Identification of Vernonia patula Merr. and Its Similar Varieties Based on a Combination of HPLC Fingerprinting and Chemical Pattern Recognition.

Vernonia patula Merr. (VP) is a traditional medicine used by the Zhuang and Yao people, known for its therapeutic properties in treating anemopyretic cold and other diseases. Distinguishing VP from similar varieties such as Praxelis clematidea (PC), Ageratum conyzoides L. (AC) and Ageratum houstonianum Mill (AH) was challenging due to their similar traits and plant morphology. The HPLC fingerprints of 40 batches of VP and three similar varieties were established. SPSS 20.0 and SIMCA-P 13.0 were used to statistically analyze the chromatographic peak areas of 37 components. The results showed that the similarity of the HPLC fingerprints for each of the four varieties was >0.9, while the similarity between the control chromatogram of VP and its similar varieties was <0.678. Cluster analysis and partial least squares discriminant analysis provided consistent results, indicating that all four varieties could be individually clustered together. Through further analysis, we found isochlorogenic acid A and isochlorogenic acid C were present only in the original VP, while preconene II was present in the three similar varieties of VP. These three components are expected to be identification points for accurately distinguishing VP from PC, AC and AH.

Rapid analysis of Radix Astragali using a portable Raman spectrometer with 1064-nm laser excitation and data fusion with PLS-DA.

Radix Astragali is a medicinal herb with various physiological activities. There were high similarities among Radix Astragali samples from different regions owing to similarities in their major chemical compositions. Raman spectroscopy is a non-invasive and non-des- tructive technique that can be used in in-situ analysis of herbal samples. Dispersive Raman scattering, excited at 1064 nm, produced minimal fluorescence background and facilitated easy detection of the weak Raman signal. By moving the portable Raman probe point-by- point from the centre of the Radix Astragali sample to the margin, the spectral fingerprints, composed of dozens of Raman spectra representing the entire Radix Astragali samples, were obtained. Principal component analysis and partial least squares discriminant analysis (PLS-DA) were applied to the Radix Astragali spectral data to compare classification results, leading to efficient discrimination between genuine and counterfeit products. Furthermore, based on the PLS-DA model using data fusion combined with different pre- processing methods, the samples from Shanxi Province were separated from those belonging to other habitats. The as-proposed combination method can effectively improve the recognition rate and accuracy of identification of herbal samples, which can be a valuable tool for the identification of genuine medicinal herbs with uneven qualities and various origins.

Discrimination and prediction of Qingzhuan tea storage year using quantitative chemical profile combined with multivariate analysis: Advantages of MRMHR based targeted quantification metabolomics.

The duration of storage significantly influences the quality and market value of Qingzhuan tea (QZT). Herein, a high-resolution multiple reaction monitoring (MRMHR) quantitative method for markers of QZT storage year was developed. Quantitative data alongside multivariate analysis were employed to discriminate and predict the storage year of QZT. Furthermore, the content of the main biochemical ingredients, catechins and alkaloids, and free amino acids (FAA) were assessed for this purpose. The results show that targeted marker-based models exhibited superior discrimination and prediction performance among four datasets. The R2Xcum, R2Ycum and Q2cum of orthogonal projection to latent structure-discriminant analysis discrimination model were close to 1. The correlation coefficient (R2) and the root mean square error of prediction of the QZT storage year prediction model were 0.9906 and 0.63, respectively. This study provides valuable insights into tea storage quality and highlights the potential application of targeted markers in food quality evaluation.

Discrimination of opium from Afghanistan and Myanmar by infrared spectroscopy coupled with machine learning methods.

