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Introduction, antioxidant compounds in banana fruits, carotenoids, phenolic compounds, health benefits of bioactive components in banana fruits.

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Bioactive compounds in banana fruits and their health benefits

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Jiwan S Sidhu, Tasleem A Zafar, Bioactive compounds in banana fruits and their health benefits, Food Quality and Safety , Volume 2, Issue 4, December 2018, Pages 183–188, https://doi.org/10.1093/fqsafe/fyy019

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Banana is an edible fruit and is herbaceous flowering plant belonging to the genus Musa and the family Musaceae . Banana is also eaten as cooked vegetable (and is then called plantains). All the edible banana fruits are seedless (parthenocarpic) and belong to two main species, Musa acuminata Colla and Musa balbisiana Colla. The hybrid from these two species Musa x paradisiaca L. is also available nowadays. Although banana is native to Indomalaya and Australia, Papua New Guinea was the first to domesticate this fruit. Banana has now spread to almost 135 countries around the world. As per 2016 data, nearly 28 per cent of the total world’s banana production comes from India and China. Cavendish group banana, being the main export item from the banana-exporting countries, usually refers to soft, sweet, and dessert banana in the Western countries, but the plantain bananas have firm, starchy fruit which is suitable for cooking as a vegetable. Banana is known to be rich not only in carbohydrates, dietary fibres, certain vitamins and minerals, but is also rich in many health-promoting bioactive phytochemicals. General composition including various bioactives and their health contributions has been reviewed in this paper.

The consumption of fruits and fruit products is known not only to promote general good health but also lower the risk of various chronic diseases, such as heart diseases, stroke, gastrointestinal disorders, certain types of cancer, hypertension, age-related macular degeneration, cataract of the eye, skin conditions, lowering of low-density lipoprotein (LDL) cholesterol, and improved immune function. To promote healthy eating lifestyle, the USDA recommends filling up half the plate with fruits and vegetables, because these provide a good amount of dietary fibres, certain vitamins ( e.g . ascorbic acid, folic acid, and vitamin A precursors), many minerals ( e.g . potassium, magnesium, iron, and calcium), and many other important phytochemicals having strong antioxidative properties. Fruits make an important part of the balanced diet adopted by the humans. USDA recommends daily five servings of fruits to obtain most of the health benefits. Depending upon their origin and production area temperature, fruits are classified into temperate fruits, sub-tropical fruits, and tropical fruits. Banana belongs to the tropical fruits as it grows more profusely in tropical rain forest areas. Interestingly, banana fruit has flesh not only rich in starch which changes into sugars on ripening but is also a good source of resistant starch. Banana is known to be rich in carbohydrates, dietary fibres, certain vitamins, and minerals ( Table 1 ). The presence of various bioactive phytochemicals and their nutritional significance has been discussed in this review paper ( Figure 1 ).

Chemical composition of banana fruit (as is basis per 100 g)

Adopted from: Wikipedia, Internet, USDA databases.

Banana tree and banana fruits of various maturities. (Source: Internet Wikipedia.)

The reactive oxygen species (ROS) and reactive nitrogen species (RON), such as hydroxyl radicals, superoxide ions, nitric oxide radicals, and singlet oxygen and hydrogen peroxide, have now been implicated in the causation of many disorders like arthritis, diabetes, arteriosclerosis, age-related macular degeneration, certain types of cancer, inflammation, genotoxicity, and Alzheimer disease. The exact mechanism is not known but the reaction of these ROS and RNS species with biomolecules such as lipids, proteins, and DNA may be the cause of these disease conditions ( Shukla et al ., 2009 ; Septembre-Malaterre et al ., 2016 ). Kandaswamy and Aradhya (2014) have shown the banana rhizome to be a rich source of many polyphenolic compounds having antioxidant activities. Pazmino-Duran et al. (2001) have suggested the use of anthocyanins from banana bracts (florets) as natural colourants. They identified various anthocyanins such as cyanidin-3-rutinoside (main one as 80 per cent of total pigments, being 32.3 mg/100 g) and 3-rutinoside derivatives of delphinidin, pelargonidin, peonidin, and malvidin. Interestingly, the addition of heat-treated onion extract was found to inhibit the polyphenol oxidase (PPO) during ripening of banana fruit at room temperature ( Lee, 2007 ). Even the Maillard reaction products (MRP) significantly affected the banana PPO activity. The phytochemistry and pharmacology of wild banana ( Musa acuminata Colla) have been reviewed by Mathew and Negi (2017) and they suggested the use of banana pulp and peel for the development of drugs and use in functional foods.

Not only banana pulp, but pseudo stem and banana fruit peel have been found to be the good sources of antioxidants ( Table 2 ). Aziz et al . (2011) have analysed the native banana pseudo-stem flour (NBPF) and tender core of pseudo-stem flour (TCBPF) for chemical and functional properties. They found higher content of polyphenols, flavonoids, total dietary fibre, insoluble dietary fibre, lignin, hemicellulose, cellulose, antioxidant capacity, and free-radical scavenging capacity in NBPF than TCBPF. In exhaustive reviews, Pereira and Maraschin (2015) and Singh et al . (2016) have reported that banana is rich in many bioactive compounds, such as carotenoids, flavonoids, phenolics, amines, vitamin C, and vitamin E having antioxidant activities to provide many human health benefits. Recently, Vu et al . (2018) have also reviewed the phenolic compounds and their potential health benefits coming from banana peel. They have suggested the use of this valuable by-product from banana fruit processing industry in food and pharmaceutical industry. Anyasi et al . (2018) have analysed the essential macro and trace minerals as well as phenolic compounds in unripe banana flour obtained from the pulp of four cultivars treated with ascorbic, citric, and lactic acids before drying in a forced air dryer at 70°C. Results of their liquid chromatography-mass spectrometry-electrospray ionization (LC-MS-ESI) assay of phenolics revealed the presence of two flavonoids, epicatechin and 3-O-rhmnosyl-glucoside in varying concentrations. Among the essential minerals, zinc had the lowest concentration of 3.55 mg/kg, but the potassium was the highest, 14746.73 mg/kg in these cultivars.

Antioxidant activity, total polyphenol, and individual polyphenolic compounds present in organic acid treated (20 g/l) unripe banana flour

Means with different letters across rows are significantly different at P < 0.05. Values are Means ± SE of triplicate measurements. DPPH, 1,1-diphenyl-2-picrylhydrazyl. (Adopted and modified from Anyasi et al ., 2018 .)

Carotenoids is a class of compounds having some 600 members in this family. Some of these are precursors for vitamin A, and others are known to have strong antioxidant capacity to scavenge ROS. Among the carotenoids present in banana fruit, α-carotene, β-carotene, and β-cryptoxanthin have provitamin A activity, but others like lycopene and lutein have a strong antioxidant capacity ( Erdman et al ., 1993 ). Lycopene is known to provide protection against prostate cancer among men, and lutein offers human health benefits to serve as an inhibitor of age-related macular degeneration ( Davey et al ., 2006 ). Later, Davey et al . (2009) have analysed 171 different genotypes of Musa spp. for provitamin A carotenoids and 47 genotypes for two minerals (iron and zinc). They found a great variability in provitamin A among the various cultivars, but a low variability in iron and zinc, irrespective of the soil type and growing environmental conditions. They suggested the use of high provitamin A and trace mineral cultivars as development strategies to improve the nutritional health and alleviation of micronutrient deficiencies among the Musa -consuming populations.

Yellow- and orange-fleshed banana cultivars are known to be richer in trans-β-carotene content ( Englberger et al ., 2006 ). Carotenoid content of some of the banana cultivars is presented in Table 3 . Consumption of fruits rich in carotenoids is reported to boost immunity and reduce the risk of various diseases, such as cancer, type II diabetes, and cardiovascular problems ( Krinsky and Johnson, 2005 ). Certain banana cultivars rich in provitamin A carotenoids can be grown and consumed by the poor population of the world that is having serious vitamin A deficiency, and the consumption of such banana fruit would alleviate vitamin A deficiency ( Fungo and Pillay, 2013 ).

Carotenoid content of different banana cultivars (µg/100 g)

Source: Adopted and modified from: Singh et al ., 2016 .

Phenolics present in banana fruit are the major bioactive compounds having antioxidant properties and are known for providing health benefits ( Table 4 ). Various phenolics present in banana have been identified as follows: gallic acid, catechin, epicatechin, tannins, and anthocyanins. Banana rhizome is used as food and for medicinal properties as well in South India as it is very rich in phenolics ( Kandasamy and Aradhya, 2014 ). Russel et al . (2009) have detected many phenolics in banana, such as ferulic, sinapic, salicylic, gallic, p-hydroxybenzoic, vanillic, syringic, gentisic, and p-coumaric acids as major components. However, ferulic acid content was the highest (69 per cent of cinnamic acids) among these phenolics. Banana peel is also a rich source of phenolic compounds. Tsamo et al . (2015) analysed banana pulp and peel from nine plantain cultivars and two dessert banana cultivars. According to their results, hydroxycinnamic derivatives, such as ferulic acid-hexoside, were the major ones (4.4–85.1 µg/g DW) in plantain pulp. They observed large variations in the phenolic contents among the cultivars tested. In the peel from plantain cultivars, rutin was the most abundant flavonol glycoside (242.2–618.7 µg/g DW). Thus, the banana peel and pulp both are good sources of health-promoting phenolic compounds. Among the flavonoids detected in banana are as follows: quercetin, myricetin, kaempferol, and cyanidin which provide health benefits mainly because they act as free radicals, ROS, and RNS scavengers ( Kevers et al ., 2007 ). Most of these phenolics are known to also exhibit antibacterial, antiviral, anti-inflammatory, antiallergenic, antithrombotic, and vasodilatory activities ( Cook and Sammon, 1996 ). Sulaiman et al . (2011) have determined the total phenolic and mineral contents in pulp and peel from eight banana ( Musa spp.) cultivars grown in Malaysia. With a few exceptions, the peel extracts had the higher total phenolics and total antioxidant activities than the pulp. Among minerals, potassium was the major element found in both the peel and pulp followed by phosphorus, magnesium, and sodium.

Uses and health benefits of bioactive compounds in banana

Health benefits of phenolics

A flavonoid, leucocyanidin, has been identified as a predominant component of aqueous extract of unripe banana pulp that showed significant anti-ulcerogenic activity ( Lewis et al ., 1999 ). Thus, many flavonoids, especially leucocyanidin analogues, may offer immense therapeutic potential in the treatment of gastric disease conditions.

The structure–activity relationship of flavonoids indicates that their antioxidant capacity, scavenging free radicals, and chelating action are related to the presence of functional groups in their nuclear structure ( Heim et al ., 2002 ). They also attributed most of the health benefits from the consumption of flavonoids to their antioxidant and chelating properties. Because of these properties, flavonoids are also shown to exhibit antimutagenic and antitumoral activities ( Rice-Evans et al ., 1996 ). The flavonoids can also inhibit many enzymes, such as oxygenases (prostaglandin synthase), required in the synthesis of eicosanoids. Thus, the flavonoids inhibit hyaluronidase activity and help in maintaining the proteoglycans of connective tissues. This would prevent the spread of bacterial or tumour metastases ( Havsteen, 2002 ). As the flavonoids get preferentially oxidized, they are reported to prevent the oxidation of body’s natural water-soluble antioxidants like ascorbic acid ( Korkina and Afanas’ev, 1997 ). Generally, after the consumption of banana fruit, the peel ends up as a feed for the animals only. The disposal of peel (pomace) and other by-products from banana-processing industry causes a serious environmental problem ( Zhang et al ., 2005 ). Banana peel is reported to be rich in many high-value health-promoting antioxidant phytochemicals, such as anthocyanins, delphinidin, and cyanidins ( Seymour, 1993 ). In a recent study, Rebello et al . (2014) have also shown the banana peel extract to be a rich source of total phenolics (29 mg/g as GAE), which are responsible for the very high antioxidant activity. They also determined various other antioxidant compounds, namely, highly polymerized prodelphinidins (~3952 mg/kg), flavonol glycosides (mainly 3-rutinosides and predominantly quercetin-based compounds, ~129 mg/kg), B-type procyanidin dimers, and monomeric flavan-3-ols (~126 mg/kg).

Health benefits of biogenic amines

Banana peel and pulp are known to be good sources of certain biogenic amines (catecholamines) which are produced by the decarboxylation of amino acids or by the amination of aldehydes and ketones. Catecholamines include dopamine, serotonin, epinephrine, and norepinephrine and are reported to occur in many plants in considerable amounts ( Ponchet et al ., 1982 ). In animals, these biogenic amines are reported to work as neurotransmitters for the hormonal regulation of glycogen metabolism ( Kimura, 1968 ). When banana is consumed by humans, serotonin present in the pulp (ranging from 8 to 50 µg/g) creates a feeling of well-being and happiness. Banana contains a large amount of dopamine and norepinephrine ( Buckley, 1961 ). Waalkes et al . (1958) were the first to report the amount of various catecholamines in banana pulp as follows: serotonin, 28 µg/g; norepinephrine, 1.9 µg/g; and dopamine, 7.9 µg/g. The concentrations of dopamine in the pulp of yellow banana ( M. acuminata ), red banana ( Musa sapientum ), and plantain has been reported to be 42, 54, and 5.5 µg/g, respectively ( Feldman et al ., 1987 ). They highlighted the role of dopamine in human brain and body as a neurotransmitter having a strong influence on mood and emotional stability. Dopamine in the peel and pulp of commercially ripened Musa Cavendish is reported to range from 80 to 560 mg/100 g, and 2.5 to 10 mg/100 g, respectively ( Kanazawa and Sakakhibara, 2000 ). Tryptophan being one of the precursors for the synthesis of dopamine, the presence of this amino acid in banana peel increases the interest in possibilities of preventing neurodegenerative diseases like Parkinson’s using this by-product of food-processing industry by developing pharmaceutical formulations. However, the increase in dopamine content from unripe to the ripened stage in both the peel and pulp has been reported by many workers ( Romphophak et al ., 2005 ; Gonzalez-Montelongo et al ., 2010 ). They also suggested that the decline in dopamine concentration during over-ripening stage may be due to its oxidation to quinones which may further polymerize to melanin pigments.

Using peroxide value and thiobarbituric activity determination, the antioxidant compounds present in water extract of banana peel have shown to suppress the autooxidation of linoleic acid by 65 to 70 per cent after 5 days of incubation ( Kanazawa and Sakakhibara, 2000 ). When they compared dopamine with other natural antioxidants, such as ascorbic acid, reduced glutathione, and phenolic acids ( e.g. gallocatechin gallate), the dopamine showed higher antioxidant activity in vitro (DPPH assay). Gonzalez-Montelongo et al . (2010) have reported the banana peel extracts to be rich in dopamine, L-dopa, and catecholamines with a significant antioxidant capacity. They found no significant difference in the antioxidant activity in the banana peel extracts from different cultivars. The biogenic amines are also shown to play an important role in offering plants’ resistance to various invading pathogens through their interaction with phytohormones ( via auxin oxidation), thus affecting the growth and development of plants ( Newman et al ., 2001 ; Roepenack-Lahaye et al ., 2003 ).

