Cargando…
Rapid Detection of Adulterants in Whey Protein Supplement by Raman Spectroscopy Combined with Multivariate Analysis
The growing demand for whey protein supplements has made them the target of adulteration with cheap substances. Therefore, Raman spectroscopy in tandem with chemometrics was proposed to simultaneously detect and quantify three common adulterants (creatine, l-glutamine and taurine) in whey protein co...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6571825/ https://www.ncbi.nlm.nih.gov/pubmed/31100965 http://dx.doi.org/10.3390/molecules24101889 |
_version_ | 1783427499119607808 |
---|---|
author | Jiao, Xianzhi Meng, Yaoyong Wang, Kangkang Huang, Wei Li, Nan Liu, Timon Cheng-Yi |
author_facet | Jiao, Xianzhi Meng, Yaoyong Wang, Kangkang Huang, Wei Li, Nan Liu, Timon Cheng-Yi |
author_sort | Jiao, Xianzhi |
collection | PubMed |
description | The growing demand for whey protein supplements has made them the target of adulteration with cheap substances. Therefore, Raman spectroscopy in tandem with chemometrics was proposed to simultaneously detect and quantify three common adulterants (creatine, l-glutamine and taurine) in whey protein concentrate (WPC) powder. Soft independent modeling class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) models were built based on two spectral regions (400–1800 cm(−1) and 500–1100 cm(−1)) to classify different types of adulterated samples. The most effective was the SIMCA model in 500–1100 cm(−1) with an accuracy of 96.9% and an error rate of 5%. Partial least squares regression (PLSR) models for each adulterant were developed using two different Raman spectral ranges (400–1800 cm(−1) and selected specific region) and data pretreatment methods. The determination coefficients (R(2)) of all models were higher than 0.96. PLSR models based on typical Raman regions (500–1100 cm(−1) for creatine and taurine, the combination of range 800–1000 cm(−1) and 1300–1500 cm(−1) for glutamine) were superior to models in the full spectrum. The lowest root mean squared error of prediction (RMSEP) was 0.21%, 0.33%, 0.42% for creatine, taurine and glutamine, and the corresponding limit of detection (LOD) values for them were 0.53%, 0.71% and 1.13%, respectively. This proves that Raman spectroscopy with the help of multivariate approaches is a powerful method to detect adulterants in WPC. |
format | Online Article Text |
id | pubmed-6571825 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-65718252019-06-18 Rapid Detection of Adulterants in Whey Protein Supplement by Raman Spectroscopy Combined with Multivariate Analysis Jiao, Xianzhi Meng, Yaoyong Wang, Kangkang Huang, Wei Li, Nan Liu, Timon Cheng-Yi Molecules Article The growing demand for whey protein supplements has made them the target of adulteration with cheap substances. Therefore, Raman spectroscopy in tandem with chemometrics was proposed to simultaneously detect and quantify three common adulterants (creatine, l-glutamine and taurine) in whey protein concentrate (WPC) powder. Soft independent modeling class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) models were built based on two spectral regions (400–1800 cm(−1) and 500–1100 cm(−1)) to classify different types of adulterated samples. The most effective was the SIMCA model in 500–1100 cm(−1) with an accuracy of 96.9% and an error rate of 5%. Partial least squares regression (PLSR) models for each adulterant were developed using two different Raman spectral ranges (400–1800 cm(−1) and selected specific region) and data pretreatment methods. The determination coefficients (R(2)) of all models were higher than 0.96. PLSR models based on typical Raman regions (500–1100 cm(−1) for creatine and taurine, the combination of range 800–1000 cm(−1) and 1300–1500 cm(−1) for glutamine) were superior to models in the full spectrum. The lowest root mean squared error of prediction (RMSEP) was 0.21%, 0.33%, 0.42% for creatine, taurine and glutamine, and the corresponding limit of detection (LOD) values for them were 0.53%, 0.71% and 1.13%, respectively. This proves that Raman spectroscopy with the help of multivariate approaches is a powerful method to detect adulterants in WPC. MDPI 2019-05-16 /pmc/articles/PMC6571825/ /pubmed/31100965 http://dx.doi.org/10.3390/molecules24101889 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jiao, Xianzhi Meng, Yaoyong Wang, Kangkang Huang, Wei Li, Nan Liu, Timon Cheng-Yi Rapid Detection of Adulterants in Whey Protein Supplement by Raman Spectroscopy Combined with Multivariate Analysis |
title | Rapid Detection of Adulterants in Whey Protein Supplement by Raman Spectroscopy Combined with Multivariate Analysis |
title_full | Rapid Detection of Adulterants in Whey Protein Supplement by Raman Spectroscopy Combined with Multivariate Analysis |
title_fullStr | Rapid Detection of Adulterants in Whey Protein Supplement by Raman Spectroscopy Combined with Multivariate Analysis |
title_full_unstemmed | Rapid Detection of Adulterants in Whey Protein Supplement by Raman Spectroscopy Combined with Multivariate Analysis |
title_short | Rapid Detection of Adulterants in Whey Protein Supplement by Raman Spectroscopy Combined with Multivariate Analysis |
title_sort | rapid detection of adulterants in whey protein supplement by raman spectroscopy combined with multivariate analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6571825/ https://www.ncbi.nlm.nih.gov/pubmed/31100965 http://dx.doi.org/10.3390/molecules24101889 |
work_keys_str_mv | AT jiaoxianzhi rapiddetectionofadulterantsinwheyproteinsupplementbyramanspectroscopycombinedwithmultivariateanalysis AT mengyaoyong rapiddetectionofadulterantsinwheyproteinsupplementbyramanspectroscopycombinedwithmultivariateanalysis AT wangkangkang rapiddetectionofadulterantsinwheyproteinsupplementbyramanspectroscopycombinedwithmultivariateanalysis AT huangwei rapiddetectionofadulterantsinwheyproteinsupplementbyramanspectroscopycombinedwithmultivariateanalysis AT linan rapiddetectionofadulterantsinwheyproteinsupplementbyramanspectroscopycombinedwithmultivariateanalysis AT liutimonchengyi rapiddetectionofadulterantsinwheyproteinsupplementbyramanspectroscopycombinedwithmultivariateanalysis |