Assessment of a Nondestructive Method for Rapid Discrimination of Moroccan Date Palm Varieties via Mid-Infrared Spectroscopy Combined with Chemometric Models

Background: Morocco is an important world producer and consumer of several varieties of date palm. In fact, the discrimination between varieties remains difficult and requires the use of complex and high-cost techniques. Objective: We evaluated in this work the potential of mid-IR (MIR) spectroscopy and chemometric models to discriminate eight date palm varieties. Method: Four chemometric models were applied for the analysis of the spectral data, including principal-component analysis (PCA), support-vector machine discriminant analysis (SVM-DA), linear discriminant analysis (LDA), and partial-least-squares (PLS) analysis. MIR spectroscopic data were recorded from the wavenumber range 4000-600cm(-1), with a spectral resolution of 4cm(-1). Results: The discriminant analysis was performed by LDA and SVM-DA with a 100% correct classification rate for the date mesocarp. PLS analysis was applied as a complementary chemometric tool aimed at quantifying moisture content; the validation of this model shows a good predictive capacity with a regression coefficient of 84% and a root-mean-square error of cross-validation of 0.50. Conclusions: The present study clearly demonstrates that MIR spectroscopy combined with chemometric approaches constitutes a promising analytical method to classify date palms according to their varietal origin and to establish a regression model for predicting moisture content. Highlights: An alternative analytical method to discriminate date palm cultivars by FTIR-attenuated total reflection spectroscopy coupled with chemometric approaches is described.