期刊
FOOD SCIENCE & NUTRITION
卷 8, 期 3, 页码 1471-1479出版社
WILEY
DOI: 10.1002/fsn3.1430
关键词
data fusion; intact protein fingerprints; milk adulteration; peptide fingerprints; principle component analysis
资金
- National Key Research and Development Program of China [2018YFD0400600]
- ChinaCanada Joint Lab of Food Nutrition and Health (Beijing) [KFKT-ZJ-201803]
- SJTU startup fund for young talent [18X100040051]
Detection of the presence of milk powder in liquid whole milk is challenging due to their similar chemical components. In this study, a sensitive and robust approach has been developed and tested for potential utilization in discriminating adulterated milk from liquid whole milk by analyzing the intact protein and hydrolyzed peptide using ultra-performance liquid chromatography with quadrupole time-of-flight mass spectrometer (UPLC-QTOF-MS) fingerprints combined with data fusion. Two different datasets from intact protein and peptide fingerprints were fused to improve the discriminating ability of principle component analysis (PCA). Furthermore, the midlevel data fusion coupled with PCA could completely distinguish liquid whole milk from the milk. The limit of detection of milk powder in liquid whole milk was 0.5% (based on the total protein equivalence). These results suggested that fused data from intact protein and peptide fingerprints created greater synergic effect in detecting milk quality, and the combination of data fusion and PCA analysis could be used for the detection of adulterated milk.
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