Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy

Consumers concern about food adulteration. Pork meat is the principal adulterated species of beef and mutton. The conventional detection methods have their own limitations; therefore, we sought to develop an efficient and economical identification method using an infrared spectroscopy technique for...

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Main Authors: Ling Yang, Ting Wu, Yun Liu, Juan Zou, Yunmao Huang, Sarath Babu V., Li Lin
Format: Article
Language:English
Published: Wiley 2018-01-01
Series:Journal of Spectroscopy
Online Access:http://dx.doi.org/10.1155/2018/2413874
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author Ling Yang
Ting Wu
Yun Liu
Juan Zou
Yunmao Huang
Sarath Babu V.
Li Lin
author_facet Ling Yang
Ting Wu
Yun Liu
Juan Zou
Yunmao Huang
Sarath Babu V.
Li Lin
author_sort Ling Yang
collection DOAJ
description Consumers concern about food adulteration. Pork meat is the principal adulterated species of beef and mutton. The conventional detection methods have their own limitations; therefore, we sought to develop an efficient and economical identification method using an infrared spectroscopy technique for meat. The Mahalanobis distance method was used to remove outliers in spectrum data. Interferences were eliminated using multiple scatter correction, standard normal variate, Savitzky-Golay smoothing, and normalization. The partial least square discriminant analysis (PLS-DA) and support vector machine (SVM) were used to establish identification models. In the Mahalanobis distance method, the coefficient of test sets was increased from 0.93 to 0.99; the RMSEC and RMSECV were decreased from 0.17 to 0.09 and 0.21 to 0.11 accordingly. The coefficient of determination in-between the calibration and testing sets in PLS-DA reached 0.99 and 0.99, RMSEC was 0.06, and both the RMSECV and RMSEP were 0.08. In contrast, in SVM, methods were 0.97 and 0.96. The RMSEC, RMSECV, and RMSEP were 0.15, 0.17, and 0.24, respectively. In summary, using a combination of infrared spectroscopy technology with PLS-DA was a better identification method than the SVM method that can be used as an effective method to identify pork, beef, and mutton meat samples.
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institution Kabale University
issn 2314-4920
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language English
publishDate 2018-01-01
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series Journal of Spectroscopy
spelling doaj-art-3f19f5610d7149b79a0f98ad815650bd2025-02-03T01:10:58ZengWileyJournal of Spectroscopy2314-49202314-49392018-01-01201810.1155/2018/24138742413874Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared SpectroscopyLing Yang0Ting Wu1Yun Liu2Juan Zou3Yunmao Huang4Sarath Babu V.5Li Lin6School of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaSchool of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaSchool of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaSchool of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaGuangzhou Key Laboratory of Aquatic Animal Diseases and Waterfowl Breeding, Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Sciences and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong 510225, ChinaGuangzhou Key Laboratory of Aquatic Animal Diseases and Waterfowl Breeding, Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Sciences and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong 510225, ChinaGuangzhou Key Laboratory of Aquatic Animal Diseases and Waterfowl Breeding, Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Sciences and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong 510225, ChinaConsumers concern about food adulteration. Pork meat is the principal adulterated species of beef and mutton. The conventional detection methods have their own limitations; therefore, we sought to develop an efficient and economical identification method using an infrared spectroscopy technique for meat. The Mahalanobis distance method was used to remove outliers in spectrum data. Interferences were eliminated using multiple scatter correction, standard normal variate, Savitzky-Golay smoothing, and normalization. The partial least square discriminant analysis (PLS-DA) and support vector machine (SVM) were used to establish identification models. In the Mahalanobis distance method, the coefficient of test sets was increased from 0.93 to 0.99; the RMSEC and RMSECV were decreased from 0.17 to 0.09 and 0.21 to 0.11 accordingly. The coefficient of determination in-between the calibration and testing sets in PLS-DA reached 0.99 and 0.99, RMSEC was 0.06, and both the RMSECV and RMSEP were 0.08. In contrast, in SVM, methods were 0.97 and 0.96. The RMSEC, RMSECV, and RMSEP were 0.15, 0.17, and 0.24, respectively. In summary, using a combination of infrared spectroscopy technology with PLS-DA was a better identification method than the SVM method that can be used as an effective method to identify pork, beef, and mutton meat samples.http://dx.doi.org/10.1155/2018/2413874
spellingShingle Ling Yang
Ting Wu
Yun Liu
Juan Zou
Yunmao Huang
Sarath Babu V.
Li Lin
Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy
Journal of Spectroscopy
title Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy
title_full Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy
title_fullStr Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy
title_full_unstemmed Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy
title_short Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy
title_sort rapid identification of pork adulterated in the beef and mutton by infrared spectroscopy
url http://dx.doi.org/10.1155/2018/2413874
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