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Ribbon porosity is a critical parameter to monitor in the roller compaction process. In this study, six techniques for measuring the porosity of solid compacts, i.e., manually by caliper (Caliper), X-ray microtomography (µCT), off-line near-infrared spectroscopy (NIR), laser triangulation (Laser), mercury intrusion porosimetry (MIP), and GeoPyc, were compared using a set of rectangular ribblets of microcrystalline cellulose (MCC). These ribblets, which were compressed at 8-130 MPa on a compaction simulator, exhibited porosities over the range of 0.09 - 0.52. Subsequently, porosities of MCC ribbons made on a roller compactor at specific roll forces of 1.8 kN/cm and 8.8 kN/cm were measured. The Caliper method is convenient for samples with a simple shape but not suitable for real ribbons. The accuracy of GeoPyc measurement relies on accurate conversion factor (unit in cm 3 /mm), sample shape and size, and sufficient sample volume percentage in the medium. The µCT data is more accurate at lower porosities (< 0.2), while the MIP data is more accurate at higher porosities (> 0.4). The Laser method has good accuracy and is more reproducible compared to other methods in the ribblets measurement. The NIR method is fast, which makes it suitable for in-line monitoring of changes in ribbon quality, but porosity quantification is sensitive to sample presentation, such as surface curvature and roughness. These insights could assist in the choice of the most appropriate method for monitoring ribbon porosity to guide the development and optimization of a roller compaction process for a given formulation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Rapid characterization of solid waste using near-infrared hyperspectral imaging (HSI) coupled with machine learning models has been increasingly investigated to replace the traditional time- and labor-intensive methods. However, contamination by waste-derived leachates or other fractions etc., can cause the spectra evolutions and significantly influences the identification performance, which has not been investigated before. The first attempt was made by using hyperspectral unmixing (HU) to extract the endmember components and demonstrate their contributions (abundance) to solid waste, taking the non-linear reflectance changes due to the O-H vibration of water and unclear variation associated with oil and leachates as an example. The HSI spectra of various solid waste components influenced by pure water, oil and three kinds of leachates were acquired. A novel method based on HU models, including multivariate curve resolution with alternating least squares and state-of-the-art autoencoder architectures (deep learning models), was developed to estimate the spectra of endmembers as well as their abundances in individual pixel. Their spatial distribution overview in solid waste was then yielded. The selected models were validated via an independent test data set, with lower spectral angle distance, 12.3° ± 6.5°, indicating the similarity of the predicted endmembers with real components. And the lowest root of mean square error on endmember distribution maps was 0.17. The non-linear liquid's effects by water and oil on spectra variations of solid waste were clearly illuminated. Additionally, the proposed method can extract information from mixed spectroscopic images and generate reconstructed spectra., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: The sense of smell is fully developed in newborns and plays an important role in their early development. There are several approaches to studying olfactory processing in the newborn brain, including EEG, fMRI, and near-infrared spectroscopy (NIRS). Understanding the processing of olfactory stimuli in the newborn brain is of fundamental importance for the development of supportive therapeutic odorant delivery, e.g. for weaning by gavage, and for adapting it to the developing brain. This study aimed to investigate the effect of different odors (milk, farnesol odor, and water as a control) on changes in brain activation in newborns in two different brain regions., Methods: Newborns older than 72 h and below an age of seven days were divided into two groups with different optode positioning strategies of NIRS, group I parietal and group II frontal. Olfactory stimulation was administered using milk, farnesol (floral odor), and water as a control., Results: A total of 26 newborns participated in the study. In the final analysis, 19 children were included. Allthough the optode positioning does not differ significantly, in group I, farnesol stimulation resulted in a significant increase in oxygenated hemoglobin compared to the control, while milk odor showed a decreased amplitude, particularly in the more parietal optode position. In group II, a significant difference was observed between the milk odor and the control, in the frontal areas., Conclusions: This study revealed significant changes in hemoglobin oxygenation, indicating neuronal activation following different olfactory stimulation in both optode positionings. Whereas milk had more impact in frontal areas, the floral odor caused an effect in parietal areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Quality of pet foods can be affected by many factors such as raw materials, formulations, and processing techniques. The pet food manufacturers require fast analyses to control the nutritional quality of their products. Herein, near-infrared spectroscopy (NIR) was evaluated to quantify the chemical composition of pet food, and the performances of two NIR spectrometers were investigated and compared: a benchtop instrument (1000-2500 nm) and a low-cost handheld instrument (900-1700 nm). Seventy cat food and thirty-six dog samples were characterized using reference methods for crude protein, crude fat, crude fibre, crude ash, moisture, calcium (Ca), and phosphorus (P). Principal component regression (PCR) and partial least squares regression (PLSR) were used to establish the models that involved the cat food and mixed model. The characteristic wavelengths were selected using a competitive adaptive reweighted-sampling (CARS) algorithm. The Optimal models obtained from the benchtop instrument for crude protein, crude fat, and moisture were classified as "Good" or "Very good" (Residual prediction variation (RPD) > 3), for crude fibre were classified as "Poor" (RPD>2), and for crude ash, Ca and P (RPD<2) were classified as "Very poor". The Optimal calibrations obtained from the handheld instrument for crude protein, crude fat, and moisture were classified as "Good" or "Very good" (RPD>3), for crude fibre, crude ash, Ca, and P were classified as "Very poor" (RPD<2). Generally, the the performance of benchtop and handheld instrument was close, and the cat food model outperformed the mixed model. Results from the current study revealed the potential to monitor the chemical compositions in pet food on a large scale., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

In the current study, a colorimetric sensor array combined with near-infrared (NIR) spectroscopy was used to quantitatively analyze zearalenone in wheat. The portable NIR spectrometer was used to scan the porphyrin reaction points of the wheat colorimetric sensor and collect spectral data. Subsequently, based on all the NIR spectral data, the two models and three feature selection algorithms are compared, and the best performance model and the best feature variable input are selected. Concurrently, the Kernel-based Extreme Learning Machine (KELM) model optimized by the two parameter optimization algorithms was compared, and the best parameter optimization algorithm was selected. Among all evaluation models, the KELM model optimized by the Competitive Adaptive Reweighted Sampling algorithm combined with the rime optimization algorithm has the best prediction effect. The predicted R P 2 is 0.9900, and the root mean square error of prediction (RMSEP) is 18.4610 μg∙kg -1 ., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Hydrogen sulfide (H 2 S) can act as a gaseous signaling mediator closely associated with inflammation development. In this work, we designed a fluorescence turn-on near-infrared (NIR) fluorescent probe CIT-H 2 S based on Intermolecular Charge Transfer (ICT) for the detection of H 2 S in living inflammatory cells and zebrafish. On this basis, a dicyanoisophorone fluorophore was chosen as the fluorescence signal reporting group of CIT-H 2 S, and an azide group was constructed as the recognition group of H 2 S. CIT-H 2 S is characterized by high selectivity and sensitivity for H 2 S over other interference species. The fluorescence intensity at 661 nm showed good linearity in the range of H 2 S concentration from 0 to 10 μM, with an excellent limit of detection (LOD) as low as 81.52 nM. Impressively, CIT-H 2 S has been visualized for detecting H 2 S in drug-induced inflammatory cell and zebrafish models, thus indicating that CIT-H 2 S is a robust tool with the ability to study the occurrence and development of hydrogen sulfide and inflammation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The development of an efficient palladium probe holds significant application value, considering the detrimental impact of palladium contaminants on human health. Thus, it is critical to create a sensitive detection method. To this end, a fluorescent probe TM-TPA-Pd based on benzothianone structure was designed, using allyl carbonate as the Pd 0 recognition unit. TM-TPA-Pd exhibited high sensitivity (1.4 eq), selectivity, near-infrared (NIR) fluorescence (798 nm), and low detection limit (0.46 μM) for Pd 0 with a rapid "turn-on" fluorescence signal (5 min). Furthermore, TM-TPA-Pd has extremely low cytotoxicity and has been successfully applied to detecting cells and zebrafish, which has great potential for palladium detection in biological systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

