Varietal discrimination of coconut using a UV–Vis and NIR spectroscopy coupled with chemometric techniques

Abstract This study investigates the discrimination of coconut varieties using ultraviolet–visible (UV–Vis) and near infrared (NIR) spectroscopy combined with multivariate chemometric techniques. Spectral data acquired from coconut water and kernel samples were analyzed to explore varietal differences and to develop classification and prediction models. The approach aims to provide a rapid and non-destructive alternative to conventional analytical methods for coconut quality assessment. The study involves five dried coconut cultivars: Tacuna, Catigan, VTT, MYD, and SGD. PLSR was used to determine the predictive capacity of the model with the UV–Vis spectroscopy recording an R 2 and R 2 CV values above 0.99 and 0.86, respectively, with RMSE and RMSECV below 1% and 0.3%, respectively, for predicting total soluble solids, titratable acidity, and pH. NIR spectroscopy recorded an R 2 and R 2 CV values above 0.68 and 0.4294, respectively, with RMSE and RMSECV below 1.3 and 1.8% w/w, respectively, for the prediction of proximate parameters. The study demonstrates accurately classifying coconut varieties, suggesting a promising application for food quality control.