Gongju(Chrysanthemum morifolium) is one of the five major medicinal Chrysanthemum varieties included in the Chinese Pharmacopoeia. In recent years, its cultivation areas have changed significantly, resulting in mixed quality of the medicinal herbs. In this study, Gongju cultivated in Anhui, Yunnan, Chongqing, and other places were selected as research objects. Hyperspectral data were collected in the visible-near-infrared(VNIR) and short-wave infrared(SWIR) bands using different modes, such as corolla facing up(A) and flower base facing up(B). After pre-processing the hyperspectral data using five methods, including multiplicative scatter correction(MSC), Savitzky-Golay smoothing(SG), first derivative(D1), second derivative(D2), and standard normal variate(SNV), partial least squares discriminant analysis(PLSDA), random forest(RF), and support vector machine(SVM) were used to establish origin identification models of Gongju at the two geographical scales of the province and the city-county in Anhui province. The accuracy of the prediction results was used as an evaluation index to select the optimal models, and the classification performance of the models was evaluated by confusion matrix. The results showed that the flower base facing up(B) collection model combined with second derivative pretreatment and RF method was the best model for both geographical scale identification models. The modeling effect of the full-band(VNIR + SWIR) was slightly better than that of the single band, with the accuracy of the prediction set in the province and city-county regions reaching 99.69% and 99.40%, respectively. The competitive adaptive reweighted sampling algorithm(CARS), successive projections algorithm(SPA), and variable iterative space shrinkage approach(VISSA) were further used to screen the feature wavelength modeling. The number of feature wavelengths screened by CARS was fewer, and the prediction set accuracy of the two geographical scales models after optimization could reach 99.56% and 98.65%, which was basically comparable to the full-band model. However, the removal of redundant variables could greatly reduce the complexity of the model. The hyperspectral technology combined with the chemometrics model established in this study can achieve the origin identification of Gongju at different geographical scales, providing a theoretical basis and technical reference for the construction of a rapid detection system for Gongju origin and the development of exclusive miniaturized instrumentation and equipment systems.
Chrysanthemum/growth & development* ; China ; Support Vector Machine ; Geography ; Discriminant Analysis ; Spectroscopy, Near-Infrared/methods* ; Spectrum Analysis/methods* ; Drugs, Chinese Herbal/analysis* ; Least-Squares Analysis

This study aims to establish a rapid and non-destructive method for recognizing the origins and cultivation patterns of Astragali Radix. A hyperspectral imaging system(spectral ranges: 400-1 000 nm, 900-1 700 nm; detection time: 15 s) was used to examine the samples of Astragali Radix with different origins and cultivation patterns. The collected hyperspectral datasets were highly correlated and numerous, which required the establishment of stable and reliable dimension reduction and classification models. Firstly, the original spectra were preprocessed by normalization, Gaussian smoothing, and masking. Then, principal component analysis(PCA), partial least squares-discriminant analysis(PLS-DA), and competitive adaptive reweighted sampling(CARS) were performed to reduce the dimension of the hyperspectral data. Finally, support vector machine(SVM), feedforward neural network(FFNN), and convolutional neural network(CNN) were used for data training of the spectral images and spectral curves with dimension reduction. The results showed that applying CARS as a variable selection method before PLS-DA on the hyperspectral data of Astragali Radix achieved the accuracy, precision, and recall of 100% on the CNN test dataset. The F_1-score and area under the curve of ROC(AUC) reached 1. This method is convenient, quick, sample-saving, and non-destructive, providing technical support for rapid identification of the origins and cultivation patterns of Astragali Radix.
Drugs, Chinese Herbal/chemistry* ; Neural Networks, Computer ; Algorithms ; Support Vector Machine ; Principal Component Analysis ; Discriminant Analysis ; Hyperspectral Imaging/methods* ; Least-Squares Analysis ; Astragalus Plant/growth & development* ; Astragalus propinquus/growth & development*

