Visible-short wavelength near infrared hyperspectral imaging coupled with multivariate curve resolution-alternating least squares for diagnosis of breast cancer.

This study investigates the application of visible-short wavelength near-infrared hyperspectral imaging (Vis-SWNIR HSI) in the wavelength range of 400-950 nm and advanced chemometric techniques for diagnosing breast cancer (BC). The research involved 56 ex-vivo samples encompassing both cancerous and non-cancerous breast tissue from females. First, HSI images were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to exploit pure spatial and spectral profiles of active components. Then, the MCR-ALS resolved spatial profiles were arranged in a new data matrix for exploration and discrimination between benign and cancerous tissue samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA classification accuracy of 82.1 % showed the potential of HSI and chemometrics for non-invasive detection of BC. Additionally, the resolved spectral profiles by MCR-ALS can be used to track the changes in the breast tissue during cancer and treatment. It is concluded that the proposed strategy in this work can effectively differentiate between cancerous and non-cancerous breast tissue and pave the way for further studies and potential clinical implementation of this innovative approach, offering a promising avenue for improving early detection and treatment outcomes in BC patients.

Smartphone video imaging: A versatile, low-cost technology for food authentication.

This study presents a low-cost smartphone-based imaging technique called smartphone video imaging (SVI) to capture short videos of samples that are illuminated by a colour-changing screen. Assisted by artificial intelligence, the study develops new capabilities to make SVI a versatile imaging technique such as the hyperspectral imaging (HSI). SVI enables classification of samples with heterogeneous contents, spatial representation of analyte contents and reconstruction of hyperspectral images from videos. When integrated with a residual neural network, SVI outperforms traditional computer vision methods for ginseng classification. Moreover, the technique effectively maps the spatial distribution of saffron purity in powder mixtures with predictive performance that is comparable to that of HSI. In addition, SVI combined with the U-Net deep learning module can produce high-quality images that closely resemble the target images acquired by HSI. These results suggest that SVI can serve as a consumer-oriented solution for food authentication.

Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

A Microscope Setup and Methodology for Capturing Hyperspectral and RGB Histopathological Imaging Databases.

The digitization of pathology departments in hospitals around the world is now a reality. The current commercial solutions applied to digitize histopathological samples consist of a robotic microscope with an RGB-type camera attached to it. This technology is very limited in terms of information captured, as it only works with three spectral bands of the visible electromagnetic spectrum. Therefore, we present an automated system that combines RGB and hyperspectral technology. Throughout this work, the hardware of the system and its components are described along with the developed software and a working methodology to ensure the correct capture of histopathological samples. The software is integrated by the controller of the microscope, which features an autofocus functionality, whole slide scanning with a stitching algorithm, and hyperspectral scanning functionality. As a reference, the time to capture and process a complete sample with 20 regions of high biological interest using the proposed method is estimated at a maximum of 79 min, reducing the time required by a manual operator by at least three times. Both hardware and software can be easily adapted to other systems that might benefit from the advantages of hyperspectral technology.

Early surveillance of rice bakanae disease using deep learning and hyperspectral imaging.

Bakanae disease, caused by Fusarium fujikuroi, poses a significant threat to rice production and has been observed in most rice-growing regions. The disease symptoms caused by different pathogens may vary, including elongated and weak stems, slender and yellow leaves, and dwarfism, as example. Bakanae disease is likely to cause necrosis of diseased seedlings, and it may cause a large area of infection in the field through the transmission of conidia. Therefore, early disease surveillance plays a crucial role in securing rice production. Traditional monitoring methods are both time-consuming and labor-intensive and cannot be broadly applied. In this study, a combination of hyperspectral imaging technology and deep learning algorithms were used to achieve in situ detection of rice seedlings infected with bakanae disease. Phenotypic data were obtained on the 9th, 15th, and 21st day after rice infection to explore the physiological and biochemical performance, which helps to deepen the research on the disease mechanism. Hyperspectral data were obtained over these same periods of infection, and a deep learning model, named Rice Bakanae Disease-Visual Geometry Group (RBD-VGG), was established by leveraging hyperspectral imaging technology and deep learning algorithms. Based on this model, an average accuracy of 92.2% was achieved on the 21st day of infection. It also achieved an accuracy of 79.4% as early as the 9th day. Universal characteristic wavelengths were extracted to increase the feasibility of using portable spectral equipment for field surveillance. Collectively, the model offers an efficient and non-destructive surveillance methodology for monitoring bakanae disease, thereby providing an efficient avenue for disease prevention and control. Supplementary Information: The online version contains supplementary material available at 10.1007/s42994-024-00169-1.