Health benefits of phytosterols

These naturally occurring plant sterols have attracted the attention of food manufacturers to produce functional foods having higher health benefits. Because of their structural similarity with cholesterol, they compete with cholesterol for absorption in the gut, thus lowering the blood cholesterol levels ( Marangoni and Poli, 2010 ). They reported that a daily intake of 3 g of phytosterols results in marked reduction of LDL cholesterol levels. Various phytosterols reported in the banana peel are stigmasterol, -sitosterol, campesterol, 24-methylene cycloartenol, cycloeucalenol, and cycloartenol ( Knapp and Nicholas, 1969 ). Now the health professionals recommend the consumption of plant sterols–rich diet to lower the LDL cholesterol in patients who do not tolerate cholesterol-lowering statin drugs ( Ostlund et al ., 2003 ). Banana fruit has been shown to contain a good amount of phytosterols both in the peel and pulp ( Akihisa et al ., 1986 ). The phytosterols content in unripe banana in the range of 2.8 to 12.4 g·kg DW has been reported by Vilaverde et al. (2013) . According to their results, the Musa balbisiana cultivars, such as ‘Dwarf Red’ and ‘Silver’, had higher amounts of phytosterols than the M. acuminata cultivars. The lipophilic extract of ripe banana pulp from several cultivars of the M. acuminata and M. balbisiana species has been found to be a source of ω-3 and ω-6 fatty acids, phytosterols, long-chain aliphatic alcohols, and α-tocopherol, thus offering well-established nutritional and health benefits ( Vilela et al ., 2014 ).

The above discussion brings out the importance of consuming banana fruits for obtaining various health benefits. It is not only the banana fruit pulp, but also the peel of this fruit is known to contain many important phytochemicals and offers many health benefits. More research is needed to be carried out to find ways of using banana fruit peel in the development of many new functional foods.

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ORIGINAL RESEARCH article

Functional characterisation of banana ( musa spp.) 2-oxoglutarate-dependent dioxygenases involved in flavonoid biosynthesis.

• 1 Genetics and Genomics of Plants, Faculty of Biology, Bielefeld University, Bielefeld, Germany
• 2 Fondazione Edmund Mach, Research and Innovation Centre, San Michele All’ Adige, Italy

Bananas ( Musa ) are non-grass, monocotyledonous, perennial plants that are well known for their edible fruits. Their cultivation provides food security and employment opportunities in many countries. Banana fruits contain high levels of minerals and phytochemicals, including flavonoids, which are beneficial for human nutrition. To broaden the knowledge on flavonoid biosynthesis in this major crop plant, we aimed to identify and functionally characterise selected structural genes encoding 2-oxoglutarate-dependent dioxygenases, involved in the formation of the flavonoid aglycon. Musa candidates genes predicted to encode flavanone 3-hydroxylase (F3H), flavonol synthase (FLS) and anthocyanidin synthase (ANS) were assayed. Enzymatic functionalities of the recombinant proteins were confirmed in vivo using bioconversion assays. Moreover, transgenic analyses in corresponding Arabidopsis thaliana mutants showed that MusaF3H , MusaFLS and MusaANS were able to complement the respective loss-of-function phenotypes, thus verifying functionality of the enzymes in planta . Knowledge gained from this work provides a new aspect for further research towards genetic engineering of flavonoid biosynthesis in banana fruits to increase their antioxidant activity and nutritional value.

Introduction

Banana ( Musa spp.) plants are well known for their edible fruit and serve as a staple food crop in Africa, Central and South America ( Arias et al., 2003 ). With more than 112 million tons produced in 2016, bananas are among the most popular fruits in the world and provide many employment opportunities ( FAO, 2019 ). Furthermore, banana fruits are rich in health promoting minerals and phytochemicals, including flavonoids, a class of plant specialised metabolites, which contribute to the beneficial effects through their antioxidant characteristics ( Forster et al., 2003 ; Wall, 2006 ; Singh et al., 2016 ). Flavonoid molecules share a C6-C3-C6 aglycon core, which can be reorganised or modified, e.g. by oxidation or glycosylation ( Tanaka et al., 2008 ; Le Roy et al., 2016 ). Modifications at the central ring structure divide flavonoids into 10 major subgroups (i.e. chalcones, aurones, flavanones, flavones, isoflavones, dihydroflavonols, flavonols, leucoanthocyanidins, anthocyanidins and flavan-3-ols). The diversity in chemical structure is closely related to diverse bioactivities of flavonoids in plant biology and human nutrition ( Falcone Ferreyra et al., 2012 ). For example, anthocyanins are in many cases well known to colour flowers and fruits to attract animals and thus promoting pollination and dispersion of seeds ( Ishikura and Yoshitama, 1984 ; Gronquist et al., 2001 ; Grotewold, 2006 ). Flavonols can interact with anthocyanins to modify the colour of fruits ( Andersen and Jordheim, 2010 ) and play a prominent role in protection against UV-B irradiation ( Li et al., 1993 ) and also in plant fertility ( Mo et al., 1992 ).

Many researchers have attributed positive effects on human health to flavonoids: e.g. antigenotoxic ( Dauer et al., 2003 ), anticarcinogenic and antioxidative ( Kandil et al., 2002 ) effects, as well as the prevention of cardiovascular diseases has been suggested (summarised in Perez-Vizcaino and Duarte, 2010 ). Additionally, Sun et al. (2019) suggested an involvement of flavonoids in the plant defence against the tropical race 4 (TR4) of the Musa Fusarium wilt (commonly known as ‘panama disease’) pathogen Fusarium oxysporum f. sp. cubense (Foc), which is a threat to the global banana production. It is certainly interesting to take a closer look at the biosynthesis of flavonoids in Musa .

Flavonoids are derived from the amino acid L-phenylalanine and malonyl-coenzyme A. Their biosynthesis ( Figure 1 ) has been analysed in many different species including the dicotyledonous model plant Arabidopsis thaliana ( Hahlbrock and Scheel, 1989 ; Lepiniec et al., 2006 ) and the monocotyledonous crop plants Zea mays (summarised in Tohge et al., 2017 ) and Oryza sativa (summarised in Goufo and Trindade, 2014 ). The first committed step, catalysed by the enzyme chalcone synthase (CHS), is the formation of naringenin chalcone from p -coumaroyl CoA and malonyl CoA ( Kreuzaler and Hahlbrock, 1972 ). A heterocyclic ring is introduced during the formation of naringenin (a flavanone) from naringenin chalcone, which can occur spontaneously or catalysed by chalcone isomerase (CHI; Bednar and Hadcock, 1988 ). The enzyme flavanone 3-hydroxylase (F3H or FHT) converts flavanones to dihydroflavonols by hydroxylation at the C-3 position ( Forkmann et al., 1980 ). Alternatively, flavanones can be converted to flavones by flavone synthase I or II (FNSI or II; Britsch, 1990 ). Flavonol synthase (FLS) catalyses the conversion of dihydroflavonols to the corresponding flavonols by introducing a double bond between C-2 and C-3 ( Forkmann et al., 1986 ; Holton et al., 1993 ). Moreover, dihydroflavonols can be converted to leucoanthocyanidins by dihydroflavonol 4-reductase (DFR; Heller et al., 1985 ), which competes with FLS for substrates ( Luo et al., 2016 ). Anthocyanidin synthase (ANS; also termed leucoanthocyanidin dioxygenase, LDOX) converts leucoanthocyanidins to anthocyanidins ( Saito et al., 1999 ).

Figure 1 . Schematic, simplified illustration of the core flavonoid aglycon biosynthesis pathway in plants. Chalcone synthase, the first committed enzyme in flavonoid biosynthesis, connects a CoA ester and malonyl CoA, forming a chalcone. Chalcone isomerase introduces the heterocyclic ring. The resulting flavanone is converted to dihydroflavonol by flavanone 3-hydroxylase (F3H) or to flavones by flavone synthase (FNSI or FNSII). Dihydroflavonols are converted to flavonols by flavonol synthase (FLS). Alternatively, dihydroflavonol 4-reductase can reduce dihydroflavonols to the corresponding leucoanthocyanidin, which is converted to anthocyanidin by the activity of anthocyanidin synthase (ANS). Flavonoid 3' hydroxylase hydroxylates 3' position of the B-ring using different flavonoid substrates. 2-oxoglutarate-dependant oxygenases are given in bold underlined. Enzymes in the focus of this work are highlighted by grey boxes.

The flavonoid biosynthesis enzymes belong to different functional classes (summarised in Winkel, 2006 ): polyketide synthases (e.g. CHS), 2-oxoglutarate-dependent dioxygenases (2-ODD; e.g. F3H, ANS, FLS and FNSI), short-chain dehydrogenases/reductases (e.g. DFR), aldo-keto reductases (e.g. chalcone reductase, CHR) and cytochrome P450 monooxygenases (e.g. FNSII and F3'H). Flavonoid biosynthesis is evolutionary old in plants ( Wen et al., 2020 ) and, although differences exist, quite similar in dicots like A. thaliana and monocots-like Musa .

In the present study, we focus on the functional class of flavonoid aglycon forming enzymes, namely, the 2-ODDs. 2-ODDs occur throughout the kingdom of life and play important roles in many biological processes including oxygen sensing and DNA repair ( Jaakkola et al., 2001 ; Trewick et al., 2002 ). They are a class of non-heme iron-containing enzymes, which require 2-oxoglutarate, Fe 2+ and ascorbate for substrate conversion (summarised in Prescott and John, 1996 ).

Plant 2-ODDs share conserved amino acid residues which coordinate ferrous iron binding (HxDxnH) and binding of 2-oxoglutarate (RxS; Cheng et al., 2014 ). They can be divided into three distinct evolutionary classes, DOXA, DOXB and DOXC, based on amino acid similarity ( Kawai et al., 2014 ). The 2-ODDs involved in specialised metabolism were classified into the DOXC class. Yet, all flavonoid biosynthesis related 2-ODDs were found in the DOXC subclades 28 (F3H and FNSI) or 47 (FLS and ANS; Kawai et al., 2014 ). In some cases, the high amino acid similarity between the different enzymes leads to overlapping functions. For example, FLSs from Ginkgo biloba ( Xu et al., 2012 ) and O. sativa ( Park et al., 2019 ) can accept flavanones and dihydroflavonols as substrates. Also, ANSs from A. thaliana ( Stracke et al., 2009 ) and Malus domestica ( Yan et al., 2005 ) can catalyse the formation of flavonol glycosides.

Based on the Musa acuminata genome sequence annotation ( DHont et al., 2012 ; Pandey et al., 2016 ) identified 28 putative Musa flavonoid biosynthesis enzymes. This includes seven 2-ODD-type enzymes, namely, two F3H, four FLS and one ANS, while no suitable FNSI candidate was identified. While the expression of the respective genes and correlations of expression to flavonoid metabolite accumulation have been studied, the functionality of these enzymes has not been analysed until now.

Here, we describe the sequence-based and functional characterisation of 2-ODDs from the non-grass monocot Musa . Functionalities of recombinant Musa enzymes were analysed using in vivo bioconversion assays and by in planta complementation of corresponding A. thaliana loss-of-function mutants. This resulted in the experimental confirmation that the Musa flavonoid 2-ODDs studied have the predicted functions. The presented results contribute to the understanding of flavonoid biosynthesis in Musa . They provide a strong basis for further research to enhance the efficiency of flavonoid production in banana, in order to increase the fruits’ health promoting effects.

Materials and Methods

Plant material.

Banana plants (Grand Naine, plantain) for RNA extraction were grown in the field in Lucknow, India. Musa gene annotation identifiers refer to the study of Martin et al. (2016) . Columbia-0 (Col-0, NASC ID N1092) and Nössen-0 (Nö-0, NASC ID N3081) were used as wild-type controls. The A. thaliana mutants tt6-2 ( f3h , GK-292E08, Col-0 background; Appelhagen et al., 2014 ) and ans/fls1-2 (synonym ldox/fls1-2, ldox: SALK_028793 , Col-0 background; fls1-2: RIKEN_PST16145, Nö-0 background; Stracke et al., 2009 ) were used for complementation experiments.

Phylogenetic Analysis

Multiple protein sequence alignments were created with MAFFT v7 ( Katoh and Standley, 2013 ) using default settings. The approximately maximum-likelihood phylogenetic tree of 33 plant 2-ODDs with proven F3H, FNSI, FLS and ANS functionality ( Supplementary Table S1 ) and seven Musa 2-ODDs was constructed as described by Pucker et al. (2020b) : MAFFT alignments were cleaned with pxclsq ( Brown et al., 2017 ) and the tree was constructed with FastTree v2.1.10 using the WAG+CAT model ( Price et al., 2010 ). The tree was visualised with interactive tree of life ( Letunic and Bork, 2019 ), branch lengths were neglected.

Expression Analysis

Expression data for Musa 2-ODD genes were extracted from previous analyses ( Pucker et al., 2020a ). Short Read Archive IDs can be found in Supplementary Table S2 .

Total RNA Extraction, cDNA Synthesis and Molecular Cloning

Isolation of RNA from different Musa plant organs (leaf, pseudostem, bract, fruit peel and fruit pulp) was performed according to a protocol from Asif et al. (2000) . cDNA synthesis was performed from 1 μg total RNA using the ProtoScript ® First Strand cDNA Synthesis Kit [New England Biolabs, (NEB)] with the provided random primer mix according to the suppliers’ instructions. Amplification of predicted full-length CDSs ( Pandey et al., 2016 ) were done using Q5 ® High-Fidelity DNA polymerase (NEB) and gene-specific primers ( Supplementary Table S3 ) according to standard protocols. Creation of full-length coding sequence constructs (CDS) was performed using the GATEWAY ® Technology (Invitrogen). MusaF3H1 (Ma02_t04650), MusaF3H2 (Ma07_t17200), MusaFLS1 (Ma03_t06970), MusaFLS3 (Ma10_t25110) and MusaANS (Ma05_t03850) CDSs were successfully amplified on different cDNA pools. The resulting PCR products were recombined into pDONR ™ /Zeo (Invitrogen) with BP clonase (Invitrogen) resulting in Entry plasmids, which were sequenced by Sanger technology ( Sanger et al., 1977 ) on 3730XL sequencers using BigDye terminator v3.1 chemistry (Thermo Fisher). Entry plasmids for AtF3H , AtFLS1 and AtMYB12 were available from previous studies ( Preuss et al., 2009 ; Stracke et al., 2017 ), Petroselinum crispum FNSI ( PcFNSI ) was amplified on a plasmid from a previous study ( Martens et al., 2003 ). The full-length CDSs were introduced from the Entry plasmids into the inducible Escherichia coli expression vector pDEST17 (Invitrogen) and the binary expression vector pLEELA ( Jakoby et al., 2004 ) using GATEWAY LR reaction (Invitrogen).

Heterologous Expression in E. coli

pDEST17-based plasmids containing proT7 -RBS-6xHis-CDS- T7term expression cassettes ( proT7: T7 promoter , RBS: ribosome-binding site, 6xHis: polyhistidine tag and T7term: T7 transcription terminator ) were transformed into BL21-AI cells (Invitrogen). Cultures were grown in LB to an OD 600 of about 0.4 and expression was induced with 0.2% L-arabinose.

