To identify Glycyrrhizae Radix et Rhizoma from different geographical origins, spectrum and image features were extracted from visible and near-infrared(VNIR, 435-1 042 nm) and short-wave infrared(SWIR, 898-1 751 nm) ranges based on hyperspectral imaging technology. The spectral features of Glycyrrhizae Radix et Rhizoma samples were extracted from hyperspectral data and denoised by a variety of pre-processing methods. The classification models were established by using Partial Least Squares Discriminate Analysis(PLS-DA), Support Vector Classification(SVC) and Random Forest(RF). Meanwhile, Gray-Level Co-occurrence matrix(GLCM) was employed to extract textural variables. The spectrum and image data were implemented from three dimensions, including VNIR and SWIR fusion, spectrum and image fusion, and comprehensive data fusion. The results indicated that the spectrum in SWIR range performed better classification accuracy than VNIR range. Compared with other four pre-processing methods, the second derivative method based on Savitzky-Golay(SG) smoothing exhibited the best performance, and the classification accuracy of PLS-DA and SVC models were 93.40% and 94.11%, separately. In addition, the PLS-DA model was superior to SVC and RF models in terms of classification accuracy and model generalization capability, which were evaluated by confusion matrix and receiver operating characteristic curve(ROC). Comprehensive data fusion on SPA bands achieved a classification accuracy of 94.82% with only 28 bands. As a result, this approach not only greatly improved the classification efficiency but also maintained its accuracy. The hyperspectral imaging system, a non-invasively, intuitively and quickly identify technology, could effectively distinguish Glycyrrhizae Radix et Rhizoma samples from different origins.
Drugs, Chinese Herbal ; Hyperspectral Imaging ; Technology

In order to realize rapid and non-destructive identification of the origin of Gardeniae Fructus, a technical method based on hyperspectral imaging technology was established in this study. Spectral information of Gardeniae Fructus samples from eight production origins was acquired from visible NIR(410-990 nm, VNIR) and short wavelength NIR(950-2 500 nm, SWIR) bands based on hyperspectral imaging techniques. The average spectral reflectance within the region of interest was extracted and calculated using the ENVI 5.3 software, resulting in 1 600 sample data. The visible short wavelength infrared band(fused bands) spectral data covering the range 410-2 500 nm were obtained after combining the spectral data of VNIR and SWIR. Data were de-noised by five common preprocessing methods, including multivariate scatter correction, Savitzky-Golay smoothing, standard normal variate, first derivative(FD), and second derivative from VNIR, SWIR, and fused bands(VNIR+SWIR). Partial least squares discriminant analysis, linear support vector classification(LinearSVC), and random forest were used to establish the model for origin identification of Gardeniae Fructus. The results indicated that the identification model of Gardeniae Fructus origin established after FD pretreatment of the spectral data in the fused bands could yield good results. According to the confusion matrix evaluation results, the model prediction set using LinearSVC reached 100% accuracy, so the optimum identification model of Gardeniae Fructus origin was determined as fusion bands-FD-LinearSVC. Therefore, the hyperspectral imaging technology can achieve rapid, nondestructive, and accurate identification of Gardeniae Fructus samples of different origins, which provides a technical reference for the differential detection of Gardeniae Fructus and other Chinese medicines.
Gardenia ; Hyperspectral Imaging ; Fruit ; Least-Squares Analysis ; Technology

In order to realize the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, this paper first prepared the sulphur-fumigated Achyranthis Bidentatae Radix samples with the usage amount of sulphur being 0, 2.5%, and 5% of the mass of Achyranthis Bidentatae Radix pieces. The SO_2 content in different batches of sulphur-fumigated Achyranthis Bidentatae Radix was determined using the method in Chinese Pharmacopoeia, followed by the acquisition of their hyperspectral data within both visible-near infrared(435-1 042 nm) and short-wave infrared(898-1 751 nm) regions by hyperspectral imaging. Meanwhile, the first derivative, AUTO, multiplicative scatter correction, Savitzky-Golay(SG) smoothing, and standard normal variable transformation algorithms were used to pre-process the original hyperspectral data, which were then subjected to characteristic band extraction based on competitive adaptive reweighted sampling(CARS) and the partial least square regression analysis for building a quantitative model of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix. It was found that the accuracy of the quantitative model built depending on the visible-near infrared spectra was high, with the determination coefficient of prediction set(R■) reaching 0.900 1. The established quantitative model has enabled the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, which can serve as an effective supplement to the method described in Chinese Pharmacopeia.
Hyperspectral Imaging ; Least-Squares Analysis ; Plant Roots ; Sulfur

