Abstract
Authentication assurance of meat or meat products is critical in the meat industry. Various methods including DNA- or protein-based techniques are accurate for assessing meat authenticity, however, they are destructive, expensive, or laborious. This study explores the feasibility of chemometrics in tandem with hyperspectral imaging (HSI) for identifying raw and cooked mutton rolls substitution by pork and duck rolls. Raw or cooked samples (n = 180) of three meat species were prepared to collect hyperspectral images in range of 400–1000 nm. Spectra were extracted from representative regions of interest (ROIs), and spectral principal component analysis (PCA) revealed that PC1 and PC2 were effective for the identification. Different methods including standard normal variable (SNV), first and second derivatives, and normalization were individually employed for spectral preprocessing, and modeling methods of partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) were also individually applied to develop classification models for both the raw and the cooked. Results showed that PLS-DA model developed by raw spectra presented the highest 100% correct classification rate (CCR) of success in all sets. After that, effective wavelengths selected by successive projections algorithm (SPA) built optimal simplified models which didn’t influence the modeling results compared with full spectra regardless of the meat roll states. Therefore, SPA-PLS-DA models were subsequently used to visualize the raw and cooked meat rolls classification. As a consequence, the general meat species of both raw and cooked meat rolls were readily discernible in pixel-wise manner by generating classification maps. The results showed that HSI combined with chemometrics can be used to identify the authentication of raw and cooked mutton rolls substituted by pork and duck rolls accurately. This promising methodology provides a reference which can be extended to the classification or grading of other meat rolls.
Highlights
The global production and consumption of meats are expected to continue to grow steadily in the decade [1]
Cooked meat rolls showed higher reflectance levels than raw meat rolls, which was mainly due to the water leakage during cooking [18]
This study presented that hyperspectral imaging (HSI) coupled with chemometrics had great potential in detecting mutton rolls substitution by pork and duck
Summary
The global production and consumption of meats are expected to continue to grow steadily in the decade [1]. The analysis of quality and safety related issues is attracting global concerns which will significantly influence the consumers’ choices to buy or re-buy. Intentional meat adulteration or meat fraud conducted by unscrupulous businessmen frequently occurred which substituted valuable species by cheap cuts [2]. The horsemeat scandal in 2013 and spoiled meat scandal in 2017 caused huge panic [3]. These cases raise a number of issues related to economy (e.g., earn more money), religion (e.g., pork for Muslims), diet (e.g., calories), and lifestyle (e.g., vegetarianism) [4]. The detection of meat authentication is indispensable to consumers and to the meat industry
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