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.

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.

Hyperspectral Imaging Combined with Deep Transfer Learning to Evaluate Flavonoids Content in Ginkgo biloba Leaves.

Ginkgo biloba is a famous economic tree. Ginkgo leaves have been utilized as raw materials for medicines and health products due to their rich active ingredient composition, especially flavonoids. Since the routine measurement of total flavones is time-consuming and destructive, rapid, non-destructive detection of total flavones in ginkgo leaves is of significant importance to producers and consumers. Hyperspectral imaging technology is a rapid and non-destructive technique for determining the total flavonoid content. In this study, we discuss five modeling methods, and three spectral preprocessing methods are discussed. Bayesian Ridge (BR) and multiplicative scatter correction (MCS) were selected as the best model and the best pretreatment method, respectively. The spectral prediction results based on the BR + MCS treatment were very accurate (RTest2 = 0.87; RMSETest = 1.03 mg/g), showing a high correlation with the analytical measurements. In addition, we also found that the more and deeper the leaf cracks, the higher the flavonoid content, which helps to evaluate leaf quality more quickly and easily. In short, hyperspectral imaging is an effective technique for rapid and accurate determination of total flavonoids in ginkgo leaves and has great potential for developing an online quality detection system for ginkgo leaves.

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.

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.

New insights into the relationship between optical response and physicochemical properties in apple flesh: Hyperspectral microscope imaging technology.

Hyperspectral microscope imaging (HMI) technique was employed to assess the changes in physicochemical parameters and microstructure of 'Golden Delicious' apples flesh during storage. Four regions of interest (ROIs), including whole-cell ROI, intercellular space ROI, cytoplasm ROI, and cell wall ROI were investigated to assess their relationships with physicochemical parameters. Different ROIs presented similar vibrational profiles, but with slight differences in spectral intensity, especially in the range of 800-1000 nm. Spectral angle mapper (SAM) was applied to the HMI of apple tissues at different storage stages to clearly show the structural changes of parenchyma cells, while principal component analysis (PCA) could highlight the distribution of sugars, water and pigments in apple flesh at the cellular scale. Simultaneously with the degradation of acid-soluble pectin (ASP), middle lamella dissolution and increased intercellular space were observed using SEM and TEM. Single feature variables were used to construct linear models based on pearson correlation analysis, with R2 of 0.96 for moisture at 982 nm, 0.85 for water-soluble pectin (WSP) at 420 nm, 0.82 for L* at 946 nm, 0.77 for soluble solids content (SSC) at 484 nm, and 0.66 for firmness at 490 nm. This work demonstrated the great potential of HMI technology as a fast, accurate and efficient solution for assessing the quality of 'Golden Delicious' apples.

Classification of soybean seeds based on RGB reconstruction of hyperspectral images.

Soyabean is an incredibly significant component of Chinese agricultural product, and categorizing soyabean seeds allows for a better understanding of the features, attributes, and applications of many species of soyabean. This enables farmers to choose appropriate seeds for sowing in order to increase production and quality. As a result, this thesis provides a method for classifying soybean seeds that uses hyperspectral RGB picture reconstruction. Firstly, hyperspectral images of seven varieties of soybean, H1, H2, H3, H4, H5, H6 and H7, were collected by hyperspectral imager, and by using the principle of the three base colours, the R, G and B bands which have more characteristic information are selected to reconstruct the images with different texture and colour characteristics to generate a new dataset for seed segmentation, and finally, a comparison is made with the classification effect of the seven models. The experimental results in ResNet34 show that the classification accuracy of the dataset before and after RGB reconstruction increases from 88.87% to 91.75%, demonstrating that RGB image reconstruction can strengthen image features; ResNet18, ResNet34, ResNet50, ResNet101, CBAM-ResNet34, SENet-ResNet34, and SENet-ResNet34-DCN models have classification accuracies of 72.25%, 91.75%, 89%, 88.48%, 92.28%, 92.80%, and 94.24%, respectively.SENet-ResNet34-DCN achieves the greatest classification accuracy results, with a model loss of roughly 0.3. The proposed SENet-ResNet34-DCN model is the most effective at classifying soybean seeds. By classifying and optimally selecting seed varieties, agricultural production can become more scientific, efficient, and sustainable, resulting in higher returns for farmers and contributing to global food security and sustainable development.