Visible and near-infrared spectral imaging combined with robust regression for predicting firmness, fatness, and compositional properties of fresh pork bellies.

Belly is a widely consumed pork product with very variable properties. Meat industry needs real-time quality assessment for maintaining superior pork quality throughout the production. This study explores the potential of using visible and near-infrared (VNIR,386-1015 nm) spectral imaging for predicting firmness, fatness and chemical compositional properties in pork belly samples, offering robust spectral calibrations. A total of 182 samples with wide variations in firmness and compositional properties were analysed using common laboratory analyses, whereas spectral images were acquired with a VNIR spectral imaging system. Exploratory analysis of the studied properties was performed, followed by a robust regression approach called iterative reweighted partial least-squares regression to model and predict these belly properties. The models were also used to generate spatial maps of predicted chemical compositional properties. Chemical properties such as fat, dry matter, protein, ashes, iodine value, along with firmness measures as flop distance and angle, were predicted with excellent, very good and fair models, with a ratio prediction of standard deviation (RPD) of 4.93, 3.91, 2.58, 2.54, 2.41, 2.53 and 2.51 respectively. The methodology developed in this study showed that a short wavelength spectral imaging system can yield promising results, being a potential benefit for the pork industry in automating the analysis of fresh pork belly samples. VNIR spectral imaging emerges as a non-destructive method for pork belly characterization, guiding process optimization and marketing strategies. Moreover, future research can explore advanced data analytics approaches such as deep learning to facilitate the integration of spectral and spatial information in joint modelling.

Transportable hyperspectral imaging setup based on fast, high-density spectral scanning for in situ quantitative biochemical mapping of fresh tissue biopsies.

Significance: Histopathological examination of surgical biopsies, such as in glioma and glioblastoma resection, is hindered in current clinical practice by the long time required for the laboratory analysis and pathological screening, typically taking several days or even weeks to be completed. Aim: We propose here a transportable, high-density, spectral scanning-based hyperspectral imaging (HSI) setup, named HyperProbe1, that can provide in situ, fast biochemical analysis, and mapping of fresh surgical tissue samples, right after excision, and without the need for fixing, staining nor compromising the integrity of the tissue properties. Approach: HyperProbe1 is based on spectral scanning via supercontinuum laser illumination filtered with acousto-optic tunable filters. Such methodology allows the user to select any number and type of wavelength bands in the visible and near-infrared range between 510 and 900 nm (up to a maximum of 79) and to reconstruct 3D hypercubes composed of high-resolution (4 to 5 µ m ), widefield images ( 0.9 × 0.9 mm 2 ) of the surgical samples, where each pixel is associated with a complete spectrum. Results: The HyperProbe1 setup is here presented and characterized. The system is applied to 11 fresh surgical biopsies of glioma from routine patients, including different grades of tumor classification. Quantitative analysis of the composition of the tissue is performed via fast spectral unmixing to reconstruct the mapping of major biomarkers, such as oxy-( HbO 2 ) and deoxyhemoglobin (HHb), as well as cytochrome-c-oxidase (CCO). We also provided a preliminary attempt to infer tumor classification based on differences in composition in the samples, suggesting the possibility of using lipid content and differential CCO concentrations to distinguish between lower and higher-grade gliomas. Conclusions: A proof of concept of the performances of HyperProbe1 for quantitative, biochemical mapping of surgical biopsies is demonstrated, paving the way for improving current post-surgical, histopathological practice via non-destructive, in situ streamlined screening of fresh tissue samples in a matter of minutes after excision.

Machine Learning Phenotyping and GWAS Reveal Genetic Basis of Cd Tolerance and Absorption in Jute.