F3H and FLS Bioconversion Assay in E. coli

The enzyme assay was performed using 20 ml E. coli cultures expressing the respective constructs right after induction with L-arabinose. 100 μl substrate [10 mg/ml naringenin, eriodictyol or dihydroquercetin (DHQ)], 50 μl 2-oxoglutaric acid, 50 μl FeSo 4 and 50 μl 1 M sodium ascorbate were added. The cultures were incubated at 28°C overnight. To extract flavonoids, 1 ml was removed from each culture and mixed with 200 μl ethyl acetate by vortexing for 30 s. Samples were taken after 0 h, 1 h, 2 h, 3 h, 4 h and 24 h. After centrifugation for 2 min at 14,000 g, the organic phase was transferred into a fresh reaction tube. Flavonoid content was analysed by high-performance thin-layer chromatography (HPTLC). Naringenin (Sigma), dihydrokaempferol (DHK; Sigma), kaempferol (Roth), eriodictyol (TransMIT PlantMetaChem), apigenin (TransMIT PlantMetaChem), DHQ (Roth) and quercetin (Sigma) were dissolved in methanol and used as standards. 3 μl of each methanolic extract was spotted on a HPTLC Silica Gel 60 plate (Merck). The mobile phase was composed of chloroform, acetic acid and water mixed in the ratio (50:45:5). Flavonoid compounds were detected as described previously ( Stracke et al., 2007 ), using diphenylboric acid 2-aminoethyl ester (DPBA) and UV light ( Sheahan and Rechnitz, 1992 ).

Agrobacterium -Mediated Transformation of A. thaliana

T-DNA from pLEELA-based plasmids containing 2xpro35S -driven Musa2-ODDs was transformed into A. thaliana plants via Agrobacterium tumefaciens [Agrobacterium, GV101::pMP90RK, ( Koncz and Schell, 1986 )] mediated gene transfer using the floral dip method ( Clough and Bent, 1998 ). Successful T-DNA integration was verified by BASTA selection and PCR-based genotyping.

Flavonoid Staining of A. thaliana Seedlings

In situ visualisation of flavonoids in norflurazon-bleached A. thaliana seedlings was performed according to Stracke et al. (2007) using DPBA/Triton X-100 and epifluorescence microscopy.

Analysis of Flavonols in Methanolic Extracts

Flavonol glycosides were extracted and analysed as previously described ( Stracke et al., 2009 ). A. thaliana rosette leaves were homogenised in 80% methanol, incubated for 15 min at 70°C and centrifuged for 10 min at 14,000 g . Supernatants were vacuum dried. The dried pellets were dissolved in 80% methanol and analysed by HPTLC on a Silica Gel 60 plate (Merck). Ethyl acetate, formic acid, acetic acid and water (100:26:12:12) were used as a mobile phase. Flavonoid compounds were detected as described above.

Determination of Anthocyanin Content

To induce anthocyanin production, A. thaliana seedlings were grown on 0.5 MS plates with 4% sucrose and 16 h of light illumination per day at 22°C. Six-day-old seedlings were used to photometrically quantify anthocyanins as described by Mehrtens et al. (2005) . All samples were measured in three independent biological replicates. Error bars indicate the standard error of the average anthocyanin content. Statistical analysis was performed using the Mann–Whitney U test ( Mann and Whitney, 1947 ).

Creation of cDNA Constructs and Sequence-Based Characterisation of Musa Flavonoid 2-ODDs

Previously described putative Musa flavonoid biosynthesis enzymes encoded in the M. acuminata , Pahang DH reference genome sequence were used. This included two F3Hs, four FLSs and one ANS, which were classified as 2-ODDs. To functionally characterise these Musa enzymes, we amplified the corresponding CDSs from a cDNA template collection derived from different Musa organs, using primers designed on the Pahang DH reference genome sequence. The successfully amplified cDNAs of MusaF3H1 and MusaF3H2 were derived from plantain pulp, MusaFLS1 from Grand Naine young leaf, MusaFLS3 on Grand Naine bract and MusaANS on Grand Naine peel. Unfortunately, we were not able to amplify MusaFLS2 and MusaFLS4 from our template collection.

Comparison of the resulting 2-ODD cDNA sequences with the reference sequence revealed several single-nucleotide polymorphisms ( Supplementary File S1 ). The derived amino acid sequences show close similarity to other plant 2-ODD proteins known to be involved in flavonoid biosynthesis ( Supplementary File S2 ). The amino acids well known to coordinate ferrous iron binding (HxDxnH) and binding of 2-oxoglutarate (RxS) are also conserved in the Musa 2-ODDs. Additionally, the At FLS1 residues which have been shown to be involved in flavonoid substrate binding ( Supplementary File S2 ) are conserved. The residue F293 (all positions refer to At FLS1) is conserved in all Musa 2-ODD proteins, F134 and K202 are found in the Musa FLSs and Musa ANS and E295 is conserved in Musa ANS.

To analyse the evolutionary relationship between Musa 2-ODDs and 33 known flavonoid biosynthesis-related 2-ODDs, a phylogenetic tree was built ( Figure 2 ). The phylogenetic tree revealed two distinct clades, which correspond to the DOXC28 and DOXC47 classes of the 2-ODD superfamily. In the F3H- and FNSI-containing DOXC28 class, Musa F3H1 and Musa F3H2 cluster with other F3Hs from monocotyledonous plants. In the FLS- and ANS-containing DOXC47 class, the Musa ANS clusters with ANSs from monocotyledonous plants, while the FLSs from monocotyledonous plants do not form a distinct group, although the Mus aFLSs are in proximity to Zm FLS and Os FLS.

Figure 2 . Rooted approximately maximum-likelihood (ML) phylogenetic tree of 2-ODDs involved in flavonoid biosynthesis. ODDs from banana are given in blue, 39 enzymes with proven F3H, FNSI, FLS or ANS activity were included. Different grey scales indicate F3H, FLS, ANS and two evolutionary FNSI clades. At GA3ox1 (gibberellin 3 beta-hydroxylase1) was used as an outgroup. Branch points to DOXC28 and DOXC47 classes are marked with black arrowheads.

Our analyses clearly show that the CDSs identified for Musa F3H1, Musa F3H2, Musa FLS1, Musa FLS3 and Musa ANS have the potential to encode functional flavonoid 2-ODDs.

Expression Profiles of Musa 2-ODD Genes

Analysis of the expression patterns of the genes studied was performed using published RNA-Seq data. We obtained normalised RNA-Seq read values for Musa 2-ODD genes from several organs and developmental stages ( Table 1 ). MusaF3H1 and MusaF3H2 are expressed in almost all analysed organs and developmental stages. MusaF3H1 expression is highest in early developmental stages of pulp (S1 and S2), followed by intermediate developmental stages of peel (S2 and S3). MusaF3H2 shows highest expression in adult leaves. MusaFLS2 was not expressed in any of the analysed samples. All four MusaFLS genes show low or no expression in seedlings and embryogenic cell suspension. MusaFLS1 shows variance in transcript abundance with particularly high levels in peel (S2) and in young and adult leaves. MusaFLS3 and MusaFLS4 transcript levels are comparatively constant and low, with highest transcript abundance in root and peel (S1 and S3, respectively). While MusaFLS1 transcript abundance is highest in adult leaf, MusaFLS3 and MusaFLS4 lack expression in this tissue. MusaANS shows highest transcript abundance in pulp (S1, S2 and S4) and peel (S3) and very low expression in embryogenic cell suspension, seedlings and leaves.

Table 1 . Expression profiles of Musa2 -ODD genes in different organs and developmental stages based on RNA-Seq data.

Musa F3H1 and Musa F3H2 Are Functional F3H

To confirm F3H activity in vivo , we used a bioconversion assay. MusaF3H1 and MusaF3H2 were heterologously expressed in E. coli and the bacterial cultures were fed with naringenin or eriodictyol as substrate of F3H. Both recombinant proteins, Musa F3H1 and Musa F3H2, were able to convert naringenin to DHK in the presence of 2-oxoglutarate and ferrous iron ( Figure 3A ). After 24 h of incubation, only small amounts of naringenin remained un-converted. Conversion of the formed DHK to kaempferol by Musa F3H1 or Musa F3H2 was not observed. Furthermore, eriodictyol was converted to DHQ by Musa F3H1 and Musa F3H2 ( Supplementary File S3 ). A further conversion to the quercetin was not observed. Since Musa F3H2 was previously considered as FNSI candidate, we analysed FNSI activity in a bioconversion assay. While Musa F3H2 was able to convert naringenin to DHK we detected apigenin as product only for PcFNSI but not for Musa F3H2 ( Supplementary File S4 ). Accordingly, Musa F3H2 does not show FNSI activity in our assay. For further in planta analysis, we chose a complementation assay with an A. thaliana f3h mutant (tt6-2) which expresses FLS and ANS but no F3H . MusaF3H1 or MusaF3H2 were expressed in tt6-2 plants under the control of the constitutive 2x35S promoter. The accumulation of flavonol glycosides was analysed in herbizide-bleached seedlings using DPBA staining ( Figure 3B ). While Col-0 wild-type seedlings appeared yellow under UV light, indicating the accumulation of flavonol glycosides, tt6-2 seedlings showed a red fluorescence. tt6-2 mutants expressing MusaF3H1 or MusaF3H2 were able to complement the mutant phenotype, showing the characteristic yellow flavonol glycoside fluorescence of the wild type. These results demonstrate that Musa F3H1 and Musa F3H2 are functional enzymes with F3H activity and are able to catalyse the conversion of naringenin to DHK.

Figure 3 . Musa F3H1 and Musa F3H2 are functional F3Hs. (A) HPTLC analysis of F3H bioconversion assays using extracts from Escherichia coli expressing recombinant Musa F3H1 or Musa F3H2. The substrate naringenin (N) and the product dihydrokaempferol (DHK) were used as standards. At F3H was used as a positive control. At MYB12 was used as a negative control. (B) Analysis of tt6-2 mutant seedlings complemented with Musa F3H1 and Musa F3H2. Col-0 wild type and tt6-2 were used as controls. Representative pictures of DPBA-stained seedlings under UV light are given. Yellow fluorescence indicates flavonol glycoside accumulation. The different numbers indicate individual transgenic lines. The scale bar indicates 0.5 mm.

Musa FLS1 and Musa FLS3 Are Functional FLS

To confirm FLS activity in vivo , the enzymes were assayed in a coupled bioconversion experiment ( Figure 4A ). Two E. coli cultures expressing MusaF3H1 and a MusaFLS were mixed and fed with naringenin in the presence of 2-oxoglutarate and ferrous irons. Here, Musa F3H1 converts naringenin to DHK, thus providing the substrate for the FLS enzyme to be tested. While Musa FLS1 was found to be able to convert DHK to kaempferol under the assay conditions, Musa FLS3 was not. In a second bioconversion experiment, E. coli cultures expressing MusaFLS1 or MusaFLS3 were fed with DHQ ( Supplementary File S5 ). Again, Musa FLS1 was able to convert the dihydroflavonol to the corresponding flavonol, while Musa FLS3 was not. We checked the expression of recombinant Musa FLS1 and Musa FLS3 in the E. coli cultures by SDS-PAGE and found both Musa FLS proteins being expressed at similar levels ( Supplementary File S7 ). Moreover, we analysed a possible F3H/FLS biofunctional activity of Musa FLS1 and Musa FLS3 by feeding naringenin to E. coli cultures. While Musa FLS1 was not able to convert naringenin to DHK in the assay, Musa FLS3 showed F3H activity but not a further conversion to kaempferol ( Supplementary File S6 ). To further analyse the enzymatic properties in planta , MusaFLS1 or MusaFLS3 was expressed in the flavonol and anthocyanin-deficient A. thaliana ans/fls1-2 double mutant. HPTLC analyses of methanolic extracts from rosette leaves from greenhouse-grown plants ( Figure 4B ) showed that wild-type plants (Col-0 and Nö-0) contained flavonol glycosides, including the prominent derivatives kaempferol 3-O-rhamnoside-7-O-rhamnoside (K-3R-7R), kaempferol 3-O-glucoside-7-O-rhamnoside (K-3G-7R) and kaempferol 3[-O-rhamnosyl-glucoside]-7-O-rhamnoside (K-3[G-R]-7R). ans/fls1-2 plants accumulated several dihydroflavonols but did not show flavonol derivatives. ans/fls1-2 mutants transformed with 2x35S:: MusaFLS1 or 2x35S:: MusaFLS3 constructs were able to form several flavonol derivatives. Nevertheless, intensities and accumulation patterns of flavonol glycosides varied. We also analysed if MusaFLS1 and MusaFLS3 are able to complement the anthocyanin deficiency of the ans/fls1-2 double mutant. For this, seedlings were grown on 4% sucrose to induce anthocyanin accumulation. As shown in Figure 4C , 6-day-old Col-0 and Nö-0 seedlings were able to accumulate red anthocyanin pigments, while ans/fls1-2 transformed with MusaFLS1 or MusaFLS3 did not show visible anthocyanins. These results confirm that Musa FLS1 and Musa FLS3 are functional proteins with FLS activity and are enzymes able to catalyse the conversion of dihydroflavonol to flavonol.

Figure 4 . Musa FLS1 and Musa FLS3 are functional FLSs. (A) HPTLC analysis of a FLS bioconversion assays using extracts from E. coli expressing recombinant Musa FLS1 or Musa FLS3. The F3H substrate naringenin (N), the FLS substrate DHK and the product kaempferol (K) were used as standards. At FLS1 served as a positive control and At MYB12 was used as a negative control. (B) Flavonol glycoside accumulation in Musa FLS-complemented ans/fls1-2 seedlings analysed by HPTLC analysis. Col-0, Nö-0 (both wild type) and ans/fls1-2 were used as controls. Bright green spots belong to derivatives of kaempferol, orange spots are derivatives of quercetin and faint blue shows sinapate derivatives. Dark green and yellow spots indicate DHK and DHQ, respectively. G, glucose; K, kaempferol; Q, quercetin; and R, rhamnose. (C) Representative pictures of anthocyanin (red) accumulation in 6-day-old Musa FLS-complemented ans/fls1-2 seedlings growing on 4% sucrose. The scale bar indicates 0.5 mm.

Musa ANS Is a Functional ANS

We analysed Musa ANS functionality in a complementation assay with the ans/fls1-2 A. thaliana double mutant. To examine the ability of MusaANS to complement the ans/fls1-2 anthocyanin deficiency phenotype, seedlings were grown on anthocyanin-inducing media. Anthocyanin accumulation was analysed visually and quantified photometrically ( Figures 5A , B ). While wild-type seedlings showed red pigmentation, the ans/fls1-2 seedlings did not. ans/fls1-2 seedlings expressing MusaANS showed accumulation of anthocyanins. The anthocyanin content in the complemented seedlings was strongly increased compared to ans/fls1-2 knockout plants, indicating ANS activity. Furthermore, we analysed the ability of MusaANS to complement the flavonol deficiency phenotype of ans/fls1-2 plants. For this, methanolic extracts of seedlings were analysed by HPTLC followed by DPBA staining ( Figure 5C ). While wild-type seedlings accumulated several kaempferol and quercetin glycosides, ans/fls1-2 mutants expressing MusaANS showed a flavonoid pattern identical to the ans/fls1-2 mutant, accumulating dihydroflavonol derivatives, but no flavonol derivatives. These results indicate that Musa ANS is a functional enzyme with ANS activity and is able to catalyse the conversion of leucoanthocyanidin to anthocyanidin.

Figure 5 . Musa ANS is a functional anthocyanin synthase. Analysis of ans/fls1-2 double mutant seedlings complemented with 2x35S-driven MusaANS demonstrate in planta ANS functionality of Musa ANS by anthocyanin accumulation. The different numbers indicate individual transgenic lines. (A,B) Sucrose induced anthocyanin accumulation in 6-day-old Arabidopsis thaliana seedlings. (A) Representative pictures of seedlings (the scale bar indicates 0.5 mm) and (B) corresponding relative anthocyanin content. Error bars indicate the standard error for three independent measurements. (C) Musa ANS does not show in planta FLS activity. Flavonol glycoside accumulation in MusaANS -complemented ans/fls1-2 seedlings analysed by HPTLC analysis. Col-0, Nö-0 (both wild type) and ans/fls1-2 were used as controls. Bright green spots belong to derivatives of kaempferol, orange spots are derivatives of quercetin and faint blue shows sinapate derivatives. Dark green and yellow spots indicate DHK and DHQ, respectively. G, glucose; K, kaempferol; Q, quercetin; and R, rhamnose.

