Feasibility of a rapid and non-destructive methodology for the study and discrimination of pine nuts using near-infrared hyperspectral analysis and chemometrics

Abstract

Spanish pine nut is highly appreciated globally for its aroma and taste. Nevertheless, its market is affected by the growing presence of Chinese pine nuts, entailing mislabeling and counterfeits. In this study, near-infrared hyperspectral imaging (940–1625 nm) coupled to chemometrics, was applied, for the first time, to perform a spectral study (identification of chemical distribution and composition) of commercial pine nuts labeled on their package as Spanish and Chinese and to develop a single class-modelling classification model. Sixty-three pine nuts from both marketed origin labels and different qualities were analysed. Principal component analysis (PCA) and multivariate curve resolution (MCR) showed the chemical distribution of the major compounds (bands around 1170–1210 nm and 1485–1550 nm, associated with fats and fatty acids and water and proteins, respectively) of each marketed origin. Soft independent modelling of class analogies (SIMCA) classified the samples according to their labeling of origin, in a pixel-based and nut-based approach, obtaining 89–98% and 84–100% of correct prediction, respectively. This preliminary study demonstrated that the proposed methodology could be used as a fast, comprehensive and innovative quality control tool (for characterisation and classification) for the pine nut industry. • NIR-HSI was for first-time applied for studying and classifying pine nuts. • It allows studying internal pine nut composition and chemical distribution. • Satisfactory classification models were achieved according to two marketed origins. • This approach shows a comprehensive fast and reliable quality control for pine nuts. • NIR-HSI could enhance pine nut traceability and detection of compositional changes.