Fast detection and quantification of pork meat in other meats by reflectance FT-NIR spectroscopy and multivariate analysis

被引:32
作者
Mabood, Fazal [1 ]
Boque, Ricard [2 ]
Alkindi, Abdulazi Y. [1 ]
Al-Harrasi, Ahmed [4 ]
Al Amri, Iss S. [1 ]
Boukra, Salah [1 ]
Jabeen, Farah [5 ]
Hussain, Javid [1 ]
Abbas, Ghulam [1 ]
Naureen, Zakira [1 ]
Haq, Quazi M., I [1 ]
Shah, Hakikull H. [1 ]
Khan, Ajmal [4 ]
Khalaf, Samer K. [3 ]
Kadim, Isam [1 ]
机构
[1] Univ Nizwa, Coll Arts & Sci, Dept Biol Sci & Chem, Nizwa 616, Oman
[2] Univ Rovira & Virgili, Dept Analyt Chem & Organ Chem, Tarragona, Spain
[3] Univ Nizwa, DARIS Ctr Res & Technol Dept, Nizwa, Oman
[4] Univ Nizwa, Nat & Med Sci Res Ctr, Nizwa 616, Oman
[5] Univ Malakand, Dept Chem, Lower Dir Kp, Pakistan
关键词
Near infrared reflectance spectroscopy; Pork meat; PCA; PLSR; PLS-DA; NEAR-INFRARED REFLECTANCE; ADULTERATION; BEEF; IDENTIFICATION; AUTHENTICATION; PREDICTION; QUALITY; ASSAY;
D O I
10.1016/j.meatsci.2020.108084
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
This study aimed to develop a fast analytical method, combining near infrared reflectance spectroscopy and multivariate analysis, for detection and quantification of pork meat in other meat samples. A total of 5952 mixture samples from 39 types of meat were prepared in triplicate, with the inclusion of pork at 0%, 1%, 5%, 10%, 30%, 50%, 70%, 90% and 100%. Each sample was scanned using an FT-NIR spectrophotometer in the reflection mode. Spectra were collected in the wavenumber range from 10,000 to 4000 cm(-1), at a resolution of 2 cm(-1) and a total path length of 0.5 mm. Principal Component Analysis (PCA) revealed the similarities and differences among the various types of meat samples and Partial Least-Squares Discriminant Analysis (PLS-DA) showed a good discrimination between pure and pork-spiked meat samples. A Partial Least-Squares Regression (PLSR) model was built to predict the pork meat contents in other meats, which provided the R-2 value of 0.9774 and RMSECV value of 1.08%. Additionally, an external validation was carried out using a test set, providing a rather good prediction error, with an RMSEP value of 1.84%.
引用
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页数:6
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