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.

Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies.

Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.

Deep learning for rapid virtual H&E staining of label-free glioma tissue from hyperspectral images.

Hematoxylin and eosin (H&E) staining is a crucial technique for diagnosing glioma, allowing direct observation of tissue structures. However, the H&E staining workflow necessitates intricate processing, specialized laboratory infrastructures, and specialist pathologists, rendering it expensive, labor-intensive, and time-consuming. In view of these considerations, we combine the deep learning method and hyperspectral imaging technique, aiming at accurately and rapidly converting the hyperspectral images into virtual H&E staining images. The method overcomes the limitations of H&E staining by capturing tissue information at different wavelengths, providing comprehensive and detailed tissue composition information as the realistic H&E staining. In comparison with various generator structures, the Unet exhibits substantial overall advantages, as evidenced by a mean structure similarity index measure (SSIM) of 0.7731 and a peak signal-to-noise ratio (PSNR) of 23.3120, as well as the shortest training and inference time. A comprehensive software system for virtual H&E staining, which integrates CCD control, microscope control, and virtual H&E staining technology, is developed to facilitate fast intraoperative imaging, promote disease diagnosis, and accelerate the development of medical automation. The platform reconstructs large-scale virtual H&E staining images of gliomas at a high speed of 3.81 mm2/s. This innovative approach will pave the way for a novel, expedited route in histological staining.

Internal quality prediction technology for 'Sulhyang' strawberry fruit using organic analysis and hyperspectral imaging.

In recent years, hyperspectral imaging combined with machine learning techniques has garnered significant attention for its potential in assessing fruit maturity. This study proposes a method for predicting strawberry fruit maturity based on the harvest time. The main features of this study are as follows. 1) Selection of wavelength band associated with strawberry growth season; 2) Extraction of efficient parameters to predict strawberry maturity 3) Prediction of internal quality attributes of strawberries using extracted parameters. In this study, experts cultivated strawberries in a controlled environment and performed hyperspectral measurements and organic analyses on the fruit with minimal time delay to facilitate accurate modeling. Data augmentation techniques through cross-validation and interpolation were effective in improving model performance. The four parameters included in the model and the cumulative value of the model were available for quality prediction as additional parameters. Among these five parameter candidates, two parameters with linearity were finally identified. The predictive outcomes for firmness, soluble solids content, acidity, and anthocyanin levels in strawberry fruit, based on the two identified parameters, are as follows: The first parameter, ps, demonstrated RMSE performances of 1.0 N, 2.3 %, 0.1 %, and 2.0 mg per 100 g fresh fruit for firmness, soluble solids content, acidity, and anthocyanin, respectively. The second parameter, p3, showed RMSE performances of 0.6 N, 1.2 %, 0.1 %, and 1.8 mg per 100 g fresh fruit, respectively. The proposed non-destructive analysis method shows the potential to overcome the challenges associated with destructive testing methods for assessing certain internal qualities of strawberry fruit.

Combining Hyperspectral Techniques and Genome-Wide Association Studies to Predict Peanut Seed Vigor and Explore Associated Genetic Loci.

Seed vigor significantly affects peanut breeding and agricultural yield by influencing seed germination and seedling growth and development. Traditional vigor testing methods are inadequate for modern high-throughput assays. Although hyperspectral technology shows potential for monitoring various crop traits, its application in predicting peanut seed vigor is still limited. This study developed and validated a method that combines hyperspectral technology with genome-wide association studies (GWAS) to achieve high-throughput detection of seed vigor and identify related functional genes. Hyperspectral phenotyping data and physiological indices from different peanut seed populations were used as input data to construct models using machine learning regression algorithms to accurately monitor changes in vigor. Model-predicted phenotypic data from 191 peanut varieties were used in GWAS, gene-based association studies, and haplotype analyses to screen for functional genes. Real-time fluorescence quantitative PCR (qPCR) was used to analyze the expression of functional genes in three high-vigor and three low-vigor germplasms. The results indicated that the random forest and support vector machine models provided effective phenotypic data. We identified Arahy.VMLN7L and Arahy.7XWF6F, with Arahy.VMLN7L negatively regulating seed vigor and Arahy.7XWF6F positively regulating it, suggesting distinct regulatory mechanisms. This study confirms that GWAS based on hyperspectral phenotyping reveals genetic relationships in seed vigor levels, offering novel insights and directions for future peanut breeding, accelerating genetic improvements, and boosting agricultural yields. This approach can be extended to monitor and explore germplasms and other key variables in various crops.

