期刊
FOOD CHEMISTRY
卷 230, 期 -, 页码 68-75出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2017.03.022
关键词
Milk adulteration; Adulterant detection; Multivariate SIMCA screening; One-class modelling; Multi-class modelling
资金
- CAPES Foundation [99999.006216/2015-03]
A sequential strategy was proposed to detect adulterants in milk using a mid-infrared spectroscopy and soft independent modelling of class analogy technique. Models were set with low target levels of adulterations including formaldehyde (0.074 g.L-1), hydrogen peroxide (21.0 g.L-1), bicarbonate (4.0 g.L-1), carbonate (4.0 g.L-1), chloride (5.0 g.L-1), citrate (6.5 g.L-1), hydroxide (4.0 g.L-1), hypochlorite (0.2 g. L-1), starch (5.0 g.L-1), sucrose (5.4 g.L-1) and water (150 g.L-1). In the first step, a one-class model was developed with unadulterated samples, providing 93.1% sensitivity. Four poorly assigned adulterants were discarded for the following step (multi-class modelling). Then, in the second step, a multi-class model, which considered unadulterated and formaldehyde-, hydrogen peroxide-, citrate-, hydroxide and starch-adulterated samples was implemented, providing 82% correct classifications, 17% inconclusive classifications and 1% misclassifications. The proposed strategy was considered efficient as a screening approach since it would reduce the number of samples subjected to confirmatory analysis, time, costs and errors. (C) 2017 Elsevier Ltd. All rights reserved.
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