LAUSR

In order to explore a new method to detect the freshness of lotus seeds, the lotus seeds stored for 0, 1, 2, and 3 years, respectively, were used as experimental materials and analyzed by DAPCI-MS (desorption atmospheric pressure chemical ionization-mass spectrometry). The obtained data were processed by principal component analysis (PCA) and backpropagation artificial neural networks (BP-ANNs). The result showed that DAPCI-MS could obtain abundant chemical material information from the slice surface of lotus seeds. The BP-ANNs model could be applied not only to distinguish fresh and aged lotus seeds with the testing set accuracies of 95.0% and 91.7%, respectively, but also to classify lotus seeds with different storage times with the testing set accuracies of 90.0%, 85.0%, 85.0%, and 90.0%, respectively. The paper developed a fast, convenient, and accurate method for the freshness detection of lotus seed and would provide reliable reference value for rapid authentication of food freshness by the rapid mass spectrometry technique.