Microscopic Hyperspectral Imaging Research Articles

Effect of a mesa with different sidewall angles on the spatially resolved optical properties and light extraction efficiency for GaN-based blue and green micro-LEDs.

In this paper, we studied the sidewall conditions of 28 × 52 µm2 InGaN-based blue and green micro-LEDs with different sidewall angles and their effects on external quantum efficiency (EQE). Our findings indicate that steeper sidewall mesas can reduce non-radiative recombination and leakage current, which is beneficial for achieving high internal quantum efficiency (IQE). However, as the sidewall angle increases, the light output from the micro-LED tends to concentrate in the internal region, leading to a decrease in light extraction efficiency (LEE). Using microscopic hyperspectral imaging, we observed distinct chromaticity characteristic distributions in the internal area of mesas with different sidewall angles, compared to the entire micro-LEDs. Mesas with gentler sidewalls exhibited lower chromaticity stability. For both blue and green micro-LEDs, the optimal sidewall tilt angle was found to be 48°, yielding the highest EQE. This result reflects a trade-off between LEE and IQE. Notably, the improvement in IQE for green micro-LEDs was not as significant as that for blue micro-LEDs, likely due to the higher indium content in green InGaN micro-LEDs, which results in deeper localized potential wells and a higher dislocation density. This work demonstrates the variation of IQE and LEE for blue and green micro-LEDs fabricated on mesas with different sidewall angles, aimed at achieving the highest EQE.

Detection of Viable but Nonculturable E. coli Induced by Low-Level Antimicrobials Using AI-Enabled Hyperspectral Microscopy.

Rapid detection of bacterial pathogens is essential for food safety and public health, yet bacteria can evade detection by entering a viable but nonculturable (VBNC) state under sublethal stress, such as antimicrobial residues. These bacteria remain active but undetectable by standard culture-based methods without extensive enrichment, necessitating advanced detection methods. This study developed an AI-enabled hyperspectral microscope imaging (HMI) framework for rapid VBNC detection under low-level antimicrobials. The objectives were to (i) induce the VBNC state in Escherichia coli K-12 by exposure to selected antimicrobial stressors, (ii) obtain HMI data capturing physiological changes in VBNC cells, and (iii) automate the classification of normal and VBNC cells using deep learning image classification. The VBNC state was induced by low-level oxidative (0.01% hydrogen peroxide) and acidic (0.001% peracetic acid) stressors for 3days, confirmed by live-dead staining and plate counting. HMI provided spatial and spectral data, extracted into pseudo-RGB images using three characteristic spectral wavelengths. An EfficientNetV2-based convolutional neural network architecture was trained on these pseudo-RGB images, achieving 97.1% accuracy of VBNC classification (n=200), outperforming the model trained on RGB images at 83.3%. The results highlight the potential for rapid, automated VBNC detection using AI-enabled hyperspectral microscopy, contributing to timely intervention to prevent foodborne illnesses and outbreaks.

SSTar-TransGAN: Transformer-based Unsupervised Multi-modality Hyperspectral Digital Staining Network

Immunohistochemistry (IHC) plays an important role in accurate cancer screening and diagnosis, the clinical application of which has been restricted by complex operation process, high costs and demand of professional skills from pathologists. Digital staining methods based on deep learning provide the possibility for Hematoxylin & Eosin (H&E) stained images to be converted into IHC stained images, but it needs to train multiple staining networks for various modalities of IHC images from different cancers, thus weakening the versatility and convenience of digital staining in the process of practical application. At the same time, microscopic hyperspectral imaging technology can provide abundant spectral information for pathological images, which has been proved effective in digital staining tasks to transform staining modalities from one to many, but microscopic hyperspectral imaging has also been troubled by time-consuming acquisition process and huge data storage. In order to overcome the above challenges, we propose SSTar-TransGAN network for digital staining tasks. With the inhabitation of StarGAN structure, SSTar-TransGAN transfers the training burden of generators into lightweight style encoders between different modalities, while in addition to the introduction of transformer structure into the encoder, the application of spectral super-resolution Swin-Spectral Transformer U-Net (SSTU) network enables the convertion from H&E stained RGB images into hyperspectral images as well, which ensure the spatial structure and color information of IHC images under multiple staining modalities and the further conversion between different staining modalities. Qualitative and quantitative experiments prove the performance of SSTar-TransGAN on digital staining tasks superior to other state-of-the-art digital staining methods.