Hyperspectral imaging technology can obtain the spatial and spectral three-dimensional imaging of substances simultaneously, and obtain the unique continuous characteristic spectrum of substances in a wide spectrum range at a certain spatial resolution, which has outstanding advantages in the fine classification and identification of biological substances. With the development of hyperspectral imaging technology, a large amount of data has been accumulated in the exploration of data acquisition, image processing and material inspection. As a new technology means, hyperspectral imaging technology has its unique advantages and wide application prospects. It can be combined with the common biological physical evidence of blood (stains), saliva, semen, sweat, hair, nails, bones, etc., to achieve rapid separation, inspection and identification of substances. This paper introduces the basic theory of hyperspectral imaging technology and its application in common biological evidence examination research and analyzes the feasibility and development of biological evidence testing and identification, in order to provide a theoretical basis for the development of new technology and promote hyperspectral imaging technology in related biological examination, to better serve the forensic practice.
Spectrum Analysis/methods* ; Hyperspectral Imaging ; Forensic Medicine ; Blood Stains ; Technology

To realize the non-destructive and rapid origin discrimination of Poria cocos in batches, this study established the P. cocos origin recognition model based on hyperspectral imaging combined with machine learning. P. cocos samples from Anhui, Fujian, Guangxi, Hubei, Hunan, Henan and Yunnan were used as the research objects. Hyperspectral data were collected in the visible and near infrared band(V-band, 410-990 nm) and shortwave infrared band(S-band, 950-2 500 nm). The original spectral data were divided into S-band, V-band and full-band. With the original data(RD) of different bands, multiplicative scatter correction(MSC), standard normal variation(SNV), S-G smoothing(SGS), first derivative(FD), second derivative(SD) and other pretreatments were carried out. Then the data were classified according to three different types of producing areas: province, county and batch. The origin identification model was established by partial least squares discriminant analysis(PLS-DA) and linear support vector machine(LinearSVC). Finally, confusion matrix was employed to evaluate the optimal model, with F1 score as the evaluation standard. The results revealed that the origin identification model established by FD combined with LinearSVC had the highest prediction accuracy in full-band range classified by province, V-band range by county and full-band range by batch, which were 99.28%, 98.55% and 97.45%, respectively, and the overall F1 scores of these three models were 99.16%, 98.59% and 97.58%, respectively, indicating excellent performance of these models. Therefore, hyperspectral imaging combined with LinearSVC can realize the non-destructive, accurate and rapid identification of P. cocos from different producing areas in batches, which is conducive to the directional research and production of P. cocos.
Hyperspectral Imaging ; Wolfiporia ; China ; Least-Squares Analysis ; Support Vector Machine

Puerariae Lobatae Radix, the dried root of Pueraria lobata, is a traditional Chinese medicine with a long history. Puerariae Lobatae Caulis as an adulterant is always mixed into Puerariae Lobatae Radix for sales in the market. This study employed hyperspectral imaging(HSI) to distinguish between the two products. VNIR lens(spectral scope of 410-990 nm) and SWIR lens(spectral scope of 950-2 500 nm) were used for image acquiring. Multi-layer perceptron(MLP), partial least squares discriminant analysis(PLS-DA), and support vector machine(SVM) were employed to establish the full-waveband models and select the effective wavelengths for the distinguishing between Puerariae Lobatae Caulis and Puerariae Lobatae Radix, which provided technical and data support for the development of quick inspection equipment based on HSI. The results showed that MLP model outperformed PLS-DA and SVM models in the accuracy of discrimination with full wavebands in VNIR, SWIR, and VNIR+SWIR lens, which were 95.26%, 99.11%, and 99.05%, respectively. The discriminative band selection(DBS) algorithm was employed to select the effective wavelengths, and the discrimination accuracy was 93.05%, 98.05%, and 98.74% in the three different spectral scopes, respectively. On this basis, the MLP model combined with the effective wavelengths within the range of 2 100-2 400 nm can achieve the accuracy of 97.74%, which was close to that obtained with the full waveband. This waveband can be used to develop quick inspection devices based on HSI for the rapid and non-destructive distinguishing between Puerariae Lobatae Radix and Puerariae Lobatae Caulis.
Pueraria ; Hyperspectral Imaging ; Medicine, Chinese Traditional ; Algorithms ; Neural Networks, Computer