In this study, we isolated several cDNAs of in silico annotated 2-ODD-type flavonoid biosynthesis enzyme coding genes from Musa and tested the in vivo functionality of the encoded proteins in E. coli and A. thaliana .

Musa F3H1 and Musa F3H2

In vivo E. coli bioconversion assays revealed that Musa F3H1 and Musa F3H2 can covert naringenin to DHK. Moreover, MusaF3H1 and MusaF3H2 are able to complement the loss-of-function phenotype of A. thaliana tt6-2 seedlings, showing in planta F3H activity. Therefore, we conclude that Musa F3H1 and Musa F3H2 are functional F3Hs.

Previous studies annotated Ma02_g04650 ( MusaF3H1 ) and Ma07_g17200 ( MusaF3H2 ) as genes encoding F3Hs ( Martin et al., 2016 ; Pandey et al., 2016 ; Pucker et al., 2020b ). However, the automatic approach developed by Pucker et al. (2020b) also considered Ma07_g17200 as a candidate to encode a FNSI enzyme. For a Petroselinum crispum F3H protein, it has been found that a replacement of three amino acids was sufficient to cause FNSI side activity and seven amino acid exchanges almost lead to a complete change in enzyme activity towards FNSI functionality ( Gebhardt et al., 2007 ). These findings underline the particular high similarity between FNSI and F3H. A closer inspection of the deduced peptide sequence of Ma07_g17200 gene revealed a lack of conservation of amino acid residues known to be relevant for FNSI function (i.e. T106M, T115I, I116V, F131I, E195D, I200V, V215L and R216K). Furthermore, Ma07_g17200 did not show FNSI activity in our bioconversion assay. Together with the confirmed F3H activity, this indicates that the classification of Ma07_g17200 as a FNSI encoding gene was inaccurate. Even though Ma07_p17200 is no functional FNSI, flavone derivatives have been identified in Musa ( Fu et al., 2018 ). In Gerbera and Glycine max , FNSII is responsible for the formation of flavones ( Martens and Forkmann, 1999 ; Fliegmann et al., 2010 ). A candidate Musa FNSII, encoded by Ma08_g26160 , has been identified ( Pucker et al., 2020b ) and is probably responsible for the accumulation of flavones in Musa . Since FNSII enzymes are NADPH-dependent cytochrome P450 monooxygenases ( Jiang et al., 2016 ), the putative Musa FNSII (encoded by Ma08_g26160 ) was not studied in this work.

Our expression study that was based on published RNA-Seq data ( Pucker et al., 2020a ), revealed highest MusaF3H1 transcript abundance in early developmental stages of pulp and intermediate stages of peel development, while highest MusaF3H2 expression was found in adult leaves ( Table 1 ). This is, at first glance, in contrast to the quantitative real-time PCR-derived expression data presented by Pandey et al. (2016) , reporting high transcript levels of MusaF3H1 and MusaF3H2 in young leaves and bract. In this study, the authors also show increased MusaF3H1 expression in pseudostem, root and ripe pulp, compared to MusaF3H2 . These discrepancies are probably due to the different growth conditions, germplasms/cultivars and sampling time points in the generation of the expression data and cannot be reasonably analysed further at this point. It should be noted, however, that MusaF3H2 expression in leaves of plantlets was found to be increased following treatment with the phytohormone methyl jasmonate (MJ), while MusaF3H1 expression was not ( Pandey et al., 2016 ). As MJ is involved in various regulatory processes, including the response against biotic and abiotic stresses (summarised in Cheong and Choi, 2003 ), the MJ-dependent induction of MusaF3H2 expression could imply that the Musa F3H2 enzyme plays a specialised role in the formation of flavonoids in response to stresses. Such stress response induction of F3H encoding genes was previously shown for a F3H gene from the dessert plant Reaumuria soongorica , which is induced by UV light ( Liu et al., 2013 ) and two F3H genes from Camellia sinensis , which are induced by UV light and by treatment with abscisic acid (ABA) or sucrose ( Han et al., 2017 ). In addition, overexpression of F3H from C. sinensis ( Mahajan and Yadav, 2014 ) and from Lycium chinense ( Song et al., 2016 ) in tobacco improved the tolerance to salt stress and fungal pathogens in the first case and to drought stress in the latter case. In conclusion, our results clearly show that Musa F3H1 and Musa F3H2 are functional F3Hs and that Musa F3H2 might play a role in stress response.

Musa FLS1 and Musa FLS3

Musa FLS1 was found to be able to convert DHK and DHQ to the corresponding flavonol in the E. coli bioconversion assays. This ability was validated in planta by successful complementation of the flavonol deficiency of A. thaliana ans/fls1-2 double mutant seedlings, while the anthocyanin deficiency phenotype was not restored. These observations could hint to an exclusive FLS activity of Musa FLS1. FLS and DFR, the first enzymes of the flavonol and anthocyanin branches of flavonoid biosynthesis, compete for dihydroflavonol substrates ( Figure 1 ). This is demonstrated by several A. thaliana fls1 single mutants which accumulate higher levels of anthocyanin pigments ( Owens et al., 2008 ; Stracke et al., 2009 ). Falcone Ferreyra et al. (2010) found that overexpression of ZmFLS1 in an A. thaliana fls1 mutant decreases the accumulation of anthocyanins to wild-type level, again indicating that the anthocyanin and flavonol branches of flavonoid biosynthesis can compete for dihydroflavonol substrates in the same cell or tissue. Accordingly, the overlapping substrate usage of FLS and ANS is difficult to analyse in a fls1 single mutant. Here, the use of an A. thaliana ans/fls1-2 double mutant is a simple way to analyse the FLS and a possible ANS side activity of FLSs and vice versa in planta . To analyse further side activities, a f3h fls ans mutant would be beneficial. A. thaliana ans/fls1-2 plants expressing MusaFLS3 showed the accumulation of several kaempferol derivatives. In contrast, plants complemented with MusaFLS1 also accumulate quercetin derivatives. This is most likely an effect of different environmental conditions in plant growth. One set of plants was grown in the greenhouse ( MusaFLS1 ), another set was grown in a growth chamber ( MusaFLS3 ). In the plants used for Musa FLS3 experiments, these conditions promoted the accumulation of DHQ, while the other growth conditions did not in the plants used for MusaFLS1 experiments. Accordingly, DHQ was probably not available as a substrate for Musa FLS1 and could not be converted to quercetin. A possible explanation for the varying DHQ amounts could be the light-induced expression of flavonoid 3'hydroxylase (F3’H), which converts DHK to DHQ. The expression of F3’H in cultured A. thaliana cells can be induced by UV light ( Schoenbohm et al., 2000 ) and the activity of the F3’H promoter from Vitis vinifera is increased under light exposure ( Sun et al., 2015 ). Consequently, a different light quality or higher light intensity could be responsible for increased F3’H expression, causing the accumulation of DHQ in the plants grown in the growth chamber. While MusaFLS3 was able to complement the flavonol deficiency in the ans/fls1-2 mutant, it did not lead to an accumulation of anthocyanins. In contrast to the in planta results, Musa FLS3 did not convert DHK or DHQ to the corresponding flavonol in the in vivo bioconversion assays, even though the protein was successfully expressed in the E. coli culture. We used the bioconversion assays as a simple and versatile tool to analyse enzymatic activities. Nevertheless, heterologous expression of eukaryotic proteins in E. coli is an artificial system. It can cause a divergent pattern of post-translational modifications or production of high amounts of protein in inclusion bodies, leading to inactive protein ( Sahdev et al., 2008 ). Preuss et al. (2009) reported that At FLS3 did not show FLS activity in E. coli , but did convert dihydroflavonols to the corresponding flavonols upon expression in yeast, indicating that the stability of At FLS3 was improved under the latter assay conditions. The approach carried out in this work might have similar limitations. Further limitations can be caused by different substrate preferences, as reported for Os FLS ( Park et al., 2019 ). Despite the lack of FLS activity in the bioconversion assay, Musa FLS3 showed F3H activity. Nevertheless, the in planta complementation of the flavonol deficiency in the A. thaliana ans/fls1-2 mutant and the lacking complementation of the anthocyanin deficiency phenotype in the ans/fls1-2 mutant indicate that Musa FLS3 is a bifunctional enzyme with FLS and F3H activity, which does not exhibit significant ANS activity.

MusaFLS1 transcript abundance was found to be high in adult leaves and low in roots. MusaFLS3 and MusaFLS4 revealed opposed transcript levels. Such divergent expression patterns have previously been observed for FLS1 and FLS2 from Freesia hybrida and FLS1 and FLS2 from Cyclamen purpurascens ( Akita et al., 2018 ; Shan et al., 2020 ). These opposed expression patterns could point to differential activity in distinct organs. Furthermore, the tandemly arranged MusaFLS3 and MusaFLS4 genes show very similar expression patterns ( Table 1 ), possibly indicating functional redundancy. While we could not find expression of MusaFLS2 in our expression analyses, Pandey et al. (2016) observed MusaFLS2 expression in several organs (including bract, pseudostem and root), as well as in different developmental stages of peel and pulp. Again, these results show that different cultivars, growth conditions, sampling time points and analysed organs can have a strong influence on the resulting data. Accordingly, data from different studies should be evaluated carefully. MusaFLS1 and MusaFLS2 expression in leaves of plantlets does not significantly increase after MJ treatment ( Pandey et al., 2016 ). However, UV radiation induces the expression of FLS from Z. mays ( Falcone Ferreyra et al., 2010 ) and M. domestica ( Henry-Kirk et al., 2018 ) and the relative expression of FLS from Triticum aestivum increases during drought stress ( Ma et al., 2014 ). Together with the knowledge that flavonols act as antioxidants ( Wang et al., 2006 ), an involvement of Musa FLSs in stress response seems feasible and should be further analysed under a broader range of conditions. In summary, our results indicate that Musa FLS1 and Musa FLS3 are functional FLS enzymes and hint at possible differential organ-specific activities of Musa FLS1 and Musa FLS3/ Musa FLS4.

A. thaliana ans/fls1-2 seedlings expressing MusaANS show a strong, red pigmentation, revealing that MusaANS can complement the anthocyanin deficiency caused by mutation of AtANS . However, the seedlings did not display flavonol derivatives in HPTLC analyses, which have been reported to detect flavonol glycosides at levels of 50 pMol ( Stracke et al., 2010 ). These results indicate that Musa ANS is a functional ANS but does not have FLS activity. The RNA-Seq data-derived expression profiles revealed high MusaANS transcript abundance in pulp and peel. Additionally, MusaANS expression has been reported to be high in bract tissue ( Pandey et al., 2016 ), the specialised leaves surrounding the flowers and usually coloured red or purple due to anthocyanin accumulation ( Pazmiño-Durána et al., 2001 ). This spatial correlation of MusaANS transcripts and anthocyanin metabolites in bract tissue supports the proposed biological functionality of this enzyme, catalysing the conversion of leucoanthocyanidins to anthocyanidins in Musa . MusaANS expression increases 24 h after MJ treatment and decreases following dark treatment ( Pandey et al., 2016 ). As supposed for MusaF3H2 , the increased expression of MusaANS after MJ treatment could imply an involvement in the formation of anthocyanins as a consequence of stress response. ABA, salicylic acid (SA), UV-B and cold treatments have been shown to enhance ANS transcript abundance in G. biloba ( Xu et al., 2008 ) and overexpression of OsANS raises the antioxidant potential in O. sativa ( Reddy et al., 2007 ). The accumulation of anthocyanins has often been shown to be induced by (UV-) light ( Takahashi et al., 1991 ; Stapleton and Walbot, 1994 ; Meng et al., 2004 ). We therefore assume that also Musa ANS could be involved in such stress response.

Very recently, a R2R3-MYB-type transcription factor (MaMYB4) has been identified as a negative regulator of anthocyanin biosynthesis in Musa acting as repressor on the MusaANS promoter ( Deng et al., 2021 ). These results give a first insight into the transcriptional regulation of MusaANS expression and confirm the role of MusaANS in the anthocyanin biosynthesis in Musa . Taking all available evidence into account, MusaANS encodes a functional ANS with a possible involvement in the plants’ stress response.

To deepen the knowledge about 2-ODDs and other enzymes involved in Musa flavonoid biosynthesis, it would be beneficial to acquire even more spatially and timely highly resolved transcriptome and in particular metabolite data. This data could serve as a starting point for the analysis of organ-, stress- or development-specific enzyme activities and functions, as well as possible substrate preferences. It could also be used to elucidate the regulatory network of Musa flavonoid biosynthesis. Furthermore, knowledge about the influence of different stresses (e.g. pathogens, light and temperature) on specific transcriptomes and metabolomes of the Musa plant could help to widen the knowledge of flavonoid biosynthesis and particular the functionality of the Musa 2-ODD enzymes. This could also lead to the detection of possible restricted side activities or overlapping functionalities as described for 2-OODs in some other plant species ( Falcone Ferreyra et al., 2010 ; Park et al., 2019 ) and to further decode the cause of these multifunctionalities in 2-ODDs.

To conclude, in this study, the functionality of five 2-ODDs involved in flavonoid biosynthesis in Musa was demonstrated in vivo in bacterial cells and in planta . Knowledge gained about the structural genes MusaF3H1 , MusaF3H2 , MusaFLS1 , MusaFLS3 and MusaANS in a major crop plant provides a basis for further research towards engineered, increased flavonoid production in banana, which could contribute to the fruits’ antioxidant activity and nutritional value, and possibly even enhanced the plants’ defence against Foc -TR4.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , and further inquiries can be directed to the corresponding author.

Author Contributions

MB and RS planned the experiments. MB and CA performed the experiments and analysed the data. MB and SM interpreted the TLC data. RS and BW supervised the project and revised the manuscript. MB wrote the initial draft. All authors read and approved the final manuscript version.

This work was supported by the basic funding of the chair of Genetics and Genomics of Plants provided by the Bielefeld University/Faculty of Biology and the Open Access Publication Fund of Bielefeld University.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We are grateful to Melanie Kuhlmann for her excellent assistance in the laboratory and to Andrea Voigt for her competent help in the greenhouse. We thank Anika Beckers who contributed to creation of constructs with MusaFLS cDNAs and Prisca Viehöver for sequencing. In addition, we thank Thomas Baier for his support with the SDS-PAGE and Ashutosh Pandey for providing us with the banana cDNAs. We acknowledge support for the publication costs by the Open Access Publication Fund of Bielefeld University.

Supplementary Material

The Supplementary Material for this article can be found online at https://www.frontiersin.org/articles/10.3389/fpls.2021.701780/full#supplementary-material

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Keywords: banana, specialised metabolites, flavanone 3-hydroxylases, flavonol synthase, anthocyanidin synthase

Citation: Busche M, Acatay C, Martens S, Weisshaar B and Stracke R (2021) Functional Characterisation of Banana ( Musa spp.) 2-Oxoglutarate-Dependent Dioxygenases Involved in Flavonoid Biosynthesis. Front. Plant Sci . 12:701780. doi: 10.3389/fpls.2021.701780

Received: 28 April 2021; Accepted: 20 July 2021; Published: 17 August 2021.

