Only 30% of embryos from in vitro fertilized oocytes successfully implant and develop to term, leading to repeated transfer cycles. To reduce time-to-pregnancy and stress for patients, there is a need for a diagnostic tool to better select embryos and oocytes based on their physiology. The current standard employs brightfield imaging, which provides limited physiological information. Here, we introduce METAPHOR: Metabolic Evaluation through Phasor-based Hyperspectral Imaging and Organelle Recognition. This non-invasive, label-free imaging method combines two-photon illumination and AI to deliver the metabolic profile of embryos and oocytes based on intrinsic autofluorescence signals. We used it to classify i) mouse blastocysts cultured under standard conditions or with depletion of selected metabolites (glucose, pyruvate, lactate); and ii) oocytes from young and old mouse females, or in vitro-aged oocytes. The imaging process was safe for blastocysts and oocytes. The METAPHOR classification of control vs. metabolites-depleted embryos reached an area under the ROC curve (AUC) of 93.7%, compared to 51% achieved for human grading using brightfield imaging. The binary classification of young vs. old/in vitro-aged oocytes and their blastulation prediction using METAPHOR reached an AUC of 96.2% and 82.2%, respectively. Finally, organelle recognition and segmentation based on the flavin adenine dinucleotide signal revealed that quantification of mitochondria size and distribution can be used as a biomarker to classify oocytes and embryos. The performance and safety of the method highlight the accuracy of noninvasive metabolic imaging as a complementary approach to evaluate oocytes and embryos based on their physiology.

The improper storage of seeds can potentially compromise agricultural productivity, leading to reduced crop yields. Therefore, assessing seed viability before sowing is of paramount importance. Although numerous techniques exist for evaluating seed conditions, this research leveraged hyperspectral imaging (HSI) technology as an innovative, rapid, clean, and precise nondestructive testing method. The study aimed to determine the most effective classification model for watermelon seeds. Initially, purchased watermelon seeds were segregated into two groups: One underwent sterilization in a dehydrator machine at 40°C for 36 h, whereas the other batch was stored under favorable conditions. Watermelon seeds\' spectral images were captured using an HSI with a charge-coupled device camera ranging from 400 to 1000 nm, and the segmented regions of all samples were measured. Preprocessing techniques and wavelength selection methods were applied to manage spectral data workload, followed by the implementation of a support vector machine (SVM) model. The initial hybrid-SVM model achieved a predictive accuracy rate of 100%, with a test set accuracy of 92.33%. Subsequently, an artificial bee colony (ABC) optimization was introduced to enhance model precision. The results indicated that, with kernel parameters (c, g) set at 13.17 and 0.01, respectively, and a runtime of 4.19328 s, the training and evaluation of the dataset achieved an accuracy rate of 100%. Hence, it was practical to utilize HSI technology combined with the PCA-ABC-SVM model to detect different watermelon seeds. As a result, these findings introduce a novel technique for accurately forecasting seed viability, intended for use in agricultural industrial multispectral imaging. PRACTICAL APPLICATION: The traditional methods for determining the condition of seeds primarily emphasize aesthetics, rely on subjective assessment, are time-consuming, and require a lot of labor. On the other hand, HSI technology as green technology was employed to alleviate the aforementioned problems. This work significantly contributes to the field of industrial multispectral imaging by enhancing the capacity to discern various types of seeds and agricultural crop products.

Flap necrosis continues to occur in skin free flap autologous breast reconstruction. Therefore, we investigated the benefits of indocyanine green angiography (ICGA) using quantitative parameters for the objective, perioperative evaluation of flap perfusion. In addition, we investigated the feasibility of hyperspectral (HSI) and thermal imaging (TI) for postoperative flap monitoring. A single-center, prospective observational study was performed on 15 patients who underwent deep inferior epigastric perforator (DIEP) flap breast reconstruction (n=21). DIEP-flap perfusion was evaluated using ICGA, HSI, and TI using a standardized imaging protocol. The ICGA perfusion curves and derived parameters, HSI extracted oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) values, and flap temperatures from TI were analyzed and correlated to the clinical outcomes. Post-hoc quantitative analysis of intraoperatively collected data of ICGA application accurately distinguished between adequately and insufficiently perfused DIEP flaps. ICG perfusion curves identified the lack of arterial inflow (n=2) and occlusion of the venous outflow (n=1). In addition, a postoperatively detected partial flap epidermolysis could have been predicted based on intraoperative quantitative ICGA data. During postoperative monitoring, HSI was used to identify impaired perfusion areas within the DIEP flap based on deoxyHb levels. The results of this study showed a limited added value of TI. Quantitative, post-hoc analysis of ICGA data produced objective and reproducible parameters that enabled the intraoperative detection of arterial and venous congested DIEP flaps. HSI appeared to be a promising technique for postoperative flap perfusion assessment. A diagnostic accuracy study is needed to investigate ICGA and HSI parameters in real-time and demonstrate their clinical benefit.

