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Moisture content prediction in durian husk biomass via near infrared spectroscopy coupled with aquaphotomics and explainable machine learning
(2025-12-15)
Shrestha, Zenisha
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Shrestha, Bijendra
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Sirisomboon, Panmanas
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Pun, Umed Kumar
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Bajracharya, Tri Ratna
Accurate determination of moisture content is essential for energy efficiency and biomass management for fuel materials such as durian husk. Traditional methods of determining biomass moisture content are time-consuming and require specialized expertise, posing challenges for continuous monitoring. To address this limitation, this study applies Near Infrared Spectroscopy (NIRS) combined with machine learning models to rapidly and accurately assess moisture content. Both linear Partial Least Squares Regression (PLSR) and non-linear approaches were used, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Extreme Gradient Boosting (XGB). The application of preprocessing techniques, notably the Savitzky-Golay second derivative (SD) and Standard Normal Variate (SNV), significantly augmented the predictive performance, highlighting the importance of data preprocessing in spectral analysis. Synthetic spectral augmentation using Gaussian noise revealed that while SVM and ANN exhibited near-perfect performance, SVM demonstrated quantifiable reliability. This study also demonstrates SVM as the most sensitive and reliable method for detecting and quantifying moisture content in durian husk. This research contributes novel insights to biomass analysis, highlighting the benefits of integrating NIRS and feasibility of explainable machine learning techniques to identify water related spectral parameters to advance aquaphotomics, thereby advancing rapid and accurate biomass characterization.
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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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Machine learning-based prediction of nutritional status in oil palm leaves using proximal multispectral images
(2022-07-01)
Chungcharoen, Thatchapol
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Donis-Gonzalez, Irwin
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Phetpan, Kittisak
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Udompetaikul, Vasu
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Sirisomboon, Panmanas
This study evaluated the application of proximal multispectral images accompanied by 4 machine learning approaches for estimating the nutritional status of oil palm leaves. The image responded for five bands: blue, green, red, red edge, and near-infrared regions with a center wavelength of 475, 560, 668, 717, and 840 nm. Average and standard deviation (SD) values from the leaf pixels of each band were extracted, obtaining 5 average and 5 SD values from 5 bands. Thirty-four vegetation variables were generated based on those average and SD values. In total, forty-four variables consisted of 10 average-and SD-based features, and 34 vegetation variables were used as the input candidates for analyses against 10 target variables: nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), zinc (Zn), boron (B), and chlorophyll (SPAD). No significant input came out for modeling with P and Zn based on the stepwise selection. Therefore, 8 nutritional models were proposed in this study. A training set with 50 samples was used to be modeled for each target, and a test set with 15 samples was employed to evaluate the models' performances. Based on random forest (RF), support vector regression (SVR), partial least square regression (PLSR), and artificial neuron network (ANN) applied to be modeled, the models for chlorophyll, N, and Ca predictions were acceptable for screening, and those for K and Mg predictions were acceptable for rough screening. The chlorophyll model developed based on the RF had the predictive statistics in terms of coefficient of determination for prediction (r2), root mean square error of prediction (RMSEP), and standard error of prediction (SEP) of 0.752, 5.46 SPAD, and 5.65 SPAD, respectively. The other 2 screening models developed based on SVR and RF for N and Ca, respectively, gave the performances with the r2, RMSEP, and SEP ranging from 0.655 to 0.718, 0.12 to 0.17%, and 0.12 to 0.18%, respectively. In the case of the 2 rough screening models established using the RF algorithm, the predictive statistics ranged from 0.496 to 0.530 for the r2 and 0.07–0.16% for both RMSEP and SEP. In this study, the Fe, Mn, and B models had poor results presenting the range of r2, RMSEP, and SEP of 0.308–0.491, 2.39–72.9 ppm, and 2.45–62.8 ppm, respectively. Based on the results, this study confirmed that the proximal multispectral information of oil palm leaves had enough significance to account for the status of chlorophyll and macro-nutrients: N, K, Ca, and Mg in the leaves.
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Rapid ripening stage classification and dry matter prediction of durian pulp using a pushbroom near infrared hyperspectral imaging system
(2022-02-15)
Sharma, Sneha
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Sumesh, K. C.
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Sirisomboon, Panmanas
This research examined the potential of a pushbroom near infrared hyperspectral imaging (NIR-HSI) system (900–1600 nm) for ripening stage (unripe, ripe, and overripe) classification based on the days after anthesis (DAA) and dry matter (DM) prediction of durian pulp. The performance of five supervised machine learning classifiers was compared including support vector machines (SVM), random forest (RF), linear discriminant analysis (LDA) partial least squares-discriminant analysis (PLS-DA), and k-nearest neighbors (kNN) for the ripening stage classification and a partial least squares regression (PLSR) model was developed for the DM prediction. The classification and regression models were developed and compared using the full and selected wavelengths by genetic algorithms (GA) and principal component analysis (PCA). For classification, LDA showed the best result with a test accuracy of 100% for both full wavelength and selected 135 wavelengths by GA. A total of 11 wavelengths selected from PCA achieved a test accuracy of 93.6% by LDA. The PLSR models predicted the DM with the coefficient of determination of prediction (R<inf>p</inf>2) greater than 0.80 and a root mean square error of prediction (RMSEP) less than 1.6%. The results show that NIR-HSI has the potential to identify ripeness correctly, predict the DM and visualize the spatial distribution of durian pulp. This approach can be implemented in the packaging firms to solve the problems related to uneven ripeness and to inspect the quality of durian based on DM content.

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