Reviewed by:

Copyright © 2021 Busche, Acatay, Martens, Weisshaar and Stracke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ralf Stracke, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Utilization of banana waste as a resource material for biofuels and other value-added products

• Original Article
• Published: 17 January 2022
• Volume 13 , pages 12717–12736, ( 2023 )

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• Geetika Gupta 1 ,
• Manoj Baranwal 1 ,
• Sanjai Saxena 1 &
• M. Sudhakara Reddy 1  

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Banana is one of the most important food crops which is generally planted in tropical countries and has beneficial applications in the food industry. A large amount of by-products such as leaves, inflorescence, pseudostem, and rhizomes serves as a source for different industries. Most of these by-products may serve as an undervalued commodity with a limited commercial value, application and in some cases, it is considered as an agricultural waste. This also paves the way to utilize a huge amount of untapped biomass and resolve some of the environmental issues. Most of the edible bananas are cultivated mainly for their fruits, thus, banana farms could generate several tons of underused by-products and wastes. The present review mainly discusses the utilization of banana by-products such as peels, leaves, pseudostem, pseudostem juice, stalk, and inflorescence in various industries as a thickening agent, alternative source for renewable energy, nutraceuticals, livestock feed, natural fibers, coloring agents, bioactive compounds, and bio-fertilizers. Banana waste serves as a potential source for the production of valuable products and preserves renewable resources and provides additional income to the farming industries.

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Acknowledgements

Authors thankfully acknowledge the financial assistance provided by the Department of Science and Technology, Ministry of Science and Technology, Government of India under the project “Utilization of Banana Stem Juice for renewable energy and value added products.” The authors are grateful to the Director, Thapar Institute of Engineering and Technology, Patiala, India, for providing the facilities to complete the research work.

The financial support for carrying out the study was received under project entitled “Utilization of banana stem juice for renewable energy and value added products” from Department of Science and Technology, Govt. of India (DST/SSTP/Haryana/345).

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MSR involved in conceptualization and funding acquisition; GG participated in writing—original draft preparation; MBW and SS participated in review and editing. All authors participated in the final improvement of the manuscript.

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Gupta, G., Baranwal, M., Saxena, S. et al. Utilization of banana waste as a resource material for biofuels and other value-added products. Biomass Conv. Bioref. 13 , 12717–12736 (2023). https://doi.org/10.1007/s13399-022-02306-6

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DOI : https://doi.org/10.1007/s13399-022-02306-6

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AI-powered banana diseases and pest detection

• Michael Gomez Selvaraj   ORCID: orcid.org/0000-0003-2394-0399 1 ,
• Alejandro Vergara 1 ,
• Henry Ruiz 2 ,
• Nancy Safari 3 ,
• Sivalingam Elayabalan 4 ,
• Walter Ocimati 5 &
• Guy Blomme 6  

Plant Methods volume  15 , Article number:  92 ( 2019 ) Cite this article

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Banana ( Musa spp.) is the most popular marketable fruit crop grown all over the world, and a dominant staple food in many developing countries. Worldwide, banana production is affected by numerous diseases and pests. Novel and rapid methods for the timely detection of pests and diseases will allow to surveil and develop control measures with greater efficiency. As deep convolutional neural networks (DCNN) and transfer learning has been successfully applied in various fields, it has freshly moved in the domain of just-in-time crop disease detection. The aim of this research is to develop an AI-based banana disease and pest detection system using a DCNN to support banana farmers.

Large datasets of expert pre-screened banana disease and pest symptom/damage images were collected from various hotspots in Africa and Southern India. To build a detection model, we retrained three different convolutional neural network (CNN) architectures using a transfer learning approach. A total of six different models were developed from 18 different classes (disease by plant parts) using images collected from different parts of the banana plant. Our studies revealed ResNet50 and InceptionV2 based models performed better compared to MobileNetV1. These architectures represent the state-of-the-art results of banana diseases and pest detection with an accuracy of more than 90% in most of the models tested. These experimental results were comparable with other state-of-the-art models found in the literature. With a future view to run these detection capabilities on a mobile device, we evaluated the performance of SSD (single shot detector) MobileNetV1. Performance and validation metrics were also computed to measure the accuracy of different models in automated disease detection methods.

Our results showed that the DCNN was a robust and easily deployable strategy for digital banana disease and pest detection. Using a pre-trained disease recognition model, we were able to perform deep transfer learning (DTL) to produce a network that can make accurate predictions. This significant high success rate makes the model a useful early disease and pest detection tool, and this research could be further extended to develop a fully automated mobile app to help millions of banana farmers in developing countries.

Bananas ( Musa spp.) are one of the world’s most important fruit crops in terms of production volume and trade [ 1 ]. Though a major staple food in Africa, Asia, and Latin America, only 13% of bananas produced are globally traded [ 2 ], clearly indicating the fruit’s importance in domestic markets and food security. In East and Central Africa, it is a substantial dietary component, accounting for over 50% of daily total food intake in parts of Uganda and Rwanda [ 3 ]. Smallholder farmers, representing 85% of the world’s farms [ 4 ], face many abiotic and biotic constraints. Several banana pests and diseases have caused significant yield losses across production landscapes [ 5 ] and are a significant threat to global food security. Therefore, early detection of pests and diseases in the field is a first crucial step. Traditional pest and disease identification approaches rely on agricultural extension specialists, but these approaches are limited in developing countries with low human infrastructure capacity. Many smallholder farmers rely on empirical knowledge, which is less effective in overcoming farming challenges [ 6 ]. The early identification of a crop disease or pest can lead to faster interventions with resulting reduced impacts on food supply chains.

Artificial intelligence (AI) with deep learning models which help to identify plant diseases by the plant’s appearance and visual symptoms that mimic human behavior should be considered [ 7 ]. Smartphone-based AI apps could alert farmers and expedite disease diagnosis, thus preventing the possible outbreak of pests and diseases [ 8 ]. Even though many farmers of developing countries do not have access to these advanced tools, internet infiltration and smartphone penetration offer new outfits for in-field crop disease detection. The Global System for Mobile Association (GMSA) predicted that global smartphone subscriptions would reach 5 billion by 2020, of which nearly one billion in Africa [ 9 ]. We do believe that cutting-edge technologies like AI, IoT (Internet of Things), robotics, satellites, cloud computing, and machine learning are transfiguring agriculture and helping farmers foresee their near future.

Deep learning is a novel method for image processing and object detection with greater accuracy in the classification of various crop diseases [ 10 ]. Transfer learning is one such popular approach in deep learning, where pre-trained models are adapted to do a new task. Deep transfer learning (DTL) generates a fresh framework for digital image processing and predictive analytics, with greater accuracy and has huge potential in crop disease detection. DTL approach also offers a promising avenue for in-field disease recognition using large trained image datasets and bids a shortcut to the developed models to meet the restrictions that are offered by mobile application [ 11 ]. This would have a distinct practical value for real field environment.

Earlier investigations have validated AI-based recognition of crop diseases in wheat [ 12 ], cassava [ 11 ] and on datasets of healthy and diseased plants [ 8 , 13 ]. Crop disease recognition based on a computerized image system through feature extraction has revealed promising results [ 14 ] but extracting features is computationally rigorous and involves expert knowledge for robust depiction. Only few restricted large, curated image datasets of crop disease library exists [ 10 ]. The PlantVillage platform holds over 50,000 images of different crops and diseases [ 15 ]. However, most of these images were taken with detached leaves on a plain background, and CNN trained on these images did not achieve well when using real field images [ 8 ]. To build robust and more practical detection models, plenty of healthy and diseased images taken from different infected parts of the plants, and growing under different environmental conditions are needed. These images subsequently need to be labeled and pre-screened by plant pathology experts. So far, existing crop disease detection models are mostly focusing on leaf symptoms. Unfortunately, numerous symptoms also appear in other parts of the plant and the best examples are banana pest and disease linked symptoms.

The objective of this study was to apply state-of-the-art deep learning techniques for the detection of visible banana disease and pest symptoms on different parts of the banana plant. We also considered the potential for adapting pre-trained deep learning CNN models to detect banana disease and pest symptoms using a large dataset of experts’ pre-screened real field images collected from Africa and India.

Materials and methods

System description.

Our DTL system dataset consists of five major banana diseases along with their respective healthy classes; dried/old age leaves and banana corm weevil ( Cosmopolites sordidus ) damage symptom classes (Table  1 ). Since these major diseases and pest can affect different parts of the banana plant, we ended up with six different models (entire plant, leaves, pseudostem, fruit bunch, cut fruits and corm) and 18 different classes (Table  1 ) to achieve maximum accuracy. An overview of the DTL system is illustrated in Fig.  1 .

Overview of deep transfer learning (DTL) system for banana disease and pest detection

Dataset collection

Our dataset comprises of about 18,000 field images of banana, collected by banana experts, from Bioversity International (Africa) and Tamil Nadu Agricultural University (TNAU, Southern India) (Additional file 2 : Table S1). These field images were captured under different environmental conditions to build a robust model. For that purpose, various banana experts visited several banana farms located in disease/pest hotspots of Africa (Eastern Democratic Republic of Congo, Central Uganda, Burundi and Benin Republic) and Southern India (Tamil Nadu and Kerala). Our current dataset consists of various types of data, including images with various resolutions (cell phone, tablets, standard RGB camera); light conditions depending on time of image taking (e.g., illumination), season (e.g., temperature, humidity), and different environmental locations (e.g., Africa, India). We have collected the images at different growing phases of the crop (i.e., vegetative and reproductive). To prevent our model from being confused between dried/old leaves and diseased leaves, we also collected numerous images of dried and old age leaves at different plant growth stages. Images of a specific disease were collected from different varieties, at different plant growth stages and in different environments (Africa and India) in order to enrich the image library (Additional file 2 : Table S1).

Our current CIAT banana image library consists of approximately 18,000 real field images. But in this present study, our datasets cover healthy plants (HP), dried/old age leaves (DOL) and a balanced number of images (700 images) from five major diseases such as, Xanthomonas wilt of banana (BXW), Fusarium wilt of banana (FWB), black sigatoka (BS), yellow sigatoka (YS) and banana bunchy top disease (BBTV) along with the banana corm weevil (BCW) pest class. The major pest (corm weevil) and disease class symptoms and their control measures are presented in Additional file 2 : Table S2. Since symptoms of different diseases and pests are seen at different parts of the banana plants, we captured images of all the plant parts (Fig.  2 ). Our current library was structured based on the disease and the affected plant parts so each part of the plant represents a model.

Detected classes and expected output from each model. a Entire plant affected by banana bunchy top virus (BBTV), b leaves affected by black sigatoka (BS), c cut pseudostem of Xanthomonas wilt (BXW) affected plant showing yellow bacterial ooze, d fruit bunch affected by Xanthomonas wilt (BXW), e cut fruit affected by Xanthomonas wilt (BXW), f corm affected by banana corm weevil (BCW)

Data labeling

The image tagging process was done using LabelImg software [ 16 ]. Labels and coordinates of the boxes were saved as an XML file, in the same format (PASCAL VOC) used by ImageNet [ 17 ]. The number of annotated samples corresponded to the number of bounding boxes labeled in each image. Every image could contain more than one annotation depending on the number of infected areas of the plant parts (Fig.  3 ).

Demonstration of the disease detection process during training. a Original raw images, b labeled process (desired output), c disease detection

CNN architectures

To train the models, we used three different architectures, such as ResNet50 [ 18 ], InceptionV2 [ 19 ] and MobileNetV1 [ 20 ]. For the object detector model architecture, we chose Faster RCNN with ResNet50 and InceptionV2 due to their accuracy. Single Shot Multibox (SSD) model was selected with the MobileNetV1 since this was one of the fastest object detection models available in TensorFlow [ 21 ]. To train these models, we used a python deep learning library called TensorFlow and its object detection Application Programming Interface (API) with the Graphics Process Unit (GPU) version [ 22 ]. Pre-trained models were trained with COCO (Common objects in context) data set [ 23 ], and it is openly available in the TensorFlow object detection API zoo models. These three architectures were re-trained using the transfer learning approach from the pre-trained versions. To finetune the original hyperparameters, the following configuration changes were executed, batch size and epoch number. The batch size was changed only in the MobileNetV1 from 24 to 6, and the epoch number was kept 15,000 for all the architectures trained.

One of the most challenging tasks in machine learning is splitting the data without suffering from overfitting, under fitting or generalization hitches. Nevertheless, there are several refined statistical sampling methods which provide a path to deal with these common disputes [ 24 ]. For developing banana model, our dataset was divided into the following proportions of 70%, 20%, 10%, for training (Ttr), validation (Tv) and testing (Tt), respectively. The simple random sampling (SRS) technique was selected, considering that it is efficient and simple to implement [ 24 ].

Performance metrics

Loss function.

Classification loss is used to measure the model’s confidence by classifying the pixels region delimitated by the bounding box [ 25 ] and the localization loss measures the geometric distance between the predicted bounding box and the ground truth annotation (validation bounding boxes). In this paper, we used the object detection API [ 26 ] to estimate the total loss function to measure model performance. The overall loss function or total loss was a weighted combination of the classification loss (classif) and the localization loss (loc).

The mean average precision (mAP) was used as the validation metric for banana disease and pest detection. Precision refers to the accuracy. mAP score was calculated as follows: Average across the number of classes of the true positive divided by the true positives plus false positive as in the following equation

Confusion matrix

In addition to mAP score, we also computed a confusion matrix (CM) for each selected model based on the object detection script [ 27 ]. Computation of CM protocol is described below. For each detection, the algorithm mines all the ground-truth boxes and classes, along with the detected boxes, classes, and scores of Intersection over Union (IoU). Only detections with a score ≥ 0.5 were considered and anything under this threshold were excluded. For each ground-truth box, the algorithm creates the IoU with each detected box. A match was found if both boxes had an IoU ≥ 0.5. The list of matches was trimmed to remove duplicates (ground-truth boxes that match with more than one detection box or vice versa). If there are duplicates, the best match (greater IoU) was continually selected. The CM was updated to reflect the resultant matches between ground-truth and detections. A detected box was reflected as correct where the intersection over union (IoU) of that box and the corresponding ground-truth box was ≥ 0.5. The formula for calculating IoU is shown in Fig.  4 . In the final step, the CM was normalized.

Diagram explaining intersect over union (IOU) calculation. a Intersection over union (IoU) formula where B 1 : ground truth bounding box and B 2 : predicted bounding box, b samples of calculated scores

Software and hardware system

The list of hardware and software used in this study was depicted in Table  2 . For algorithm implementation, and data wrangling scripts, python 3.6 was used. Then models were re-trained using the powerful library called TensorFlow object detection API [ 28 ] developed by Google, this library support control process unit (CPU) and GPU training and inference.

Results and discussion

Banana dataset collection and annotation.