Afghanistan and Myanmar are two overwhelming opium production places. In this study, rapid and efficient methods for distinguishing opium from Afghanistan and Myanmar were developed using infrared spectroscopy (IR) coupled with multiple machine learning (ML) methods for the first time. A total of 146 authentic opium samples were analyzed by mid-IR (MIR) and near-IR (NIR), within them 116 were used for model training and 30 were used for model validation. Six ML methods, including partial least squares discriminant analysis (PLS-DA), orthogonal PLS-DA (OPLS-DA), k-nearest neighbour (KNN), support vector machine (SVM), random forest (RF), and artificial neural networks (ANNs) were constructed and compared to get the best classification effect. For MIR data, the average of precision, recall and f1-score for all classification models were 1.0. For NIR data, the average of precision, recall and f1-score for different classification models ranged from 0.90 to 0.94. The comparison results of six ML models for MIR and NIR data showed that MIR was more suitable for opium geography classification. Compared with traditional chromatography and mass spectrometry profiling methods, the advantages of MIR are simple, rapid, cost-effective, and environmentally friendly. The developed IR chemical profiling methodology may find wide application in classification of opium from Afghanistan and Myanmar, and also to differentiate them from opium originating from other opium producing countries. This study presented new insights into the application of IR and ML to rapid drug profiling analysis.

Nutrient supplementation-induced metabolic profile changes and early appearance of free N-glycans in nutrient deficient tomato plants revealed by mass spectrometry.

Inorganic fertilizers are routinely used in large scale crop production for the supplementation of nitrogen, phosphorus, and potassium in nutrient poor soil. To explore metabolic changes in tomato plants grown on humic sand under different nutritional conditions, matrix-assisted laser desorption ionization (MALDI) mass spectrometry was utilized for the analysis of xylem sap. Variations in the abundances of metabolites and oligosaccharides, including free N-glycans (FNGs), were determined. Statistical analysis of the sample-related peaks revealed significant differences in the abundance ratios of multiple metabolites, including oligosaccharides, between the control plants, grown with no fertilizers, and plants raised under "ideal" and "nitrogen deficient" nutritional conditions, i.e., under the three treatment types. Among the 36 spectral features tentatively identified as oligosaccharides, the potential molecular structures for 18 species were predicted based on their accurate masses and isotope distribution patterns. To find the spectral features that account for most of the differences between the spectra corresponding to the three different treatments, multivariate statistical analysis was carried out by orthogonal partial least squares-discriminant analysis (OPLS-DA). They included both FNGs and non-FNG compounds that can be considered as early indicators of nutrient deficiency. Our results reveal that the potential nutrient deficiency indicators can be expanded to other metabolites beyond FNGs. The m/z values for 20 spectral features with the highest variable influence on projection (VIP) scores were ranked in the order of their influence on the statistical model.

Quality difference analysis of raw and vinegar-processed products of Qingpi based on color and component correlation.

The dried young fruit of Citrus reticulata Blanco, known as Qingpi, is commonly used in clinic both with its raw and vinegar-processed products. However, the distinctions in quality between these two products remain unclear, and the methods for identification are considerably intricate. In this study, an electronic eye technique was applied to assess the overall color of Qingpi products before and after processing. The luminosity (L*) and yellow-blue (b*) values of Qingpi decreased after vinegar processing, while red-green (a*) values increased. The discriminant function models based on color parameters were established to effectively classify the two products. The chemical compositions of different Qingpi products were characterized using ultra-high performance liquid chromatography fingerprint technology, and 10 distinct components were considered as potential chemical markers. The correlation analysis revealed a significant relationship between chromatic values and chemical components. In conclusion, the results of this study suggested that chromaticity can be effectively considered as a valuable instrument for the prediction of component content in both raw and vinegar-processed Qingpi products. This study will provide new ideas and methods for identification and quality evaluation of Qingpi processed products, as well as provide a reference for standardizing traditional Chinese medicine processing techniques.

Automatic Classification of Antimalarial Herbal Drugs Exposed to Ultraviolet Radiation from Unexposed Ones Using Laser-Induced Autofluorescence with Chemometric Techniques.