In the 21 st century, the rise of artificial intelligence(AI) marks the arrival of the intelligence era or the era of Industry 4.0. In addition to the rapid development of computer and electronic information science, machine learning, as the core intelligence of AI, provides a new methodology for the modernization of traditional Chinese medicine. The algorithms of machine learning include support vector machine(SVM), extreme learning machine(ELM), convolutional neural network(CNN), and recurrent neural network(RNN). The combination of machine learning algorithms and hyperspectral imaging analysis could be used for the identification of fake and inferior herbs, the origin of herbs and the content determination of bioactive ingredients in herbs, which has largely solved the difficulty in strictly controlling the quality of traditional Chinese medicine. The integration of high spectral imaging(HSI) and deep lear-ning will make the predicted results more reliable and suitable for analysis of great amounts of samples. This paper summarizes the application of hyperspectral imaging technology(HSI) and machine learning algorithms in the field of traditional Chinese medicine in recent years, focuses on the principles of hyperspectral imaging technology, preprocessing methods and deep learning algorithms, and gives the prospects of evolution of hyperspectral imaging technology in the field.
Algorithms ; Artificial Intelligence ; Deep Learning ; Hyperspectral Imaging ; Medicine, Chinese Traditional ; Neural Networks, Computer

In order to establish a rapid and non-destructive evaluation method for the identification of Armeniacae Semen Amarum and Persicae Semen from different origins, the spectral information of Armeniacae Semen Amarum and Persicae Semen in the range of 898-1 751 nm was collected based on hyperspectral imaging technology. Armeniacae Semen Amarum and Persicae Semen from different origins were collected as research objects, and a total of 720 Armeniacae Semen Amarum samples and 600 Persicae Semen samples were used for authenticity discrimination. The region of interest(ROI) and the average reflection spectrum in the ROI were obtained, followed by comparing five pre-processing methods. Then, partial least squares discriminant analysis(PLS-DA), support vector machine(SVM), and random forest(RF) method were established for classification models, which were evaluated by the confusion matrix of prediction results and receiver operating characteristic curve(ROC). The results showed that in the three sample sets, the se-cond derivative pre-processing method and PLS-DA were the best model combinations. The classification accuracy of the test set under the 5-fold cross-va-lidation was 93.27%, 96.19%, and 100.0%, respectively. It was consistent with the confusion matrix of the predicted results. The area under the ROC curve obtained the highest values of 0.992 3, 0.999 6, and 1.000, respectively. The study revealed that the near-infrared hyperspectral imaging technology could accurately identify the medicinal materials of Armeniacae Semen Amarum and Persicae Semen from different origins and distinguish the authentication of these two varieties.
Drugs, Chinese Herbal ; Hyperspectral Imaging ; Least-Squares Analysis ; Semen ; Support Vector Machine ; Technology

With the development of imaging technology and artificial intelligence, hyperspectral imaging technology provides a fast, non-destructive, intelligent, and precise new method for the analysis of Chinese materia medica(CMM). This paper summarized the methods and applications of hyperspectral imaging technology combined with intelligent analysis technology in the field of CMM in recent years, focusing on the acquisition and preprocessing of hyperspectral data, intelligent analysis methods of hyperspectral data, and practical cases of these technologies in the field of CMM. Hyperspectral data of CMM can provide spectral information with nanometer-level resolution and rich spatial texture information simultaneously. This paper summarized the acquisition process, including black-and-white board calibration and region-of-interest extraction, and preprocessing methods including smoothing, differentiation, scale-space, and scattering correction. The feature extraction methods in terms of spectral, spatial, color, and texture were briefly described, and common modeling methods were summarized. Finally, this paper reviewed the research cases of the application of the above methods to the fields of CMM, such as authenticity identification, origin tracing, variety recognition, year identification, sulfur fumigation degree determination, and quantitative measurement.
Humans ; Artificial Intelligence ; Drugs, Chinese Herbal ; Hyperspectral Imaging ; Materia Medica ; Medicine, Chinese Traditional ; Technology