Predicting the oil content of individual corn kernels combining NIR-HSI and multi-stage parameter optimization techniques.

Predicting the oil content of individual corn kernels using hyperspectral imaging and ML offers the advantages of being rapid and non-destructive. However, traditional methods rely on expert experience for setting parameters. In response to these limitations, this study has designed an innovative multi-stage grid search technique, tailored to the characteristics of spectral data. Initially, the study automatically screening the best model from up to 504 algorithm combinations. Subsequently, multi-stage grid search is utilized for improving precision. We collected 270 kernel samples from different parts of the ear from 15 high oil and regular corn materials, with oil contents ranging from 1.4% to 13.1%. Experimental results show that the combinations SG + NONE+KS + PLSR(R2: 0.8570) and MA + LAR+Random+MLR(R2: 0.8523) performed optimally. After parameter optimization, their R2 values increased to 0.9045 and 0.8730, respectively. Additionally, the ACNNR model achieved an R2 of 0.8878 and an RMSE of 0.2243. The improved algorithm significantly outperforms traditional methods and ACNNR model in prediction accuracy and adaptability, offering an effective method for field applications.

Design and validation of a high-speed hyperspectral laparoscopic imaging system.

Significance: Minimally invasive surgery (MIS) has shown vast improvement over open surgery by reducing post-operative stays, intraoperative blood loss, and infection rates. However, in spite of these improvements, there are still prevalent issues surrounding MIS that may be addressed through hyperspectral imaging (HSI). We present a laparoscopic HSI system to further advance the field of MIS. Aim: We present an imaging system that integrates high-speed HSI technology with a clinical laparoscopic setup and validate the system's accuracy and functionality. Different configurations that cover the visible (VIS) to near-infrared (NIR) range of electromagnetism are assessed by gauging the spectral fidelity and spatial resolution of each hyperspectral camera. Approach: Standard Spectralon reflectance tiles were used to provide ground truth spectral footprints to compare with those acquired by our system using the root mean squared error (RMSE). Demosaicing techniques were investigated and used to measure and improve spatial resolution, which was assessed with a USAF resolution test target. A perception-based image quality evaluator was used to assess the demosaicing techniques we developed. Two configurations of the system were developed for evaluation. The functionality of the system was investigated in a phantom study and by imaging ex vivo tissues. Results: Multiple configurations of our system were tested, each covering different spectral ranges, including VIS (460 to 600 nm), red/NIR (RNIR) (610 to 850 nm), and NIR (665 to 950 nm). Each configuration is capable of achieving real-time imaging speeds of up to 20 frames per second. RMSE values of 3.51 ± 2.03 % , 3.43 ± 0.84 % , and 3.47% were achieved for the VIS, RNIR, and NIR systems, respectively. We obtained sub-millimeter resolution using our demosaicing techniques. Conclusions: We developed and validated a high-speed hyperspectral laparoscopic imaging system. The HSI system can be used as an intraoperative imaging tool for tissue classification during laparoscopic surgery.

Hyperspectral Imaging Technique to Characterize Digestate and Visualize Physical Impurities in Anaerobically Digested Biowaste.

