Near-Infrared Hyperspectral Imaging to Predict Intact Sweet Tamarind Fruit Quality

The quality of sweet tamarind fruit, as determined by its total soluble solids (TSS), titratable acidity (TA), and TSS/TA ratio, is important for consumer satisfaction. Nondestructive techniques are therefore required to assess the quality of sweet tamarind fruit. This study investigated whether near-infrared hyperspectral imaging (NIR-HSI) in the wavelength range of 935–1720 nm can be used as a non-destructive method to assess TSS, TA, and the TSS/TA ratio of sweet tamarind fruit and to classify it under commercial standards. NIR-HSI, combined with deep learning and chemometrics, was applied for quantification and qualification analyses. Calibration models for determining TSS, TA, and the TSS/TA ratio were developed using partial least squares regression (PLSR) and support vector machine regression (SVMR). A combination of 1st derivative and SNV spectral pretreatment, was optimized to establish an SVMR model for TSS determination. MSC spectral pretreatment was optimized to develop the SVMR model for TA assessment, and the 1st derivative spectral pretreatment was optimized to establish an SVMR model for the TSS/TA ratio. Correlation coefficients of prediction (Rp) of 0.959, 0.961, and 0.956 were obtained with root mean square errors of prediction (RMSEP) of 1.102%, 0.369%, and 11.282 for TSS, TA, and the TSS/TA ratio evaluation, respectively. Partial least squares discriminant analysis (PLS-DA) and support vector machine classification (SVMC) were used for classifying sweet tamarind fruit under a commercial acidity standard ( 4%). The SVMC with SNV spectral pretreatment produced the best prediction results for distinguishing standard and off-standard sweet tamarind fruit with an 82.86% accuracy. NIR HSI can be used to non-destructively predict the quality of tamarind fruit. It can be applied for online sorting to evaluate individual sweet tamarind fruits for grading and quality control in factory environments.