From the perspective of market classification of Cnidii Fructus, this paper revealed the scientific connotation of evaluating the quality grade of Cnidii Fructus by its appearance traits. Thirty batches of Cnidii Fructus in different grades were selected as the research objects. The canonical correlation analysis and principal component analysis(PCA) were used to explore the measurement values of 15 appearance traits and intrinsic content indexes. The results of correlation analysis showed that except the aspect ratio, the 5 appearance trait indexes(length, width, 1 000-grain weight, broken grain weight proportion, and chroma) and 9 internal content indexes(the content of moisture, total ash, acid insoluble ash, osthole, imperatorin, 5-methoxy psoralen, isopimpinellin, xanthotoxin, and xanthotol) showed significant correlation to varying degrees. In addition, there was a significant positive correlation between the first typical variable U_1 composed of appearance traits and the first typical variable V_1 composed of internal content indexes(CR_1=0.963, P<0.01). The results of PCA showed that the classification results of appearance traits for 30 batches of Cnidii Fructus were consistent with the actual information of the samples. Under the same analysis conditions, 30 batches of Cnidii Fructus were reclassified by 9 groups of internal content indexes, and the analysis results were consistent. From the classification standard of the appearance traits of the system study, the statistical results of 6 appearance traits of Cnidii Fructus showed a correlation with grades. There was a good correlation between the appearance and the internal content of Cnidii Fructus, and the appearance quality effectively predicted the level of the internal content. There is a certain scientific basis for the quality classification of Cnidii Fructus by main appearance traits. Appearance classification can replace quality grading to realize the "quality evaluation through morphological identification" of Cnidii Fructus.
Fruit ; Phenotype ; Principal Component Analysis ; Social Group

The volatile oil of Chuanxiong Rhizoma(CX) is known as an effective fraction. In order to seek a suitable method for processing CX and its decoction pieces, this study selected 16 volatile components as indices to investigate how different processing methods such as washing/without washing, sun-drying, baking, oven-drying and far-infrared drying at different temperatures affected the quality of CX and its decoction pieces(fresh CX was partially dried, cut into pieces, and then dried) by headspace gas chromatography-mass spectrometry(GC-MS), cluster analysis, principal component analysis and comprehensive weighted scoring. The results showed that the rapid washing before processing did not deteriorate the volatile components of CX. Considering the practical condition of production area, oven-drying was believed to be more suitable than sun-drying, baking, and far-infrared drying. The CX decoction pieces with a thickness of 0.3-0.4 cm were recommended to be oven-dried at 50 ℃. The integrated processing(partial drying, cutting into pieces, and drying) did not cause a significant loss of volatile components. For the fresh CX, the oven-drying at 60 ℃ is preferred. The temperature should not exceed 60 ℃, and drying below 60 ℃ will prolong the processing time, which will produce an unfavorable effect on volatile components. This study has provided the scientific evidence for field processing of CX, which is conducive to realizing the normalization and standardization of CX processing in the production area and stabilizing the quality of CX and its decoction pieces.
Desiccation ; Gas Chromatography-Mass Spectrometry/methods* ; Oils, Volatile ; Principal Component Analysis ; Rhizome/chemistry* ; Volatile Organic Compounds/analysis*

The present study established the ultra-high performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS) method for simultaneous determination of the content of eight major active components in Caesalpinia decapetala and performed the quality evaluation of C. decapetala from different habitats with the chemical pattern recognition. The analysis was carried out on a Waters BEH C_(18) column(2.1 mm×100 mm, 1.7 μm) at 40 ℃, with the mobile phase of water containing 0.1% formic acid(A) and acetonitrile containing 0.1% formic acid under gradient elution, the flow rate of 0.3 mL·min~(-1), and the injection volume of 1 μL. The electrospray ionization(ESI) source in the negative mode and multiple reaction monitoring(MRM) were used for MS quantitative analysis. The content results were analyzed by the hierarchical cluster analysis(HCA) and principal component analysis(PCA) for the evaluation of the quality difference. Eight components showed good linear relationships within their respective concentration ranges(r>0.999), with the average recoveries of 96.85%-103.4% and RSD of 0.52%-2.8%. The analysis results showed that the quality of samples from different batches was different. The samples were classified into three clusters by HCA and PCA. The method is simple, sensitive, accurate, and efficient, and can be used for the quality evaluation of C. decapetala.
Caesalpinia ; Chromatography, High Pressure Liquid ; Chromatography, Liquid ; Principal Component Analysis ; Tandem Mass Spectrometry