Cadmium (Cd) is a dangerous environmental contaminant. Jute (Corchorus sp.) is an important natural fiber crop with strong absorption and excellent adaptability to metal-stressed environments, used in the phytoextraction of heavy metals. Understanding the genetic and molecular mechanisms underlying Cd tolerance and accumulation in plants is essential for efficient phytoremediation strategies and breeding novel Cd-tolerant cultivars. Here, machine learning (ML) and hyperspectral imaging (HSI) combining genome-wide association studies (GWAS) and RNA-seq reveal the genetic basis of Cd resistance and absorption in jute. ML needs a small number of plant phenotypes for training and can complete the plant phenotyping of large-scale populations with efficiency and accuracy greater than 90%. In particular, a candidate gene for Cd resistance (COS02g_02406) and a candidate gene (COS06g_03984) associated with Cd absorption are identified in isoflavonoid biosynthesis and ethylene response signaling pathways. COS02g_02406 may enable plants to cope with metal stress by regulating isoflavonoid biosynthesis involved in antioxidant defense and metal chelation. COS06g_03984 promotes the binding of Cd2+ to ETR/ERS, resulting in Cd absorption and tolerance. The results confirm the feasibility of high-throughput phenotyping for studying plant Cd tolerance by combining HSI and ML approaches, facilitating future molecular breeding.

Innovations in forensic science: Comprehensive review of hyperspectral imaging for bodily fluid analysis.

Hyperspectral imaging (HSI) has become a crucial innovation in forensic science, particularly for analysing bodily fluids. This advanced technology captures both spectral and spatial data across a wide spectrum of wavelengths, offering comprehensive insights into the composition and distribution of bodily fluids found at crime scenes. In this review, we delve into the forensic applications of HSI, emphasizing its role in detecting, identifying, and distinguishing various bodily fluids such as blood, saliva, urine, vaginal fluid, semen, and menstrual blood. We examine the benefits of HSI compared to traditional methods, noting its non-destructive approach, high sensitivity, and capability to differentiate fluids even in complex mixtures. Additionally, we discuss recent advancements in HSI technology and their potential to enhance forensic investigations. This review highlights the importance of HSI as a valuable tool in forensic science, opening new pathways for improving the accuracy and efficiency of crime scene analyses.

Technological Frontiers in Brain Cancer: A Systematic Review and Meta-Analysis of Hyperspectral Imaging in Computer-Aided Diagnosis Systems.

Brain cancer is a substantial factor in the mortality associated with cancer, presenting difficulties in the timely identification of the disease. The precision of diagnoses is significantly dependent on the proficiency of radiologists and neurologists. Although there is potential for early detection with computer-aided diagnosis (CAD) algorithms, the majority of current research is hindered by its modest sample sizes. This meta-analysis aims to comprehensively assess the diagnostic test accuracy (DTA) of computer-aided design (CAD) models specifically designed for the detection of brain cancer utilizing hyperspectral (HSI) technology. We employ Quadas-2 criteria to choose seven papers and classify the proposed methodologies according to the artificial intelligence method, cancer type, and publication year. In order to evaluate heterogeneity and diagnostic performance, we utilize Deeks' funnel plot, the forest plot, and accuracy charts. The results of our research suggest that there is no notable variation among the investigations. The CAD techniques that have been examined exhibit a notable level of precision in the automated detection of brain cancer. However, the absence of external validation hinders their potential implementation in real-time clinical settings. This highlights the necessity for additional studies in order to authenticate the CAD models for wider clinical applicability.

A direct comparison of a next generation hyperspectral camera to state-of-the-art.

Hyperspectral camera technology is advancing rapidly, and this paper seeks to compare a state-of-the-art industrial dual-camera setup to a single-camera system employing the latest chip technology (IMX990 from Sony). The hyperspectral cameras are compared over both the Visual and Short-Wave Infrared range (400-1700 nm) of the electromagnet spectrum. The spectral range and resolution, as well as spatial parameters and spectroscopic information are quantified with comparable optics, electronics, and test targets. Generally, enhanced spectral detail and reduced noise were observed for the single-camera compared to its peers. Thus, the IMX990 shows promising performance for the new generation of hyperspectral cameras directly relevant to industrial applications, such as detection, documentation, and sorting.

Fluorescence hyperspectral imaging technology combined with chemometrics for kiwifruit quality attribute assessment and non-destructive judgment of maturity.