A monochrome pipelined HMI system for foodborne microorganisms testing

Hyperspectral microscopy imaging (HMI) is an efficient and non-destructive method to detect microbial contaminants in food, as it can provide both spatial morphology and spectral signature. Aims at reducing thermal effect, low cost, and improving spectral resolution in testing, a pipeline-operated LEDs monochromatic illumination mode is proposed, which integrates the design concepts of both grating-based and LED-based HMI systems. By design of the LED set, shared grating monochromatic optical path, and coordinated control system, an HMI system has been developed that could obtain the hyperspectral data cube with 101 bands in 400–700 nm. Hyperspectral datasets of three species of Aspergillus are prepared using the prototype, and efficient results have been achieved in the training and testing of classical classification algorithms (1D-CNN (97.33 %), k-NN (96.33 %), SVM (97.67 %) and ResNet-18 (95.67 %)). The results demonstrate that the proposed monochromatic illumination mode and associated system are potential detection solutions for foodborne microbial contaminants with low-cost and high-accurate.

Assessing nanotoxicity of food-relevant particles: A comparative analysis of cellular responses in cell monolayers versus 3D gut epithelial cultures

Engineered nanoparticles (NPs) are extensively used in the food industry, yet safety concerns remain. The lack of validated methodologies is a bottleneck towards resolving this uncertainty. Hence, the current study aims to compare two cell models by examining the toxicological impacts of two food-relevant NPs (SiO2 and Ag) on intestinal epithelia using monolayer Caco-2 cells and full-thickness 3D tissue models of human small intestines (EpiIntestinal™). Comprehensive characterization and dosimetric analysis of the NPs were performed to determine effective doses and model realistic exposures. Neither genotoxicity nor cytotoxicity were detected in the 3D tissues after NP treatment, while the 2D cultures exhibited cytotoxic response from Ag NP treatment for 24 h at 1 μg/ml. Hyperspectral imaging and transmission electron microscopy confirmed uptake of both NPs by cells in both 2D and 3D culture models. Ag NPs caused an increase in autophagy, whereas SiO2 NPs induced increased cytoplasmic vacuolization. Based on realistic exposure levels studied, the 3D small intestinal tissue model was found to be more resilient to NP treatment compared to 2D cell monolayers. This comparative approach towards toxicological assessment of food relevant NPs could be used as a framework for future analysis of NP behavior and nanotoxicity in the gut.

A novel ensemble approach with deep transfer learning for accurate identification of foodborne bacteria from hyperspectral microscopy

The detection of foodborne bacteria is critical in ensuring both consumer safety and food safety. If these pathogens are not properly identified, it can lead to dangerous cross-contamination. One of the most common methods for classifying bacteria is through the examination of Hyperspectral microscope imaging (HMI). A widely used technique for measuring microbial growth is microscopic cell counting. HMI is a laborious and expensive process, producing voluminous data and needing specialized equipment, which might not be widely available. Machine learning (ML) methods are now frequently utilized to automatically interpret data from hyperspectral microscopy. The objective of our study is to devise a technique that employs deep transfer learning to address the challenge of limited data and utilizes four base classifiers - InceptionResNetV2, MobileNet, ResNet101V2, and Xception - to create an ensemble-based classification model for distinguishing live and dead bacterial cells of six pathogenic strains. In order to determine the optimal weights for the base classifiers, a Powell's optimization method was utilized in conjunction with a weighted average ensemble (WAVE) technique. We carried out an extensive experimental study to verify the efficiency of our proposed ensemble model on live and dead cell images of six different foodborne bacteria. In order to gain a better understanding of the regions, we performed a Grad-CAM analysis to explain the predictions made by our model. Through a series of experiments, our proposed framework has proven its capacity to effectively and precisely detect numerous bacterial pathogens. Specifically, it achieved a perfect identification rate of 100 % for Escherichia coli (EC), Listeria innocua (LI), and Salmonella Enteritidis (SE), while achieving rates of 96.30 % for Salmonella Typhimurium (ST), 87.13 % for Staphylococcus aureus (SA), and 94.12 % for Salmonella Heidelberg (SH). As a result, it can be considered as an effective tool for the identification of foodborne pathogens, due to its high level of efficiency.

