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Deep neural networks (DNNs) chemical compositions estimation of fresh durian in-line via near infrared spectroscopy
Author(s)
Posom, Jetsada
Saenphon, Chirawan
Ditcharoen, Sirirak
Pitak, Lakkana
Maraphum, Kanvisit
Date Issued
June 1, 2025
Type
Article
Abstract
Near infrared (NIR) spectroscopy and deep neural network (DNN) models were adopted for evaluating the nutritional compositions in durian pulp. The quality inspection of durians through online channels remains challenging because consumers cannot directly touch or smell the fruit. This leads to issues with substandard durians, such as unripe ones or those infested with pests. One hundred and sixty durian samples of Mon Thong varieties were used in this experiment. Then, the collected NIR spectra were augmented to improve the generalization ability of regression models. Deep neural network (DNNs) regressions were developed. The results showed that deep neural network (DNN) regression model possessed the best prediction performance, which were provided performance index. Almost all the best performance models were developed from the second derivative, except for the fat content model, which was developed from raw spectra. The best total soluble solids (TSS) model provided the coefficient of determination of calibration (R2c) and root mean square error of calibration (RMSEc) were 0.84 and 2.25 % and coefficient of determination of calibration (R2p), root mean square error of prediction (RMSEp) and ratio of performance to deviation (RPD) were 0.72 2.92 % and 1.92, respectively, and those of dry matter content (DMC) were provided R2c and RMSEc were 0.997, 0.58 %, and for prediction set provided R2p, RMSEp and RPD were 0.94, 3.13 %, and 4.21, respectively. For fat content (FC), they have also provided R2c and RMSEc were 0.84, 0.36 (g/100 g), while it provided R2p, RMSEp and RPD were 0.86, 0.50 (g/100 g), and 2.72, respectively. Moreover, for total sugar content (TSC) value, it also gave high accuracy, which provided R2c and RMSEc were 0.93, 0.49 (g/100 g) and R2p, RMSEp, and RPD were 0.91, 0.81 (g/100 g), 3.44, and for starch content (SC) also provided R2c and RMSEc were 0.93, 0.49(g/100 g) and R2p, RMSEp, and RPD were 0.76, 2.79 (g/100 g), 2.09, respectively. Therefore, the proposed method offers an ultrasensitive and effective strategy for estimation of nutritional compositions.
Citation
Journal of Food Composition and Analysis, 142, 2025