Flavor Characterization of Rhei Radix et Rhizoma Processed with Nine-time Repeating Steaming and Sun-drying Based on Intelligent Sensory Technology / 中国实验方剂学杂志

ObjectiveTo analyze the flavor substances and change rules of Rhei Radix et Rhizoma during the process of nine-time repeating steaming and sun-drying. MethodThe flavor response values of Rhei Radix et Rhizoma samples were obtained by using PEN3 electronic nose system. The data were processed and analyzed by principal component analysis （PCA）， linear discriminant analysis （LDA） and Loadings analysis. ResultRhei Radix et Rhizoma processed with nine-time repeating steaming and sun-drying could be effectively distinguished into two categories as the sixth sample was the turning point. The samples steamed and dried for one to five times could be grouped into one category， the other four samples were obviously distinguished from them. The main flavor components reached the maximum response in the sample processed with six-time repeating steaming and sun-drying， and its response value of inorganic sulfur compounds was about 2.7 times that of the sample processed with one-time repeating steaming and sun-drying. In addition， compared with the raw products， the flavors of Rhei Radix et Rhizoma processed with nine-time repeating steaming and sun-drying and wine stewing changed significantly， and the response value of inorganic sulfur compounds in sample processed with nine-time repeating steaming and sun-drying was about 2.2 times that of raw products. From the perspective of flavor analysis， the response values of inorganic sulfur compounds and nitrogen-oxygen compounds in sample processed with nine-time repeating steaming and sun-drying were higher than those of wine-stewed products， and the two were not completely equivalent. ConclusionElectronic nose technology preliminarily clarifies the dynamic change rules of the flavor of Rhei Radix et Rhizoma during the process of nine-time repeating steaming and sun-drying from the flavor characteristics， and clarifies the difference between products processed with nine-time repeating steaming and sun-drying and wine-stewed products from the odor characteristics， which lays a foundation for revealing the processing principle of Rhei Radix et Rhizoma processed with nine-time repeating steaming and sun-drying.

Rapid identification of microbial contaminants in pharmaceutical products using a PCA/LDA-based FTIR-ATR method

Microbiological quality of pharmaceuticals is fundamental in ensuring efficacy and safety of medicines. Conventional methods for microbial identification in non-sterile drugs are widely used; however they can be time-consuming and laborious. The aim of this paper was to develop a chemometric-based rapid microbiological method (RMM) for identifying contaminants in pharmaceutical products using Fourier transform infrared with attenuated total reflectance spectrometry (FTIR-ATR). Principal components analysis (PCA) and linear discriminant analysis (LDA) were used to obtain a predictive model capable of distinguishing Bacillus subtilis (ATCC 6633), Candida albicans (ATCC 10231), Enterococcus faecium (ATCC 8459), Escherichia coli (ATCC 8739), Micrococcus luteus (ATCC 10240), Pseudomonas aeruginosa (ATCC 9027), Salmonella typhimurium (ATCC 14028), Staphylococcus aureus (ATCC 6538), and Staphylococcus epidermidis (ATCC 12228) microbial growth. FTIR-ATR spectra provide data on proteins, DNA/RNA, lipids, and carbohydrates constitution of microbial growth. Microbial identification provided by PCA/LDA based on FTIR-ATR method were compatible with those obtained using traditional microbiological methods. The chemometric-based FTIR-ATR method for rapid identification of microbial contaminants in pharmaceutical products was validated by assessing the sensitivity (93.5%), specificity (83.3%), and limit of detection (17-23 CFU/mL of sample). Therefore, we propose that FTIR-ATR spectroscopy may be used for rapid identification of microbial contaminants in pharmaceutical products and taking into account the samples studied

Rapid identification of microbial contaminants in pharmaceutical products / Identificação rápida de contaminantes microbianos em produtos farmacêuticos

A qualidade microbiológica de medicamentos é fundamental para garantir sua eficácia e segurança. Os métodos convencionais para identificação microbiana em produtos não estéreis são amplamente utilizados, entretanto são demorados e trabalhosos. O objetivo deste trabalho é desenvolver método microbiológico rápido (MMR) para a identificação de contaminantes em produtos farmacêuticos utilizando a espectrofotometria de infravermelho com transformada de Fourier com reflectância total atenuada (FTIR-ATR). Análise de componentes principais (PCA) e análise de discriminantes (LDA) foram utilizadas para obter um modelo de predição com a capacidade de diferenciar o crescimento de oriundo de contaminação por Bacillus subtilis (ATCC 6633), Candida albicans (ATCC 10231), Enterococcus faecium (ATCC 8459), Escherichia coli (ATCC 8739), Micrococcus luteus (ATCC 10240), Pseudomonas aeruginosa (ATCC 9027), Salmonella Typhimurium (ATCC 14028), Staphylococcus aureus (ATCC 6538) e Staphylococcus epidermidis (ATCC 12228). Os espectros de FTIR-ATR forneceram informações quanto à composição de proteínas, DNA/RNA, lipídeos e carboidratos provenientes do crescimento microbiano. As identificações microbianas fornecidas pelo modelo PCA/LDA baseado no método FTIR-ATR foram compatíveis com aquelas obtidas pelos métodos microbiológicos convencionais. O método de identificação microbiana rápida por FTIR-ATR foi validado quanto à sensibilidade (93,5%), especificidade (83,3%) e limite de detecção (17-23 UFC/mL de amostra). Portanto, o MMR proposto neste trabalho pode ser usado para fornecer uma identificação rápida de contaminantes microbianos em produtos farmacêuticos

