Comparison of Multiple NIR Instruments for the Quantitative Evaluation of Grape Seed and Other Polyphenolic Extracts with High Chemical Similarities

被引:0
作者
Lukacs, Matyas [1 ]
Vitalis, Flora [1 ]
Bardos, Adrienn [1 ]
Tormasi, Judit [2 ]
Bec, Krzysztof B. [3 ]
Grabska, Justyna [3 ]
Gillay, Zoltan [1 ]
Tomoskozi-Farkas, Rita A. [2 ]
Abranko, Laszlo [2 ]
Albanese, Donatella [4 ]
Malvano, Francesca [4 ]
Huck, Christian W. [3 ]
Kovacs, Zoltan [1 ]
机构
[1] Hungarian Univ Agr & Life Sci, Inst Food Sci & Technol, Dept Food Measurement & Proc Control, H-1118 Budapest, Hungary
[2] Hungarian Univ Agr & Life Sci, Inst Food Sci & Technol, Dept Food Chem & Analyt, H-1118 Budapest, Hungary
[3] Leopold Franzens Univ, Inst Analyt Chem & Radiochem, CCB Ctr Chem & Biomed, Innrain 80-82, A-6020 Innsbruck, Austria
[4] Univ Salerno, Dept Ind Engn, I-84084 Salerno, Italy
关键词
dietary supplement; grape seed extract; chemometrics; handheld device; near-infrared spectroscopy; machine learning; NEAR-INFRARED SPECTROSCOPY; PLANT FOOD SUPPLEMENTS; ANTIOXIDANT CAPACITY; GALLIC ACID; IN-VIVO; PROCYANIDINS; ADULTERANTS; FLAVANOLS; BIOAVAILABILITY; IDENTIFICATION;
D O I
10.3390/foods13244164
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
Grape seed extract (GSE), one of the world's bestselling dietary supplements, is prone to frequent adulteration with chemically similar compounds. These frauds can go unnoticed within the supply chain due to the use of unspecific standard analytical methods for quality control. This research aims to develop a near-infrared spectroscopy (NIRS) method for the rapid and non-destructive quantitative evaluation of GSE powder in the presence of multiple additives. Samples were prepared by mixing GSE with pine bark extract (PBE) and green tea extract (GTE) on different levels between 0.5 and 13% in singular and dual combinations. Measurements were performed with a desktop and three different handheld devices for performance comparison. Following spectral pretreatment, partial least squares regression (PLSR) and support vector regression (SVR)-based quantitative models were built to predict extract concentrations and various chemical parameters. Cross- and external-validated models could reach a minimum R2p value of 0.99 and maximum RMSEP of 0.27% for the prediction of extract concentrations using benchtop data, while models based on handheld data could reach comparably good results, especially for GTE, caffeic acid and procyanidin content prediction. This research shows the potential applicability of NIRS coupled with chemometrics as an alternate, rapid and accurate quality evaluation tool for GSE-based supplement mixtures.
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页数:25
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