明胶
重复性
色谱法
化学
串联质谱法
Boosting(机器学习)
基质(化学分析)
质谱法
液相色谱-质谱法
人工智能
生物化学
计算机科学
作者
Seung Hoon Han,Zhiye Yan,Xiaozheng Huang,Shuo Cai,Ming Zhao,Yun‐Feng Zheng,Xiao Liu,Hao-Kun Xu,Yu Xie,Rong Hou,Jin‐Ao Duan,Rui Liu
出处
期刊:Food Chemistry
[Elsevier]
日期:2022-10-01
卷期号:390: 133111-133111
被引量:4
标识
DOI:10.1016/j.foodchem.2022.133111
摘要
Response-boosting of MS signal was observed in gelatin samples due to abundant Glycine residues produced by collagen enzymolysis. In this work, a new strategy utilizing response-boosting to enhance detection sensitivity was developed for absolute quantification of Asini Corii Colla, a kind of gelatin commonly used as food therapy products in Asia, by high performance liquid chromatography coupled to tandem mass spectrometry. Peptidomics analysis was used to evaluate the similarity between eight different protein matrices, and deer-hide gelatin was selected as the appropriate simulated matrix. Isotope-labelled internal standard was used to compensate the matrix effect and construct matrix-matched calibration curves. The established method showed reliability in absolute quantification of three species-specific gelatin peptides with good linearity (r2 > 0.997), precision (RSD < 8.5%), repeatability (RSD < 8.9%), accuracy (recovery 89.4%∼106.5%) and sensitivity (LOD 0.02 ∼ 0.98 ng/mL). Thus, the present response-boosting based protocol provides a promising application in quality control of food rich in gelatins.
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