Comparing Machine Learning and PLSDA Algorithms for Durian Pulp Classification Using Inline NIR Spectra.

The aim of this study was to evaluate and compare the performance of multivariate classification algorithms, specifically Partial Least Squares Discriminant Analysis (PLS-DA) and machine learning algorithms, in the classification of Monthong durian pulp based on its dry matter content (DMC) and soluble solid content (SSC), using the inline acquisition of near-infrared (NIR) spectra. A total of 415 durian pulp samples were collected and analyzed. Raw spectra were preprocessed using five different combinations of spectral preprocessing techniques: Moving Average with Standard Normal Variate (MA+SNV), Savitzky-Golay Smoothing with Standard Normal Variate (SG+SNV), Mean Normalization (SG+MN), Baseline Correction (SG+BC), and Multiplicative Scatter Correction (SG+MSC). The results revealed that the SG+SNV preprocessing technique produced the best performance with both the PLS-DA and machine learning algorithms. The optimized wide neural network algorithm of machine learning achieved the highest overall classification accuracy of 85.3%, outperforming the PLS-DA model, with overall classification accuracy of 81.4%. Additionally, evaluation metrics such as recall, precision, specificity, F1-score, AUC ROC, and kappa were calculated and compared between the two models. The findings of this study demonstrate the potential of machine learning algorithms to provide similar or better performance compared to PLS-DA in classifying Monthong durian pulp based on DMC and SSC using NIR spectroscopy, and they can be applied in the quality control and management of durian pulp production and storage.

Rapid evaluation of the texture properties of melon (Cucumis melo L. Var. reticulata cv. Green net) using near infrared spectroscopy.

The near infrared (NIR) spectroscopy as the rapid nondestructive method was aimed to be applied for determination of the texture properties of melon intact fruit and pulp including initial firmness, rupture force, average firmness, rupture distance, toughness, average penetrating force and penetrating energy. The data from the reference method of texture analyzer were correlated with the NIR spectral data. The result showed that, only the two properties including rupture force and penetrating force in pulp could be predicted by NIR spectroscopy technique. The determination coefficient of validation (r2 ) for prediction of rupture force and penetrating force in the pulp of melon using intact fruit spectra were 0.850 and 0.845, respectively. The r2 , for prediction of rupture force and penetrating force in the pulp of melon using pulp spectra were 0.813 and 0.778, respectively. This indicated that the NIR spectroscopy protocol developed here was useful for research works such as breeding and postharvest research, the melon processing factory and also the import and export of melon. PRACTICAL APPLICATIONS: The near infrared spectroscopy protocol developed for determination of rupture force and penetrating force in pulp using intact fruit spectra as a nondestructive method will be useful for research works such as breeding and postharvest research, the melon processing factory and also the import and export of melon. There are also the protocol developed using pulp spectra can be used for texture determination of fresh-cut melon.