As a common food raw material in daily life, the quality and safety of wheat flour are directly related to people's health. In this study, a model was developed for the rapid identification and detection of three illegal additives in flour, namely azodicarbonamide (ADA), talcum powder, and gypsum powder. This model utilized a combination of near-infrared spectroscopy with chemometric methods. A one-dimensional convolutional neural network was used to reduce data dimensionality, while a support vector machine was applied for non-linear classification to identify illegal additives in flour. The model achieved a calibration set F1 score of 99.38% and accuracy of 99.63%, with a validation set F1 score of 98.81% and accuracy of 98.89%. Two cascaded wavelength selection methods were introduced: The first method involved backward interval partial least squares (BiPLS) combined with an improved binary particle swarm optimization algorithm (IBPSO). The second method utilized the CARS-IBPSO algorithm, which integrated competitive adaptive reweighted sampling (CARS) with IBPSO. The two cascade wavelength selection methods were used to select feature wavelengths associated with additives and construct partial least squares quantitative detection models. The models constructed using CARS-IBPSO selected feature wavelengths for detecting ADA, talcum powder, and gypsum powder exhibited the highest overall performance. The model achieved validation set determination coefficients of 0.9786, 0.9102, and 0.9226, with corresponding to root mean square errors of 0.0024%, 1.3693%, and 1.6506% and residual predictive deviations of 6.8368, 3.5852, and 3.9253, respectively. Near-infrared spectroscopy in combination with convolutional neural network dimensionality reduction and support vector machine classification enabled rapid identification of various illegal additives. The combination of CARS-IBPSO feature wavelength selection and partial least squares regression models facilitated rapid quantitative detection of these additives. This study introduces a new approach for rapidly and accurately identifying and detecting illegal additives in flour., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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 (R 2  > 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The accumulation of volatile fatty acids (VFAs) over anaerobic digestion (AD) leads to malfunctioning of industrial reactors, hence decreasing biogas production. Real-time monitoring of VFAs is a challenge due to the complexity and high cost of current methods for their quantification. For this reason, this research evaluated the application of near infrared (NIR) spectroscopy to quantify volatile fatty acids as a tool for AD reactors monitoring. To do that, 129 samples from various AD reactors fed with olive oil pomace were taken and their NIR spectra were acquired with a hand-held spectrometer. After performing grid search, three spectral variable selection methods, namely competitive adaptive reweighted sampling, uninformative variable elimination (UVE) and successive projections algorithm, were assayed before developing PLRS models to correlate the NIR light transmittance through the samples at the wavelengths selected by those methods with their VFAs concentrations. UVE led to the best performance for all the VFAs assayed. Thus, R 2 of validation of UVE-PLSR models for acetic, propionic, butyric, valeric and total VFAs were 0.895, 0.622, 0.866, 0.898 and 0.871, respectively. The predictive model for total VFAs achieved the highest accuracy (RMSEV = 539.5 mg/L), explained by the correlation between the light absorption at the wavelengths selected by UVE and the chemical characteristics of VFAs. All in all, the prediction errors achieved suggest that a portable near infrared spectrometer can be used for monitoring VFAs in AD processes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Brassica juncea depends heavily on nitrogen (N) fertilizers for growth and accumulation of seed protein. However, it is an inefficient mobilizer of applied N which leads to accumulation of excess N in the soil, posing environmental risks. Hence, it is imperative to systematically examine spatial-temporal pattern of crop N to efficiently manage N application. The Kjeldahl method is commonly used to estimate N status of crops but it is a destructive method that entails the use of perilous and expensive chemicals. Near-infrared reflectance spectroscopy (NIRS) offers a safe, accurate, and non-destructive alternative for large-scale screening of seed metabolites. Currently, no NIRS model exists to quickly estimate N content in shoots and roots from large germplasm sets in any rapeseed-mustard crop. Developing such a model is essential to breed for enhanced nitrogen use efficiency (NUE). We used 738 shoot and 346 root samples from a B. juncea diversity set to construct the NIRS models. A diverse range of genetic variation in N content was recorded in the stem (0.21-6.61%) and root (0.15-3.04%) tissues of the crop raised on two different N levels (N0 and N100). Modified partial least squares (MPLS) method was employed to establish a regression equation linking reference N values with spectral changes. The developed models exhibited strong associations with reference values, with RSQ values of 0.884 for stem and 0.645 for roots. Furthermore, external validation confirms the reliability of the developed models. The developed models have strong predictive capabilities for rapid and reliable N estimation in various tissues of B. juncea plants., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Calcium ions (Ca 2+ ) are key players in intracellular signaling as