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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]

An accurate and nondestructive prediction method of lime acidity was established based on near infrared (NIR) spectroscopy and ensemble learning strategy. Dual-band spectra were obtained nondestructively with a single scan using a desk-top Fourier transform spectrometer and a grating portable spectrometer. Spectral preprocessing methods were used to eliminate the interferences in the spectra. The quantification models of available acidity (pH and 10−pH) and total acidity (TA) were developed with the ensemble learning strategy compared with partial least squares (PLS) and variable selection methods. The results indicated that due to the high-energy light source, the models of the grating portable spectrometer were much better than those of the Fourier transform spectrometer. Short-wave NIR (SWNIR) was more suitable for quantitative analysis of available acidity, while long-wave NIR (LWNIR) was more effective for quantitative analysis of TA. Besides, the models of available acidity were ahead of those of TA. Compared with PLS and variables selection methods, the ensemble learning strategy can produce models with higher prediction accuracy and better robustness. In the optimized models, the correlation coefficients of pH, 10−pH and TA for the prediction set were 0.84, 0.82 and 0.66, respectively. The experiment results show that accurate and nondestructive prediction of lime acidity can be achieved with the grating portable NIR spectrometer and ensemble learning strategy. [Display omitted] • Simultaneous and nondestructive detection of pH and TA in lime was achieved. • Dual-band spectra were obtained with a single scan using two spectrometers. • Models of the grating portable spectrometer can satisfy practical application. • Ensemble learning strategy can produce models with higher prediction accuracy. • SWNIR was more suitable for pH, while LWNIR was more effective for TA. [ABSTRACT FROM AUTHOR]

[Display omitted] An automatic, rapid and non-targeted detection method for multi-pesticides in plant-derived foods was developed by gas chromatography–mass spectrometry and chemometrics. In this method, a novel algorithm named moving window iterative target transformation factor analysis was proposed. Although there are challenges of peak overlapping and background interference, the retention time and corrected mass spectra of unknown pesticides can be automatically obtained through iteration calculation in the 'moving window' with reference to the pesticide mass spectral library. One mixed pesticide standard and nine varieties of plant-derived foods were investigated with the proposed method. By contrast, a fast temperature programme was used to shorten detection time compared to the standard temperature programme. For the mixed standard, the mass spectra and retention times of all 39 pesticides were successfully obtained from the overlapping signal. Furthermore, all spiked pesticides were successfully detected in plant-derived foods within 10 min using a fast temperature programme. [ABSTRACT FROM AUTHOR]