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Application of Non-Destructive Rapid Determination of Piperine in Piper nigrum L. (Black Pepper) Using NIR and Multivariate Statistical Analysis: A Promising Quality Control Tool
Piperine is a bioactive alkaloid compound which provides a unique spicy flavor derived from plants of the Piper nigrum L. Black pepper (n = 160) collected from Vietnam was studied using non-destructive near infrared spectroscopy (NIRS). The spectral acquisition ranged from 1100 to 2500 nm, and a che...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7600935/ https://www.ncbi.nlm.nih.gov/pubmed/33050560 http://dx.doi.org/10.3390/foods9101437 |
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author | Park, Jong-Rak Kang, Hyun-Hee Cho, Jong-Ku Moon, Kwang-Deog Kim, Young-Jun |
author_facet | Park, Jong-Rak Kang, Hyun-Hee Cho, Jong-Ku Moon, Kwang-Deog Kim, Young-Jun |
author_sort | Park, Jong-Rak |
collection | PubMed |
description | Piperine is a bioactive alkaloid compound which provides a unique spicy flavor derived from plants of the Piper nigrum L. Black pepper (n = 160) collected from Vietnam was studied using non-destructive near infrared spectroscopy (NIRS). The spectral acquisition ranged from 1100 to 2500 nm, and a chemometrics analysis program was performed to quantify the piperine contents. High performance liquid chromatography (HPLC) analysis was carried out to develop a chemometric model based on reference values. The black pepper samples were divided into two groups used for calibration (n = 120) and prediction (n = 40) sets. The optimum calibration model was developed by pretreatment of the spectra. The analyses results based on the prediction samples included a coefficient of determination (R(2)) of 0.914, a root mean square error of prediction (RMSEP) and a standard error of prediction (SEP) of about 0.220 g/100 g, and a ratio performance to deviation (RPD) value of 3.378 regarding the partial least square (PLS) regression model, and an R(2) of 0.921, an RMSEP and SEP of 0.210 g/100 g, and an RPD of 3.571, with respect to the principal components (PC) regression model. These results indicate that NIRS can be applicable as a control, or as an alternative rapid and effective method to quantify piperine in P. nigrum L. |
format | Online Article Text |
id | pubmed-7600935 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76009352020-11-01 Application of Non-Destructive Rapid Determination of Piperine in Piper nigrum L. (Black Pepper) Using NIR and Multivariate Statistical Analysis: A Promising Quality Control Tool Park, Jong-Rak Kang, Hyun-Hee Cho, Jong-Ku Moon, Kwang-Deog Kim, Young-Jun Foods Article Piperine is a bioactive alkaloid compound which provides a unique spicy flavor derived from plants of the Piper nigrum L. Black pepper (n = 160) collected from Vietnam was studied using non-destructive near infrared spectroscopy (NIRS). The spectral acquisition ranged from 1100 to 2500 nm, and a chemometrics analysis program was performed to quantify the piperine contents. High performance liquid chromatography (HPLC) analysis was carried out to develop a chemometric model based on reference values. The black pepper samples were divided into two groups used for calibration (n = 120) and prediction (n = 40) sets. The optimum calibration model was developed by pretreatment of the spectra. The analyses results based on the prediction samples included a coefficient of determination (R(2)) of 0.914, a root mean square error of prediction (RMSEP) and a standard error of prediction (SEP) of about 0.220 g/100 g, and a ratio performance to deviation (RPD) value of 3.378 regarding the partial least square (PLS) regression model, and an R(2) of 0.921, an RMSEP and SEP of 0.210 g/100 g, and an RPD of 3.571, with respect to the principal components (PC) regression model. These results indicate that NIRS can be applicable as a control, or as an alternative rapid and effective method to quantify piperine in P. nigrum L. MDPI 2020-10-11 /pmc/articles/PMC7600935/ /pubmed/33050560 http://dx.doi.org/10.3390/foods9101437 Text en © 2020 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 Park, Jong-Rak Kang, Hyun-Hee Cho, Jong-Ku Moon, Kwang-Deog Kim, Young-Jun Application of Non-Destructive Rapid Determination of Piperine in Piper nigrum L. (Black Pepper) Using NIR and Multivariate Statistical Analysis: A Promising Quality Control Tool |
title | Application of Non-Destructive Rapid Determination of Piperine in Piper nigrum L. (Black Pepper) Using NIR and Multivariate Statistical Analysis: A Promising Quality Control Tool |
title_full | Application of Non-Destructive Rapid Determination of Piperine in Piper nigrum L. (Black Pepper) Using NIR and Multivariate Statistical Analysis: A Promising Quality Control Tool |
title_fullStr | Application of Non-Destructive Rapid Determination of Piperine in Piper nigrum L. (Black Pepper) Using NIR and Multivariate Statistical Analysis: A Promising Quality Control Tool |
title_full_unstemmed | Application of Non-Destructive Rapid Determination of Piperine in Piper nigrum L. (Black Pepper) Using NIR and Multivariate Statistical Analysis: A Promising Quality Control Tool |
title_short | Application of Non-Destructive Rapid Determination of Piperine in Piper nigrum L. (Black Pepper) Using NIR and Multivariate Statistical Analysis: A Promising Quality Control Tool |
title_sort | application of non-destructive rapid determination of piperine in piper nigrum l. (black pepper) using nir and multivariate statistical analysis: a promising quality control tool |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7600935/ https://www.ncbi.nlm.nih.gov/pubmed/33050560 http://dx.doi.org/10.3390/foods9101437 |
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