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Spectral data of the outer wall and inner capsule of Pericarpium Citri Reticulatae Viride from different regions (Anhui, Guangdong, and Sichuan) were collected with portable near infrared spectrometer. Multiple interferences in the spectra were eliminated using single and combined pretreatment methods. Pattern recognition methods such as principal component analysis (PCA), cluster independent soft pattern classification (SIMCA) and Fisher linear discriminant analysis (FLDA) were used to establish traceability models of Pericarpium Citri Reticulatae Viride origin. The results showed that spectral pretreatment could eliminate the interferences of baseline drift, background, and spectral peak overlap to a certain extent. However, the traceability analysis of the origin couldn't be achieved. Among the three pattern recognition methods, accurate traceability analysis of Pericarpium Citri Reticulatae Virid couldn't be achieved with PCA method. The whole identification rate of SIMCA model with the outer wall and inner capsule original spectra were 99.14% and 98.28%, respectively. The whole identification rates of FLDA models were both 99.57%, better than that of SIMCA models. The identification rates of SIMCA and FLDA models with appropriate spectral pretreatment were 100%. Therefore, portable near infrared spectroscopy technology combined with the supervised pattern recognition methods can achieve non-destructive traceability analysis of Pericarpium Citri Reticulatae Viride origin, expanding a new way for the traceability analysis of food and drug homologous substances origin. [ABSTRACT FROM AUTHOR]

To the increasing difficulty of the detection of multi-pesticide residues in cowpea, optimized moving window target conversion factor analysis (OMWTTFA) algorithm was proposed, in which an adaptive iterative re-weighted penalized least squares calculation had been added to the moving window target transfer factor analysis (MWTTFA) algorithm. And a new method for non-targeted detection of multi-pesticide residues in cowpea was established by combining with gas chromatography-mass spectrometry (GC-MS). A mass spectrometry database of 500 common pesticides was established and high throughput analysis of cowpea GC-MS data was realized by using OMWTTFA algorithm. The method was verified with actual cowpea and spiked samples. The results showed that the method could realize non-targeted detection of all pesticide residues in the two groups of spiked cowpea samples within 30 min retention time, while no pesticide residues were detected in the actual cowpea samples. The matching values of all components were larger than 800‰, and this method was more accurate than MWTTFA algorithm. The above results showed that this method promised to become a new way for non-targeted and high-throughput detection of multi-pesticide residues in fruits and vegetables. [ABSTRACT FROM AUTHOR]