The odor fingerprint of Pollygonati Rhizoma samples with different mildewing degrees was analyzed and the relationship between the odor variation and the mildewing degree was explored. A fast discriminant model was established according to the response intensity of electronic nose. The α-FOX3000 electronic nose was applied to analyze the odor fingerprint of Pollygonati Rhizoma samples with different mildewing degrees and the radar map was used to analyze the main contributors among the volatile organic compounds. The feature data were processed and analyzed by partial least squares discriminant analysis(PLS-DA), K-nearest neighbor(KNN), sequential minimal optimization(SMO), random forest(RF) and naive Bayes(NB), respectively. According to the radar map of the electronic nose, the response values of three sensors, namely T70/2, T30/1, and P10/2, increased with the mildewing, indicating that the Pollygonati Rhizoma produced alkanes and aromatic compounds after the mildewing. According to PLS-DA model, Pollygonati Rhizoma samples of three mildewing degrees could be well distinguished in three areas. Afterwards, the variable importance analysis of the sensors was carried out and then five sensors that contributed a lot to the classification were screened out: T70/2, T30/1, PA/2, P10/1 and P40/1. The classification accuracy of all the four models(KNN, SMO, RF, and NB) was above 90%, and KNN was most accurate(accuracy: 97.2%). Different volatile organic compounds were produced after the mildewing of Pollygonati Rhizoma, and they could be detected by electronic nose, which laid a foundation for the establishment of a rapid discrimination model for mildewed Pollygonati Rhizoma. This paper shed lights on further research on change pattern and quick detection of volatile organic compounds in moldy Chinese herbal medicines.
Electronic Nose ; Odorants/analysis* ; Volatile Organic Compounds/analysis* ; Bayes Theorem ; Drugs, Chinese Herbal/analysis* ; Discriminant Analysis

This study explored the feasibility of mineral element content and ratios of nitrogen isotopes to discriminate the cultivation mode of Dendrobium nobile in order to provide theoretical support for the discrimination of the cultivation mode of D. nobile. The content of 11 mineral elements(N, K, Ca, P, Mg, Na, Fe, Cu, Zn, Mn, and B) and nitrogen isotope ratios in D. nobile and its substrate samples in three cultivation methods(greenhouse cultivation, tree-attached cultivation, and stone-attached cultivation) were determined. According to the analysis of variance, principal component analysis, and stepwise discriminant analysis, the samples of different cultivation types were classified. The results showed that the nitrogen isotope ratios and the content of elements except for Zn were significantly different among different cultivation types of D. nobile(P<0.05). The results of correlation analysis showed that the nitrogen isotope ratios, mineral element content, and effective component content in D. nobile were correlated with the nitrogen isotope ratio and mineral element content in the corresponding substrate samples to varying degrees. Principal component analysis can preliminarily classify the samples of D. nobile, but some samples overlapped. Through stepwise discriminant analysis, six indicators, including δ~(15)N, K, Cu, P, Na, and Ca, were screened out, which could be used to establish the discriminant model of D. nobile cultivation methods, and the overall correct discrimination rates after back-substitution test, cross-check, and external validation were all 100%. Therefore, nitrogen isotope ratios and mineral element fingerprints combined with multivariate statistical analysis could effectively discriminate the cultivation types of D. nobile. The results of this study provide a new method for the identification of the cultivation type and production area of D. nobile and an experimental basis for the quality evaluation and quality control of D. nobile.
Dendrobium ; Minerals ; Discriminant Analysis ; Multivariate Analysis ; Nitrogen Isotopes

Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.
Humans ; Brain-Computer Interfaces ; Evoked Potentials, Visual ; Algorithms ; Discriminant Analysis ; Electroencephalography

Error self-detection based on error-related potentials (ErrP) is promising to improve the practicability of brain-computer interface systems. But the single trial recognition of ErrP is still a challenge that hinters the development of this technology. To assess the performance of different algorithms on decoding ErrP, this paper test four kinds of linear discriminant analysis algorithms, two kinds of support vector machines, logistic regression, and discriminative canonical pattern matching (DCPM) on two open accessed datasets. All algorithms were evaluated by their classification accuracies and their generalization ability on different sizes of training sets. The study results show that DCPM has the best performance. This study shows a comprehensive comparison of different algorithms on ErrP classification, which could give guidance for the selection of ErrP algorithm.
Algorithms ; Brain ; Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Support Vector Machine

