Cargando…
Discrimination and prediction of the origin of Chinese and Korean soybeans using Fourier transform infrared spectrometry (FT-IR) with multivariate statistical analysis
The ability to determine the origin of soybeans is an important issue following the inclusion of this information in the labeling of agricultural food products becoming mandatory in South Korea in 2017. This study was carried out to construct a prediction model for discriminating Chinese and Korean...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5916519/ https://www.ncbi.nlm.nih.gov/pubmed/29689113 http://dx.doi.org/10.1371/journal.pone.0196315 |
_version_ | 1783317025439875072 |
---|---|
author | Lee, Byeong-Ju Zhou, Yaoyao Lee, Jae Soung Shin, Byeung Kon Seo, Jeong-Ah Lee, Doyup Kim, Young-Suk Choi, Hyung-Kyoon |
author_facet | Lee, Byeong-Ju Zhou, Yaoyao Lee, Jae Soung Shin, Byeung Kon Seo, Jeong-Ah Lee, Doyup Kim, Young-Suk Choi, Hyung-Kyoon |
author_sort | Lee, Byeong-Ju |
collection | PubMed |
description | The ability to determine the origin of soybeans is an important issue following the inclusion of this information in the labeling of agricultural food products becoming mandatory in South Korea in 2017. This study was carried out to construct a prediction model for discriminating Chinese and Korean soybeans using Fourier-transform infrared (FT-IR) spectroscopy and multivariate statistical analysis. The optimal prediction models for discriminating soybean samples were obtained by selecting appropriate scaling methods, normalization methods, variable influence on projection (VIP) cutoff values, and wave-number regions. The factors for constructing the optimal partial-least-squares regression (PLSR) prediction model were using second derivatives, vector normalization, unit variance scaling, and the 4000–400 cm(–1) region (excluding water vapor and carbon dioxide). The PLSR model for discriminating Chinese and Korean soybean samples had the best predictability when a VIP cutoff value was not applied. When Chinese soybean samples were identified, a PLSR model that has the lowest root-mean-square error of the prediction value was obtained using a VIP cutoff value of 1.5. The optimal PLSR prediction model for discriminating Korean soybean samples was also obtained using a VIP cutoff value of 1.5. This is the first study that has combined FT-IR spectroscopy with normalization methods, VIP cutoff values, and selected wave-number regions for discriminating Chinese and Korean soybeans. |
format | Online Article Text |
id | pubmed-5916519 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-59165192018-05-05 Discrimination and prediction of the origin of Chinese and Korean soybeans using Fourier transform infrared spectrometry (FT-IR) with multivariate statistical analysis Lee, Byeong-Ju Zhou, Yaoyao Lee, Jae Soung Shin, Byeung Kon Seo, Jeong-Ah Lee, Doyup Kim, Young-Suk Choi, Hyung-Kyoon PLoS One Research Article The ability to determine the origin of soybeans is an important issue following the inclusion of this information in the labeling of agricultural food products becoming mandatory in South Korea in 2017. This study was carried out to construct a prediction model for discriminating Chinese and Korean soybeans using Fourier-transform infrared (FT-IR) spectroscopy and multivariate statistical analysis. The optimal prediction models for discriminating soybean samples were obtained by selecting appropriate scaling methods, normalization methods, variable influence on projection (VIP) cutoff values, and wave-number regions. The factors for constructing the optimal partial-least-squares regression (PLSR) prediction model were using second derivatives, vector normalization, unit variance scaling, and the 4000–400 cm(–1) region (excluding water vapor and carbon dioxide). The PLSR model for discriminating Chinese and Korean soybean samples had the best predictability when a VIP cutoff value was not applied. When Chinese soybean samples were identified, a PLSR model that has the lowest root-mean-square error of the prediction value was obtained using a VIP cutoff value of 1.5. The optimal PLSR prediction model for discriminating Korean soybean samples was also obtained using a VIP cutoff value of 1.5. This is the first study that has combined FT-IR spectroscopy with normalization methods, VIP cutoff values, and selected wave-number regions for discriminating Chinese and Korean soybeans. Public Library of Science 2018-04-24 /pmc/articles/PMC5916519/ /pubmed/29689113 http://dx.doi.org/10.1371/journal.pone.0196315 Text en © 2018 Lee et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Lee, Byeong-Ju Zhou, Yaoyao Lee, Jae Soung Shin, Byeung Kon Seo, Jeong-Ah Lee, Doyup Kim, Young-Suk Choi, Hyung-Kyoon Discrimination and prediction of the origin of Chinese and Korean soybeans using Fourier transform infrared spectrometry (FT-IR) with multivariate statistical analysis |
title | Discrimination and prediction of the origin of Chinese and Korean soybeans using Fourier transform infrared spectrometry (FT-IR) with multivariate statistical analysis |
title_full | Discrimination and prediction of the origin of Chinese and Korean soybeans using Fourier transform infrared spectrometry (FT-IR) with multivariate statistical analysis |
title_fullStr | Discrimination and prediction of the origin of Chinese and Korean soybeans using Fourier transform infrared spectrometry (FT-IR) with multivariate statistical analysis |
title_full_unstemmed | Discrimination and prediction of the origin of Chinese and Korean soybeans using Fourier transform infrared spectrometry (FT-IR) with multivariate statistical analysis |
title_short | Discrimination and prediction of the origin of Chinese and Korean soybeans using Fourier transform infrared spectrometry (FT-IR) with multivariate statistical analysis |
title_sort | discrimination and prediction of the origin of chinese and korean soybeans using fourier transform infrared spectrometry (ft-ir) with multivariate statistical analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5916519/ https://www.ncbi.nlm.nih.gov/pubmed/29689113 http://dx.doi.org/10.1371/journal.pone.0196315 |
work_keys_str_mv | AT leebyeongju discriminationandpredictionoftheoriginofchineseandkoreansoybeansusingfouriertransforminfraredspectrometryftirwithmultivariatestatisticalanalysis AT zhouyaoyao discriminationandpredictionoftheoriginofchineseandkoreansoybeansusingfouriertransforminfraredspectrometryftirwithmultivariatestatisticalanalysis AT leejaesoung discriminationandpredictionoftheoriginofchineseandkoreansoybeansusingfouriertransforminfraredspectrometryftirwithmultivariatestatisticalanalysis AT shinbyeungkon discriminationandpredictionoftheoriginofchineseandkoreansoybeansusingfouriertransforminfraredspectrometryftirwithmultivariatestatisticalanalysis AT seojeongah discriminationandpredictionoftheoriginofchineseandkoreansoybeansusingfouriertransforminfraredspectrometryftirwithmultivariatestatisticalanalysis AT leedoyup discriminationandpredictionoftheoriginofchineseandkoreansoybeansusingfouriertransforminfraredspectrometryftirwithmultivariatestatisticalanalysis AT kimyoungsuk discriminationandpredictionoftheoriginofchineseandkoreansoybeansusingfouriertransforminfraredspectrometryftirwithmultivariatestatisticalanalysis AT choihyungkyoon discriminationandpredictionoftheoriginofchineseandkoreansoybeansusingfouriertransforminfraredspectrometryftirwithmultivariatestatisticalanalysis |