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 Detection of Adulteration of Fritillariae Cirrhosae Bulbus Based on Portable Near Infrared Spectroscopy / 中国实验方剂学杂志

ObjectiveIn order to establish a systematic quality evaluation system for Fritillariae Cirrhosae Bulbus adulteration， portable near-infrared （NIR） spectroscopy was used to identify Fritillariae Cirrhosae Bulbus and its adulterants and detect their adulteration quantity. MethodA total of 72 batches of Fritillariae Cirrhosae Bulbus samples were collected and 570 batches of adulterated products （dry bulbs of Fritillaria thunbergii， F. ussuriensis， F. pallidiflora and F. hupehensis， Bulbus Tulipae， flour） were prepared， NIR spectral data of samples were collected by the portable NIR spectrometer. Linear discriminant analysis （LDA） was used to establish the qualitative correction models of Fritillariae Cirrhosae Bulbus-adulterants and adulterants of different categories， partial least squares （PLS） was used to establish the quantitative correction models of adulteration quantity of different kinds of adulterants. ResultThe recognition rates of qualitative analysis model of Fritillariae Cirrhosae Bulbus and its adulterants were 99.49% （calibration set） and 100.00% （validation set）， respectively. In different adulterant models， the recognition rates of calibration set and validation set were 70.47% and 73.68%， respectively. Moreover， the correlation coefficients of validation set （R2P） of the six quantitative models of adulteration ratio were 0.840 2 （Fritillariae Cirrhosae Bulbus adulterated with F. thunbergii dry bulbs）， 0.960 2 （Fritillariae Cirrhosae Bulbus adulterated with F. ussuriensis dry bulbs）， 0.765 7 （Fritillariae Cirrhosae Bulbus adulterated with F. pallidiflora dry bulbs）， 0.902 5 （Fritillariae Cirrhosae Bulbus adulterated with F. hupehensis dry bulbs）， 0.957 4 （Fritillariae Cirrhosae Bulbus adulterated with Bulbus Tulipae）， 0.976 1 （Fritillariae Cirrhosae Bulbus adulterated with flour）， the root mean square error of prediction （RMSEP） were 10.948 5， 5.463 9， 13.256 4， 8.549 2， 5.655 3， 4.235 6， respectively. The two qualitative models and six quantitative models showed good prediction performance. ConclusionThe portable NIR spectroscopy can be used to identify Fritillariae Cirrhosae Bulbus and its adulterants in real time， the method is rapid and accurate， which can meet the requirements of nondestructive identification of Fritillariae Cirrhosae Bulbus on site.

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

Taste-Masking Effect of Montelukast Sodium Orally Disintegrating Tablets Using Electronic Tongue Analysis and Human Sensory Evaluation Method / 中国药学杂志

OBJECTIVE: To investigate the taste-masking effect of montelukast sodium orally disintegrating tablets using electronic tongue technology and human sensory evaluation and determine the optimum formulation. METHODS: Orally disintegrating tablets were prepared with five different concentrations of flavoring agents or without flavoring agent.The tastes of those tablets were determined by electronic tongue, and principal component analysis and linear discriminant analysis were used to evaluate differences of different formulations. The taste-masking effect of the tablets was investigated combined with electronic tongue analysis and sensory evaluation of subjects. RESULTS: The taste of orally disintegrating tablet was the best when the total amount of flavoring agent was 1.6 mg, and the ratio of sweetening agent to aromatic agent was 5∶3. CONCLUSION: The combination of electronic tongue and human sensory assess can evaluate the taste-masking effect of orally disintegrating tablets and provide the basis for determining the optimum formulation.

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.

Recognition method of single trial motor imagery electroencephalogram signal based on sparse common spatial pattern and Fisher discriminant analysis / 生物医学工程学杂志

This paper aims to realize the decoding of single trial motor imagery electroencephalogram (EEG) signal by extracting and classifying the optimized features of EEG signal. In the classification and recognition of multi-channel EEG signals, there is often a lack of effective feature selection strategies in the selection of the data of each channel and the dimension of spatial filters. In view of this problem, a method combining sparse idea and greedy search (GS) was proposed to improve the feature extraction of common spatial pattern (CSP). The improved common spatial pattern could effectively overcome the problem of repeated selection of feature patterns in the feature vector space extracted by the traditional method, and make the extracted features have more obvious characteristic differences. Then the extracted features were classified by Fisher linear discriminant analysis (FLDA). The experimental results showed that the classification accuracy obtained by proposed method was 19% higher on average than that of traditional common spatial pattern. And high classification accuracy could be obtained by selecting feature set with small size. The research results obtained in the feature extraction of EEG signals lay the foundation for the realization of motor imagery EEG decoding.

A TrAdaBoost-based method for detecting multiple subjects' P300 potentials / 生物医学工程学杂志

Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects' fatigue and degrade the system performance. TrAdaBoost is a method that transfers the knowledge from source area to target area, which improves learning effect in the target area. Our research purposed a TrAdaBoost-based linear discriminant analysis and a TrAdaBoost-based support vector machine to recognize the P300 potentials across multiple subjects. This method first trains two kinds of classifiers separately by using the data deriving from a small amount of data from same subject and a large amount of data from different subjects. Then it combines all the classifiers with different weights. Compared with traditional training methods that use only a small amount of data from same subject or mixed different subjects' data to directly train, our algorithm improved the accuracies by 19.56% and 22.25% respectively, and improved the information transfer rate of 14.69 bits/min and 15.76 bits/min respectively. The results indicate that the TrAdaBoost-based method has the potential to enhance the generalization ability of brain-computer interface on the individual differences.