Dry matter content (DMC), firmness and soluble solid content (SSC) are important indicators for assessing the quality attributes and determining the maturity of kiwifruit. However, traditional measurement methods are time-consuming, labor-intensive, and destructive to the kiwifruit, leading to resource wastage. In order to solve this problem, this study has tracked the flowering, fruiting, maturing and collecting processes of Ya'an red-heart kiwifruit, and has proposed a non-destructive method for kiwifruit quality attribute assessment and maturity identification that combines fluorescence hyperspectral imaging (FHSI) technology and chemometrics. Specifically, first of all, three different spectral data preprocessing methods were adopted, and PLSR was used to evaluate the quality attributes (DMC, firmness, and SSC) of kiwifruit. Next, the differences in accuracy of different models in discriminating kiwifruit maturity were compared, and an ensemble learning model based on LightGBM and GBDT models was constructed. The results indicate that the ensemble learning model outperforms single machine learning models. In addition, the application effects of the 'Convolutional Neural Network'-'Multilayer Perceptron' (CNN-MLP) model under different optimization algorithms were compared. To improve the robustness of the model, an improved whale optimization algorithm (IWOA) was introduced by modifying the acceleration factor. Overall, the IWOA-CNN-MLP model performs the best in discriminating the maturity of kiwifruit, with Accuracytest of 0.916 and Loss of 0.23. In addition, compared with the basic model, the accuracy of the integrated learning model SG-MSC-SEL was improved by about 12%-20 %. The research findings will provide new perspectives for the evaluation of kiwifruit quality and maturity discrimination using FHSI and chemometric methods, thereby promoting further research and applications in this field.

WeedCube: Proximal hyperspectral image dataset of crops and weeds for machine learning applications.

WeedCube dataset consists of hyperspectral images of three crops (canola, soybean, and sugarbeet) and four invasive weeds species (kochia, common waterhemp, redroot pigweed, and common ragweed). Plants were grown in two separate greenhouses and plant canopies were captured from a top-down camera angle. A push-broom hyperspectral sensor in the visible near infrared region of 400-1000 nm was used for data collection. The dataset includes 160 calibrated images. The number of images can be further increased by selection of smaller region of interests (ROIs). Dataset is supplemented by Jupyter Notebook scripts that help in data augmentation, spectral pre-processing, ROI selection for points and images, and data visualization. The primary purpose of this dataset is to support weed classification or identification studies by enhancing existing training datasets and validating the generalization capabilities of existing models. Owing to the three-dimensional (3D) nature of hyperspectral images, this dataset can also be utilized by researchers and educators across various domains for the development and testing of deep learning algorithms, the creation of automated data processing pipelines effective for 3D data, the development of tools for 3D data visualization, the creation of innovative solutions for data compression, and addressing system memory issues associated with high-dimensional data.

Hyperspectral imaging with deep learning for quantification of tissue hemoglobin, melanin, and scattering.

Significance: Hyperspectral cameras capture spectral information at each pixel in an image. Acquired spectra can be analyzed to estimate quantities of absorbing and scattering components, but the use of traditional fitting algorithms over megapixel images can be computationally intensive. Deep learning algorithms can be trained to rapidly analyze spectral data and can potentially process hyperspectral camera data in real time. Aim: A hyperspectral camera was used to capture 1216 × 1936 pixel wide-field reflectance images of in vivo human tissue at 205 wavelength bands from 420 to 830 nm. Approach: The optical properties of oxyhemoglobin, deoxyhemoglobin, melanin, and scattering were used with multi-layer Monte Carlo models to generate simulated diffuse reflectance spectra for 24,000 random combinations of physiologically relevant tissue components. These spectra were then used to train an artificial neural network (ANN) to predict tissue component concentrations from an input reflectance spectrum. Results: The ANN achieved low root mean square errors in a test set of 6000 independent simulated diffuse reflectance spectra while calculating concentration values more than 4000× faster than a conventional iterative least squares approach. Conclusions: In vivo finger occlusion and gingival abrasion studies demonstrate the ability of this approach to rapidly generate high-resolution images of tissue component concentrations from a hyperspectral dataset acquired from human subjects.

Simultaneously predicting SPAD and water content in rice leaves using hyperspectral imaging with deep multi-task regression and transfer component analysis.

