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Application of SPORT algorithm on ATR-FTIR data: A rapid and green tool for the characterization and discrimination of three typical Italian Pecorino cheeses
被引:5
|作者:
Di Donato, Francesca
[1
]
Biancolillo, Alessandra
[1
]
Foschi, Martina
[1
]
D'Archivio, Angelo Antonio
[1
]
机构:
[1] Univ Aquila, Dipartimento Sci Fis & Chim, Via Vetoio, I-67010 Laquila, Italy
关键词:
Ewes' milk cheese;
Attenuated total reflectance-fourier transformed infrared spectroscopy;
Data fusion;
Sequential preprocessing through orthogonalization;
Sequential and orthogonalized partial least squares;
Partial least squares-discriminant analysis;
Food analysis;
Food composition;
INFRARED-SPECTROSCOPY;
MIDINFRARED SPECTROSCOPY;
CHEMICAL-PARAMETERS;
LEAST-SQUARES;
DI FARINDOLA;
PIG RENNET;
BODY-FAT;
TEXTURE;
CLASSIFICATION;
PROTEOLYSIS;
D O I:
10.1016/j.jfca.2022.104784
中图分类号:
O69 [应用化学];
学科分类号:
081704 ;
摘要:
Three certified Pecorino cheeses, protected designation of origin (PDO) Pecorino Romano, PDO Pecorino Sardo, and Pecorino di Farindola (Slow Food Presidium specialty), produced in Central Italy were analyzed by attenuated total reflectance-fourier transformed infrared (ATR-FTIR) spectroscopy and classified according to their manufacture and/or different cheesemaking process they underwent. The ATR-FTIR spectra were processed by partial least squares-discriminant analysis (PLS-DA) to be used as benchmark method. Moreover, the multi-block strategy sequential preprocessing through orthogonalization (SPORT), based on sequential and orthogonalized partial least squares (SO-PLS), was applied in order to test whether association of spectra preprocessings could enhance the prediction rates. Eventually, the best results were achieved by the multi-block approach, which allows obtaining an accuracy of 98.33% (corresponding to two misclassified samples over 120) in external validation.
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页数:7
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