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Identification and quantification of quality of intact durian fruits using NIR spectroscopy
(2026-01-01)
Pitak, Lakkana
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Ditcharoen, Sirirak
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Maraphum, Kanvisit
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Khamwan, Buathip
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Warorost, Nithithada
Quality classification of durian fruits is based on the dry matter (DM) content of the pulp. According to Thai agricultural standards, durian fruit (Monthong variety) must contain at least 32% DM. This study aimed to develop a classification model for assessing durian quality based on DM content, categorizing fruits as either “rejected” (DM < 32%) or “accepted” (DM ≥ 32%). Near-infrared (NIR) spectra were collected as the durian fruits moved along a conveyor belt. The models were developed using two spectral ranges: short-wavelength near-infrared (SWNIR; 4501000 nm) and long-wavelength near-infrared (LWNIR; 8601750 nm). Owing to the imbalance in the dataset between the two classes, the data were adjusted using the synthetic minority oversampling technique to create a balanced dataset. Prediction models were built using different spectral preprocessing methods and algorithms. For the LWNIR range, the models constructed using LDA, SVM, KNN, and SDA achieved accuracies of 95%, 90%, 93%, and 93%, respectively, for the test set. The SWNIR models, developed using the same algorithms, achieved accuracies of 90%, 88%, 90%, and 90%, respectively, for the test set. PLS-regression was used to predict the DM content from both LWNIR and SWNIR data. With the 2nd derivative preprocessing method, the models achieved R² values of 0.89 and 0.79, SEP values of 5% and 6.89%, and RPD values of 2.29 and 1.66, respectively. The wavelength range significantly influenced the model performance, whereas spectral pretreatment had a minor effect on the model's predictive ability. Overall, NIR spectroscopy demonstrated the potential for nondestructive quality grading of whole durian fruits. This work is the first to establish real-time, in-line models for durian grading based on DM content, advancing beyond the previous destructive method. The findings demonstrate the feasibility of automated, nondestructive, and objective quality assessment, supporting industrial automation, precision agriculture, and export quality assurance.
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Deep neural networks (DNNs) chemical compositions estimation of fresh durian in-line via near infrared spectroscopy
(2025-06-01)
Posom, Jetsada
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Saenphon, Chirawan
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Ditcharoen, Sirirak
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Pitak, Lakkana
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Sirisomboon, Panmanas
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.
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Near-infrared hyperspectral imaging combined with machine learning for physicochemical-based quality evaluation of durian pulp
(2023-06-01)
Sharma, Sneha
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Sirisomboon, Panmanas
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K.C, Sumesh
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Terdwongworakul, Anupun
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Phetpan, Kittisak
This research reports on the application of near-infrared hyperspectral imaging (NIR-HSI) system for predicting the physicochemical properties; dry matter (DM), total soluble solids (TSS), and fat content (FC) of durian. Partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and 1D convolution neural network (CNN) models: custom, U-Net, and VGG19; were developed to predict DM, TSS, and FC of durian pulp. Feature wavelengths were selected using a genetic algorithm (GA) and successive projection algorithm (SPA). The selected wavelengths were then validated based on the algorithms for regression model development. GA-PLSR model was compelling to predict the DM and FC in durian pulp, which obtained the coefficient of determination for the test set (r2) and root mean square error of prediction (RMSEP) of 0.97 and 1.12% for DM and 0.86 and 0.64% for FC, respectively. The GA-PLSR model provided the best result for the TSS prediction with r2, and RMSEP of 0.90 and 1.40%, respectively, whereas the SPA-PLSR model based on only thirteen wavelengths attained fair result with the r2 and RMSEP of 0.79 and 2.03%, respectively. The above results show that the pushbroom NIR-HSI system achieved promising results for estimating DM, TSS, and FC in durian pulp. This research identified the featured wavelengths that can be used to develop a portable and reliable HSI or multispectral system to be installed at durian packaging firms for quality inspection and grading.
