Fourier transform near infrared spectroscopy as a tool to discriminate olive wastes: The case of monocultivar pomaces

Olive pomace, a wet semi-solid paste that remains after olive oil extraction, is a major waste of the process and its recovery is mandatory due to its phytotoxicity when rejected directly into the soil. Innovative applications have been studied, but simple and reliable methods that fulfil the gap between the recovery of compounds and their use by industries (contributing to the sustainability and circular economy of the chain) still need to be explored. In this work, four monocultivar olive pomaces (Arbequina, Arbosana, Oliana, and Koroneiki) were studied regarding their nutritional composition, fatty acids and vitamin E profiles, total phenolic and flavonoid contents, antioxidant activity, and Fourier Transform Near Infrared and Mid Infrared spectra. Principal Component Analysis and Partial Least Square Discriminant Analysis were used to discriminate samples. Arbosana pomace presented the highest total fat (15%, dw) and vitamin E contents (1.4 mg/g of oil), being α-tocopherol the main vitamer in all samples. Koroneiki pomace was the richest in phenolic compounds (9 g gallic acid eq./kg). The major fatty acid was oleic acid. Stearic acid, linoleic acid, and FRAP levels differed significantly among cultivars. NIR spectra showed differences in all spectral regions (best separation from 6504 to 5389 cm^-1 and 4961 to 4035 cm^-1), while MIR spectra presented differences only in some spectral regions. The results showed that Near Infrared spectroscopy together with Principal Component Analysis is a powerful tool to discriminate olive pomace cultivars, with ability to be used in an industrial context.

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site.