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Author: search.filters.author.Sharma, SnehaSubject: search.filters.subject.Machine learning

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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 ripening stage classification and dry matter prediction of durian pulp using a pushbroom near infrared hyperspectral imaging system
    (2022-02-15)
    Sharma, Sneha
    ;
    Sumesh, K. C.
    ;
    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><sup>2</sup>) 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.