Banana is liable to various types of pests and diseases for which symptoms occur in different parts of the plant (Table  1 ). The occurrences of these diseases depends on many factors, such as environment, temperature, humidity, rainfall, variety, season, nutrition, etc. For instance, certain diseases are localized in a particular country, region or continent, such as, Xanthomonas wilt of banana which is very specific to Africa. Therefore, reliable and accurate image collection at hotspots and strong labeling is very important. Since we are aiming for a global solution, we collected the image dataset of major banana diseases from different disease hotspots through our CGIAR network. Publicly available datasets poorly cover banana disease/pest symptom images, and the PlantVillage public dataset so far doesn’t include banana images. We collected our own datasets of leaves infected by specific pathogens at different infection stages and other infected plant parts such as entire plants, fruit bunch, cut fruits, pseudostem and corms etc. with the help of well-trained banana experts using different cameras with various resolutions (Table  1 , Fig.  2 ). Currently our CIAT-Bioversity, CGIAR dataset contains more than 18,000 expert pre-screened original field images, but in this study we utilized only 12,600 images to create banana image data sets. Since our ultimate aim is to develop a mobile-assisted banana disease detection tool targeting banana farmers across the globe/wordwide and the scientific community around the world, we enriched our image library with a diverse collection of images from different disease hot spots (Additional file 2 : Table S1). To build a robust model, images were captured in real field scenarios on banana farms. A heterogeneous background is an essential feature of any real field images, most of the publicly available datasets are images of leaves in a controlled environment and simple background. For this reason, we tried to create many variations while collecting data from the field. The more the variation in the dataset, the better is the generalization of the trained model. The images were captured with different camera devices (Additional file 2 : Table S1) with diverse background. Furthermore, the challenging part of our image dataset is the background variations caused by the surroundings of the field, dried leaves on the floor, overlapping leaves from neighboring plants etc. This made our model more robust to adapt any changes in the real-time background.

We annotated the images to train our CNN by setting the images of different classes in distinct folders. We randomly picked 75% of images of each class and put them into a training set. Likewise, another 25% of images of each class were put into a test set. The training and the test set both contained 700 real field images per class (700 × 18 classes = 12,600) which has made the data set well balanced. The categories and the number of annotated samples used in our system can be seen in Table  1 . We carried out a strong labeling approach whereby the banana experts confirmed the typical symptoms on each and every image of the data set, as a result we ended up with a total of 30,952 annotations (Table  1 ). Even though this strategy is time-consuming, we worked with three human experts to annotate the whole banana dataset which took almost 4 weeks. The tediousness of data collection and labeling had forced earlier studies [ 29 , 30 ] to use small datasets to train and test classifiers. The use of small labeled datasets is also a limiting factor in machine learning, and it can lead to over or underfitting [ 31 ]. Most of the publicly available data sets are weakly labeled and resulted in poor performance.

We summarized the total loss function (Additional file 1 : Fig. S1a–f) only for the winner models (Additional file 2 : Table S3). In general, we could observe that the accuracy increased while loss decreased gradually with epoch. For Corm damage images, the reported error was high until the 1500th iteration, then started to go down and after the 4000th step remained constant (Additional file 1 : Fig. S1f), the same behavior was noticed in Pseudostem and Cut Fruits (Additional file 1 : Fig. S1c, e), where after 2000 iterations the error remained constant until the end. For the entire plant and leaves (Additional file 1 : Fig. S1a, b), although a loss was found to be below 0.3 in the last iteration, it suffered due to lot of variations, which was evident since these two models (entire plant and leaf) were found to be low accurate compared to other models studied (Table  3 ). The probable reason was clearly explained further by other performance metrics below.

Performance metrics and validation of developed models

In recent years, deep learning techniques, and in particular convolutional neural networks (CNNs), recurrent neural networks and long-short term memories (LSTMs), have shown great success in visual data recognition, classification, and sequence learning tasks. In the field of computer vision specifically, a set of CNN architectures have been emerging and they have proved to achieve tasks like object classification, detection and segmentation. In this paper, we retrained MobileNetV1, InceptionV2 and RestNet50 architectures using transfer learning to detect the banana pest and diseases. In order to improve the accuracy, the diseases were grouped by plant parts, and a different model was trained for each plant part (Table  1 ). Transfer learning is a progress that has the huge potential of being extensively used in crop phenomics and pest and disease detection. Transfer learning is particularly interesting, as its improved performance of deep neural networks by evading intricate data mining and labeling efforts [ 32 ].

There are different metrics to measure the accuracy and effectiveness in object detection models. In this study, we used mAP which is one of the widely used metrics in the literature [ 33 , 34 ], especially for detection. Additionally, for each best model, a confusion matrix was generated. Earlier studies on detection revealed that the mAP score had become the accepted and standard way in competitions such as PASCAL VOC [ 35 ], ImageNet, and COCO datasets. More detail results are described below.

The accuracy of the models based on mAP score is presented in Table  3 . For the entire plant, leaves, pseudostem and fruit bunch models performed better in Faster R-CNN (faster regions with convolutional neural network) ResNet50 than others tested, which achieved an mAP score of 73%, 70%, 99%, and 97%, respectively. For cut fruits and corm, Faster R-CNN InceptionV2 worked better with the mAP accuracy of 95% and 98%, respectively. Fuentes et al. 2017 [ 33 ], used three CNN-based systems (Faster R-CNN, R-FCN and SSD) which performed object localization and disease diagnosis processes simultaneously and their system achieved more than 86.0% mean average precision on annotated tomato leaf images. In this present study, ResNet50 and InceptionV2 models have almost similar performance in all the cases compared to MobileNetV1 (Table  3 ). In generalized recognition, Faster R-CNN [ 36 ], models have been widely used and have achieved good results.

In this research, to achieve greater accuracy, we considered the complexity of the model as an important factor to select the best architectures for the training set. This characteristic could be measured by counting the total amount of learnable parameters or the number of operations. As a result, we selected three architectures (Inception, ResNet, MobileNet). Since complexity is associated with the capacity of the model to extract more features from the images, it is expected inceptionV2 to be the most accurate among three architectures. However, it is always a trade-off between complex and simple architecture especially when you specifically think about mobile application.

We also noticed higher accuracy (more than 95%) for pseudostem, fruit bunch and corm compared to entire plant (73%) and leaf models (70%). This was expected in the entire plant model due to background noise in field environment, multiple classes (Fig.  5 b) in the single image and wide angle. Wide-angle images are often more complex due to the substantial overlap of multiple leaves and symptoms are scattered in different leaves. In the case of banana it is much more complex because of the specific plant morphology and large leaf size. We also observed that during the labeling process, a single class per image was working as a ground-truth for the model (Fig.  5 a, b). But in real life scenario, one image could have multiple classes as seen in Fig.  3 . Developed entire plant and leaf model from this study is finding multiple classes in single image (Fig.  5 b) which is more practical and useful in the real-time field application, since our ground-truth data is solely grouped on single class (Fig.  5 a) which brought the mAP score lower than expected and it was the main cause. It was a little surprise for leaf model where we expect more than 90% accuracy since it is not very wide angle like the entire plant class. But these wide-angle images from field environment expected to have more background noises. To confirm these results in leaf model, we did an additional test to select images containing only one class per image, which reflected on higher accuracy more than 70% (Additional file 2 : Table S4). Moreover, this accuracy was further increased (more than 90%) when the new image dataset contain only one focused leaf per image (Additional file 2 : Table S4). From these results, it is clear the complexity of the banana leaf morphology, disease symptoms, multiple classes in single image, field background noises etc. Unlike other crops such as rice, wheat, and cassava, banana leaves are very big, that makes the angle wider than other crops which increases the complexity in real-time field images (Fig.  5 ). In the case of pseudostem, fruit bunch, cut fruits and corm field images used in this study have more focused images towards the object with less background variation and single class per image which reduced the complexity and improved mAP score (Table  3 ).

Comparison between ground-truth labeled image and the predicted classes by model. a Ground-truth labeled image of FWB, b image after predicted by a model

In the field of deep learning, specifically the problem of statistical classification, the confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of the performance of an algorithm. It considers different metrics: the true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) etc. Based on the results obtained on the test dataset, we generated a confusion matrix for each of the best architectures (Fig.  6 a–f). Each confusion matrix gave us a accuracy per disease (classes) and quantitative representation of the classes in which the model is misclassified or confused (Fig.  6 a–f). Due to the complexity of the patterns shown in each class from different plant parts, the system tends to be confused on several classes that results in lower performance. Based on the results, we can visually evaluate the performance of the classifier and determine which classes and features are more prone to confusion. If the number of misclassifications between two particular classes becomes high, it indicates that we need to collect more data on those classes to properly train the convolutional architecture so that it can differentiate between those two classes. For this purpose, we also generated confusion matrix on our validation set for each best CNN architecture. Furthermore, it helps us to identify a future solution in order to avoid those inter-class confusions.

Confusion matrix for the best models identified in this study. a Entire plant—ResNet, b leaves—ResNet, c pseudostem—ResNet, d fruit bunch—ResNet, e cut fruits—inception, f corm—inception

On comparing among the models, leaves produced a lot of confusion and low accuracy (57%) especially yellow sigatoka leaf spot classes (Fig.  6 b), this was expected since YS and BS commonly produce similar symptoms in advanced stages, but early stage symptoms are unique (Additional file 1 : Fig. S2). It is worth mentioning that yellow leaf spot disease appearing more frequently in Asia and Latin America and black leaf spot in Africa, and their treatment and disease controlling measures are almost similar. To handle these issues, we are currently collecting and labeling images of early stage symptoms for improving the accuracy of the model, and the ability to generalize. Because the dataset is not big enough, it was not considered in this study. We also observed medium prediction accuracy in the dried/old age leaf classes (Fig.  6 b), it was obvious that, advanced stages of all leaf diseases will turn to be like dried/old age leaves and we expected this results. So early and mid-stage leaf symptoms are very important to detect the diseases with more accuracy. As we expected, the entire plant, corm, pseudostem, fruit bunch and cut fruits models, we had not found any accuracy or misclassification problems (Fig.  6 a, c–f), which was ranged between 90 and 100% accuracy.

Conclusions and future directions

Many computer visioned approaches for automated crop disease detection and classification have been reported, but still, a detailed exploration of real-time pest and diseases recognition is lagging. In this paper, a novel method of using deep transfer learning method was explored in order to automatically detect banana pest and disease symptoms on different parts of the banana plants using real-time field images. This system introduces a practical and applicable solution for detecting the class and location of diseases in banana plants, which represents a main comparable difference with other methods for plant diseases classification. The developed model was able to detect the difference between healthy and infected plant parts for different banana diseases. All images used in this study are available upon formal request through PestDisPlace ( https://pestdisplace.org/ ) [ 37 ]. It consists of more than 18,000 original expertly pre-screened banana images collected on real farmer’s field in Africa, Latin America and South India and was extended to more than 30,952 annotations. The experimental results achieved accuracy between 70 and 99%, of the different models tested. The robust models developed from this research will be more useful to develop the decision-support system to help early identification of pest and diseases and their management. Models developed in this study are currently utilized to develop a banana mobile app which is currently being tested by collaborative partners in Benin, DR Congo, Uganda, Colombia, and India (Additional file 1 : Fig. S3). The developed model system from this study is easily transferable to other CGIAR mandatory crops.

Future work will comprise the development of a broad structure consisting of server side machinery containing a trained model and an application for smartphone devices with features such as displaying recognized diseases in other CGIAR mandatory crop such as Brachiaria, common bean, cassava, potato and sweet potato. Additionally, future work will involve disseminating the usage of the model by training it for banana disease recognition on wider applications, merging aerial images of banana growing regions captured by drones and convolution neural networks for instant segmentation of multiple diseases. By extending this research, we are hoping to achieve a valuable impact on sustainable development and strengthen banana value chains.

Availability of data and materials

The remotely sensed and field sampling data used in this study is available from the corresponding author upon reasonable request.

Abbreviations

artificial intelligence

Application Programming Interface

banana bunchy top virus

banana corm weevil

black sigatoka

Xanthomonas wilt of banana

Consultative Group on International Agricultural Research

International Center for Tropical Agriculture

convolutional neural network

common objects in context

confusion matrix

control process unit

deep convolutional neural networks

dried/old leaves

deep transfer learning

faster regions with convolutional neural network

false negatives

false positives

Fusarium wilt of banana

graphics process unit

healthy plant

Internet of Things

intersection over union

long-short term memories (LSTMs)

mean average precision

single random sampling

single shot detector

true negatives

true positives

yellow sigatoka

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Acknowledgements

The authors would like to thank the International Center for Tropical Agriculture (CIAT) IT unit for providing facilities and logistics support. Joe Tohme, Manabu Ishitani and Wilmer Cuellar from CIAT for guidance and support to do the research. The authors would also like to acknowledge Milton Valencia, Jorge Casas, Maria Montoya, Crysthian Delgado and Frank Montenegro for their help in image annotation and data collection. Thanks to Jules Ntamwira, Jean-Pierre Mafuta and Aman Omondi of Bioversity International, Africa and Deo Kantungeko of IITA, Burundifor their immense support to collect smartphone images. The farmers of Tamil Nadu Banana grower’s federation, Trichy and planters of Tamil Nadu Hill Banana Growers Federation, Lower Palani Hills, Tamil Nadu India are also acknowledged for helping to collect data images. The authors also thank two anonymous reviewers for their detailed suggestions for improving the manuscript. As well as Angela Fernando, CIAT and Escalin Fernando, India for formatting and technical editing.

Funding for field smartphone image collection was provided by Bioversity International in the framework of the RTB-CC3.1 cluster and by the CIAT Agrobiodiversity Research Area to carry out the image processing work & preliminary app development (AGBIO1). This study was supported by the CGIAR Research Program on Roots, Tubers and Bananas (RTB). We thank the RTB Program Management Unit that supported this study and the CGIAR Fund Donors who support RTB ( www.cgiar.org/who-we-are/cgiar-fund/fund-donors-2 ).

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Michael Gomez Selvaraj & Alejandro Vergara

Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, USA

Bioversity International, Bukavu, South Kivu Province, Democratic Republic of Congo

Nancy Safari

Department of Biotechnology, Imayam Institute of Agriculture and Technology (IIAT), Affiliated to Tamil Nadu Agricultural University (TNAU), Tiruchirappalli, Tamil Nadu, India

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MGS, HR and AV designed the study, performed the experiments and are the main contributing authors of the paper. HR, AV, and MGS carried out data annotations, trained algorithms and analyzed the data. GB, SE, NS and WO collected over 17,000 images of disease and pest symptoms/damage, confirmed the symptoms and pre-screened all the images collected in Africa, Malaysia and India. MGS written the paper. All authors read and approved the final manuscript.

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Additional file 1: figure s1..

Loss function curve for the winner models. a Entire plant—ResNet, b Leaves—ResNet, c Pseudostem—ResNet, d Fuit bunch—ResNet, e Cut fruits—Inception, f Corm—Inception. Fig. S2. Early and late stage symptoms of banana leafspots. a Black sigatoka (BS) late stage, b Yellow sigatoka (YS) late stage, c Black sigatoka (BS) early stage, d Yellow sigatoka (YS) early stage. Fig. S3. Developed mobile application for Banana disease and pest detection. a Initial screen, b Image taking and Scan, c Diagnostic screen, d Recommendations and management.

Additional file 2: Table S1.

Overview of banana data set collections, locations and image acquisition. Table S2. Description of major banana diseases and pest symptoms with their control measures. Table S3. Winner architecture for the models developed in this study. Table S4. mAP score metrics of leaf classes before and after segmentation.

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Selvaraj, M.G., Vergara, A., Ruiz, H. et al. AI-powered banana diseases and pest detection. Plant Methods 15 , 92 (2019). https://doi.org/10.1186/s13007-019-0475-z

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Production, application and health effects of banana pulp and peel flour in the food industry

Amir amini khoozani.

