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Subject: search.filters.subject.Near-infrared

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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
    ;
    Sirisomboon, Panmanas
    ;
    K.C, Sumesh
    ;
    Terdwongworakul, Anupun
    ;
    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 (r<sup>2</sup>) 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 r<sup>2</sup>, and RMSEP of 0.90 and 1.40%, respectively, whereas the SPA-PLSR model based on only thirteen wavelengths attained fair result with the r<sup>2</sup> 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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    Rapid and accurate classification of Aspergillus ochraceous contamination in Robusta green coffee bean through near-infrared spectral analysis using machine learning
    (2023-03-01)
    Ruttanadech, Nuttapong
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    Phetpan, Kittisak
    ;
    Srisang, Naruebodee
    ;
    Srisang, Siriwan
    ;
    Chungcharoen, Thatchapol
    Near-infrared (NIR) spectral-based classification of Aspergillus ochraceous contamination in the Robusta green coffee bean was investigated. Six different learning algorithms, including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbors (KNN), decision tree (Tree), Naive Bayes (NB), and quadratic discriminant analysis (QDA), were applied for the investigating purpose. Four classes of fungal contamination on coffee beans, non-fungal contaminated beans on day 1 and day 3 (NCB-D1 and NCB-D3) and fungal contaminated beans on day 1 and day 3 (CB-D1 and CB-D3), were set for the classification intention. Based on the 6 learning algorithms, the Tree approach was optimal, displaying a training accuracy of 97.5%. As proven by the testing dataset, the classification accuracy of the Tree was also at 97.5%. With this number, the Tree could correctly classify 100% between the contaminated and non-contaminated coffee beans. These findings exhibit the potential of the NIR spectroscopy accompanied by machine learning for the early detection of fungal contamination in green coffee beans.
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    Evaluation of informative spectral wavelengths for estimating soluble solids content in sugarcane billets
    (2022-02-22)
    Udompetaikul, Vasu
    ;
    Phetpan, Kittisak
    ;
    Sirisomboon, Panmanas
    This study proposed individual spectral wavelengths significant to estimate soluble solids content (SSC) in sugarcane billets moving on the conveyor. At the same time, an all-in-one quality and yield monitor using those wavelengths for a sugarcane harvester was also proposed. Seven wavelengths, 475, 560, 668, 717, 755, 840, and 890 nm, were arranged into three groups for modeling. Group 1, consisting of 475, 560, 668, 717, and 840 nm, was based on the spectral responses of a commercial multispectral camera, while group 2 (717 and 840 nm) was based on the invisible (RedEdge and near-infrared or NIR) responses of the camera. For group 3, two sugar-related wavelengths at 755 and 890 nm were selected as the candidates for modeling. Partial least squares regression (PLSR) was employed to model those three groups with corresponding soluble solids content (SSC). The results showed that the developed models based on two sugar-related wavelengths at 755 and 890 nm provided the best performance, explaining 80.2 % of the variance in the SSC and displaying a root mean square error of calibration (RMSEC) of 0.32 ºBrix. The predictive performance had the root mean square error of prediction (RMSEP) of 0.33 ºBrix. This finding confirmed the effectiveness of the sugar wavelengths and conveyed the possibility to develop the sugarcane quality and yield monitor.