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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]

The nondestructive characterization of citrus varieties (Egyptian sweet orange, Lane Late navel orange, Australian orange, and Blood orange) was developed based on near-infrared diffuse reflectance spectroscopy (NIRDRS) together with principal component analysis (PCA) and Fisher linear discriminant analysis (FLDA). An experiment for the penetration of NIRDRS into the peel was designed and the effects of different spectral acquisition points were investigated. Pretreatments were used to eliminate the spectral interferences. As an unsupervised pattern recognition method, PCA was used to establish the characterization models. Furthermore, supervised pattern recognition based on PCA and FLDA was employed to enhance the accuracy. The results of the penetration experiments show that near-infrared light enters the citrus peel and is able to characterize the internal composition. Even with the optimized spectral pretreatment, accurate characterization of citrus varieties was not achieved by PCA. However, the accurate characterization of citrus varieties was provided by PCA-FLDA. The accuracies of four spectral acquisition points are 95%, while the characterization accuracies of six spectral acquisition points are 100% combined with optimized spectral pretreatment. Therefore, NIRDRS with PCA-FLDA is suitable for the rapid and nondestructive characterization of citrus varieties. [ABSTRACT FROM AUTHOR]