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

Identifier:  imarina:6112171

Handle: https://hdl.handle.net/20.500.11797/imarina6112171

Authors:  Mabood F; Boqué R; Alkindi AY; Al-Harrasi A; Al Amri IS; Boukra S; Jabeen F; Hussain J; Abbas G; Naureen Z; Haq QMI; Shah HH; Khan A; Khalaf SK; Kadim I

Abstract:
© 2020 Elsevier Ltd 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 R2 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%.