Detection of milk powder in liquid whole milk using hydrolyzed peptide and intact protein mass spectral fingerprints coupled with data fusion technologies.
Lijuan DuWeiying LuYaqiong ZhangBoyan GaoLiangli YuPublished in: Food science & nutrition (2020)
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.
Keyphrases
- mass spectrometry
- electronic health record
- liquid chromatography
- big data
- protein protein
- ms ms
- computed tomography
- risk assessment
- magnetic resonance imaging
- machine learning
- simultaneous determination
- climate change
- real time pcr
- loop mediated isothermal amplification
- high resolution mass spectrometry
- quantum dots