The geographical origin assessment of Italian hazelnuts is nowadays a relevant topic, aimed at the protection of provenience certificates. Near Infrared (NIR) spectroscopy could be a functional candidate for preventing and fighting illegal activities related to this matrix. The present study focuses on the exploitability of the NIR technique on the ‘hazelnut chain’ (fresh, roasted and paste), against the false origin declaration frauds, mainly concerning some of the best Italian varieties (‘Nocciola Piemonte’, ‘Tonda Gentile Romana’, ‘Mortarella’). 216 spectra were recorded, for a total of n = 144 for the training set, and n = 72 for the validation set, considering fresh (n = 57), roasted (n = 107), and paste (n = 52) hazelnuts as different matrices. The training set sample selection was made according to a Design of Experiment (DoE), that considered diverse factors, such as harvesting year, storage shelf life, and presence of peel. The validation set was composed of blended samples generated by mixing Italian and non-Italian ones, and real samples bought from local markets. Multivariate Statistical Analysis was employed for data handling and elaboration, both unsupervised and supervised models, Principal Component Analysis, and Partial Least Square-Discriminant Analysis were built to simplify, observe, and classify the samples. A variables selection was performed by filtering the most important ones considering the Variable Importance in Projection (VIP) scores. The predictive ability of the technology was evaluated by applying Classification List and Confusion Matrix approaches to a prediction set, providing a fit of the observations of this set into the selected supervised model. The outcomes highlight valuable discrimination between authentic samples (related to two different harvesting year campaigns) with classification accuracy rates between 89 % and 100 %. Promising results about the application on blended and real samples were also obtained, especially as regards fresh and roasted hazelnuts, which presented classification accuracy rates of 81 % and 91 %. Therefore, this analytical technique could play a strategic role in the geographical origin assessment considering it is a rapid, direct, non-destructive, and cost-effective approach.

Near infrared spectroscopy and multivariate statistical analysis as rapid tools for the geographical origin assessment of Italian hazelnuts / Sammarco, G.; Dall'Asta, C.; Suman, M.. - In: VIBRATIONAL SPECTROSCOPY. - ISSN 0924-2031. - 126:(2023), p. 103531.103531. [10.1016/j.vibspec.2023.103531]

Near infrared spectroscopy and multivariate statistical analysis as rapid tools for the geographical origin assessment of Italian hazelnuts

Sammarco G.;Dall'Asta C.;Suman M.
2023-01-01

Abstract

The geographical origin assessment of Italian hazelnuts is nowadays a relevant topic, aimed at the protection of provenience certificates. Near Infrared (NIR) spectroscopy could be a functional candidate for preventing and fighting illegal activities related to this matrix. The present study focuses on the exploitability of the NIR technique on the ‘hazelnut chain’ (fresh, roasted and paste), against the false origin declaration frauds, mainly concerning some of the best Italian varieties (‘Nocciola Piemonte’, ‘Tonda Gentile Romana’, ‘Mortarella’). 216 spectra were recorded, for a total of n = 144 for the training set, and n = 72 for the validation set, considering fresh (n = 57), roasted (n = 107), and paste (n = 52) hazelnuts as different matrices. The training set sample selection was made according to a Design of Experiment (DoE), that considered diverse factors, such as harvesting year, storage shelf life, and presence of peel. The validation set was composed of blended samples generated by mixing Italian and non-Italian ones, and real samples bought from local markets. Multivariate Statistical Analysis was employed for data handling and elaboration, both unsupervised and supervised models, Principal Component Analysis, and Partial Least Square-Discriminant Analysis were built to simplify, observe, and classify the samples. A variables selection was performed by filtering the most important ones considering the Variable Importance in Projection (VIP) scores. The predictive ability of the technology was evaluated by applying Classification List and Confusion Matrix approaches to a prediction set, providing a fit of the observations of this set into the selected supervised model. The outcomes highlight valuable discrimination between authentic samples (related to two different harvesting year campaigns) with classification accuracy rates between 89 % and 100 %. Promising results about the application on blended and real samples were also obtained, especially as regards fresh and roasted hazelnuts, which presented classification accuracy rates of 81 % and 91 %. Therefore, this analytical technique could play a strategic role in the geographical origin assessment considering it is a rapid, direct, non-destructive, and cost-effective approach.
2023
Near infrared spectroscopy and multivariate statistical analysis as rapid tools for the geographical origin assessment of Italian hazelnuts / Sammarco, G.; Dall'Asta, C.; Suman, M.. - In: VIBRATIONAL SPECTROSCOPY. - ISSN 0924-2031. - 126:(2023), p. 103531.103531. [10.1016/j.vibspec.2023.103531]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2944653
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