Exposure of antimalarial herbal drugs (AMHDs) to ultraviolet radiation (UVR) affects the potency and integrity of the AMHDs. Instant classification of the AMHDs exposed to UVR (UVR-AMHDs) from unexposed ones (Non-UVR-AMHDs) would be beneficial for public health safety, especially in warm regions. For the first time, this work combined laser-induced autofluorescence (LIAF) with chemometric techniques to classify UVR-AMHDs from Non-UVR-AMHDs. LIAF spectra data were recorded from 200 ml of each of the UVR-AMHDs and Non-UVR-AMHDs. To extract useful data from the spectra fingerprint, principal components (PCs) analysis was used. The performance of five chemometric algorithms: random forest (RF), neural network (NN), support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbour (KNN), were compared after optimization by validation. The chemometric algorithms showed that KNN, SVM, NN, and RF were superior with a classification accuracy of 100% for UVR-AMHDs while LDA had a classification accuracy of 98.8% after standardization of the spectra data and was used as an input variable for the model. Meanwhile, a classification accuracy of 100% was obtained for KNN, LDA, SVM, and NN when the raw spectra data was used as input except for RF for which a classification accuracy of 99.9% was obtained. Classification accuracy above 99.74 ± 0.26% at 3 PCs in both the training and testing sets were obtained from the chemometric models. The results showed that the LIAF, combined with the chemometric techniques, can be used to classify UVR-AMHDs from Non-UVR-AMHDs for consumer confidence in malaria-prone regions. The technique offers a non-destructive, rapid, and viable tool for identifying UVR-AMHDs in resource-poor countries.

Identification of Commercial Antimalarial Herbal Drugs Using Laser-Induced Autofluorescence Technique and Multivariate Algorithms.

In malaria-prone developing countries the integrity of Anti-Malarial Herbal Drugs (AMHDs) which are easily preferred for treatment can be compromised. Currently, existing techniques for identifying AMHDs are destructive. We report on the use of non-destructive and sensitive technique, Laser-Induced-Autofluorescence (LIAF) in combination with multivariate algorithms for identification of AMHDs. The LIAF spectra were recorded from commercially prepared decoction AMHDs purchased from accredited pharmacy shop in Ghana. Deconvolution of the LIAF spectra revealed secondary metabolites belonging to derivatives of alkaloids and classes of phenolic compounds of the AMHDs. Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were able to discriminate the AMHDs base on their physicochemical properties. Based on two principal components, the PCA- QDA (Quadratic Discriminant Analysis), PCA-LDA (Linear Discriminant Analysis), PCA-SVM (Support Vector Machine) and PCA-KNN (K-Nearest Neighbour) models were developed with an accuracy performance of 99.0, 99.7, 100.0, and 100%, respectively, in identifying AMHDs. PCA-SVM and PCA-KNN provided the best classification and stability performance. The LIAF technique in combination with multivariate techniques may offer a non-destructive and viable tool for AMHDs identification.

Detection of the Adulteration of Dendrobium Huoshanense with Dendrobium Henanense by UV-Vis-Shortwave Near-Infrared Diffuse Reflectance Spectroscopy Combined with Chemometrics.

BACKGROUND: Dendrobium huoshanense (DHS) is a classic traditional Chinese medicine (TCM) with distinctive medicinal benefits and great economic worth; nevertheless, because of similar tastes and looks, it is simple to adulterate with less expensive substitutes (such as Dendrobium henanense [DHN]). OBJECTIVE: This work aimed to develop a reliable tool to detect and quantify the adulteration of DHS with DHN by using UV-Vis-shortwave near-infrared diffuse reflectance spectroscopy (UV-Vis-SWNIR DRS) combined with chemometrics. METHODS: Adulterated samples prepared in varying concentrations (0-100%, w/w) were analyzed with UV-Vis-SWNIR DRS methods. Partial least-square-discriminant analysis (PLS-DA) and partial least-squares (PLS) regression techniques were used for the differentiation of adulterated DHN from pure DHS and the prediction of adulteration levels. RESULTS: The PLS-DA classification models successfully differentiated adulterated and nonadulterated DHS with an over 100% correct classification rate. UV-Vis-SWNIR DRS data were also successfully used to predict adulteration levels with a high coefficient of determination for calibration (0.9924) and prediction (0.9906) models and low error values for calibration (3.863%) and prediction (5.067%). CONCLUSION: UV-Vis-SWNIR DRS, as a fast and environmentally friendly tool, has great potential for both the identification and quantification of adulteration practices involving herbal medicines and foods. HIGHLIGHTS: UV-Vis-SWNIR DRS combined with chemometrics can be applied to identify and quantify the adulteration of herbal medicines and foods.