Evaluation of Focus Measures for Hyperspectral Imaging Microscopy Using Principal Component Analysis.

An automatic focusing system is a crucial component of automated microscopes, adjusting the lens-to-object distance to find the optimal focus by maximizing the focus measure (FM) value. This study develops reliable autofocus methods for hyperspectral imaging microscope systems, essential for extracting accurate chemical and spatial information from hyperspectral datacubes. Since FMs are domain- and application-specific, commonly, their performance is evaluated using verified focus positions. For example, in optical microscopy, the sharpness/contrast of visual peculiarities of a sample under testing typically guides as an anchor to determine the best focus position, but this approach is challenging in hyperspectral imaging systems (HSISs), where instant two-dimensional hyperspectral images do not always possess human-comprehensible visual information. To address this, a principal component analysis (PCA) was used to define the optimal ("ideal") optical focus position in HSIS, providing a benchmark for assessing 22 FMs commonly used in other imaging fields. Evaluations utilized hyperspectral images from visible (400-1100 nm) and near-infrared (900-1700 nm) bands across four different HSIS setups with varying magnifications. Results indicate that gradient-based FMs are the fastest and most reliable operators in this context.

Optimal antioxidant enzyme activity estimation in melon plant leaves based on microhyperspectral imaging technique

BackgroundIn order to select the most suitable type of light for plant growth, we have developed a quantitative model for the detection of antioxidant enzyme activities on melon leaves, which allows us to monitor plant growth in a timely and accurate manner. Leaf of melon ‘M5147’ and ‘M5346’ were used as test material. Six treatments have been established with different illuminance ratios. By the measurement of leaf superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) activities, the varying response of melons leaf to different grades of light have been studied using microscopic hyperspectral imaging at the plant factory. ResultThe original spectrum has been pre-processed and optimized. And then the characteristic wavelengths were extracted by ant colony optimization (ACO), iterative retained information variable (IRIV), un-information variable elimination (UVE) and genetic partial least squares (GAPLS). Partial Least Squares Regression (PLSR), least squares support vector machine (LSSVM) and convolutional neural network (CNN) are built based on the optimal feature wavelengths. T4 treatment (Light ratio: 7R/3B/5W/1UVa, Flux: μmol (m2-s), photoperi 12 h) was found to be optimum. In the pre-processing of the raw spectral data, orthogonal signal correction (OSC) methods were selected for SOD, POD and CAT. The best performance of SOD prediction model was constructed based on GAPLS-CNN (Rc = 0.751, Rp = 0.638); The best performance of POD prediction model was constructed based on UVE-CNN (Rc = 0.753, Rp = 0.530); The best performance of CAT prediction model constructed based on ACO-CNN (Rc = 0.742, Rp = 0.504). SignificanceThe aim of this study was to provide technical support for the dynamic monitoring of antioxidant enzyme activity in the leaves of other plants by establishing a quantitative detection model for antioxidant enzyme activity in melon leaves by combining chemical methods with microscopic hyperspectral imaging technology. This work provides data and a theoretical basis for screening light ratios in other plants and offers technical guidance for dynamically monitoring antioxidant enzyme activities in their leaves.

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.

Impacts and effectiveness of sidewall treatment on the spatially resolved optical properties and efficiency enhancement for GaN-based blue and green micro-LEDs

InGaN-based micro-light-emitting diodes (micro-LEDs) are crucial for efficient full-color micro-displays, but suffer from severe degradation of external quantum efficiency (EQE) with size reduction. In this study, the impacts of chemical treatment on the sidewall condition and the EQE of the 10 μm InGaN-based blue and green micro-LEDs were demonstrated. The sidewall treatment can effectively alleviate the residual damage caused by dry etching and suppress the nonradiative recombination, facilitating the achievement of high internal quantum efficiency (IQE). Nevertheless, the sidewall light extraction of the micro-LEDs with smoother sidewall morphology after tetramethylammonium hydroxide (TMAH) treatment is lower than those with rough and prismatic sidewall surface. By utilizing microscopic hyperspectral imaging, the difference in chromatic properties between the inner region and the whole micro-LED caused by dry etching can hinder the stability of chromatic properties of InGaN-based blue micro-LEDs, which can be alleviated by sidewall treatment. The optimal duration for TMAH treatment is obtained as 10 min for both blue and green micro-LEDs, which is the consequence of the compromise between sidewall light extraction efficiency (LEE) and IQE, whereas the improvement of IQE by sidewall treatment for green micro-LEDs is less significant compared to that for blue ones, which can be attributed to the higher dislocation density and recombination via localized states of green InGaN micro-LEDs. This work demonstrates the evolution of IQE and LEE for blue and green micro-LEDs with different sidewall treatment strategies to achieving the ultimate EQE.

Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks.

Hyperspectral imaging (HSI) is an emerging imaging modality for oncological applications and can improve cancer detection with digital pathology. The study aims to highlight the increased accuracy and sensitivity of detecting the margin of thyroid carcinoma in hematoxylin and eosin (H&E)-stained histological slides using HSI and data augmentation methods. Using an automated microscopic imaging system, we captured 2599 hyperspectral images from 65 H&E-stained human thyroid slides. Images were then preprocessed into 153,906 image patches of dimension . We modified the TimeSformer network architecture, which used alternating spectral attention and spatial attention layers. We implemented several data augmentation methods for HSI based on the RandAugment algorithm. We compared the performances of TimeSformer on HSI against the performances of pretrained ConvNext and pretrained vision transformers (ViT) networks on red, green, and blue (RGB) images. Finally, we applied attention unrolling techniques on the trained TimeSformer network to identify the biological features to which the network paid attention. In the testing dataset, TimeSformer achieved an accuracy of 90.87%, a weighted score of 89.79%, a sensitivity of 91.50%, and an area under the receiving operator characteristic curve (AU-ROC) score of 97.04%. Additionally, TimeSformer produced thyroid carcinoma tumor margins with an average Jaccard score of 0.76 mm. Without data augmentation, TimeSformer achieved an accuracy of 88.23%, a weighted score of 86.46%, a sensitivity of 85.53%, and an AU-ROC score of 94.94%. In comparison, the ViT network achieved an 89.98% accuracy, an 88.14% weighted score, an 84.77% sensitivity, and a 96.17% AU-ROC. Our visualization results showed that the network paid attention to biological features. The TimeSformer model trained with hyperspectral histological data consistently outperformed conventional RGB-based models, highlighting the superiority of HSI in this context. Our proposed augmentation methods improved the accuracy, the score, and the sensitivity score.

Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells.

This work investigated the high-throughput classification performance of microscopic images of mesenchymal stem cells (MSCs) using a hyperspectral imaging-based separable convolutional neural network (CNN) (H-SCNN) model. Human bone marrow mesenchymal stem cells (hBMSCs) were cultured, and microscopic images were acquired using a fully automated microscope. Flow cytometry (FCT) was employed for functional classification. Subsequently, the H-SCNN model was established. The hyperspectral microscopic (HSM) images were created, and the spatial-spectral combined distance (SSCD) was employed to derive the spatial-spectral neighbors (SSNs) for each pixel in the training set to determine the optimal parameters. Then, a separable CNN (SCNN) was adopted instead of the classic convolutional layer. Additionally, cultured cells were seeded into 96-well plates, and high-functioning hBMSCs were screened using both manual visual inspection (MV group) and the H-SCNN model (H-SCNN group), with each group consisting of 96 samples. FCT served as the benchmark to compare the area under the curve (AUC), F1 score, accuracy (Acc), sensitivity (Sen), specificity (Spe), positive predictive value (PPV), and negative predictive value (NPV) between the manual and model groups. The best classification Acc was 0.862 when using window size of 9 and 12 SSNs. The classification Acc of the SCNN model, ResNet model, and VGGNet model gradually increased with the increase in sample size, reaching 89.56 ± 3.09, 80.61 ± 2.83, and 80.06 ± 3.01%, respectively at the sample size of 100. The corresponding training time for the SCNN model was significantly shorter at 21.32 ± 1.09 min compared to ResNet (36.09 ± 3.11 min) and VGGNet models (34.73 ± 3.72 min) (P < 0.05). Furthermore, the classification AUC, F1 score, Acc, Sen, Spe, PPV, and NPV were all higher in the H-SCNN group, with significantly less time required (P < 0.05). Microscopic images based on the H-SCNN model proved to be effective for the classification assessment of hBMSCs, demonstrating excellent performance in classification Acc and efficiency, enabling its potential to be a powerful tool in future MSCs research.

