[Research progress on common pesticide residues in Chinese medicinal materials].

Chinese medicinal materials are the precious resources of China and favored by patients at home and abroad because of their natural sources and curative effects. Pesticides are often used to prevent and control diseases and insect pests and regulate the growth of Chinese medicinal plants, so as to improve the yield and quality of Chinese medicinal materials. Most of the pesticides can play a role in pest control through systemic action, stomach toxicity, contact, fumigation and other ways, especially the systemic pesticides can kill hidden pests by entering the Chinese medicinal plants. Despite the good pest control effect, it is difficult to remove the systemic pesticides by simple cleaning, which poses a great risk to the safety of Chinese medicinal materials. At the same time, excessive or non-standard use of pesticides leads to serious pesticide residues in Chinese medicinal materials, which affects not only the quality and efficacy of the materials and harm human health but also the international development of Chinese medicinal materials industry. Pesticide residues have become a bottleneck affecting the industry development and hindering the export of Chinese medicinal materials. Therefore, it is of great significance to study how to quickly, sensitively, and accurately detect and remove pesticide residues in Chinese medicinal materials. We reviewed the common pesticide residues in Chinese medicinal materials in recent years in terms of characteristics, harm, and detection and removal techniques, and discussed the future development of the detection and removal deve-lopment. With this review, we aimed to provide a reference for the quality control of Chinese medicinal materials and promote the healthy development of Chinese medicine industry.

Rapid determination of sulfamethoxazole and trimethoprim illegally added to health products using excitation-emission matrix fluorescence coupled with the second-order calibration method.

In this work, a simple and fast analytical method based on a self-weighted alternating trilinear decomposition (SWATLD) algorithm coupled with excitation-emission matrix (EEM) fluorescence was developed for the simultaneous determination of sulfamethoxazole (SMZ) and trimethoprim (TMP) illegally added to health products. With the second-order advantage, the proposed method obtained satisfactory results in the presence of peak overlap and unknown interferences. The analysis time for a single sample is only 0.8 minutes. The average spiked recoveries of SMZ and TMP in three health product spiked samples were in the range of 91.0-106.2% and 86.8-107.8%, respectively. The relative standard deviations (RSDs) were lower than 8.6%. In addition, verification parameters including sensitivity (SEN), selectivity (SEL), the limit of detection (LOD), the limit of quantification (LOQ), intra-day precision, and inter-day precision were calculated, and the results show that the proposed method is feasible. The quantitative results of the proposed method were further confirmed by the LC-MS/MS method, which proved that the proposed method was efficient and green for drug-abuse monitoring of SMZ and TMP in health products.

Fast identification of the geographical origin of Gastrodia elata using excitation-emission matrix fluorescence and chemometric methods.

Geographical origin is an important factor affecting the quality of traditional Chinese medicine. In this paper, the identification of geographical origin of Gastrodia elata was performed by using excitation-emission matrix fluorescence and chemometric methods. Firstly, excitation-emission matrix (EEM) fluorescence spectra of Gastrodia elata samples from different geographical origins were obtained. And then three chemometric methods, including multilinear partial least squares discriminant analysis (N-PLS-DA), unfold partial least squares discriminant analysis (U-PLS-DA), and k-nearest neighbor (kNN) method, were applied to build discriminant models. Finally, 45 Gastrodia elata samples could be differentiated from each other by these classification models according to their geographical origins. The results showed that all models obtained good classification results. Compared with the N-PLS-DA and U-PLS-DA, kNN got more accurate and reliable classification results and could identify Gastrodia elata samples from different geographical origins with 100% accuracy on the training and test set. Therefore, the proposed method was available for easily and quickly distinguishing the geographical origin of Gastrodia elata, which can be considered as a promising alternative method for determining the geographic origin of other traditional Chinese medicines.

Excitation-emission matrix fluorescence spectroscopy coupled with multi-way chemometric techniques for characterization and classification of Chinese lager beers.

This paper proposed excitation-emission matrix fluorescence spectroscopy coupled with multi-way chemometric techniques for characterization and classification of Chinese pale lager beers produced by different manufacturers. The undiluted and diluted beer samples presented different fluorescence fingerprints. Three-way and four-way parallel factor analysis (PARAFAC) were used to decompose the skillfully constructed three-way and four-way data arrays, respectively, to further achieve beer characterization and feature extraction. Based on the features extracted in different ways, four strategies for beer classification were proposed. In each strategy, three supervised classification methods including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA) and k-nearest neighbor (kNN) were used to build discriminant models. By comparison, PARAFAC-data fusion-kNN method in strategy 3 and four-way PARAFAC-kNN method in strategy 4 obtained the best classification results. The classification strategy based on four-way sample-excitation-emission-dilution level data array was proposed to solve the problem of beer classification for the first time.