To validate the feasibility and improve the accuracy of water content detection in polyester fabrics using hyperspectral imaging, 150 sets of hyperspectral images of polyester fabrics with varying thicknesses and moisture contents were obtained, and the characteristics of the spectral curves and impact of moisture content were elucidated. In addition, the area and full width at half maximum of the characteristic peaks around 1363 and 1890 nm were determined as spectral characteristic variables. Furthermore, the models of polyester fabric moisture content detection were developed using backpropagation neural networks, and their accuracy was evaluated using correlation coefficient and mean squared error. It was observed that the change in the moisture content of polyester fabrics not only affected the reflectance of the overall spectral curve of polyester fabrics but also altered the position and overall shape of the characteristic peaks. As the moisture content increased, the proportion of pure water spectra in the mixed spectra of water-containing polyester fabrics also increased, leading to a change in the overall shape of the characteristic peaks of polyester fabrics. Because of the overlap between the near-infrared absorption bands of pure water and the polyester fabric around 1363 and 1890 nm, the area and full width at half maximum of the characteristic peaks were considered to be more representative than the reflection for modeling. The established backpropagation neural network-based moisture content quantitative detection model has shown extremely high detection accuracy, with the correlation coefficient for the test set being higher than 0.999 and the root mean square error being lower than 0.3 %, indicating that the detection error of moisture content was only about 0.3 wt%.

The sesquiterpene α-farnesene and its corresponding oxidation products, namely conjugated trienols (CTols) is well known to be correlated with the development of superficial scald, a typical physiological disorder after a long term of cold storage in pear fruit. In this work, hyperspectral imaging (HSI) technology was used for nondestructive predicting of α-farnesene and CTols [CT258, CT281 and CT(281-290)] content in \'Yali\' pear. In order to obtain the best performance of calibration model and simplify the calibration model further, various preprocessing methods together with their combinations and different wavelength selection algorithms, including successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE), were investigated and compared based on linear partial least square regression (PLSR) and nonlinear least square support vector machine (LS-SVM) models, respectively. In conclusion, compared to the PLSR models, the results of LS-SVM models based on original and preprocessing methods performed better for the prediction of α-farnesene and CTols, while the performance of LS-SVM models based on the selected characteristic wavelengths were worse. For α-farnesene, the best result was obtained by LS-SVM model based on MSC-FD pretreatment with the RPD value of 2.6, Rp = 0.925 and RMSEP = 4.387 nmol cm-2. And for CTols, CT281 performed better compared with CT258 and CT(281-290), achieving the result with RPD = 2.4, Rp = 0.913 and RMSEP = 2.734 nmol cm-2 based on LS-SVM model combined with SD pretreatment. The overall results illustrated HSI technology could be used for rapid and nondestructive prediction of α-farnesene and CTols in \'Yali\' pear, which would be helpful for supporting postharvest decision systems.

Color reintegration is a restoration treatment that involves applying paint or colored plaster to an object of cultural heritage to facilitate its perception and understanding. This study examines the impact of lighting on the visual appearance of one such restored piece: a tiled skirting panel from the Nasrid period (1238-1492), permanently on display at the Museum of the Alhambra (Spain). Spectral images in the range of 380-1080 nm were obtained using a hyperspectral image scanner. CIELAB and CIEDE2000 color coordinates at each pixel were computed assuming the CIE 1931 standard colorimetric observer and considering ten relevant illuminants proposed by the International Commission on Illumination (CIE): D65 plus nine white LEDs. Four main hues (blue, green, yellow, and black) can be distinguished in the original and reintegrated areas. For each hue, mean color difference from the mean (MCDM), CIEDE2000 average distances, volumes, and overlapping volumes were computed in the CIELAB space by comparing the original and the reintegrated zones. The study reveals noticeable average color differences between the original and reintegrated areas within tiles: 6.0 and 4.7 CIEDE2000 units for the yellow and blue tiles (with MCDM values of 3.7 and 4.5 and 5.8 and 7.2, respectively), and 16.6 and 17.8 CIEDE2000 units for the black and green tiles (with MCDM values of 13.2 and 12.2 and 10.9 and 11.3, respectively). The overlapping volume of CIELAB clouds of points corresponding to the original and reintegrated areas ranges from 35% to 50%, indicating that these areas would be perceived as different by observers with normal color vision for all four tiles. However, average color differences between the original and reintegrated areas changed with the tested illuminants by less than 2.6 CIEDE2000 units. Our current methodology provides useful quantitative results for evaluation of the color appearance of a reintegrated area under different light sources, helping curators and museum professionals to choose optimal lighting.