second messengers and play a pivotal role in various physiological processes. In this study, near-infrared water-soluble AgInS 2 quantum dots (AIS QDs) for Ca 2+ detection were synthesized by a one-step hydrothermal method. The fluorescence quantum yield (PL QY) of the quantum dots was as high as 23.99 %. With low cytotoxicity and good fluorescence properties, as well as short reaction time, the ternary AIS QDs have excellent synthesis efficiency and quantum yield, which are advantageous for Ca 2+ detection and bioimaging applications. The fluorescence quenching of the quantum dots showed a clear linear relationship with calcium ion concentration in the range of 0-250 μM (detection limit: 0.65 μM). Confocal imaging experiments demonstrate the excellent biofluorescence imaging capability of AIS QDs. By tuning the Ag/In molar ratio, AIS QDs can achieve fluorescence emission in the near-infrared wavelength band (620-700 nm), and the near-infrared fluorescence imaging has deeper tissue penetration, less tissue absorption and photodamage, and lower interference of spontaneous fluorescence, which further expands the potential of QDs for bioimaging applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Primary Objective: To evaluate the accuracy of an innovative machine-learning-powered near-infrared spectroscopy (mNIRS)-based bio-optical sensitivity parameters, namely specific tissue optical index (STOI) and intracranial tissue optical index (ITOI) for effective triaging of patients with suspected traumatic brain injury (TBI) across different age-groups to facilitate timely intervention and curtail the silent epidemic., Research Design: Prospective, observational, double-blinded, cross-sectional single-center., Methods: A total of 240 subjects suspected with TBI and recommended for head CT scan were enrolled. Findings of CT were compared with the bio-optical sensitivity parameters (STOI+ITOI) generated by the mNIRS system to detect intracranial hemorrhages (ICHs)., Statistical Software Used: Spss (ibm Version 21.0) Results: Bio-optical sensitivity parameters (STOI+ITOI) analysis of 1288 scanned lobes showed high specificity of 97% (CI 95-98%), sensitivity of 96% (CI 92-99%), and accuracy of 97% (CI 96-98%) across all ages, maintaining robust performance for detecting subdural, epidural, subarachnoid hemorrhages, and contusions. Also, the reliability in the diagnosis of ICH was evidenced by Youden's index of 0.93 and positive and negative likelihood ratios of 29.13 and 0.04, respectively., Conclusion: The mNIRS-based STOI+ITOI proves to be an effective triage tool for TBI, exhibiting superior diagnostic performance across all age-groups.

The establishment of near infrared (NIR) spectroscopy model mostly relies on chemometrics, and spectral analysis combined with artificial intelligence (AI) provides a new way of thinking for pharmaceutical quality inspection, new algorithms such as back propagation artificial neural networks (BP-ANN) and swarm intelligence optimization algorithms such as sparrow search algorithm (SSA) provide core technical support. In order to explore the application of AI in the pharmaceutical field, in this study, Angelica dahurica formula granules with a relatively complex system were selected as the research object. Quantitative analysis models were established by using partial least squares regression (PLSR) with a micro-NIR spectrometer, and BP-ANN modeling results were compared. For the best PLSR models of six characteristic components in the continuous counter-current extract of Angelica dahurica, R 2 v of imperatorin was lower than 0.90, and the RPD values of imperatorin, phellopterin, and isoimperatorin were even lower than 1. When the prediction model established by SSA-BP-ANN was used for quantitative analysis, R 2 v of six components were all higher than 0.92, and the RPD values all higher than 1.5, which proved that the BP-ANN method was better than PLSR. This study confirmed that in the continuous counter-current extraction progress of Angelica dahurica formula granules, the use of micro-NIR spectrometer combined with AI could realize the rapid prediction of the contents of six characteristic components. The comparison results provided a scientific reference for the process analysis and on-line monitoring in the production process of traditional Chinese medicine by micro-NIR spectrometer combined with AI., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Fast detection of viral infections is a key factor in the strategy for the prevention of epidemics expansion and follow-up. Hepatitis C is paradigmatic within viral infectious diseases and major challenges to elimination still remain. Near infrared spectroscopy (NIRS) is an inexpensive, clean, safe method for quickly detecting viral infection in transmission vectors, aiding epidemic prevention. Our objective is to evaluate the combined potential of machine learning and NIRS global molecular fingerprint (GMF) from biobank sera as an efficient method for HCV activity discrimination in serum. GMF of 151 serum biobank microsamples from hepatitis C patients were obtained with a FT-NIR spectrophotometer in reflectance mode. Multiple scatter correction, smoothing and Saviztsky-Golay