A model approach revealed the relationship between banana pulp acidity and composition during growth and post harvest ripening

Etienne, A; Génard, Michel; Bancel, Doriane; Benoit, Sophie; Bugaud, Christophe

doi:10.1016/j.scienta.2013.08.011

Cited by 32 publications

(35 citation statements)

References 16 publications

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“…Acidity is an important factor in the sensory quality of bananas, influencing the perception of both sourness and sweetness. In addition, acidity acts to control enzyme activity and contributes to the preservation of the product (Etienne, Génard, Bancel, Benoit, & Bugaud, 2013). The acidity of dried bananas, expressed in % (g of malic acid per 100 g of sample), was between 1.038 and 1.585%.…”

Section: Resultsmentioning

confidence: 99%

Effect of drying air temperature on drying kinetics and physicochemical characteristics of dried banana

Macedo

Vimercati

Araújo

et al. 2020

J Food Process Engineering

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This study aimed to evaluate the influence of drying air temperature on drying kinetics and the physicochemical properties of dried bananas. Banana slices were dried at 40, 60, and 80°C. Drying was terminated when the samples had a moisture of 20%. Mathematical models were fitted to the moisture ratio. A generalized model of moisture was developed, to predict the moisture of samples as a function of drying time and temperature. The effective moisture diffusivity (Deff), activation energy and the drying rate (DR) were calculated. Electrical energy consumption was measured. The moisture, water activity, reducing and total sugar content, acidity, hardness, and color of the dried bananas were evaluated. The moisture decreased during drying, obtaining a mean value of 20.132%. All mathematical models fitted well to the MR data, with a determination coefficient greater than 0.95. The Midilli model was that which best fitted. The higher temperature resulted in higher Deff (3.538 × 10−9 m2 s−1) and DR, less drying time (120 min) and electrical energy consumption (4.319 kWh), higher reducing sugar content (47.51%) and hardness (28.187 N), lower acidity (1.038%), more yellow tonality (78.04°), and higher chromaticity (18.49) of the dried bananas. The optimum temperature for drying bananas was 80°C. Practical Applications Banana is a fruit widely consumed in the world. However, it is very perishable, causing great waste and financial loss. Convective drying is a simple and low‐cost method, widely used in fruit processing to produce new products and extend the shelf life of food. The dried banana is a product of good sensory acceptance and therefore drying is a good alternative for processing the banana pulp. The temperature of the drying air is one of the principal parameters of this process since it influences the drying kinetics and the physicochemical properties of the dry product. Therefore, it is important to study different drying air temperatures to determine the best drying condition for dried banana production, to optimize the dried banana process and properties.

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Section: Resultsmentioning

confidence: 99%

Effect of drying air temperature on drying kinetics and physicochemical characteristics of dried banana

Macedo

Vimercati

Araújo

et al. 2020

J Food Process Engineering

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“…The chemical characteristics of banana pulp varied according to starch transformations that occur during the ripening process in the different cultivars (JESUS et al, 2004). Since banana is a climacteric fruit, there is a significant increase in the respiration rate on the first days after ethylene treatment, and differences in pulp acidity among cultivars may be linked to the respiratory metabolism (ETIENNE et al, 2013), more exactly to the tricarboxylic acid metabolism (Krebs cycle) during the ripening process, increasing the sugar/acid ratio (CHITARRA;CHITARRA, 2005). Therefore, in the PA-9401 cultivar, possibly in the Krebs cycle, there is higher production of tricarboxylic acid, increasing pulp acidity and reflecting in higher titratable acidity and reducing the SS and TA ratio.…”

Section: Resultsmentioning

confidence: 99%

Post-harvest characterization of ‘Prata’ banana cultivar grown under different nitrogen and potassium fertilization

et al. 2019

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There are several banana cultivars resistant to Black Sigatoka disease and to Panama disease, but due to the lack of information regarding post-harvest characteristics, many are rejected in the market. Nitrogen and potassium fertilization may help to improve the sensory characteristics of ripe banana, but there is lack of studies on its effects on banana cultivation, especially those resistant to diseases. The aim of this study was to evaluate the post-harvest characteristics of ‘Prata-Anã’, ‘BRS Platina’ and ‘PA94-01’ banana cultivars at four N and K2O fertilization levels (NK0: no fertilization, NK1: 175 and 285 kg ha-1 year-1, NK2: 350 and 570 kg ha-1 year-1, NK3: 525 and 855 kg ha-1 year-1, respectively) in three production cycles. It could be concluded that N and K doses adopted in this work did not influence the post-harvest physicochemical characteristics of ‘Prata-Anã’, ‘BRS Platina’ and ‘PA94-01’ banana cultivars. Regardless of N and K doses, ‘Prata-Anã’, ‘BRS Platina’ and PA94-01’ banana cultivars presented differences regarding post-harvest physicochemical characteristics. Production cycles did not influence the physical characteristics but differed in post-harvest chemical characteristics in ‘Prata-Anã’, ‘BRS Platina’ and ‘PA94-01’ banana cultivars.