Fluorescence excitation-scanning hyperspectral imaging with scalable 2D–3D deep learning framework for colorectal cancer detection

Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians.

Deep Learning-based Hyperspectral Microscopic Imaging for Cholangiocarcinoma Detection and Classification

Deep Learning-based Hyperspectral Microscopic Imaging for Cholangiocarcinoma Detection and Classification

Effects of phantom microstructure on their optical properties.

Developing stable, robust, and affordable tissue-mimicking phantoms is a prerequisite for any new clinical application within biomedical optics. To this end, a thorough understanding of the phantom structure and optical properties is paramount. We characterized the structural and optical properties of PlatSil SiliGlass phantoms using experimental and numerical approaches to examine the effects of phantom microstructure on their overall optical properties. We employed scanning electron microscope (SEM), hyperspectral imaging (HSI), and spectroscopy in combination with Mie theory modeling and inverse Monte Carlo to investigate the relationship between phantom constituent and overall phantom optical properties. SEM revealed that microspheres had a broad range of sizes with average and were also aggregated, which may affect overall optical properties and warrants careful preparation to minimize these effects. Spectroscopy was used to measure pigment and SiliGlass absorption coefficient in the VIS-NIR range. Size distribution was used to calculate scattering coefficients and observe the impact of phantom microstructure on scattering properties. The results were surmised in an inverse problem solution that enabled absolute determination of component volume fractions that agree with values obtained during preparation and explained experimentally observed spectral features. HSI microscopy revealed pronounced single-scattering effects that agree with single-scattering events. We show that knowledge of phantom microstructure enables absolute measurements of phantom constitution without prior calibration. Further, we show a connection across different length scales where knowledge of precise phantom component constitution can help understand macroscopically observable optical properties.

Squeezing the Threshold of Metal‐Halide Perovskite Micro‐Crystal Lasers Grown by Solution Epitaxy

AbstractMetal halide perovskite semiconductors have demonstrated remarkable progress not only in photovoltaics and X‐ray detection but also in laser technologies. Particularly appealing is the simplicity with which micro‐crystallites can be epitaxially grown, thereby forming micro‐resonators suitable for lasing. Here, the laser threshold is optimized by selecting excitation laser parameters and by improving material quality. The latter process is conducted for formamidinium lead tribromide, emitting in the green spectral range. Depending on the growth method and parameters, the sizes of the micro‐resonators can be tuned between ≈3 and 23 micrometers. Under laser excitation systematically lower thresholds are observed for micro‐resonators in the 4–7 micrometer size range, than for larger ones, irrespective of growth method. Among three optimized growth methods, epitaxial growth via antisolvent vapor‐assisted crystallization exhibits the smallest threshold powers, indicating the highest material quality. This conclusion is supported by hyperspectral microscopic luminescence imaging and by transient photoluminescence. The best laser structures exhibit record threshold powers for epitaxially grown perovskites, indicating that the selected antisolvent vapor epitaxial growth holds great promises also for other perovskite materials.

Classification of Microscopic Hyperspectral Images of Blood Cells Based on Lightweight Convolutional Neural Network

Hyperspectral imaging has emerged as a novel imaging modality in the medical field, offering the ability to acquire images of biological tissues while simultaneously providing biochemical insights for in-depth tissue analysis. This approach facilitates early disease diagnosis, presenting advantages over traditional medical imaging techniques. Addressing challenges such as the computational burden of existing convolutional neural networks (CNNs) and imbalances in sample data, this paper introduces a lightweight GhostMRNet for the classification of microscopic hyperspectral images of human blood cells. The proposed model employs Ghost Modules to replace conventional convolutional layers and a cascading approach with small convolutional kernels for multiscale feature extraction, aiming to enhance feature extraction capabilities while reducing computational complexity. Additionally, an SE (Squeeze-and-Excitation) module is introduced to selectively allocate weights to features in each channel, emphasizing informative features and efficiently achieving spatial–spectral feature extraction in microscopic hyperspectral imaging. We evaluated the performance of the proposed GhostMRNet and compared it with other state-of-the-art models using two real medical hyperspectral image datasets. The experimental results demonstrate that GhostMRNet exhibits a superior performance, with an overall accuracy (OA), average accuracy (AA), and Kappa coefficient reaching 99.965%, 99.565%, and 0.9925, respectively. In conclusion, the proposed GhostMRNet achieves a superior classification performance at a smaller computational cost, thereby providing a novel approach for blood cell detection.