Origin traceability of Cordyceps sinensis based on trace elements and stable isotope fingerprints.

Cordyceps sinensis is a kind of valuable Chinese herbal medicine, and its quality and price depend on the place of origin. Building a traceability system for Cordyceps sinensis products is an effective way to protect Cordyceps sinensis geographical indication products and consumers. In this study, concentrations of 45 trace elements and stable C, N, and Pb isotopes were used to distinguish Cordyceps sinensis samples from different habitats and different varieties (natural and artificial). The results showed that there were significant differences in the isotope compositions of N and Pb and trace elements contents in the Cordyceps sinensis samples from different sources. Stepwise discriminant analysis was used to select effective traceability indicators, and three discriminant models were successfully established. A combination of Co, Sr, Cu, Tl, and Zr indexes was selected to distinguish the naturally grown samples from the artificially cultivated ones, with an overall cross-validation correctness rate of 90.0 %; while a combination of As, Cu, Rb, Tl, W, and Zr indexes was adopted to distinguish the naturally grown samples from different regions, with a corresponding 100.0 % overall cross-validation correctness rate. To simultaneously distinguish samples between natural and artificial and between different regions, a combination of As, Cu, Rb, Tl, U, W, and δ15N indexes was employed, with an overall cross-validation correctness rate of 89.3 %.

Geographical origin discriminatory analysis of onions: Chemometrics methods applied to ICP-OES and ICP-MS analysis.

Geographical origin is an important determinant of agricultural product quality and safety. Herein, inductively coupled plasma (ICP) analysis was applied to determine the inorganic elemental content of onions and identify their geographical origin (Korean or Chinese). Chemometric, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to the ICP results. OPLS-DA distinguished each group, and 17 elements with variable importance in projection (VIP) values of ≥ 1 were selected. The receiver operating characteristic (ROC) curve had an area under the curve (AUC) of 1, indicating excellent discriminatory power. Differences in elemental content between groups were visually observed in a heatmap, and the country of origin was determined with 100% accuracy using canonical discriminant analysis (CDA). This method accurately distinguishes between Korean and Chinese onions and is expected to be beneficial for identifying agricultural products.

Comparative Metabolomic Analysis Reveals the Differences in Nonvolatile and Volatile Metabolites and Their Quality Characteristics in Beauty Tea with Different Extents of Punctured Leaves by Tea Green Leafhopper.

The fresh leaves were processed into beauty tea from the Camellia sinensis "Jinxuan" cultivar, which were punctured by tea green leafhoppers to different extents. Low-puncturing dry tea (LPDT) exhibited a superior quality. Altogether, 101 and 129 differential metabolites, including tea polyphenols, lipids, and saccharides, were identified from the fresh leaves and dry beauty tea, respectively. Most metabolite levels increased in the fresh leaves punctured by leafhoppers, but the opposite was observed for the dry beauty tea. According to relative odor activity values (rOAVs) and partial least-squares discriminant analysis (PLS-DA), four characteristic volatiles, including linalool, geraniol, benzeneacetaldehyde, and dihydrolinalool, were selected. Mechanical injury to leaves caused by leafhoppers, watery saliva secreted by the leafhopper, and different water contents of the fresh leaves in different puncturing degrees are the possible reasons for the difference in the quality of the beauty tea with different levels of puncturing. Overall, this study identified a wide range of chemicals that are affected by the degrees of leafhopper puncturing.

Qualitative identification and quantitative comparison of Physochlainae Radix from different regions based on chemometric methods.

Physochlainae Radix (PR) is an essential herbal medicine that has been generally applied for treating cough and asthma. In this study, a comprehensive strategy for quality evaluation of PR from different origins was established by integrating qualitative identification, quantitative analysis, and chemometric methods. A total of 58 chemical components were identified by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF-MS/MS), and a sensitive and rapid UHPLC-QqQ-MS/MS method was established for the simultaneous determination of 12 compounds. In addition, multivariate statistical analysis was applied for discriminant analysis to compare the differences among 30 batches of PR samples. The results showed that the 30 batches of PR collected from four provinces could be clustered into three categories, in which scoparone, protocatechuic acid, tropic acid, and scopolin were important components to distinguish the primary and non-primary producing areas, as well as superior and inferior products of PR. Chemometric results were consistent and validated each other, and systematically explained the intrinsic quality characteristics of PR. This study first demonstrated that LC-MS combined with multivariate statistical analysis, provided a comprehensive and effective means for quality evaluation of PR.