Microbiological quality of pharmaceuticals is fundamental in ensuring efficacy and safety of medicines. Conventional methods for microbial identification in non-sterile drugs are widely used, however are time-consuming and laborious. The aim of this paper was to develop a rapid microbiological method (RMM) for identification of contaminants in pharmaceutical products using Fourier transform infrared with attenuated total reflectance spectrometry (FTIR-ATR). Principal components analysis (PCA) and linear discriminant analysis (LDA) were used to obtain a predictive model with capable to distinguish Bacillus subtilis (ATCC 6633), Candida albicans (ATCC 10231), Enterococcus faecium (ATCC 8459), Escherichia coli (ATCC 8739), Micrococcus luteus (ATCC 10240), Pseudomonas aeruginosa (ATCC 9027), Salmonella Typhimurium (ATCC 14028), Staphylococcus aureus (ATCC 6538), and Staphylococcus epidermidis (ATCC 12228) microbial growth. FTIR-ATR spectra provide information of protein, DNA/RNA, lipids, and carbohydrates constitution of microbial growth. Microbial identification provided by PCA/LDA based on FTIR-ATR method were compatible to those obtained using conventional microbiological methods. FTIR-ATR method for rapid identification of microbial contaminants in pharmaceutical products was validated by assessing the sensitivity (93.5%), specificity (83.3%), and limit of detection (17-23 CFU/mL of sample). Therefore, the RMM proposed in this work may be used to provide a rapid identification of microbial contaminants in pharmaceutical products

Monosaccharide analysis and fingerprinting identification of polysaccharides from Poria cocos and Polyporus umbellatus by HPLC combined with chemometrics methods / 中草药·英文版

Objective: Poria cocos and Polyporus umbellatus are similar medicinal fungi in traditional Chinese medicines. A method for fingerprint analysis of monosaccharide composition of polysaccharides by HPLC combined with chemometrics methods has been developed for characterization and discrimination of them in this research. Methods: The polysaccharides were extracted by decocting in water, and then completely hydrolyzed with hydrochloride. Monosaccharides in the hydrolyzates were derivatized with 1-phenyl-3-methyl-5-pyrazolone (PMP) for HPLC analysis. More than 20 batches of P. cocos and P. umbellatus from different regions were analyzed. Results: The fingerprints of P. cocos showed five common characteristic peaks, which were identified by comparing with the reference substances. The five peaks corresponded to the derivatives of mannose, ribose, glucose, galactose, and fucose. At the same time, the fingerprints of P. umbellatus showed eight common characteristic peaks, of which seven were identified as the derivatives of mannose, ribose, rhamnose, glucose, galactose, xylose, and fucose. Moreover, the similarity of their fingerprints was respectively calculated by the Similarity Evaluation System for Chromatographic Fingerprint of TCM published by China Pharmacopoeia Committee (Version 2004A). And the data were further processed by hierarchical cluster analysis (HCA) and principal component analysis (PCA). The similarity evaluation and HCA indicated that there were no significant difference in P. cocos or P. umbellatus samples from different geographical regions, but PCA was performed to characterize the difference in monosaccharide constituents between P. cocos and P. umbellatus, and linear discriminant analysis (LDA) showed the overall correct classification rate was 100%. Conclusion: The fingerprint analysis method of monosaccharide composition of water-soluble polysaccharides can distinguish P. cocos and P. umbellatus, and can be applied for the authentication or quality control for P. cocos and P. umbellatus.

A standardization model based on image recognition for performance evaluation of an oral scanner

PURPOSE: Accurate information is essential in dentistry. The image information of missing teeth is used in optically based medical equipment in prosthodontic treatment. To evaluate oral scanners, the standardized model was examined from cases of image recognition errors of linear discriminant analysis (LDA), and a model that combines the variables with reference to ISO 12836:2015 was designed. MATERIALS AND METHODS: The basic model was fabricated by applying 4 factors to the tooth profile (chamfer, groove, curve, and square) and the bottom surface. Photo-type and video-type scanners were used to analyze 3D images after image capture. The scans were performed several times according to the prescribed sequence to distinguish the model from the one that did not form, and the results confirmed it to be the best. RESULTS: In the case of the initial basic model, a 3D shape could not be obtained by scanning even if several shots were taken. Subsequently, the recognition rate of the image was improved with every variable factor, and the difference depends on the tooth profile and the pattern of the floor surface. CONCLUSION: Based on the recognition error of the LDA, the recognition rate decreases when the model has a similar pattern. Therefore, to obtain the accurate 3D data, the difference of each class needs to be provided when developing a standardized model.