线性判别分析
数学
偏最小二乘回归
光学(聚焦)
投影(关系代数)
中红外
班级(哲学)
判别式
统计
人工智能
食品科学
作者
A. Biancolillo,M. Foschi,M. Di Micco,F. Di Donato,A.A. D'Archivio
标识
DOI:10.1016/j.microc.2022.107327
摘要
• Geographical Discrimination study for the traceability of Italian lentils. • Chemometric elaboration of the Mid-Infrared spectroscopic lentil profiles. • The discriminant approach correctly recognized 98.8% of the test samples. Three hundred forty-six (3 4 6) samples of lentils have been collected and analyzed by ATR-MIR spectroscopy. Of the investigated individuals, 283 were harvested in two Central-Italy regions (Umbria and Abruzzo), whereas the others were grown in Canada. At first, Partial Least Squares Discriminant Analysis (PLS-DA) was used to discriminate samples among the three origins. The outcome of PLS-DA analysis was noteworthy: only one individual (over 86 of the external test set) was erroneously assigned by the model, indicating the suitability of the proposed approach. Furthermore, Variable Importance in Projection (VIP) was exploited to inquire which spectral variables significantly contribute to the discrimination. Eventually, the focus has been circumscribed to two categories of high-valued lentils, e.g., lentils from Castelluccio di Norcia (CDN), a sub-set of the Umbria class, and from Santo Stefano di Sessanio (SSS), a sub-set of the Abruzzo class. These are of particular interest because CDN presents the Protected Geographical Indication (PGI) while SSS belongs to the Slow Food Presidium. The models for these classes provided interesting results.
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