Quantitative prediction and visualization of matcha color physicochemical indicators using hyperspectral microscope imaging technology

Color physicochemical indicators of matcha are related to sensory quality, and the distributions of color physicochemical indicators can reflect the uniformity of matcha powder. This study aimed to explore the feasibility of using hyperspectral microscope imaging (HMI) technology to predict and visualize matcha color physicochemical indicators. The average spectra (400–998 nm) were extracted from the regions of interest (ROI). Subsequently, competitive adaptive reweighted sampling (CARS), interval random frog (iRF), successive projections algorithm (SPA), and the combination of iRF and SPA (iRF-SPA) were adopted to screen the characteristic spectra variables that were associated with specific color physicochemical indicators. Partial least squares (PLS) regression was applied to develop the calibration models for predicting the color physicochemical indicators. The results showed that the iRF-SPA-PLS models obtained the optimal prediction results, with predictive correlation coefficients (Rp) of 0.9262 for L*, 0.8826 for a*, 0.8583 for b*, 0.8243 for chlorophyll a, 0.7518 for chlorophyll b, and 0.8093 for chlorophyll total. Finally, the distribution maps of matcha color physicochemical indicators were visualized using these optimized models. The results suggested that the HMI technology coupled with chemometrics has enormous potential to predict and visualize the color physicochemical indicators of matcha, which could reflect the uniformity of matcha powder to ensure the stability of matcha quality. The approach presented in this study can also be applied to quality control of other powdery foods.

Development of a large volume line scanning, high spectral range and resolution 3D hyperspectral photoluminescence imaging microscope for diamond and other high refractive index materials

Hyperspectral photoluminescence (PL) imaging is a powerful technique that can be used to understand the spatial distribution of emitting species in many materials. Volumetric hyperspectral imaging of weakly emitting color centers often necessitates considerable data collection times when using commercial systems. We report the development of a line-scanning hyperspectral imaging microscope capable of measuring the luminescence emission spectra for diamond volumes up to 2.20 × 30.00 × 6.30 mm with a high lateral spatial resolution of 1–3 µm. In an single X-λ measurement, spectra covering a 711 nm range, in a band from 400–1100 nm, with a spectral resolution up to 0.25 nm can be acquired. Data sets can be acquired with 723 (X) × 643 (Y) × 1172 (λ) pixels at a rate of 6 minutes/planar image slice, allowing for volumetric hyperspectral imaging with high sampling. This instrument demonstrates the ability to detect emission from several different color centers in diamond both at the surface and internally, providing a non-destructive method to probe their 3D spatial distribution, and is currently not achievable with any other commonly used system or technique.

An Ensemble Learning Method for Detection of Head and Neck Squamous Cell Carcinoma Using Polarized Hyperspectral Microscopic Imaging.

Head and neck squamous cell carcinoma (HNSCC) has a high mortality rate. In this study, we developed a Stokes-vector-derived polarized hyperspectral imaging (PHSI) system for H&E-stained pathological slides with HNSCC and built a dataset to develop a deep learning classification method based on convolutional neural networks (CNN). We use our polarized hyperspectral microscope to collect the four Stokes parameter hypercubes (S0, S1, S2, and S3) from 56 patients and synthesize pseudo-RGB images using a transformation function that approximates the human eye's spectral response to visual stimuli. Each image is divided into patches. Data augmentation is applied using rotations and flipping. We create a four-branch model architecture where each branch is trained on one Stokes parameter individually, then we freeze the branches and fine-tune the top layers of our model to generate final predictions. Our results show high accuracy, sensitivity, and specificity, indicating that our model performed well on our dataset. Future works can improve upon these results by training on more varied data, classifying tumors based on their grade, and introducing more recent architectural techniques.