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

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

The high-pace rise in advanced computing and imaging systems has given rise to a new research dimension called computer-aided diagnosis (CAD) system for various biomedical purposes. CAD-based diabetic retinopathy (DR) can be of paramount significance to enable early disease detection and diagnosis decision. Considering the robustness of deep neural networks (DNNs) to solve highly intricate classification problems, in this paper, AlexNet DNN, which functions on the basis of convolutional neural network (CNN), has been applied to enable an optimal DR CAD solution. The DR model applies a multilevel optimization measure that incorporates pre-processing, adaptive-learning-based Gaussian mixture model (GMM)-based concept region segmentation, connected component-analysis-based region of interest (ROI) localization, AlexNet DNN-based highly dimensional feature extraction, principle component analysis (PCA)- and linear discriminant analysis (LDA)-based feature selection, and support-vector-machine-based classification to ensure optimal five-class DR classification. The simulation results with standard KAGGLE fundus datasets reveal that the proposed AlexNet DNN-based DR exhibits a better performance with LDA feature selection, where it exhibits a DR classification accuracy of 97.93% with FC7 features, whereas with PCA, it shows 95.26% accuracy. Comparative analysis with spatial invariant feature transform (SIFT) technique (accuracy—94.40%) based DR feature extraction also confirms that AlexNet DNN-based DR outperforms SIFT-based DR.

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.

Analysis of the relationship between EEG signal and aging through Linear Discriminant Analysis (LDA) / Análise das relações entre o sinal EEG e o envelhecimento através da Análise Discriminante Linear (ADL)

This paper aims to establish the correlation between statistical parameters and Electroencephalographic (EEG) signals as a function of age, in subjects without neurological disorders. EEG signals were recorded during the task of following an Archimedes spiral. There were 59 healthy subjects who voluntarily participated in this study which were divided into 7 groups, aging between 20 to 86  years from both gender, in order to identify differences and allow discrimination between the features of each group. Initially, comparisons were made among several features (F20, F50, F80, F95, Mean Frequency, Root Mean Square value, Zero Crossings, Square of the Power Spectrum, Kurtosis, Skewness, Variance, Standard Deviation and Approximate Entropy) seeking separation between young and elderly groups. Furthermore, it was sought to correlate the statistical parameters and the entire age range. For this purpose it was used Linear Discriminant Analysis  (LDA). The data were processed with MATLAB® software. Through the LDA, significant differences were observed over the distinct age ranges. The tool has satisfactorily performed the separation of discriminant features by classifying groups of subjects in function of their age range.

O objetivo deste trabalho é estabelecer as correlações entre parâmetros estatísticos e EEG em função da idade, em indivíduos não portadores de distúrbios neurológicos. Os sinais EEG foram registrados durante a tarefa de seguir uma espiral de Arquimedes. 59 indivíduos saudáveis participaram do estudo e foram divididos em 7 grupos, com idades entre 20 a 86 anos, de ambos os sexos, para identificar diferenças e permitir a discriminação entre as características de cada grupo. Inicialmente, foram feitas comparações entre as diversas variáveis (F20, F50, F80, F95, Frequência Média, RMS, Cruzamentos por zero, Quadrado do Espectro de Potência, Curtose, Assimetria, Variância, Desvio Padrão e Entropia Aproximada) procurando a separação entre os grupos jovem e idoso. Buscou-se ainda correlacionar os parâmetros estatísticos e toda a faixa etária. Para tal, a técnica de Análise Discriminante Linear (ADL) foi utilizada. Os dados foram processados com o software MATLAB®. Por meio da ADL foram observadas diferenças significativas ao longo da idade. Observou-se que a ferramenta executou de forma satisfatória a separação de características discriminantes, classificando cada grupo de indivíduos em função da idade.

Classification and Diagnosis of Multiclass ECG Signals Based on MAR Modeling and Priority / 航天医学与医学工程

Objective To propose an electrocardiogram(ECG) feature extraction method,which is able to reflect the difference of the importance existing among the classes and at the same time to overcome the deficiency of conventional feature extraction method that is unable to solve the classification problem with priority.Methods The data for analysis was obtained from MIT-BIH database,including 250 samples each of normal sinus rhythm(NSR),atria premature contraction(APC),premature ventricular contraction(PVC),ventricular tachycardia(VT),ventricular fibrillation(VF) and super-ventricular tachycardia(SVT).The projecting vectors were constructed following the introduction of separable measurement to extract the features for the classification with priority.Results The proposed feature extraction method increased the average accuracy by 12 percentages as compared with conventional linear discriminative analysis(LDA) methods.Conclusion The prior classes can be better discriminated from others and separate each of the classes as much as possible at the same time.

Differential Effects of Typical and Atypical Antipsychotics on MK- 801-induced EEG Changes in Rats

We examined whether the abnormal EEG state by NMDA receptor blocker MK-801 can be reversed by typical and atypical antipsychotics differentially by comparing their spectral profiles after drug treatment in rats. The spectral profiles produced by typical antipsychotics chlorpromazine (5 mg/kg, i.p.) and haloperidol (0.5 mg/kg, i.p.) were differ from that by atypical antipsychotic clozapine (5 mg/kg, i.p.) in the rats treated with or without MK-801 treatment (0.2 mg/kg, i.p.) which produce behavioral abnormalities like hyperlocomotion and stereotypy. The dissimilarity between the states produced by antipsychotics and the control state was examined with the distance of the location of the canonical variables calculated by stepwise discriminant analysis with the relative band powers as input variables. Although clozapine produced more different state from normal state than typical antipsychotics, clozapine could reverse the abnormal schizophrenic state induced by MK-801 to the state closer to the normal state than the typical antipsychotics. The results suggest that atypical anesthetic can reverse the abnormal schizophrenic state with negative symptom to the normal state better than typical antipsychotic. The results indicate that the multivariate discriminant analysis using the spectral parameters can help differentiate the antipsychotics with different actions.