Abstract
Cape gooseberries possess high nutritional value, characterized by elevated levels of vitamin C. This study proposed the application of near infrared hyperspectral imaging (NIR-HSI) coupled with chemometrics (Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR)) to predict vitamin C content (mg/mL), firmness (N), soluble solids content (SSC) (°Brix), and titratable acidity (TA) ( %). Vitamin C content was the property with best prediction performance (RMSEP = 4.15 (mg/mL) and RPD = 2.61.) SVMR for predicting firmness achieved an RPD of 2.31, while for predicting SSC and titratable acidity, RMSEP values were <1 %. The bottom/calyx of berries proved to be the best region for image acquisition. Overall, full-spectral-based PLSR and SVMR exhibited comparable performance regarding RPD values. However, following a meticulous variable selection process, SVMR demonstrated superior adaptability to dimensionality reduction, increasing +17 % RPD values. Finally, we demonstrate that NIR-HSI could predict the physicochemical composition of Cape gooseberries.
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