Exploring the use of NIR and Raman spectroscopy for the prediction of quality traits in PDO cheeses.

Autor:	Stocco G; Department of Veterinary Science, University of Parma, Parma, Italy., Gómez-Mascaraque LG; Department of Food Chemistry and Technology, Teagasc Food Research Centre Moorepark, Fermoy, Ireland., Deshwal GK; Department of Food Chemistry and Technology, Teagasc Food Research Centre Moorepark, Fermoy, Ireland., Sanchez JC; Escola Universitària Salesiana de Sarrià, Barcelona, Spain., Molle A; Department of Veterinary Science, University of Parma, Parma, Italy., Pizzamiglio V; Consorzio del Formaggio Parmigiano Reggiano, Reggio Emilia, Italy., Berzaghi P; Department of Animal Medicine, Production and Health, University of Padova, Padova, Italy., Gergov G; Institute of Chemical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria., Cipolat-Gotet C; Department of Veterinary Science, University of Parma, Parma, Italy.
Jazyk:	angličtina
Zdroj:	Frontiers in nutrition [Front Nutr] 2024 Feb 06; Vol. 11, pp. 1327301. Date of Electronic Publication: 2024 Feb 06 (Print Publication: 2024).
DOI:	10.3389/fnut.2024.1327301
Abstrakt:	The aims of this proof of principle study were to compare two different chemometric approaches using a Bayesian method, Partial Least Square (PLS) and PLS-discriminant analysis (DA), for the prediction of the chemical composition and texture properties of the Grana Padano (GP) and Parmigiano Reggiano (PR) PDO cheeses by using NIR and Raman spectra and quantify their ability to distinguish between the two PDO and among their ripening periods. For each dairy chain consortium, 9 cheese samples from 3 dairy industries were collected for a total of 18 cheese samples. Three seasoning times were chosen for each dairy industry: 12, 20, and 36 months for GP and 12, 24, and 36 months for PR. A portable NIR instrument (spectral range: 950-1,650 nm) was used on 3 selected spots on the paste of each cheese sample, for a total of 54 spectra collected. An Alpha300 R confocal Raman microscope was used to collect 10 individual spectra for each cheese sample in each spot for a total of 540 Raman spectra collected. After the detection of eventual outliers, the spectra were also concatenated together (NIR + Raman). All the cheese samples were assessed in terms of chemical composition and texture properties following the official reference methods. A Bayesian approach and PLS-DA were applied to the NIR, Raman, and fused spectra to predict the PDO type and seasoning time. The PLS-DA reached the best performances, with 100% correctly identified PDO type using Raman only. The fusion of the data improved the results in 60% of the cases with the Bayesian and of 40% with the PLS-DA approach. A Bayesian approach and a PLS procedure were applied to the NIR, Raman, and fused spectra to predict the chemical composition of the cheese samples and their texture properties. In this case, the best performance in validation was reached with the Bayesian method on Raman spectra for fat (R2VAL = 0.74). The fusion of the data was not always helpful in improving the prediction accuracy. Given the limitations associated with our sample set, future studies will expand the sample size and incorporate diverse PDO cheeses. Competing Interests: PB co-author of this manuscript, is also administrator of GraiNit srl, which provided the portable NIR instrument. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. (Copyright © 2024 Stocco, Gómez-Mascaraque, Deshwal, Sanchez, Molle, Pizzamiglio, Berzaghi, Gergov and Cipolat-Gotet.)
Databáze:	MEDLINE
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