An intelligent surgical knife (iKnife) coupled with rapid evaporative ionization mass spectrometry (REIMS) was employed for the lipidomic profiling of fresh and frozen-thawed beef muscle. The data were obtained by REIMS and then processed using multivariate statistical analysis methods including principal component analysis-linear discriminant analysis (PCA-LDA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The discrimination of fresh and frozen-thawed meat has been achieved, and the real-time identification accuracy was 92-100%. Changes in the composition and content of fatty acids and phospholipids were statistically analyzed by OPLS-DA, and the ions of m/z 279.2317, m/z 681.4830, and m/z 697.4882 were selected as differential compounds/metabolites. The developed method was also successfully applied in the discrimination of fresh and frozen-thawed meat samples. These results showed that REIMS as a high-throughput, rapid, and real-time mass spectrometry detection technology can be used for the identification of fresh and frozen-thawed meat samples.
Keywords: fresh meat; frozen−thawed meat; iKnife; multivariate statistical analysis; rapid evaporative ionization mass spectrometry (REIMS); real-time identification.