In this paper, a new strategy of introducing an extra solvent mode in the three-way EEMs data to construct a four-way EX–EM-solvent-samples data set was used in order to investigate the ‘third-order advantage’ by comparing the performances of two four-way calibration algorithms with those of two three-way calibration algorithms. The results of the self-weighted alternating normalized residue fitting (SWANRF) and the parallel factor analysis (PARAFAC) algorithms were both disappointing in this work due to serious collinearity and high background interference. In contrast, the satisfactory results (average recovery, 100.0–107.4% and standard deviation, 1.0–3.0%) were obtained by the third-order calibration methods based on both four-way self-weighted alternating normalized residue fitting (four-way SWANRF) and four-way parallel factor analysis (four-way PARAFAC) algorithms. In addition, for prediction of Dulbecco's modified eagle medium (DMEM) samples, the root-mean-square error of prediction (RMSEP) values for schizandrol B obtained from two three-way calibration algorithms were 60.91 and 35.46 μg mL − 1 with 80% ethanol in the aqueous solvent , which were obviously much larger than these (0.0832 and 0.0825 μg mL − 1 ) obtained using two algorithms of the four-way calibration. The results demonstrated that third-order calibration can extract more inherent information from the data, and can easily solve the problems of serious collinearity and high background interference. In short, among the advantages of this method over the existing methods, low cost, non-toxic and non-destructive analysis can be cited.
In order to characterize the volatile chemical components ofSchisandra chinensisprocessed by different Traditional Chinese Medicine Processing methods and establish fingerprint profiles, headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) technology was employed to detect, identify, and analyzeSchisandra chinensisprocessed by five different methods. Fingerprint profiles of volatile chemical components ofSchisandra chinensisprocessed by different methods were established; a total of 85 different volatile organic compounds (VOCs) were detected in the experiment, including esters, alcohols, ketones, aldehydes, terpenes, olefinic compounds, nitrogen compounds, lactones, pyrazines, sulfur compounds, thiophenes, acid, and thiazoles. Principal component analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and Pearson correlation analysis methods were used to cluster and analyze the detected chemical substances and their contents. The analysis results showed significant differences in the volatile chemical components ofSchisandra chinensisprocessed by different methods; the Variable Importance in Projection (VIP) values of the OPLS-DA model and thePvalues obtained from one-way ANOVA were used to score and screen the detected volatile chemical substances, resulting in the identification of five significant chemical substances with the highestVIPvalues: Alpha-Farnesene, Methyl acetate,1-octene, Ethyl butanoate, and citral. These substances will serve as marker compounds for the identification ofSchisandra chinensisprocessed by different methods in the future.
危险品化学品经营许可证（带存储）