Department of Food Science, University of Otago, PO Box 56, Dunedin, 9054 New Zealand

Alaa El-Din Ahmed Bekhit

The past 20 years has seen rapid development of value-added food products. Using largely wasted fruit by-products has created a potential for sustainable use of these edible materials. The high levels of antioxidant activity, phenolic compounds, dietary fibres and resistant starch in banana pulp and peel have made this tropical fruit an outstanding source of nutritive ingredient for enrichment of foodstuffs. Accordingly, processing of separate banana parts into flour has been of interest by many researchers using different methods (oven drying, spouted bed drier, ultrasound, pulsed vacuum oven, microwave, spray drying and lyophilization). Regarding the high level of bioactive compounds, especially resistant starch in banana flour, the application of its flour in starchy foods provides a great opportunity for product development, even in gluten free foods. This review aims to provide concise evaluation of the health benefits of banana bioactive components and covers a wide range of literature conducted on the application of different parts of banana and the flour produced at various ripeness stages in the food industry. Of particular interest, the impact of drying methods on banana flour properties are discussed.

Introduction

Banana is a tropical climacteric fruit and universally comprises a number of species in the genus Musa of the family Musaceae . It is one of the most favored fruits in the world and the fourth most important crop produced globally (Aurore et al. 2009 ). Nearly all of the identified cultivars derived from two diploid species, Musa acuminata and Musa balbisiana , in which the Cavendish variety is the most common. Plantain is related to the hybrid triploid cultivars of banana and is longer, more angular and diverse in shape. Even in the mature state, plantain is firmer than Cavendish and thus it is less valued as a fresh product (Zhang et al. 2005 ). According to the latest FAO statistics, Asia is the largest producer of banana with a share of 54.4% of the world’s banana production. With an average banana consumption of 12 kg per capita (FAOSTAT 2017 ), banana is amongst the world’s major food crops, after rice, wheat and maize.

Banana fruit consists of two parts: peel and pulp. Peel, which is the main by-product of banana, is about 40% of total weight of the fruit. Until recently, banana peel (Bpe) had no useful applications and was dumped as waste, contributing massive amounts of organic materials to be managed. Since researchers have begun to focus on studying the composition of BPe, several possible applications have emerged (Agama-Acevedo et al. 2016 ). Banana pulp (BP), which is the edible part of the fruit, has an abundant amount of nutrients. Studies conducted on BP have investigated different aspects ranging from its use as an ingredient for food enrichment to extraction and isolation of many health-beneficial components, such as different types of starch, cellulose and bioactive compounds (Singh et al. 2016 ). As stated by Kitts ( 1994 ), bioactive compounds are constituents with extra nutritional advantages that are naturally occurring in plants and foods in small amounts. They exert their beneficial biological effects by stimulating the probiotic growth and help in the prevention of cardiovascular disease and cancer (Kris-Etherton et al. 2002 ). Phenolics, carotenoids, flavonoids, biogenic amines, phytosterols, and other phytochemicals can be found in BP and peel (Pereira and Maraschin 2015 ). Due to the presence of these compounds, bananas have a higher antioxidant capacity than some berries, herbs and vegetables (Moongngarm et al. 2014 ). Bioactive compounds in different cultivars of banana and their health benefits were reviewed in details by Singh et al. ( 2016 ).

The increased attention to functional food products and health and wellbeing of consumers in the last decade led to an increased interest in vitamins, minerals, unsaturated fatty acids, bioactive compounds and fibre in food products. The utilization of by-products of fruits, especially banana, has become a trend as of late and many studies are in progress to evaluate their effects on food properties (Chávez-Salazar et al. 2017 ; Kaur et al. 2014 ). As approximately one-third of banana is lost due to the public tendency to consume only ripened fruit, utilization of different parts of the banana at different ripening stages has also gained interest over the past years (Sheikh et al. 2017 ).

This review discusses the health benefits of banana bioactive compounds and utilization of different parts of the banana and the flour produced at various ripeness stages in food applications. Of particular interest, various methods for producing banana flour are evaluated to highlight processing options and their influence on flour nutritional and functional properties.

Composition of banana

Banana pulp is a rich source of essential phytonutrients, including phenolic compounds and vitamins (B3, B6, B12, C and E). It also contains carotenoids, flavonoids, amine compounds and dietary fibre (DF).

Dietary fibres are indigestible carbohydrate polymers that are classified based on their water solubility into two types, soluble fibres (pectin and some hemicelluloses) and insoluble fibres (cellulose, lignin and resistant starch) (Alba et al. 2018 ). In general, it has been reported that Bpe contains more DF than BP (Garcia-Amezquita et al. 2018 ). Extracting pectin from the peels could increase their added value. In addition, BPe has high amount of lignin, cellulose and hemicelluloses fractions which can be extracted as a formed complex substrate named lignocellulosic biomass which could be used to produce bioethanol (Happi Emaga et al. 2008 ). Also Khamsucharit et al. ( 2018 ) signified that the extracted pectin from Bpe could be an alternative source for commercial pectin.

One of the most important DF which has gained a lot of attention in recent years is resistant starch (RS). It is mainly composed of the linear part of starch (amylose) which is fermented by probiotics in the colon, specifically Bifidobacterium and Lactobacillus species (Kale et al. 2002 ). This brings about the production of short chain fatty acids, mainly butyric acid, which has a key role in prevention of colorectal cancer (Amini et al. 2015 ). There are five types of RS introduced up to now: starch that is physically inaccessible in crop’s cell walls (RS1), granular native starch with high crystalline structure (RS2), retrograded starch achieved by heating and cooling of starchy foods (RS3), chemically modified starch (RS4) and amylose-lipid complex (RS5) (Amini Khoozani et al. 2019 ). Fractionation of the DF and RS is different parts of banana in different levels of maturation is shown in Table  1 .

Table 1

Composition of banana flour produced from different ripening stage (dry basis percentage)

RS resistant starch, DF dietary fiber

Unripe banana is rich in RS2, which is beneficial to colon health, while ripe banana contains more digestible starch and protein (Singh et al. 2016 ). Banana peel is a rich source of minerals, bioactive compounds and DF (Kusuma et al. 2018 ). Several studies reported the use of banana peel flour (BPeF) as a functional food source (Agama-Acevedo et al. 2016 ; Ramli et al. 2009 ; Ramli et al. 2010 ; Türker et al. 2016 ). According to some reports, both pulp and peel have high antioxidant activity (Agama-Acevedo et al. 2016 ; González-Montelongo et al. 2010 ). As lipid oxidation in food components is one of the unwanted reactions causing rancidity, food producers rely on synthetic antioxidants to minimize lipid deterioration. Potential health risks, however, is a limiting factor of using these preservatives extensively in food products, especially staple ones (Pathak et al. 2017 ). Given that BPe extract has been found to be non-toxic to human cells, more information has become available on using it as an inexpensive fruit by-product source of antioxidants (Segundo et al. 2017b ). The amount of ash, protein, crude fibre and digestible starch of BPeF was reported to be significantly higher than that of pulp, which makes the BPeF more effective as a functional additive (Nasrin et al. 2015 ). For instance, the higher quantity of ash can be valuable in treating deficiencies of minerals caused by celiac disease (Presutti et al. 2007 ). Additionally, several studies have shown the application of BPe as a low-cost precursor for producing materials such as anionic dye and heavy metal adsorbents (Mahindrakar and Rathod 2018 ; Munagapati et al. 2018 ; Oyewo et al. 2018 ; Singh et al. 2018 ; Vilardi et al. 2018 ), recovering phenolic compounds (Vu et al. 2018 ), producing cellulose nanofibers (Costa et al. 2018 ; Harini et al. 2018 ; Tibolla et al. 2018 ), as well as bioethanol (Berawi and Bimandama 2018 ; Prakash et al. 2018 ) and pectin extract (Khamsucharit et al. 2018 ). In the following sections, some of the exclusive added value components in banana are introduced.

Health effects of banana bioactive compounds

Carotenoids are natural antioxidants which contribute to the stability of foods during storage. Previous studies documented the existence of various carotenoids in banana fruit (Davey et al. 2006 ). Although some suggested that the cultivars genotype specifies the quantity of carotenoids, they mostly concurred that the amount of trans-alpha and trans-beta carotene comprised the majority of pro-vitamin A compounds (Yan et al. 2016 ). Another significant carotenoid reported was lutein, which exhibited antioxidant properties and an inhibitory effect on the age-related macular degeneration. Interestingly, it has been identified that green banana peel (GBPe) has substantially higher carotenoids than the pulp (Davey et al. 2006 ).

Phytochemicals, especially phenolic acids, are the main bioactive compounds known for exerting health benefits. Unexpectedly, the percentage of phenolic compounds have been reported to be greater in peel than in pulp (Kanazawa and Sakakibara 2000 ). Similarly, it was demonstrated that the quantity of gallocatechins in peel was five times greater than pulp. With regard to presented results, BPe extract was found to inhibit lipid oxidation better than pulp extract (Someya et al. 2002 ). Recently, it has been shown that gallocatechin extracted from GBPe was effective in the healing of surgical wounds in rats (Von Atzingen et al. 2015 ). Correspondingly, a unique flavonoid named leucocyanidin was found in aqueous extract of unripe plantain pulp, which is now known to be effective in the treatment of gastric diseases (Lewis et al. 1999 ).

Biogenic amines play a key role in the prevention of depression. Catecholamines, dopamine, norepinephrine (noradrenaline) and epinephrine (adrenaline) are the best-known examples of these bioactive compounds which regulate hormones in glycogen metabolism (González-Montelongo et al. 2010 ). Results of dopamine levels in different ripening stages of banana revealed an inverse relation between its concentration and fruit’s maturity, noting that BPe contained more dopamine than pulp (Kanazawa and Sakakibara 2000 ). The possibility of BPe application as a dopamine biomass source for the prevention of Parkinson’s disease was also discussed (Pereira and Maraschin 2015 ). Furthermore, in comparison to other plant residual biomasses, dopamine has exerted higher antioxidant capacity in vitro (Babbar et al. 2011 ).

There is a wide range of DF in banana fruit, including pectin, cellulose, lignin and hemicellulose which can be found naturally in banana flour (BF). Amongst them, RS is the most notable one which provides bioactive effects (Thebaudin et al. 1997 ). Resistant starch, which is mainly composed of the linear part of starch (amylose), is resistant to digestion in the small intestine after 2 h incubation (Homayouni et al. 2014 ). After it reaches the colon undigested, it will be fermented by membrane microbiota (mainly probiotics) and cause pH reduction. Therefore, the environment will be undesirable for the growth of pathogenic microbiota and formation of carcinogenic cells (Khalili and Amini 2015 ).

A considerable amount of literature has been published on the direct relationship between RS intake and reduction of the large bowel cancer risks either in vitro or in vivo; it seems that RS may be a major protective factor against colorectal cancer (Panebianco et al. 2017 ; Yin and Zhao 2017 ).

It was shown that the amount of RS decreased from 8 to 2% with progression in peel ripeness. However, the percentage of total dietary fibre (TDF) slightly increased in yellow banana peel (YBPe) (Ramli et al. 2010 ). In a parallel report, the RS and TDF content of green banana pulp flour (GBPF) reported 49.9% and 7.2%, respectively (Menezes et al. 2011 ). In general, it can be concluded that because approximately 70% (dry basis) of the peeled green banana comprises RS2, the unripe pulp is a remarkable source of this kind of bioactive compound (Wang et al. 2017a ).

It is well-known that phytosterols act as immune system modulators and exert cholesterol-lowering and anticancer properties in the intestine (González-Montelongo et al. 2010 ).

As reported by (Marangoni and Poli 2010 ), a daily intake of phytosterols up to 3 grams/day significantly reduced total and LDL cholesterol. A study pointed to a high amount of phytosterols, mainly beta-sitosterol, campesterol and stigmasterol, which can be found in whole green banana flour (GBF). After they investigated the effect of pre-treatments on BF and although citric acid treatment gave rise to phytosterol reduction in flour obtained from the peel, no change was detected between phytosterols of acidified and control samples from the pulp (Bertolini et al. 2010 ). Consequently, the known properties of phytosterols suggest these flours could be used as functional food components.

Methods for banana flour preparation

Due to the climacteric nature of the banana, it is highly perishable and requires drying during processing for preserving for a longer period of time. Banana flour is a product with high storability potential and long shelf life and can be readily applied to food products. The proximate composition of the flour also depends on the origin, variety, time of harvest, and drying procedure of bananas (Haslinda et al. 2009 ). Table  1 depicts a summary of the proximate composition of both green and yellow BF made from pulp and peel.

Processing steps used for flour preparation from banana pulp and peel are similar, except for the heating procedure used. Pretreatment process steps of banana flour preparation are summarized in Fig.  1 .

Pretreatment process steps of banana flour preparation

While most researchers applied oven drying for banana fruit (Gomes et al. 2016 ; Kurhade et al. 2016 ; Nasrin et al. 2015 ; Segundo et al. 2017a , b ; Türker et al. 2016 ), spouted bed drying (Bezerra et al. 2013a , b ) and lyophilization (Da Mota et al. 2000 ; Türker et al. 2016 ; Wang et al. 2012 ) were also applied in previous studies. In order to minimize enzymatic browning, soaking in sodium metabisulfite, sodium hypochlorite or citric acid solutions was a general pretreatment and considered as the first step after rinsing bananas with water.

The composition of samples obtained by lyophilization and spouted drying techniques showed a significant increase in phenolic acid content, heat sensitive vitamins and minerals compared to traditional drying methods such as solar or hot air-oven drying. Using a spouted drier for producing GBF resulted in high DF and RS content with an average of 21.91% and 68.02%, respectively. This technique did not alter the RS content; however, this effect was consistently reported since a similar study reported lower values, 13.89% and 40.14% for DF and RS, respectively (Bezerra et al. 2013a ).

Likewise, post-treatments could affect the composition of BF produced. For example, smaller particle sizes of GBPF (less than 80 µm diameter) had a higher amount of RS, while flour particles bigger than 156 µm had more TDF, ash, protein and phenolic compounds (Segundo et al. 2017a ). Briefly, depending on the enrichment purpose, selecting a proper procedure is imperative. Table  2 demonstrates the effect of various drying process on produced banana flour properties regarding the type and part of banana used.

Table 2

The effect of different drying methods on banana flours

GBPeF green banana peel flour, GBPF green banana pulp flour, YBPeF yellow banana peel flour, YBPF yellow banana pulp flour, RS resistant starch

Banana flour applications

The high dietary fibre content of GBPF and high levels of mentioned bioactive compounds have enabled the production of BF foodstuffs with remarkable functionalities. Moreover, discovering high nutrition value of BPeF represents a low-cost by-product for industrial application. As discussed earlier, depending on the ripening stage, there are four flour products produced from banana with different chemical composition. Due to the structure of BF, cereal-based products have gained more attention than other food products. The main starch-based foods targeted for enrichment with BF products are as follows.

In a study by Juarez-Garcia et al. ( 2006 ), GBF was obtained from a Mexican species ( Musa paradisiacal L. ) to develop a high gluten bread using 37% GBF in the formulation. Chemical composition analysis showed an increase in ash, protein, TDF and starch percentage of banana bread compared to wheat bread. Even though RS was decreased from 17.5% in flour to 6.7% in banana bread, it was significantly higher than the control sample. The authors also reported a significant decrease in predicted Glycemic Index (pGI) and Hydrolysis Index (HI) of the final product; a result that was in accordance with a higher value of total indigestible fraction (TIF), the main ingredients unavailable for digestion in the small intestine. However, rheological and sensory properties and shelf life of banana bread were not determined. In another study, with more attention to the sensory characteristics, GBF substitution with wheat flour resulted in lower sensory scores in 20% enriched-samples (Gomes et al. 2016 ). Moreover, darker colour, higher hardness and lower specific volume compared to control sample indicated negative effects of this enrichment. While TDF and ash content increased significantly in 20% substitution, the 10% substituted samples was regarded the best as it had no significant technological defects (Gomes et al. 2016 ).