Autonomic nervous system modulation during self-induced non-ordinary states of consciousness.

Self-induced cognitive trance (SICT) is a voluntary non-ordinary state of consciousness characterized by a lucid yet narrowed awareness of the external surroundings. It involves a hyper-focused immersive experience of flow, expanded inner imagery, modified somatosensory processing, and an altered perception of self and time. SICT is gaining attention due to its potential clinical applications. Similar states of non-ordinary state of consciousness, such as meditation, hypnosis, and psychedelic experiences, have been reported to induce changes in the autonomic nervous system. However, the functioning of the autonomic nervous system during SICT remains poorly understood. In this study, we aimed to investigate the impact of SICT on the cardiac and respiratory signals of 25 participants proficient in SICT. To accomplish this, we measured various metrics of heart rate variability (HRV) and respiration rate variability (RRV) in three conditions: resting state, SICT, and a mental imagery task. Subsequently, we employed a machine learning framework utilizing a linear discriminant analysis classifier and a cross-validation scheme to identify the features that exhibited the best discrimination between these three conditions. The results revealed that during SICT, participants experienced an increased heart rate and a decreased level of high-frequency (HF) HRV compared to the control conditions. Additionally, specific increases in respiratory amplitude, phase ratio, and RRV were observed during SICT in comparison to the other conditions. These findings suggest that SICT is associated with a reduction in parasympathetic activity, indicative of a hyperarousal state of the autonomic nervous system during SICT.

Quality Assessment and Classification of Codonopsis Radix Based on Fingerprints and Chemometrics.

In China, Codonopsis Radix (CR) is frequently consumed both as food and medicine. Here, a comprehensive strategy based on fingerprinting and chemometric approaches was created to explore the influence of origins, storage time and kneading processing on the quality of CR. Firstly, high-performance liquid chromatography with diode array detection was used to obtain the fingerprints of 35 batches of CR from six different origins and 33 batches of CR from varying storage times or kneading procedures. Secondly, chemometric methods including similarity analysis (SA), principal component analysis (PCA), hierarchical clustering analysis (HCA), and two-way orthogonal partial least square with discriminant analysis (O2PLS-DA) were used to evaluate the differences of chemical components in CR so as to identify its source and reflect its quality. Moreover, 13 and 16 major compounds were identified as marker compounds for the discrimination of CR from different origins, storage time and kneading processing, respectively. Furthermore, the relative content of the marker components and the exact content of Lobetyolin were measured, indicating that the contents of these components vary significantly between various CR samples. Meanwhile, the chemical components of CR were identified using Mass spectrometry. According to the findings of our investigation, the quality of CR from Gansu was the best, followed by Shanxi and then Sichuan. The quality of CR from Chongqing and Guizhou was poor. At the same time, the quality of CR was the best when it was kneaded and stored for 0 years, indicating that the traditional kneading process of CR is of great significance. Conclusively, HPLC fingerprint in conjunction with chemical pattern recognition and component content determination can be employed to differentiate the raw materials of different CR samples. Additionally, it is also a reliable, comprehensive and prospective method for quality control and evaluation of CR.

Discrimination of Polygonati Rhizoma Species: An Investigation Utilizing High-Performance Liquid Chromatography Fingerprints and Chemometrics.