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“…In the vacuole, the activity coefficient of the di-anion malate (a Mal 2− vac ) is related to the concentration of all ionic species [ 18 ], while its concentration is proportional to the total malate concentration and is controlled by the dissociation equation, since malate is a weak acid: where [Mal vac ] is the total concentration of malate in the vacuole (mol L −1 ), h = 10 -pHvac , and K′ 1 and K′ 2 are the apparent acidity constants of malate (mol L −1 ).…”

Section: Methodsmentioning

confidence: 99%

“…The acid/base composition of the vacuole determines a Mal 2− vac , K′ 1 , K′ 2 , and pH vac . These variables are calculated using a model of pH prediction that was described and validated on banana fruit in a previous paper [ 18 ]. As input variables, the model requires the concentrations of the three main organic acids present in banana pulp, citrate, malate, and oxalate (oxalate being present in large amounts at the green stage [ 18 ]), and of the main soluble mineral elements, namely potassium, magnesium, chloride, calcium, and phosphorus.…”

Section: Methodsmentioning

confidence: 99%

“…The main adaptation concerned the variation of the free energy of ATP hydrolysis during fruit development. Banana fruit was taken as a reference because it has the particularity of having separate growth and post-harvest ripening stages, during which malate concentration undergoes substantial changes [ 18 ]. Moreover, the concentration of malate in banana pulp varies greatly among cultivars which make possible to use the model as a tool to analyze the genotypic variability [ 11 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Modeling the vacuolar storage of malate shed lights on pre- and post-harvest fruit acidity

et al. 2014

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BackgroundMalate is one of the most important organic acids in many fruits and its concentration plays a critical role in organoleptic properties. Several studies suggest that malate accumulation in fruit cells is controlled at the level of vacuolar storage. However, the regulation of vacuolar malate storage throughout fruit development, and the origins of the phenotypic variability of the malate concentration within fruit species remain to be clarified. In the present study, we adapted the mechanistic model of vacuolar storage proposed by Lobit et al. in order to study the accumulation of malate in pre and postharvest fruits. The main adaptation concerned the variation of the free energy of ATP hydrolysis during fruit development. Banana fruit was taken as a reference because it has the particularity of having separate growth and post-harvest ripening stages, during which malate concentration undergoes substantial changes. Moreover, the concentration of malate in banana pulp varies greatly among cultivars which make possible to use the model as a tool to analyze the genotypic variability. The model was calibrated and validated using data sets from three cultivars with contrasting malate accumulation, grown under different fruit loads and potassium supplies, and harvested at different stages.ResultsThe model predicted the pre and post-harvest dynamics of malate concentration with fairly good accuracy for the three cultivars (mean RRMSE = 0.25-0.42). The sensitivity of the model to parameters and input variables was analyzed. According to the model, vacuolar composition, in particular potassium and organic acid concentrations, had an important effect on malate accumulation. The model suggested that rising temperatures depressed malate accumulation. The model also helped distinguish differences in malate concentration among the three cultivars and between the pre and post-harvest stages by highlighting the probable importance of proton pump activity and particularly of the free energy of ATP hydrolysis and vacuolar pH.ConclusionsThis model appears to be an interesting tool to study malate accumulation in pre and postharvest fruits and to get insights into the ecophysiological determinants of fruit acidity, and thus may be useful for fruit quality improvement.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-014-0310-7) contains supplementary material, which is available to authorized users.

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A model approach revealed the relationship between banana pulp acidity and composition during growth and post harvest ripening

Cited by 32 publications

References 16 publications

Effect of drying air temperature on drying kinetics and physicochemical characteristics of dried banana

Effect of drying air temperature on drying kinetics and physicochemical characteristics of dried banana

Post-harvest characterization of ‘Prata’ banana cultivar grown under different nitrogen and potassium fertilization

Modeling the vacuolar storage of malate shed lights on pre- and post-harvest fruit acidity

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