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The transformation of value-added solar-dried banana to banana syrup, which contains very high levels of fructooligosaccharides (FOS), is attractive. It has promising economic potential for the nutraceutical and functional food industry. In this study, the near-infrared (NIR) spectroscopy was used for the detection of 1-kestose (GF2), nystose (GF3), 1F-fructofuranosylnystose (GF4), and total FOS in solar-dried banana syrup in the spectral region of 4000–12,000 cm−1. Partial Least Squares (PLS) were applied to build the regression models. Considering the highest coefficient of determination (R2) and the lowest root mean square error (RMSE) of prediction, the prediction model of GF2 and GF3 yielded the greatest when original spectra at the optimization wavelength region was used. At the same time, the SNV procedure exhibited the best for the prediction model of GF4 and total FOS. Therefore, NIR spectroscopy with the PLS technique can be suitably applied for the rapid detection of FOS in solar-dried banana syrup.

While the temporal ripening behavior of mango is widely documented, the spatial distribution with respect to physicochemical composition remains largely unknown. In this study, ripening behavior of mangos cv. Nam Dokmai was investigated focusing on the variations between the different fruit parts, namely shoulder, cheek, and tip. The temporal results showed typical ripening behavior: color changes in pericarp and mesocarp with increase of a* and b* values and rises in total soluble solids (TSS) and pH, while firmness and titratable acidity (TA) decreased. Sucrose and citric acid were respectively the predominant sugar and acid in this particular cultivar. Relating to spatial variation, the shoulder (near the stem) had the highest TSS, pH, sugar to acid ratio (TSS·TA−1), and sucrose, glucose, and fructose values. The tip showed the highest TA and citric acid content. Generally, the shoulder and tip were observed to ripen faster than cheeks. PCA was used to classify the ripeness of mango based on measured parameters and suggested that the cheek part is recommended for quality measurements. This information is imperative for the fruit handling industry, especially regarding the implementation of non-destructive technologies for quality determination.

Hyperspectral imaging (HSI) techniques using a newly-developed frame camera were applied to determine internal properties of mango fruits including firmness, total soluble solids (TSS) and titratable acidity (TA). Prediction models were developed using spectral data from relative surface reflectance of 160 fruits in the visible and near infrared (vis/NIR) region of 450–998 nm analysed by PLS regression. For data reduction, MLR analysis showed 16 significant factors for firmness, 17 for TA, and 20 for TSS. The results of MLR did not substantially affect the prediction performance as compared to PLS. An original approach with combined chemometric and HSI data analyses was applied using R programming. Significant correlations were found between HSI data and firmness (R2 = 0.81 and RMSE = 2.83 N) followed by TA (R2 = 0.81 and RMSE = 0.24%) and TSS (R2 = 0.5 and RMSE = 2.0%). Prediction maps of physicochemical qualities were achieved by applying the prediction models to each pixel of HSI to visualise their spatial distribution. The variation of firmness, TSS, and TA within the fruit indicated fruit ripening started from shoulder toward to tip part. From these results, HSI can be used as a non-destructive technique for determining the quality of fruits which could potentially enhance grading capabilities in the industrial handling and processing of mango.

The effect of harvest year on near-infrared spectroscopy (NIRS) prediction models to determine postharvest quality of mango was evaluated. Diffuse reflectance spectra in region of 700–1100 nm were used to develop calibration models for firmness, total soluble solids (TSS), titratable acidity (TA) and ripening index (RPI) using partial least squares (PLS) regression analysis. The results showed that model robustness was influenced by harvest year. High prediction error was found when models from single harvest year were used to predict the data of other years, whereas using combined data from two or three years for calibration greatly enhanced the prediction accuracy. The prediction models established from three-year data performed the most suitably for prediction of TSS (R2 = 0.9; SEP = 1.2%), firmness (R2 = 0.82; SEP = 4.22 N), TA (R2 = 0.74; SEP = 0.38 %) and RPI (R2 = 0.8; SEP = 0.8). Classification of mango ripeness was successfully achieved using second derivative pretreated spectra with an accuracy of more than 80%. The results indicated that NIRS can be used as a reliable non-destructive technique for mango quality assessment and a robust model could be developed when effect of harvest year was taken into account.

Non-destructive applications for the detection of food quality, especially internal properties, are highly relevant for process control in the food industry. In this respect, colour measurement and near-infrared spectroscopy (NIRS) were evaluated and compared for their ability to predict β-carotene content in mango cv. ‘Nam Dokmai’. Colorimetric analysis of peel and flesh colour as well as NIR measurements in the short- (700–1100 nm) and long-wave regions (1000–2500 nm) were analysed for prediction ability. It was found that β-carotene content could be estimated by multiple linear regression (MLR) models developed from b* and hue angle (h°) values of the flesh with good results for coefficient of determination (R2) and standard errors of cross validation (SECV) of 0.941 and 10.2 retinol equivalents (RE) 100 g−1 edible part (EP), respectively, while peel colour showed poor results. However, flesh colour measurement is a destructive method. NIRS calibration showed good results with R2 > 0.800 and standard error of prediction (SEP) 11.642–20.2 RE 100 g−1 EP. Long-wave NIR provided better prediction ability than short-wave. From these results, NIRS can be recommended for non-destructive and reliable determination of β-carotene content in mango. The results have implications for quality control in the industrial handling and processing of fruits.

Science, Engineering and Health Studies, 12, 3, 139-148

This study investigated the effect of irrigation on the ability of near-infrared (NIR) measurements to predict maturity stage of mango. Fruits from irrigated and non-irrigated trees were sampled on six dates during the final three weeks of development and subjected to NIR and conventional laboratory analyses. Spectral assessment showed differentiation between irrigated and non-irrigated fruits on earlier dates, which was not evident later on. NIR measurements of irrigated samples correlated well to dry matter, this was not the case for non-irrigated samples ( r = 0.80 compared to 0.57, respectively), while the reverse was true for acidity with r increasing from 0.55 to 0.85 between irrigated and non-irrigated. Second derivative spectra of all samples best correlated with acid content ( r = 0.73). Although dry matter was previously proposed as the most suitable parameter for NIR calibration, this study suggests acidity might be an appropriate harvesting index when considering irrigation effects. Interestingly, NIR technology has been found to adequately predict acidity in other fruits, but results of this study require additional investigation.