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Evaluation of physiological properties and texture traits of durian pulp using near-infrared spectra of the pulp and intact fruit
(2021-04-01)
Onsawai, Phalanon
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Phetpan, Kittisak
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Khurnpoon, Lampan
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Sirisomboon, Panmanas
This study aimed to investigate the feasibility of non-destructively predicting physiological properties (color, dry matter, and soluble solids) and texture properties (initial firmness, average firmness, rupture force, rupture distance, toughness, average penetrating force, and penetrating energy) of ‘Monthong’ durian using Fourier transform near-infrared spectroscopy of the pulp of the largest locule, the intact fruit at the largest locule, and the stylar end of the intact fruit. Based on partial least squares regression modeling, the internal quality evaluation of durian obtained by scanning the pulp could provide rough screening capability, with coefficient of determination of validation (r2), root mean square error of prediction (RMSEP), and the ratio of standard error of validation to standard deviation (RPD) values for the dry matter content, average penetrating force, and rupture force of 0.89, 3.60%, and 3.27; 0.73, 5.53 N, and 1.95; and 0.74, 6.15 N, and 1.96, respectively. Only the dry matter content of the pulp could be reasonably predicted based on scanning the intact durian fruit at the largest locule, with r2, RMSEP, and RPD values of 0.79, 5.23%, and 2.18, respectively. This finding could be applied at the first stage of trade between durian agriculturalists and exporters to prevent the exportation of immature durian and would be helpful for the industries producing frozen durian pulp and intact durian fruit for export.
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Determination of dry matter and soluble solids of durian pulp using diffuse reflectance near infrared spectroscopy
(2015-01-01)
Onsawai, Phalanon
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Sirisomboon, Panmanas
Fourier transform near infrared spectroscopy was used as a non-invasive technique for the determination of dry matter and soluble solids of durian pulp. A set of 25 fruit was randomly harvested every 10 days, starting from 80 days until 127 days after the onset of fruit development covering six levels of maturity (80 days, 90 days, 100 days, 110 days, 120 days and 127 days). After applying ethephon on the fruit stems, the fruits were kept for 3 days at room temperature and allowed to ripen. Only the pulp of the durian was scanned. The dry matter and soluble solids reference values of the samples were determined by a hot-air-oven method and using a refractometer, respectively. Prediction models using half the samples related the spectral data, and dry matter and soluble solids data were subsequently established using partial least-squares regression and validated using the other half of the samples in a prediction set. A full cross-validation was also generated using all 149 samples. Both the half-of-the-samples model and the all-sample model were then validated using a true validation set of samples collected in a later year. When tested against the validation half of the samples, the halfof- the-samples model predicted dry-matter content with a coefficient of determination (r2) and root mean square error of prediction (RMSEP) of 0.89 and 3.60%, respectively, and for soluble solids content 0.55 and 1.63 °Brix (Bx), respectively. When tested on samples from a later season, the model for dry-matter content returned an r2, RMSEP and bias of 0.26, 6.10% and 2.16%, respectively, and for soluble solids content 0.27, 1.25 °Bx and 1.09 °Bx, respectively. The cross-validated model for dry matter yielded a slightly better r2 and root mean square error of cross-validation (RMSECV) of 0.90 and 3.58%, respectively, however, the model for soluble solids did not provide a better r2 and RMSECV: 0.51 and 1.81 °Bx, respectively. When tested on samples from a later season, the cross-validated models gave, r2, RMSEP and bias of 0.15, 5.17% and 1.49%, respectively, for dry-matter content, and for soluble solids content 0.37, 1.32 °Bx and 1.23 °Bx, respectively. The poor results obtained when predicting dry matter in samples in later seasons indicate that samples from several seasons must be included in the set of calibration samples. This is the first report on the application of NIR spectroscopy to evaluate the dry matter and soluble solids of durian pulp and could be useful to customers, exporters, importers and also postharvest technologists. However, prediction accuracy was not demonstrated in the model for durian pulp soluble solids, possibly because of the effect of ethephon applied after harvesting to induce ripening within 3 days to make the fruit suitable for consumption. In addition, it was found that the vibration bands of cellulose and fat, and those of aromatic, CH2 and sucrose highly affected the predictions of dry matter and soluble solids in the durian pulp, respectively.

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