A chemometric comparison of different models in fluorescence analysis of dabigatran etexilate and dabigatran.

In this paper, a simple, rapid, low-cost and potential method was established for the simultaneous quantitative analysis of dabigatran etexilate (DABE) and dabigatran (DAB) in spiked biological fluids. It combined excitation-emission matrix fluorescence (EEMF) with different second-order calibration methods, including the self-weighted alternating normalized residue fitting (SWANRF) algorithm based on trilinear decomposition model, the multivariate curve resolution - alternating least-squares (MCR-ALS) based on bilinear decomposition model and the unfolded partial least-square coupled with residual bilinearization (U-PLS/RBL) based on latent variables model. The proposed method showed "second-order advantage", that is, satisfactory quantitative results were successfully obtained even in the presence of unknown interferences and serious spectral overlap. The recoveries of DABE and DAB in spiked biological fluids were 91.7%-101.7% for SWANRF, 95.9%-117.8% for MCR-ALS, 83.0%-109.6% for U-PLS/RBL, respectively. Figures of merit and other statistical parameters were also calculated to assess the performance of the proposed method. Moreover, the modeling procedures and characteristics of three different models in EEMF analysis were discussed and compared.

A simple method for direct modeling of second-order liquid chromatographic data with retention time shifts and holding the second-order advantage.

Retention time shifts in chromatographic data severely affect the quantitative analysis of analytes of interest in complex systems. This paper offers a simple method for directly handing second-order liquid chromatographic data with retention time shift, and achieving qualitative and quantitative analysis of target analytes in the presence of overlapping peaks and unknown interference, which is the so-called "second-order advantage". The proposed method is named the alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR) because it absorbs the basic philosophy of alternating trilinear decomposition (ATLD) algorithm and multivariate curve resolution (MCR). ATLD-MCR was implemented by using the pre-decomposition results of ATLD as the initial values, MCR strategy for each sample slice matrix and the least squares optimization strategy. Three simulated data sets, a semi-simulated LC-MS data set and a real HPLC-DAD data set were investigated by the proposed method, respectively. In addition, the resolved qualitative profiles and concentration values were compared with those obtained by the other three classical second-order calibration algorithms. ATLD-MCR performed well and obtained satisfactory qualitative and quantitative results for the analytes of interest in both the simulated and experimental systems, which proved that the newly proposed method could properly model the second-order chromatographic data with retention time shifts and severe signal overlapping.

Rapid identification and quantification of cheaper vegetable oil adulteration in camellia oil by using excitation-emission matrix fluorescence spectroscopy combined with chemometrics.

Camellia oil is a high quality oil mainly produced in southern China. It is common that unscrupulous merchants attempt to make huge profits by adulterating camellia oil with other cheaper or lower-quality vegetable oils. Therefore, this paper proposed excitation-emission matrix fluorescence spectroscopy combined with chemometric methods for the rapid identification and quantification of camellia oil adulteration with other cheaper vegetable oils. A five-component parallel factor analysis (PARAFAC) model roughly completed spectral characterization of oil samples, and obtained chemically meaningful information. Four advanced chemometrics methods were used for the classification of camellia oil and other vegetable oils (model 1) and the classification of camellia oil and adulterated camellia oil (models 2 and 3), respectively. Two-directional two-dimensional linear discriminant analysis ((2D)2LDA) was used for chemical data for the first time and showed huge potential. Furthermore, the developed N-PLS regression model used for the prediction of adulteration level in camellia oil showed satisfactory accuracy.

Chemometrics-assisted liquid chromatography with full scan mass spectrometry for the interference-free determination of glucocorticoids illegally added to face masks.

A smart chemometrics-assisted strategy that combines the full scan mode of liquid chromatography with mass spectrometry with second-order calibration method based on alternating trilinear decomposition algorithm was developed for the rapid determination of 15 glucocorticoids including the epimers betamethasone and dexamethasone illegally added into face masks. Fifteen glucocorticoids were rapidly eluted (11 min) under a simple elution program. By means of the second-order calibration method, 15 target analytes were successfully quantified in the presence of peak overlaps, unknown interferences and baseline drifts. Notably, the epimers, namely, betamethasone and dexamethasone, were simultaneously quantified by the proposed method under a simple elution program. The average spiked recoveries for all target analytes ranged from 87.3 ± 2.2 to 119.4 ± 5.8%. The validation parameters including sensitivity, selectivity, limit of detection, limit of quantitation, and precision were calculated to validate the accuracy of the proposed method, and the quantitative analysis results were further confirmed by liquid chromatography with tandem mass spectrometry. All results proved that the proposed chemometrics-assisted liquid chromatography with mass spectrometry strategy was an accurate and fast method to determine epimers and multiple glucocorticoids in complex face mask samples.