Polygonati Rhizoma has been a famous traditional Chinese medicine (TCM) for two thousand years. It is increasingly being used not just as a traditional herbal medicine but also as a popular functional food. In this study, qualitative and quantitative analysis of PR from three different origins were initially performed using chemical fingerprint and chemometrics methods. Hierarchical cluster analysis (HCA) and Principal component analysis (PCA) were used to classify 60 PR samples from three different origins. The results revealed that the PR samples fell into three clusters related to the origins. In addition, pairwise comparison of varying PR and obtaining chemical markers between different species through the establishment of partial least squares discriminant analysis. Finally, chemical markers 9,13 and 17 were identified by LC/MS as disporopsin, 5,7-dihydroxy-3-(4'-hydroxybenzyl)-6,8-dimethylchroman-4-one and (3R)-5,7-dihydroxy-3-(4'-hydroxybenzyl)-6-methylchroman-4-one or isomer, respectively. In conclusion, these methods can be applied to identify and distinguish the quality of PR with other original plants and provide novel ideas for evaluating herbal products used in TCM.

[Establishment of a fast discriminant model with electronic nose for Polygonati Rhizoma mildew based on odor variation].

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.

A Recognition Method for Road Hypnosis Based on Physiological Characteristics.

Road hypnosis is a state which is easy to appear frequently in monotonous scenes and has a great influence on traffic safety. The effective detection for road hypnosis can improve the intelligent vehicle. In this paper, the simulated experiment and vehicle experiment are designed and carried out to obtain the physiological characteristics data of road hypnosis. A road hypnosis recognition model based on physiological characteristics is proposed. Higher-order spectra are used to preprocess the electrocardiogram (ECG) and electromyography (EMG) data, which can be further fused by principal component analysis (PCA). The Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbor (KNN) models are constructed to identify road hypnosis. The proposed model has good identification performance on road hypnosis. It provides more alternative methods and technical support for real-time and accurate identification of road hypnosis. It is of great significance to improve the intelligence and active safety of intelligent vehicles.

Multidimensional Chromatographic Fingerprinting Combined with Chemometrics for the Identification of Regulated Plants in Suspicious Plant Food Supplements.

The popularity of plant food supplements has seen explosive growth all over the world, making them susceptible to adulteration and fraud. This necessitates a screening approach for the detection of regulated plants in plant food supplements, which are usually composed of complex plant mixtures, thus making the approach not so straightforward. This paper aims to tackle this problem by developing a multidimensional chromatographic fingerprinting method aided by chemometrics. To render more specificity to the chromatogram, a multidimensional fingerprint (absorbance × wavelength × retention time) was considered. This was achieved by selecting several wavelengths through a correlation analysis. The data were recorded using ultra-high-performance liquid chromatography (UHPLC) coupled with diode array detection (DAD). Chemometric modelling was performed by partial least squares-discriminant analysis (PLS-DA) through (a) binary modelling and (b) multiclass modelling. The correct classification rates (ccr%) by cross-validation, modelling, and external test set validation were satisfactory for both approaches, but upon further comparison, binary models were preferred. As a proof of concept, the models were applied to twelve samples for the detection of four regulated plants. Overall, it was revealed that the combination of multidimensional fingerprinting data with chemometrics was feasible for the identification of regulated plants in complex botanical matrices.

Recognition of the rhizome of red ginseng based on spectral-image dual-scale digital information combined with intelligent algorithms.

Red ginseng is a widely used and extensively researched food and medicinal product with high nutritional value, derived from steamed fresh ginseng. The components in various parts of red ginseng differ significantly, resulting in distinct pharmacological activities and efficacies. This study proposed to establish a hyperspectral imaging technology combined with intelligent algorithms for the recognition of different parts of red ginseng based on the dual-scale of spectrum and image information. Firstly, the spectral information was processed by the best combination of first derivative as pre-processing method and partial least squares discriminant analysis (PLS-DA) as classification model. The recognition accuracy of the rhizome and the main root of red ginseng is 96.79% and 95.94% respectively. Then, the image information was processed by the You Only Look Once version 5 small (YOLO v5s) model. The best parameter combination is epoch = 30, learning rate = 0.01, and activation function is leaky ReLU. In the red ginseng dataset, the highest accuracy, recall and mean Average Precision at IoU (Intersection over Union) threshold 0.5 (mAP@0.5) were 99.01%, 98.51% and 99.07% respectively. The application of spectrum-image dual-scale digital information combined with intelligent algorithms in the recognition of red ginseng is successful, which provides a positive significance for the online and on-site quality control and authenticity identification of crude drugs or fruits.