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Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging
Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of...
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/PMC7073906/ https://www.ncbi.nlm.nih.gov/pubmed/32041126 http://dx.doi.org/10.3390/foods9020154 |
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author | Jiang, Hongzhe Cheng, Fengna Shi, Minghong |
author_facet | Jiang, Hongzhe Cheng, Fengna Shi, Minghong |
author_sort | Jiang, Hongzhe |
collection | PubMed |
description | Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with R(p)(2) = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that R(p)(2) was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork. |
format | Online Article Text |
id | pubmed-7073906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70739062020-03-19 Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging Jiang, Hongzhe Cheng, Fengna Shi, Minghong Foods Article Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with R(p)(2) = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that R(p)(2) was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork. MDPI 2020-02-06 /pmc/articles/PMC7073906/ /pubmed/32041126 http://dx.doi.org/10.3390/foods9020154 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 Jiang, Hongzhe Cheng, Fengna Shi, Minghong Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging |
title | Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging |
title_full | Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging |
title_fullStr | Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging |
title_full_unstemmed | Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging |
title_short | Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging |
title_sort | rapid identification and visualization of jowl meat adulteration in pork using hyperspectral imaging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7073906/ https://www.ncbi.nlm.nih.gov/pubmed/32041126 http://dx.doi.org/10.3390/foods9020154 |
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