Methods used to monitor anaerobic digestion (AD) indicators are commonly based on wet chemical analyses, which consume time and materials. In addition, physical disturbances, such as floating granules (FGs), must be monitored manually. In this study, we present an eco-friendly, high-throughput methodology that uses near-infrared hyperspectral imaging (NIR-HSI) to build a machine-learning model for characterizing the chemical composition of the digestate and a target detection algorithm for identifying FGs. A total of 732 digestate samples were used to develop and validate a model for calculating total nitrogen (TN), total organic carbon (TOC), total ammonia nitrogen (TAN), and chemical oxygen demand (COD), which are the chemical indicators of responses to disturbances in the AD process. Among these parameters, good model performance was obtained using the dried digestates data set, where the coefficient of determination (R2test) and the root-mean-square error (RMSEtest) were 0.82 and 1090 mg/L for TOC, and 0.86 and 690 mg/L for TN, respectively. Furthermore, the unique spectral features of the FGs in reactors with a lipid-rich substrate meant that they could also be identified by the HSI system. Based on these findings, developing NIR-HSI solutions to monitor the digestate properties in AD plants has great potential for industrial application.

Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains.

High-throughput and low-cost quantification of the nutrient content in crop grains is crucial for food processing and nutritional research. However, traditional methods are time-consuming and destructive. A high-throughput and low-cost method of quantification of wheat nutrients with VIS-NIR (400-1700 nm) hyperspectral imaging is proposed in this study. Stepwise linear regression (SLR) was used to predict hundreds of nutrients accurately (R2 > 0.6); results improved when the hyperspectral data was processed with the first derivative. Knockout materials were also used to verify their practical application value. Various nutrients' characteristic wavelengths were mainly concentrated in the visible regions of 400-500 nm and 900-1000 nm. Finally, we proposed an improved pix2pix conditional generative network model to visualize the nutrients distribution and showed better results compared with the original. This research highlights the potential of hyperspectral technology in high-throughput and non-destructive determination and visualization of grain nutrients with deep learning.

The combination of HSI and NMR techniques with deep learning for identification of geographical origin and GI markers of Lycium barbarum L.

Lycium barbarum L. (L. barbarum) is renowned worldwide for its nutritional and medicinal benefits. Rapid and accurate identification of L.barbarum's geographic origin is essential because its nutritional content, medicinal efficacy, and market price significantly vary by region. This study proposes an innovative method combining hyperspectral imaging (HSI), nuclear magnetic resonance (NMR), and an improved ResNet-34 deep learning model to accurately identify the geographical origin and geographical indication (GI) markers of L.barbarum. The deep learning model achieved a 95.63% accuracy, surpassed traditional methods by 6.26% and reduced runtime by 29.9% through SHapley Additive exPlanations (SHAP)-based feature selection. Pearson correlation analysis between GI markers and HSI characteristic wavelengths enhanced the interpretability of HSI data and further reduced runtime by 33.99%. This work lays the foundation for portable multispectral devices, offering a rapid, accurate, and cost-effective solution for quality assurance and market regulation of L.barbarum products.

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.

Genomic regions influencing the hyperspectral phenome of deoxynivalenol infected wheat.

The quantitative nature of fusarium head blight (FHB) resistance requires further exploration of the wheat genome to identify regions conferring resistance. In this study, we explored the application of hyperspectral imaging of Fusarium-infected wheat kernels and identified regions of the wheat genome contributing significantly to the accumulation of Deoxynivalenol (DON) mycotoxin. Strong correlations were identified between hyperspectral reflectance values for 204 wavebands in the 397-673 nm range and DON mycotoxin. Dimensionality reduction using principal components was performed for all 204 wavebands and 38 sliding windows across the range of wavebands. The first principal component (PC1) of all 204 wavebands explained 70% of the total variation in waveband reflectance values and was highly correlated with DON mycotoxin. PC1 was used as a phenotype in a genome wide association study and a large effect QTL on chromosome 2D was identified for PC1 of all wavebands as well as nearly all 38 sliding windows. The allele contributing variation in PC1 values also led to a substantial reduction in DON. The 2D polymorphism affecting DON levels localized to the exon of TraesCS2D02G524600 which is upregulated in wheat spike and rachis tissues during FHB infection. This work demonstrates the value of hyperspectral imaging as a correlated trait for investigating the genetic basis of resistance and developing wheat varieties with enhanced resistance to FHB.