This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLMER-ResNet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention mechanism and multi-scale image features to automatically learn the pathological organization of gliomas and obtain hierarchical feature representations, effectively removing the interference of redundant information. It also completes multi-modal, multi-scale spatial-spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3%) and a Kappa coefficient (0.954), indicating its effectiveness in improving the automatic classification of hyperspectral gliomas. The method is readily applicable in a wide range of clinical settings, offering valuable assistance in alleviating the workload of clinical pathologists. Furthermore, the study contributes to the development of more personalized and refined treatment plans, as well as subsequent follow-up and treatment adjustment, by providing physicians with insights into the underlying pathological organization of gliomas.

The process of meat postmortem aging is a complex one, in which improved tenderness and aroma coincide with negative effects such as water loss and microbial growth. Determining the optimal postmortem storage time for meat is crucial but also challenging. A new visual monitoring technique based on hyperspectral imaging (HSI) has been proposed to monitor pork aging progress. M. longissimus thoracis from 15 pigs were stored at 4 °C for 12 days while quality indexes and HSI spectra were measured daily. Based on changes in physical and chemical indicators, 100 out of the 180 pieces of meat were selected and classified into rigor mortis, aged, and spoilt meat. Discrete wavelet transform (DWT) technology was used to improve the accuracy of classification. DWT separated approximate and detailed signals from the spectrum, resulting in a significant increase in classification speed and precision. The support vector machine (SVM) model with 70 band spectra achieved remarkable classification accuracy of 97.06%. The study findings revealed that the aging and microbial spoilage process started at the edges of the meat, with varying rates from one pig to another. Using HSI and visualization techniques, it was possible to evaluate and portray the postmortem aging progress and edible safety of pork during storage. This technology has the potential to aid the meat industry in making informed decisions on the optimal storage and cooking times that would preserve the quality of the meat and ensure its safety for consumption.

Conventional diagnostic methods for glaucoma primarily rely on non-dynamic fundus images and often analyze features such as the optic cup-to-disc ratio and abnormalities in specific retinal locations like the macula and fovea. However, hyperspectral imaging techniques focus on detecting alterations in oxygen saturation within retinal vessels, offering a potentially more comprehensive approach to diagnosis. This study explores the diagnostic potential of hyperspectral imaging for glaucoma by introducing a novel hyperspectral imaging conversion technique. Digital fundus images are transformed into hyperspectral representations, allowing for a detailed analysis of spectral variations. Spectral regions exhibiting differences are identified through spectral analysis, and images are reconstructed from these specific regions. The Vision Transformer (ViT) algorithm is then employed for classification and comparison across selected spectral bands. Fundus images are used to identify differences in lesions, utilizing a dataset of 1291 images. This study evaluates the classification performance of models using various spectral bands, revealing that the 610-780 nm band outperforms others with an accuracy, precision, recall, F1-score, and AUC-ROC all approximately at 0.9007, indicating its superior effectiveness for the task. The RGB model also shows strong performance, while other bands exhibit lower recall and overall metrics. This research highlights the disparities between machine learning algorithms and traditional clinical approaches in fundus image analysis. The findings suggest that hyperspectral imaging, coupled with advanced computational techniques such as the ViT algorithm, could significantly enhance glaucoma diagnosis. This understanding offers insights into the potential transformation of glaucoma diagnostics through the integration of hyperspectral imaging and innovative computational methodologies.

The aim of this study was to assess the efficacy of hyperspectral imaging (HSI) as an intraoperative perfusion imaging modality during gender affirmation surgery (GAS). The hypothesis posited that HSI could quantify perfusion to the clitoral complex, thereby enabling the prediction of either uneventful wound healing or the occurrence of necrosis. In this non-randomised prospective clinical study, we enrolled 30 patients who underwent GAS in the form of vaginoplasty with the preparation of a clitoral complex from 2020 to 2024 and compared patients\' characteristics as well as HSI data regarding clitoris necrosis. Individuals demonstrating uneventful wound healing pertaining to the clitoral complex were designated as Group A. Patients with complete necrosis of the neo-clitoris were assigned to Group B. Patient characteristics were collected and subsequently a comparative analysis carried out. No significant difference in patient characteristics was observed between the two groups. Necrosis occurred when both StO2 and NIR PI parameters fell below 40%. For the simultaneous occurrence of StO2 and NIR PI of 40% or less, a sensitivity of 92% and specificity of 72% was calculated. Intraoperatively, the onset of necrosis in the clitoral complex can be reliably predicted with the assistance of HSI.