Abstract
The problem of adulteration and mislabeling in meat products has raised the public concerns globally. An easy-operation, fast, and robust method that is applicable to routine inspections is urgently needed. This study showed that the MALDI-TOF MS protein profiling of four meat species (beef, chicken, duck, and pork) combining with partial least squares discriminant analysis (PLS-DA) discovered 57 feature peaks for their unambiguous differentiation. Among them, 36 were identified in Uniprot. Based on the linear relation between the intensities of feature peaks, the partial least squares regression was successfully applied to build the prediction models for determining the adulteration ratios of beef meat mixtures containing one of the other three species. Blind tests were applied to evaluate the method and the average prediction accuracy at 94.7% was achieved. Taking duck meat as the adulterant, the detection sensitivity of the method could be down to 5%. Moreover, the method has also been successfully applied to analyze market samples and the results were in agreement with the PCR method, showing the potential of its practical application for qualitative and quantitative analysis of adulterated beef products.
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Funding
K. Pu acknowledges National Students’ platform for innovation and entrepreneurship training program (Grant No.: 202110560019). K.-M. Ng acknowledges Shantou University for granting the research fund (STU Scientific Research Foundation for Talents, Grant No.: NTF19043), and this work is financially supported by the Guangdong Basic and Applied Basic Research Foundation (2021A1515010106). Y. Lin acknowledges the funding support of National Natural Science Foundation of China (Grant No.: 82071973). The funding supported by 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant (Project Number: 2020LKSFG01F) is gratefully acknowledged.
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K. Pu designed and performed the experiments and data analysis, and wrote the manuscript; J. Qiu performed the experiments and data analysis, and wrote the manuscript; J. Li performed the experiments; W. Huang assisted the preparation of blind-test samples, and revised the manuscript; X. Lai assisted the data analysis, and revised the manuscript; C. Liu and Y. Lin revised the manuscript; K.-M. Ng contributed to the conceptualization, supervision of the study, checking the processed data and manuscript revision.
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Keyuan Pu declares that he has no conflict of interest. Jiamin Qiu declares that she has no conflict of interest. Jiaying Li declares that she has no conflict of interest. Wei Huang declares that he has no conflict of interest. Xiaopin Lai declares that she has no conflict of interest. Cheng Liu declares that he has no conflict of interest. Yan Lin declares that she has no conflict of interest. Kwan-Ming Ng declares that he has no conflict of interest.
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Pu, K., Qiu, J., Li, J. et al. MALDI-TOF MS Protein Profiling Combined with Multivariate Analysis for Identification and Quantitation of Beef Adulteration. Food Anal. Methods 16, 132–142 (2023). https://doi.org/10.1007/s12161-022-02403-2
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DOI: https://doi.org/10.1007/s12161-022-02403-2