In a study on gluten-added bread, substitution of 25% of ready freeze-dried banana powder (maturity level was not mentioned) with wheat flour resulted in increased volume and viscosity of leavened bread (Mohamed et al. 2010 ). Banana bread was darker than the control due to excess sugar in the BF that caused the Maillard Reaction to occur between reducing sugars and proteins. Regarding the evaluation of shelf life, while bread staling (firmness) increased in higher BF concentration regardless of storage temperature (25 °C, 4 °C and − 20 °C), the stiffness of the control was higher than BF bread (Mohamed et al. 2010 ). By comparing storage temperature, it was stated that bread stored at − 20 °C up to 7 days, experienced lower firmness compared to other samples (Mohamed et al. 2010 ). Similar findings reported by (Ho et al. 2013 ) who prepared a steam bread with 30% GBPF wheat flour substitution. In terms of mineral evaluation, the percentage of Mg, K, Na and Ca were higher than the control, and yielded consequently higher ash content. Increased TDF and RS were also reported to a level of 9% and 5%, respectively. However, the adverse impact of GBPF and added gluten caused an increase in hardness and adhesiveness in the produced bread. The cohesiveness, elasticity and chewiness of bread supplemented with 30% GBPF were decreased due to the lack of consistency in gluten structure. With reference to higher specific volume in banana bread samples containing 8% gluten, researchers explained that GBPF could affect the gluten network and attenuate the gas holding capability of the dough, which leads to low elasticity and expansion in leavened bread. These findings were in agreement with (Steel et al. 2013 ) and consistent with those reported by (Gomes et al. 2016 ; Kurhade et al. 2016 ) which focused on yellow banana peel flour (YBPeF) and GBF, correspondingly.

Considering nutritional properties, a reduction in TDF level and a slight increase in RS level (2.6%) were observed in a freeze-dried tortilla bread containing 40% GBPF substituted with corn flour. Moreover, higher values of pGI and HI than control showed that GBPF addition may not be a proper enrichment strategy for the purpose of producing a low-calorie tortilla bread (Aparicio-Saguilan et al. 2013 ). Additionally, the high amount of RS2 in corn flour, should lead to a major increase in RS amount. It can be concluded that RS2 is sensitive to the cooking process and most of it gelatinizes during the process (Robles-Ramírez et al. 2012 ).

A comprehensive research was carried out on composite bread with 10% peeled yellow banana pseudo-stem flour substitution with wheat flour. Due to the dilution of gluten in substituted samples, crumb and crust lightness improved in the banana bread. A significant increase in total phenolic content, ash and TDF demonstrated the fact that peeled mature banana pseudo-stem flour could be effective in producing a functional composite bread. Besides, no significant difference was observed between samples and control, except for colour and softness in a sensory analysis (Ho et al. 2013 ). In their following study, researchers also found an interaction between gum type and BF effect on volume, in which the addition of 0.8% sodium carboxymethyl cellulose (NaCMC) improved specific volume, minerals (Na, K, Mg and Ca) and RS content (14.98%) more than the same amount of xanthan. NaCMC, which is a soluble DF, acts like a sponge and absorbs water in the intestine. Therefore, it helps in mixing with the starchy food’s structure to form a dense structure that results in slowing down the rate of digestion (Ho et al. 2015 ). As a result, it can be a suitable additive together with peeled yellow banana pseudo-stem flour.

In two separate studies, different types of flat bread were enriched with the addition of BPeF in two stages of ripeness. In another report, (Kurhade et al. 2016 ) found that with the substitution of YBPeF at any ratio with wheat flour in chapatti bread, lightness will decline. Because of the increased water absorption, chapatti containing 10% YBPeF was found to be softer owing to a decrease in the tear force. In terms of antioxidants, total phenolic content, flavonoids and free radical scavenging activity were higher to than that of the control; which justified the high concentration of these elements in YBPeF. Though, overall acceptability score was not distinguished by panelists up to 10% of substitution. Nevertheless, when green banana peel flour (GBPeF) was added to a level of 10% in balady bread in another study, the control sample scored better in all sensory parameters, except taste and chewiness. Contrary to previous findings (Eshak 2016 ) also reported a higher amount of protein in banana bread (12.52%) which was ascribed to the slightly higher amount of protein in GBPeF (8.74%) compared to wheat flour (8.68%). As there was no textural analysis of balady bread in this research, further work is needed to address the technological quality of GBPeF enriched bread. Similarly, increased protein and TDF content were reported in leavened bread containing 20% and 30% fermented green banana slurry substituted with wheat flour. However, in accordance with the application of GBPeF in previous studies, sensory scores diminished for all treatments with more than 10% substitution (Adebayo-Oyetoro et al. 2016 ). Again, textural analysis and firmness during storage were not considered, therefore it is hard to validate the quality of bread produced.

It follows from the above that there are contrary results on BF usage in bread products. Also, more information of bread enrichment with GBPeF and its technological effect on the final product is required.

Pasta products are foodstuffs with an important role in diets. In addition to being easily produced with a long shelf life, pasta products also have a lower glycemic index (GI) in comparison with white bread or rice (Nilsson et al. 2008 ). Hence, enrichment of pasta products with different DF-enriched flours and micronutrients has been considered in the last decade (Filipović et al. 2010 ).

In 2009, the properties of spaghetti enriched with different substitution ratios of GBPF with semolina flour was investigated in two studies. Following a similar trend in most of the banana bread products, a reduction in lightness, protein and fat content of spaghetti containing 20, 35 and 40% GBPF was reported (Ovando-Martinez et al. 2009 ). However, textural results indicated increased adhesiveness and chewiness compared to the control, which was because of the release of amylose from starch granules during cooking. This also caused a rise in cooking loss (less than 7%) with increasing GBPF in the formulation. Yet, since a cooking loss below than 8% is considered as acceptable for semolina-based pasta products, it can be said it was not a negative effect. On account of the high amount of RS (12%) and incomplete gelatinization of starch granules, enriched samples showed lower values in vitro digestion tests.

In a study conducted by Agama-Acevedo et al. ( 2009 ), produced GBPF comprised 42.54% RS2 (dwb). With regard to a higher percentage of polyphenols and antioxidant capacity of banana pasta, it was concluded that enrichment of spaghetti with 30% GBPF could provide a product without off-flavours (Agama-Acevedo et al. 2009 ; Ovando-Martinez et al. 2009 ). These results were confirmed by Krishnan and Prabhasankar ( 2010 ) findings a year later. Besides, they found a synergic relation between sprouted Ragi Flour and GBPF; in which a combination of 15% sprouted Ragi Flour and 15% GBPF proved to be the best for nutritional and technological attributes.

Incorporation of green banana parts, individually, has also been considered in pasta products.

A separate substitution of 10% pulp and peel (obtained from two different varieties) with wheat flour was conducted to produce alkaline noodles. Since the GBPeF was higher in TDF but lower in RS content than the GBPF, the low pGI of banana peel noodles was primarily because of its high TDF. Whilst pulp enriched noodles originated from the Cavendish variety, exhibited higher elasticity, enriched samples with GBPeF from the Dream variety showed the lowest values. In discussion, it was declared that higher sugar content was responsible for high levels of total solid content which led to a rise in the density of the molecular structure (Ramli et al. 2009 ).

In a very recent study, banana flour was prepared from whole green banana and added to tagliatelle pasta up to 30% substitution with wheat flour. Contrary to previous findings, there was no evidence of darker colour in banana pasta which possibly was due to the use of wheat flour instead of semolina flour. But in support of previous researches, banana pasta presented higher ash, TDF and total phenolic content than the control, though 15% substitution showed more ash content. Likewise, the mentioned sample showed better sensory attributes by panelist scores (Zheng et al. 2016 ). Because rheological properties and some important physical properties, such as cooking loss, were not assessed, it is hard to determine the effect of GBF on quality of the final pasta product.

Confectionaries

Due to its high sugar content, utilization of BF in confectionaries has been heightened by the food industry, specifically cereal-based ones. The growth of pathogens in a cake premix made with 60% GBPF instead of wheat flour over 4 months of storage was investigated. Despite a high sugar concentration, the pre-mixture remained significantly unaltered in pH and pathogenic growth, fungus or yeasts (Borges et al. 2010 ). In another study, foaming stability and overall acceptability increased in the presence of 10% BPF. Also, with increased content of BPF to 20% of the formulation, higher hardness was reported, although chewiness and adhesiveness were not significantly different amongst samples. The reason behind this phenomenon was the lower amount of moisture content in banana cakes (Park et al. 2010 ).

With the aim of increasing DF and RS in layer and sponge cakes, GBPF was added at different particle sizes; ranging from 80 µm (fine) to 200 µm (coarse) in diameter. Researchers showed that the fine flour comprised 40% RS compared to 25% RS in the coarse flour. This fact specified higher RS content (about 3%) in 30% replacement samples with fine flours in both layer and sponge cakes. However, the percentage of TDF, protein, ash, lipid, phenolic compounds and amylose was higher in the coarse flour. In terms of technological properties, sponge cakes were noticeably worsened with the presence of banana flours (lower specific volume, inferior sensory characteristics and higher hardness), which was diminished at the 15% ratio; except for cohesiveness that showed a dramatic decrease in all samples compared to the control. The authors accounted for different gelatinization and retrogradation behaviour of banana starch compared to wheat starch for textural changes and decreased sensory scores in banana cakes. Still, samples made by fine particle sizes of GBPF showed better nutritional properties without negatively affecting textural attributes (Segundo et al. 2017a ). The same results were reported in a similar study by selection of YBPF in 40% substitution with sugar. Both sponge and layer cakes depicted enhancement in DF, polyphenols and antioxidant capacity values. In concurrence with their previous work, increased hardness and decreased volume led to the decline of the acceptability of cakes by panelists, especially in 40% banana cakes. Considering the correlation between volume and hardness was more significant in sponge than in layer cakes, the maturity of banana was not correlated to improvements of textural properties (Segundo et al. 2017b ). The same behavior was observed by (Oliveira de Souza et al. 2018 ), even though they used a higher concentration of GPB puree instead of flour for pound cakes.

Cookies containing 20% of YBPeF were rich in TDF and were categorized as a low-calorie product (Agama-Acevedo et al. 2012 ). The same pattern was reported by Elaveniya and Jayamuthunagai ( 2014 ) in which banana blossom powder was added at 5 g in 100 g of biscuit formulation. However, consumer acceptance of samples for colour, crispiness and taste decreased with the addition of BPe, while it was acceptable at 5% substitution.

In another study on biscuit, YBPeF was substituted up to 75% with wheat flour and caused a decline in hardness. According to the discussions, prior treatments of mashed peels together with DF led to an increase in softness of the product. While a high level of consistency and crispiness is required in a biscuit product, dilution of gluten in higher concentrations of YBPeF led to insignificant decrease of these attributes in all enriched samples. Organoleptic results were not significantly different in terms of colour, flavour, after taste and mouthfeel at even the 75% level of substitution (Joshi 2007 ). These findings were consistent with those reported by Carvalho and Conti-Silva ( 2018 ), where enrichment of cereal bars with 14% YBPeF exerted no negative effect on aroma or taste, while further incorporation caused a darker colour, more hardness and adhesiveness together with a bitter aftertaste.

Overall, replacement of BF, regardless of ripeness degree, was feasible up to 15% for achieving optimal quality and maximum 30% for functional purposes in confectionaries. Still, quality control of produced different types of cakes with BPeF is needed to be instigated.

Gluten-free products

Because of the rise of the gluten related disorders, such as celiac disease and dermatitis herpetiformis , it has been essential to expand the gluten-free (GF) food market. Moreover, considering that untreated celiac disease contributes to intestinal cancer, nutrient deficiencies and oxidative stress, developing GF products with the consideration of additional nutritional values is highly important (Wang et al. 2017b ). In this regard, bioactive compounds are a prominent ingredient in GF foods. As most of GF starchy products do not provide proper technological qualities, application of different types of BF has been considered as of late (Torres et al. 2017 ).

By incorporation of 47% GBPF in 100 g pasta formulation, Zandonadi et al. ( 2012 ) produced a GF pasta with an additional proportion of egg white and hydrocolloids. Regarding excess amount of water absorption during cooking, increased stickiness of the product led to a weakened structure. Another negative effect was the poor nutritional value of the product that was exhibited by a reduction in TDF and protein content. On the other hand, improvements in ash content and sensory scores were reported. These findings were in agreement to Radoi et al. ( 2015 ), who reported a growth in total phenolic acids, cinnamic acids and minerals (mainly Fe, Cu, Zn, Mn, Ni) with presence of 30–40% dried banana. Yet, textural and cooking properties of GF pasta were missing important assessments.

Gluten free bread has also been produced by the use of BF. In a study conducted by Sarawong et al. ( 2014 ), while crumb firmness increased in 15% of green plantain flour addition, bread volume improved by 25% addition at a lower baking temperature over a longer time. An elevated proportion of green plantain flour in GF bread contributed to higher water binding capacity and a reduction in starch retrogradation owing to the presence of extra water. In addition to darker crumb at 5% addition level, RS increased only up to 2.5% compared to the control sample.

In sweetened bread, both green and yellow banana GF bread showed lower volume, lesser height and darker colour, but, when black banana pulp flour was utilized at 20% of the formulation, those variables improved to a significant level. However, sensory evaluation, shelf life and textural properties of bread produced were overlooked (Seguchi et al. 2014 ).

The substitution of 15% of GBPeF was with rice flour in GF cake gave rise to a decrease in GF cake volume. Due to an increase in viscosity, the density of banana GF cakes rose (Türker et al. 2016 ). Once more, without considering textural, sensory and storability properties of the final product in the later study, it is of importance to note that more studies are needed to be done in GF starch-based food products with the aim of considering of high nutritional value and technological quality of final product.

It is desirable to find proper food applications for banana peel for achieving two goals; first, helping the environment through sustainability by utilizing secondary processing products and second, creating a new outlook for consumers and producers for generating value-added food products. Besides adding nutritional value to food products, GBF also stands out for not creating production waste, thus representing the complete use of the fruit, increasing the yield and reducing manpower costs due to peeling which is not required.

In gluten-added starchy products, such as bread, cake and pasta, textural properties were not negatively affected by banana flour addition to a certain extent. However, in gluten-free products, more research on improving rheological and cooking properties need to be undertaken; especially investigation on the application of green and yellow banana peel.

As discussed in this review, there are several methods for producing banana flour. Comparing new drying technologies with existing methods and their effect on bioactive components of produced products would be an important subject for future research. Also, further studies, especially clinical trials are needed to be considered in order to confirm the health benefits of banana flour enriched food products.

Acknowledgements

We would like to show our gratitude to the University of Otago for their support of this study.

Compliance with ethical standards

Conflict of interest.

The authors declare that they do not have any conflict of interest.

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Contributor Information

Amir Amini Khoozani, Email: [email protected] .

John Birch, Phone: 64 3 479 7566, Email: [email protected] .

Alaa El-Din Ahmed Bekhit, Email: [email protected] .

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