Vision is an important way for human beings to interact with the outside world and obtain information. In order to research human visual behavior under different conditions, this paper uses a Gaussian mixture-hidden Markov model (GMM-HMM) to model the scanpath, and proposes a new model optimization method, time-shifting segmentation (TSS). The TSS method can highlight the characteristics of the time dimension in the scanpath, improve the pattern recognition results, and enhance the stability of the model. In this paper, a linear discriminant analysis (LDA) method is used for multi-dimensional feature pattern recognition to evaluates the rationality and the accuracy of the proposed model. Four sets of comparative trials were carried out for the model evaluation. The first group applied the GMM-HMM to model the scanpath, and the average accuracy of the classification could reach 0.507, which is greater than the opportunity probability of three classification (0.333). The second set of trial applied TSS method, and the mean accuracy of classification was raised to 0.610. The third group combined GMM-HMM with TSS method, and the mean accuracy of classification reached 0.602, which was more stable than the second model. Finally, comparing the model analysis results with the saccade amplitude (SA) characteristics analysis results, the modeling analysis method is much better than the basic information analysis method. Via analyzing the characteristics of three types of tasks, the results show that the free viewing task have higher specificity value and a higher sensitivity to the cued object search task. In summary, the application of GMM-HMM model has a good performance in scanpath pattern recognition, and the introduction of TSS method can enhance the difference of scanpath characteristics. Especially for the recognition of the scanpath of search-type tasks, the model has better advantages. And it also provides a new solution for a single state eye movement sequence.
Algorithms ; Discriminant Analysis ; Eye Movements ; Humans ; Markov Chains ; Normal Distribution ; Probability

OBJECTIVE: To observe the changes of serum metabolites in patients with multiple myeloma (MM) by metabonomics, and explore the potential biomarkers for diagnosis, prognosis, and progression of MM. METHODS: Serum samples were collected from 26 patients with MM and 50 healthy controls. The data detected by liquid chromatography-mass spectrometry (LC-MS) was input into SIMCA-14.0 software for multivariate statistical analysis. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze the changes of metabolites. RESULTS: The metabolic change of uric acid and trans-vaccenic acid in serum samples of MM patients was 9.39 times and 2.77 times of these in healthy people, respectively, which were significantly higher than those of healthy people, and the difference was statistically significant(P<0.01). CONCLUSION Uric acid and trans-vaccenic acid are expected to be important metabolic indicators for the diagnosis, prognosis, and efficacy evaluation of MM, thus providing some clues for the pathogenesis of MM.
Biomarkers ; Chromatography, Liquid ; Discriminant Analysis ; Humans ; Mass Spectrometry ; Metabolomics ; Multiple Myeloma

Objective To construct a discriminant analysis model based on the differential expression of multiple microRNAs （miRNAs） in two kinds of blood samples （peripheral blood and menstrual blood） and three non-blood samples （saliva, semen and vaginal secretion）, to form an identification solution for peripheral blood and menstrual blood. Methods Six kinds of miRNA （miR-451a, miR-144-3p, miR-144-5p, miR-214-3p, miR-203-3p and miR-205-5p） were selected from literature, the samples of five kinds of body fluids commonly seen in forensic practice （peripheral blood, menstrual blood, saliva, semen, vaginal secretion） were collected, then the samples were divided into training set and testing set and detected by SYBR Green real-time qPCR. A discriminant analysis model was set up based on the expression data of training set and the expression data of testing set was used to examine the accuracy of the model. Results A discriminant analysis statistical model that could distinguish blood samples from non-blood samples and distinguish peripheral blood samples from menstrual blood samples at the same time was successfully constructed. The identification accuracy of the model was over 99%. Conclusion This study provides a scientific and accurate identification strategy for forensic fluid identification of peripheral blood and menstrual blood samples and could be used in forensic practice.
Body Fluids ; Discriminant Analysis ; Female ; Forensic Genetics ; MicroRNAs/genetics* ; Semen

Objective To estimate sex based on patella measurements of Sichuan Han population by computed tomography three-dimensional volume reconstruction technique, and to explore the application value of patella in sex estimation. Methods CT three-dimensional volume reconstruction images of patella of 250 individuals were collected, the four measurement indicators including patellar length, patellar width, patellar thickness, and patellar volume were measured. The t-test was used to determine measurement indicators with sex differences. Fisher discriminant analysis was used to establish the sex discriminant function and the prediction accuracy was calculated by leave-one-out cross validation. Results The sex differences of the four measurement indicators had a statistical significance （P<0.05）. The accuracy rate of the univariate discriminant function established by the patellar length was the highest （82.0%）. The accuracy rates of the all indicators discriminant function and the stepwise discriminant function were 80.4% and 81.6%, respectively. Conclusion It is feasible and accurate to estimate sex of Sichuan Han population by patella measurements with CT three-dimensional volume reconstruction technique. The method may be used as an alternative for sex estimation of Sichuan Han population when other bones with higher accuracy are not available.
Discriminant Analysis ; Female ; Forensic Anthropology ; Humans ; Imaging, Three-Dimensional ; Male ; Patella/diagnostic imaging* ; Sex Determination by Skeleton ; Tomography, X-Ray Computed