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| High Performance Liquid Chromatographic Fingerprinting Combined with Chemical Pattern Recognition for Meat Adulteration Identification |
| ZHANG Yali, CHEN Xiuhong, LIU Jia, LI Yi, XIE Yunfeng, WANG Fengxiang, YANG Dan, CHANG Dong |
| 1. National Non-staple Food Quality Inspection and Testing Center, International Trade Food Science Research Institute Co. Ltd., Beijing 102209, China; 2. Beijing Key Laboratory of Nutrition Health and Food Safety, Nutrition & Health Research Institute, COFCO Corporation, Beijing 102209, China |
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Abstract Objective: Based on high performance liquid chromatography (HPLC), fingerprinting of flavor nucleotides in meat was combined with chemical pattern recognition to identify the adulteration of pork in mutton and the adulteration of duck in beef. Methods: HPLC fingerprints of flavor nucleotides in mutton-pork systems and beef-duck systems were established respectively. Common peaks were identified and similarity evaluation was carried out. The data of the common peaks were analyzed by two pattern recognition algorithms, hierarchical clustering analysis (HCA) and principal component analysis (PCA). A regression equation describing the relationship between similarity and the amount of adulteration was fitted. Results: A total of 19 common peaks between the flavor nucleotide fingerprints of mutton and pork were found including 5’-guanosine monophosphate, 5’-inosine monophosphate, hypoxanthine, 5’-adenosine monophosphate, and inosine, and the same result was observed between the flavor nucleotide fingerprints of beef and duck. The results of similarity evaluation, HCA and PCA showed that there were significant differences in the flavor nucleotide components between mutton and pork, and between beef and duck. The developed model showed the negative correlation between similarity and adulteration amount with a correlation coefficient ≥ 0.99. This study and real-time fluorescence polymerase chain reaction gave consistent qualitative results for commercial unknown samples. Conclusion: By fingerprinting of flavor nucleotides, similarity analysis and chemical pattern recognition analysis, and regression equation fitting between similarity and adulteration amount, meat adulteration could be identified and quantified.
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2026, Vol. 40 