Rapid serial visual presentation-brain computer interface (RSVP-BCI) is the most popular technology in the early discover task based on human brain. This algorithm can obtain the rapid perception of the environment by human brain. Decoding brain state based on single-trial of multichannel electroencephalogram (EEG) recording remains a challenge due to the low signal-to-noise ratio (SNR) and nonstationary. To solve the problem of low classification accuracy of single-trial in RSVP-BCI, this paper presents a new feature extraction algorithm which uses principal component analysis (PCA) and common spatial pattern (CSP) algorithm separately in spatial domain and time domain, creating a spatial-temporal hybrid CSP-PCA (STHCP) algorithm. By maximizing the discrimination distance between target and non-target, the feature dimensionality was reduced effectively. The area under the curve (AUC) of STHCP algorithm is higher than that of the three benchmark algorithms (SWFP, CSP and PCA) by 17.9%, 22.2% and 29.2%, respectively. STHCP algorithm provides a new method for target detection.
Algorithms ; Brain ; Brain-Computer Interfaces ; Electroencephalography/methods* ; Humans ; Principal Component Analysis ; Signal Processing, Computer-Assisted

In this study, 280 batches of Zingiberis Rhizoma samples from nine producing areas were analyzed to obtain infrared spectral information based on near-infrared spectroscopy(NIRS). Pluralistic chemometrics such as principal component analysis(PCA), partial least squares-discriminant analysis(PLS-DA), orthogonal partial least squares-discriminant analysis(OPLS-DA), K-nearest neighbors(KNN), support vector machine(SVM), random forest(RF), artificial neural network(ANN), and gradient boosting(GB) were applied for tracing of origins. The results showed that the discriminative accuracy of the spectral preprocessing by standard normal variate transformation coupled with the first derivative was 93.9%, which could be used for the construction of the discrimination model. PCA and PLS-DA score plots showed that samples from Shandong, Sichuan, Yunnan, and Guizhou could be effectively distinguished, but the remaining samples were partially overlapped. As revealed by the analysis results by machine learning algorithms, the AUC values of KNN, SVM, RF, ANN, and GB algorithms were 0.96, 0.99, 0.99, 0.99, and 0.98, respectively, with overall prediction accuracies of 83.3%, 89.3%, 90.5%, 91.7%, and 89.3%. It indicated that the developed model was reliable and the machine learning algorithm combined with NIRS for origin identification was sufficiently feasible. OPLS-DA showed that Zingiberis Rhizoma from Sichuan(genuine producing areas) could be significantly distinguished from other regions, with good discriminative accuracy, suggesting that the NIRS established in this study combined with chemometrics can be used for the identification of Zingiberis Rhizoma from Sichuan. This study established a rapid and nondestructive identification and reliable data analysis method for origin identification of Zingiberis Rhizoma, which is expected to provide a new idea for the origin tracing of Chinese medicinal materials.
Algorithms ; Chemometrics ; China ; Ginger ; Least-Squares Analysis ; Plant Extracts ; Principal Component Analysis ; Support Vector Machine

Oscillating chemical fingerprint is a nonlinear dynamic fingerprint technology that reflects the overall redox activity of the entire system based on potential-time changes in multi-stage chemical reactions. This article summarizes the application of oscillating chemical fingerprint technology combined with mathematical analysis method in the qualitative and quantitative analysis of traditional Chinese medicine and food in recent years, including similarity analysis, principal component analysis, cluster analysis and other qua-litative analysis methods, as well as linear, logarithmic, exponential, polynomial, multivariate analysis and other quantitative analysis methods, so as to provide meaningful information for further quality control analysis of the oscillation chemical fingerprint technology in the field of traditional Chinese medicine and food.
Chromatography, High Pressure Liquid ; Drugs, Chinese Herbal ; Food ; Medicine, Chinese Traditional ; Principal Component Analysis ; Quality Control

Assessment of the status property(boiling time) is a challenge for the quality control of extraction process in pharmaceutical enterprises. In this study, the pilot extraction process of Phellodendron chinense was used as the research carrier to develop an online near-infrared(NIR) quality control method based on the status property(boiling time). First, the NIR spectra of P. chinense were collected during the two pilot-scale extraction processes, and the status property(boiling time) was assessed by observing the state of bubbles in the extraction tank using a transparent window during the extraction process, which was then used as a reference standard. Based on the moving block standard deviation(MBSD) algorithm, the assessment model using online NIR spectra for boiling time during extraction process was established. In addition, the model was optimized as follows: standard normal variable(SNV) for spectral pretreatment, modeling band of 800-2 200 nm, and window size of 4. The results showed that, with 0.002 0 as the MBSD model threshold, the boiling time can be accurately assessed using online NIR spectra during extraction process. Furthermore, the principal component analysis-moving block standard deviation(PCA-MBSD) model was developed by our group to reduce the influence of online NIR spectral noise and background signal on the model, and the number of principal components was optimized into 2 in the PCA-MBSD model. The results showed that, with 0.000 075 as the PCA-MBSD model threshold, the boiling time can be accurately assessed using online NIR spectra during extraction process, with improved reliability. This study can provide a assessment method for boiling time during extraction process using online NIR spectra, which can replace the empirical judgment in manual observation, and realize the digitalization of the extraction process for big brand traditional Chinese medicine.
Medicine, Chinese Traditional ; Principal Component Analysis ; Quality Control ; Reproducibility of Results ; Spectroscopy, Near-Infrared