BACKGROUND: Water content and chlorophyll content are important indicators for monitoring rice growth status. Simultaneous detection of water content and chlorophyll content is of significance. Different varieties of rice show differences in phenotype, resulting in the difficulties of establishing a universal model. In this study, hyperspectral imaging was used to detect the Soil and Plant Analyzer Development (SPAD) values and water content of fresh rice leaves of three rice varieties (Jiahua 1, Xiushui 121 and Xiushui 134). RESULTS: Both partial least squares regression and convolutional neural networks were used to establish single-task and multi-task models. Transfer component analysis (TCA) was used as transfer learning to learn the common features to achieve an approximate identical distribution between any two varieties. Single-task and multi-task models were also built using the features of the source domain, and these models were applied to the target domain. These results indicated that for models of each rice variety the prediction accuracy of most multi-task models was close to that of single-task models. As for TCA, the results showed that the single-task model achieved good performance for all transfer learning tasks. CONCLUSION: Compared with the original model, good and differentiated results were obtained for the models using features learned by TCA for both the source domain and target domain. The multi-task models could be constructed to predict SPAD values and water content simultaneously and then transferred to another rice variety, which could improve the efficiency of model construction and realize rapid detection of rice growth indicators. © 2024 Society of Chemical Industry.

Monitoring of parasite Orobanche cumana using Vis-NIR hyperspectral imaging combining with physio-biochemical parameters on host crop Helianthus annuus.

KEY MESSAGE: This study provided a non-destructive detection method with Vis-NIR hyperspectral imaging combining with physio-biochemical parameters in Helianthus annuus in response to Orobanche cumana infection that took insights into the monitoring of sunflower weed. Sunflower broomrape (Orobanche cumana Wallr.) is an obligate weed that attaches to the host roots of sunflower (Helianthus annuus L.) leading to a significant reduction in yield worldwide. The emergence of O. cumana shoots after its underground life-cycle causes irreversible damage to the crop. In this study, a fast visual, non-invasive and precise method for monitoring changes in spectral characteristics using visible and near-infrared (Vis-NIR) hyperspectral imaging (HSI) was developed. By combining the bands sensitive to antioxidant enzymes (SOD, GR), non-antioxidant enzymes (GSH, GSH + GSSG), MDA, ROS (O2-, OH-), PAL, and PPO activities obtained from the host leaves, we sought to establish an accurate means of assessing these changes and conducted imaging acquisition using hyperspectral cameras from both infested and non-infested sunflower cultivars, followed by physio-biochemical parameters measurement as well as analyzed the expression of defense related genes. Extreme learning machine (ELM) and convolutional neural network (CNN) models using 3-band images were built to classify infected or non-infected plants in three sunflower cultivars, achieving accuracies of 95.83% and 95.83% for the discrimination of infestation as well as 97.92% and 95.83% of varieties, respectively, indicating the potential of multi-spectral imaging systems for early detection of O. cumana in weed management.

The Role of Luminescent Coupling in Monolithic Perovskite/Silicon Tandem Solar Cells.

Luminescent coupling (LC) is a key phenomenon in monolithic tandem solar cells. This study presents a nondestructive technique to quantitatively evaluate the LC effect, addressing a gap in the existing predictions made by optical modeling. The method involves measuring the ratio of photons emitted from the high bandgap top cell that escape through the rear, contributing additional current to the bottom cell, and to those escaping from the front side of top cell. The findings indicate that in the analyzed monolithic perovskite/silicon tandem solar cells, more than 85% of the emitted photons escaping from the perovskite top cell are used to generate additional current in the bottom cell. This process notably reduces the mismatch in the generated current between each subcell, particularly when the current is limited by the low bandgap subcell. The presented method is applicable to a variety of monolithic tandem structures, providing vital information for subcell characterization, providing vital information for predicting energy output and optimization for outdoor applications.

Investigation of a hyperspectral Scanning Laser Optical Tomography setup for label-free cell identification.

