| Literature DB >> 27719903 |
Shuifang Li1, Xin Zhang2, Yang Shan3, Donglin Su4, Qiang Ma1, Ruizhi Wen1, Jiaojuan Li1.
Abstract
Near-infrared spectroscopy (NIR) was used for qualitative and quantitative detection of honey adulterated with high-fructose corn syrup (HFCS) or maltose syrup (MS). Competitive adaptive reweighted sampling (CARS) was employed to select key variables. Partial least squares linear discriminant analysis (PLS-LDA) was adopted to classify the adulterated honey samples. The CARS-PLS-LDA models showed an accuracy of 86.3% (honey vs. adulterated honey with HFCS) and 96.1% (honey vs. adulterated honey with MS), respectively. PLS regression (PLSR) was used to predict the extent of adulteration in the honeys. The results showed that NIR combined with PLSR could not be used to quantify adulteration with HFCS, but could be used to quantify adulteration with MS: coefficient (Rp2) and root mean square of prediction (RMSEP) were 0.901 and 4.041 for MS-adulterated samples from different floral origins, and 0.981 and 1.786 for MS-adulterated samples from the same floral origin (Brassica spp.), respectively.Entities:
Keywords: Competitive adaptive reweighted sampling (CARS); Honey adulteration; Near-infrared spectroscopy; Partial least squares linear discriminant analysis (PLS-LDA); Partial least squares regression (PLSR)
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Year: 2016 PMID: 27719903 DOI: 10.1016/j.foodchem.2016.08.105
Source DB: PubMed Journal: Food Chem ISSN: 0308-8146 Impact factor: 7.514