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Identification and quantification of quality of intact durian fruits using NIR spectroscopy
(2026-01-01)
Pitak, Lakkana
;
Ditcharoen, Sirirak
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Maraphum, Kanvisit
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Khamwan, Buathip
;
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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Total soluble solids, dry matter content prediction and maturity stage classification of durian fruit using long-wavelength NIR reflectance
(2023-12-01)
Saenphon, Chirawan
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Ditcharoen, Sirirak
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Malai, Chayuttapong
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Saengprachatanarug, Khwantri
;
Wongpichet, Seree
The DM and TSS of durian pulp moving on a conveyor belt were measured for their rapid and non-destructive qualities based on quantitative and qualitative measurements. The calibration set and prediction set equaled 209 and 69 pulps, respectively. The quantitative test compared the performance of PLS regression for DM and TSS prediction developed from full wavelength (860–1754 nm) and a few significant variables using SPA, GA, and VIP methods. The qualitative test identified the possibility of maturity stage classification by comparing three supervised machine learning classifiers, namely SVM, random forest (RF), and LDA. Effective models for DM and TSS prediction were developed from second derivatives spectra combined with the GA method, exhibiting r2, SEP, and RPD of 0.85, 4.50%, and 2.64, respectively for DM, and 0.66, 5.15%, and 1.60, respectively, for TSS. The model classifying samples into two distinct groups, namely “reject” and “pass,” utilizing the LDA algorithm, exhibited an impressive accuracy rate of 94.20%, making it a suitable choice for quality assurance purposes. This result indicates that the few effective variables were more efficient than full wavelength and improved model accuracy with greater model stability. Enhancing the classification model could involve data sample balancing in each group, leading to further improvements.
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Improving the non-destructive maturity classification model for durian fruit using near-infrared spectroscopy
(2023-03-01)
Ditcharoen, Sirirak
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Sirisomboon, Panmanas
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Saengprachatanarug, Khwantri
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Phuphaphud, Arthit
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Rittiron, Ronnarit
The maturity state of durian fruit is a key indicator of quality before trading. This research aims to improve the near-infrared (NIR) model for classifying the maturity stage of durian fruit using a completely non-destructive measurement. Both NIR spectrometers were investigated: the short wavelength NIR (SWNIR) ranging from 450 to 1000 nm and long wavelength NIR (LWNIR) ranging from 860 to 1750 nm. The samples collected for experimentation consisted of four stages: immaturity, prematurity, maturity, and ripe. Each fruit was scanned at the rind position on the main fertile lobe (header, middle, and tail) and stem. The classification models were developed using three supervised machine learning algorithms: linear discriminant analysis (LDA), support vector machine (SVM), and K-Nearest neighbours (KNN). The analysis results revealed that the use of durian rind spectra only obtained between 83.15% and 88.04% accuracy for the LWNIR spectrometer, while the SWNIR spectrometer provided 64.73 to 93.77% accuracy. The performance of model increases when developing with combination between rind and stem spectra. The LDA model developed using a combination of rind and stem spectra provided the greatest efficiency, exhibiting 97.28% and 100% accuracy for LWNIR and SWNIR spectrometers, respectively. The LDA model is therefore recommended for obtaining spectra from smoothing moving average (MA) + baseline of rind position and when used in combination with the MA + standard normal variance (SNV) of stem spectra. The NIR spectroscopy indicated high potential for non-destructive estimation of the durian maturity stage. This process could be used for quality control in the durian export industry to solve the problem of unripe durian being mixed with ripe fruit.

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