The development of non-destructive, tomographic imaging systems is a current topic of research in biomedical technologies. One of these technologies is Scanning Laser Optical Tomography (SLOT), which features a highly modular setup with various contrast mechanisms. Extending this technology with new acquisition mechanisms allows us to investigate untreated and non-stained biological samples, leaving their natural biological physiology intact. To enhance the development of SLOT, we aimed to extend the density of information with a significant increase of acquisition channels. This should allow us to investigate samples with unknown emission spectra and even allow for label-fee cell identification. We developed and integrated a hyperspectral module into an existing SLOT system. The adaptations allow for the acquisition of three-dimensional datasets containing a highly increased information density. For validation, artificial test objects were made from fluorescent acrylic and acquired with the new hyperspectral setup. In addition, measurements were made on two different human cell spheroids with an unknown spectra, to test the possibilities of label-free cell identification. The validation measurements of the artificial test target show the expected results. Furthermore, the measurements of the biological cell spheroids show small variations in their tomographic spectrum that allow for label-free cell type differentiation. The results of the biological sample demonstrate the potential of label-free cell identification of the newly developed setup.

Assessing the Efficacy of the Spectrum-Aided Vision Enhancer (SAVE) to Detect Acral Lentiginous Melanoma, Melanoma In Situ, Nodular Melanoma, and Superficial Spreading Melanoma.

Skin cancer is the predominant form of cancer worldwide, including 75% of all cancer cases. This study aims to evaluate the effectiveness of the spectrum-aided visual enhancer (SAVE) in detecting skin cancer. This paper presents the development of a novel algorithm for snapshot hyperspectral conversion, capable of converting RGB images into hyperspectral images (HSI). The integration of band selection with HSI has facilitated the identification of a set of narrow band images (NBI) from the RGB images. This study utilizes various iterations of the You Only Look Once (YOLO) machine learning (ML) framework to assess the precision, recall, and mean average precision in the detection of skin cancer. YOLO is commonly preferred in medical diagnostics due to its real-time processing speed and accuracy, which are essential for delivering effective and efficient patient care. The precision, recall, and mean average precision (mAP) of the SAVE images show a notable enhancement in comparison to the RGB images. This work has the potential to greatly enhance the efficiency of skin cancer detection, as well as improve early detection rates and diagnostic accuracy. Consequently, it may lead to a reduction in both morbidity and mortality rates.

Rapid and non-destructive identification of Panax ginseng origins using hyperspectral imaging, visible light imaging, and X-ray imaging combined with multi-source data fusion strategies.

The geographical origin of Panax ginseng significantly influences its nutritional value and chemical composition, which in turn affects its market price. Traditional methods for analyzing these differences are often time-consuming and require substantial quantities of reagents, rendering them inefficient. Therefore, hyperspectral imaging (HSI) in conjunction with X-ray technology were used for the swift and non-destructive traceability of Panax ginseng origin. Initially, outlier samples were effectively rejected by employing a combined isolated forest algorithm and density peak clustering (DPC) algorithm. Subsequently, random forest (RF) and support vector machine (SVM) classification models were constructed using hyperspectral spectral data. These models were further optimized through the application of 72 preprocessing methods and their combinations. Additionally, to enhance the model's performance, four variable screening algorithms were employed: SelectKBest, genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), and permutation feature importance (PFI). The optimized model, utilizing second derivative, auto scaling, permutation feature importance, and support vector machine (2nd Der-AS-PFI-SVM), achieved a prediction accuracy of 93.4 %, a Kappa value of 0.876, a Brier score of 0.030, an F1 score of 0.932, and an AUC of 0.994 on an independent prediction set. Moreover, the image data (including color information and texture information) extracted from color and X-ray images were used to construct classification models and evaluate their performance. Among them, the SVM model constructed using texture information from X -ray images performed the best, and it achieved a prediction accuracy of 63.0 % on the validation set, with a Brier score of 0.181, an F1 score of 0.518, and an AUC of 0.553. By implementing mid-level fusion and high-level data fusion based on the Stacking strategy, it was found that the model employing a high-level fusion of hyperspectral spectral information and X-ray images texture information significantly outperformed the model using only hyperspectral spectral information. This advanced model attained a prediction accuracy of 95.2 %, a Kappa value of 0.912, a Brier score of 0.027, an F1 score of 0.952, and an AUC of 0.997 on the independent prediction set. In summary, this study not only provides a novel technical path for fast and non-destructive traceability of Panax ginseng origin, but also demonstrates the great potential of the combined application of HSI and X-ray technology in the field of traceability of both medicinal and food products.