The employment of Fourier transform infrared spectroscopy coupled with chemometrics techniques for traceability and authentication of meat and meat products.

Abdul Rohman

doi:10.5455/javar.2019.f306

Abstract

Meat-based food such as meatball and sausages are important sources of protein needed for the human body. Due to different prices, some unethical producers try to adulterate high-price meat such as beef with lower priced meat like pork and rat meat to gain economical profits, therefore, reliable and fast analytical techniques should be developed, validated, and applied for meat traceability and authenticity. Some instrumental techniques have been applied for the detection of meat adulteration, mainly relied on DNA and protein using polymerase chain reaction and chromatographic methods, respectively. But, this method is time-consuming, needs a sophisticated instrument, involves complex sample preparation which make the method is not suitable for routine analysis. As a consequence, a simpler method based on spectroscopic principles should be continuously developed. Food samples are sometimes complex which resulted in complex chemical responses. Fortunately, a statistical method called with chemometrics could solve the problems related to complex chemical data. This mini-review highlights the application of Fourier-transform infrared spectroscopy coupled with numerous chemometrics techniques for authenticity and traceability of meat and meat-based products.

Highlights

Meat and meat-based products are taken into account as important sources of protein for the human body and have evolved as an essential diet ingredient because of its appreciated taste and flavor and is being widely consumed around the world [1]
The common chemometrics techniques used in vibrational spectroscopy including mid-IR spectroscopy are: (a) Fourier-transform infrared (FTIR) spectral data treatment intended to increase the quality of FTIR spectra by minimizing the undesired effect based on the mathematical equations and data transformations such as normalizations, derivatization, Savitzy–Golay smoothing, standard normal variate, baseline corrections, and multiplicative corrections; (b) the experiments design which include randomization, factorial design, and response surface methodology; (3) discrimination and classification among objects such as discriminant analysis (DA), partial least square-discriminant analysis (PLS-DA), principal component analysis (PCA), orthogonal projections to latent structures-DA, cluster analysis; and (4) multivariate calibrations such as classical linear regression, multiple linear regression, polymerase chain reaction (PCR), and PLS regression [29,35]
SIMCA with mean-centered data could provide best model for the identification of Beef offal (BO), while LDA using nonscaled spectra offered best performance in classifying of Pork offal (PO) PLS-kernel calibration could predict the levels of pork in the mixture of pork-beef

Summary

Introduction

Meat and meat-based products are taken into account as important sources of protein for the human body and have evolved as an essential diet ingredient because of its appreciated taste and flavor and is being widely consumed around the world [1]. Several analytical methods have been validated and developed for authentication and traceability of meat and meat-based food either using physicochemical (spectroscopy, electrophoresis, enzyme-linked immunosorbent assay or ELISA, and chromatography), enzymatic, or biological-based techniques [polymerase chain reaction (PCR)] [13], mainly via analysis of lipid, DNA, or protein present in meat products as reviewed by several authors [5,14,15,16,17]. The selection of these methods depends on several variables, including the quantity of analytes, type of analytes target, part of the meat, condition, and processing of meat [18]. Fourier-transform infrared (FTIR) spectroscopy is regarded as an ideal analytical technique for fast screening of meat due to its nature of fingerprint [20], in which there are no meats having the same FTIR spectra

Methods

Results

Conclusion