The volatile oil of Curcumae Rhizoma has many active components,which are the key to the quality of Curcumae Rhizoma. Exploring the difference between volatile oil of different kinds of Curcumae Rhizoma facilitates the quality control and rational application of resources. In this study,GC-MS was applied to realize online qualitative and semi-quantitative analysis of the chemical composition spectrum of volatile oil from Curcuma wenyujin( CW),C. phaeocaulis( CP),and C. kwangsiensis( CK). Forty components were identified and their fingerprints were compared and evaluated. Hierarchical cluster analysis( HCA),principal component analysis( PCA),and orthogonal partial least squares discrimination analysis( OPLS-DA) were adopted to analyze the overall and outlier data. The results showed that the whole data could be divided into three kinds according to each analysis mode,and the volatile components of Curcumae Rhizoma vary greatly among species. PCA explored the difference between outliers and the mean value of the group and found that some volatile oils from CW may be greatly affected by the origin. By OPLS-DA,the samples from Zhejiang were able to gather,but those from Guizhou remained isolated,indicating the influence of growing environment on Curcumae Rhizoma metabolites. Based on VIP results combined with the heat map,characteristic volatile oil components of Curcumae Rhizoma from different varieties were screened out: curdione and linalool for CW; 2-undecanone for CP; humulene,γ-selinene,and zederone for CK. The GCMS method established in this study describes Curcumae Rhizoma samples comprehensively and accurately,and the characteristic components screened based on chemometrics can be used to distinguish Curcumae Rhizoma from different varieties and give them unique pharmacodynamic significance,which is fast,convenient,stable,and reliable and supports the rational application of Curcu-mae Rhizoma resources. It is found that the region of origin has great influence on CW,which is worthy of further study.
Curcuma ; Gas Chromatography-Mass Spectrometry ; Oils, Volatile ; Principal Component Analysis ; Rhizome

The quality control of Chinese herbal medicine is a current challenge for the internationalization of traditional Chinese medicine. Traditional quality evaluation methods lack quantitative analysis, while modern quality evaluation methods ignore the origins and appearance traits. Therefore, an integrated quality evaluation method is urgent in need. Raw Rehmanniae Radix (RRR) is commonly used in Chinese herbal medicine. At present, much attention has been drwan towards its quality control, which however is limited by the existing quality evaluation methods. The present study was designed to establish a comprehensive and practical method for the quality evaluation and control of RRR pieces based on its chemical constituents, appearance traits and origins. Thirty-three batches of RRR pieces were collected from six provinces, while high-performance liquid chromatography (HPLC) was applied to determine the following five constituents, including catalpol, rehmannioside A, rehmannioside D, leonuride and verbascoside in RRR pieces. Their appearance traits were quantitatively observed. Furthermore, correlation analysis, principal components analysis (PCA), cluster analysis and t-test were performed to evaluate the qualities of RRR pieces. These batches of RRR pieces were divided into three categories: samples from Henan province, samples from Shandong and Shanxi provinces, and those from other provinces. Furthermore, the chemical constituents and appearance traits of RRR pieces were significantly different from diverse origins. The combined method of chemical contituents, appearance traits and origins can distinguish RRR pieces with different qualities, which provides basic reference for the quality control of Chinese herbal medicine.
Chromatography, High Pressure Liquid ; Drugs, Chinese Herbal/analysis* ; Medicine, Chinese Traditional ; Plant Roots/chemistry* ; Principal Component Analysis ; Quality Control ; Rehmannia/chemistry*

Objective To develop an SNP Panel for East Asian population, which has a high individual identification rate and the capability of ancestry analysis. Methods The 55 SNP Panel by Professor KIDD of Yale University and the 128 SNP Panel by Professor SELDIN of Davis School of California University, 170 SNP Panel in total was used as the basis and its test data in the East Asian population was collected. The genetic parameters of SNP loci were calculated and combined with the results of heatmap analysis to screen SNP loci suitable for East Asian population. Some Tibetan and Han samples were tested. The possibility of using the SNP loci in ancestry inference was analyzed by means of STRUCTURE analysis, principal component analysis and heatmap analysis. Results A Panel with 45 SNPs （45 SNP Panel） was screened out, and the average genetic parameters of each SNP were better than 170 SNP Panel, with the same ancestry analysis and inference ability. Conclusion In terms of ancestry inference information, the 45 SNP Panel can completely replace the 170 SNP Panel and achieve the same ancestry analysis and inference ability. In genetic parameters, 45 SNP Panel is better than 170 SNP Panel in the East Asian population, which shows its important potential forensic application value.
Asian People/genetics* ; Gene Frequency ; Genetics, Population ; Humans ; Polymorphism, Single Nucleotide ; Principal Component Analysis