ABSTRACT
Ambient mass spectrometry is an analytical approach that enables the ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrated applications in both human health and food science. Chemical fingerprints of strip loin sections were acquired using REIMS. Dimension reduction was performed using principal component analysis, feature selection, or PCA followed by FS. A total of eight machine-learning algorithms were compared for predictive accuracy of each model set. In this study, eight machine-learning algorithms were compared for the prediction of specific quality attributes in beef based on molecular profiles generated by REIMS. It includes Partial least squares discriminant analysis, Support vector machine, Random forest , K-nearest neighbor , Linear discriminant analysis , Penalized discriminant analysis , XGBoost and LogitBoost. The performance of each machine-learning algorithm and data reduction combination was assessed in the screening step.