second derivative were applied. Spectral analysis included Principal Component Analysis (PCA), Bootstrap and L1-penalized classification. Microsamples of 70 µl were sufficient for GMF acquisition. Bootstrap evidenced significant difference between HCV PCR positive and negative sera. PCA renders a neat discrimination between HCV PCR-positive and negative samples. PCA loadings together with L1-penalized classification allow the identification of discriminative bands. Active virus positive sera are associated to free molecular water, whereas water in solvation shells is associated to HCV negative samples. Divergences in the water matrix structure and the lipidome between HCV negative and positive sera, as well as the relevance of prooxidants and glucose metabolism are reported as potential biomarkers of viral activity. Our proof of concept demonstrates that NIRS GMF of hepatitis C patients' sera aided by machine learning allows for efficient discrimination of viral presence and simultaneous potential biomarker identification., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Oscar Barquero reports financial support was provided by Spanish Ministry of Education and Science. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

We propose a novel strategy for tailoring the structure of fluorescent molecules to achieve emission at the tail end of the NIR-II window. The favorable spectroscopic properties and low cytotoxicity of YNs make them powerful tools for bioimaging. Notably, YN-4 exhibits a brightness 2.5 times greater than YN-3, 6 times that of IR-783, and 5 times that of ICG. This enhanced brightness enabled high-resolution imaging of mouse thoracic and abdominal cavities, tumor vasculature, and real-time monitoring of gastrointestinal motility using YN-4. Furthermore, covalent grafting of glucose onto the YN-Glu scaffold significantly improved tumor-targeting capability and facilitated tracking of glucose metabolism. This work aims to extend the application of fluorescent molecule imaging beyond the NIR-IIa window., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

To investigate the effect of CPPU (forchlorfenuron) on optical properties of strawberry during growth, the optical properties (absorption coefficient (μ a ) and reduced scattering coefficient (μ s ')) of strawberry treated with CPPU solutions at different concentrations (0, 2.5, 5.0 and 7.5 mg/L) were measured in white, color turning and red stages by using a single integrating sphere system over near-infrared wavelength range of 900-1700 nm. The physicochemical properties, i.e., single fruit weight, soluble solids content, firmness and moisture content, as well as microstructure of strawberry were also investigated. The results showed that in white stage, the μ a of strawberry treated with 7.5 mg/L CPPU was significantly (p ≤ 0.05) lower than that of untreated strawberry at absorption peak of 1411 nm. In color turning stage, the μ s ' of strawberry treated with 5 mg/L CPPU was significantly lower than that of treated with 2.5 mg/L at absorption peaks of 975, 1197 and 1411 nm. In red stage, the μ a of strawberry treated with 2.5 mg/L CPPU was significantly (p ≤ 0.05) different from that of treated with 7.5 mg/L at 1197 nm. The study indicates that the optical properties of strawberry were affected by CPPU, and it provides useful information for identifying CPPU treated strawberry., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Norfloxacin is an antibacterial compound that belongs to the fluoroquinolone family. Currently, hyperspectral imaging (HSI) for the detection of antibiotic residues focuses mostly on individual systems. Attempts to integrate different HSI systems with complementary spectral ranges are still lacking. This study investigates the feasibility of applying data fusion strategies with two HSI techniques (Visible near-infrared and near-infrared) in combination to predict norfloxacin residue levels in mutton. Spectral data from the two spectral techniques were analyzed using partial least squares regression (PLSR), support vector regression (SVR) and stochastic configuration networks (SCN), respectively, and the two data fusion strategies were fused at the data level (low-level fusion) and feature level (middle-level fusion, mid-level fusion). The results indicated that the modeling performance of the two fused datasets was better than that of the individual systems. Mid-level fusion data achieved the best model based on uninformative variable elimination (UVE) combined with SCN, in which the determination coefficient of prediction set (R 2 p ) of 0.9312, (root mean square error of prediction set) RMSEP of 0.3316 and residual prediction deviation (RPD) of 2.7434, in comparison with all others. Therefore, two HSI systems with complementary spectral ranges, combined with data fusion strategies and feature selection, could be used synergistically to improve the detection of norfloxacin residues. This study may provide a valuable reference for the non-destructive detection of antibiotic residues in meat., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The objective of this study was to evaluate the ability of a handheld near-infrared device (900-1600 nm) to predict fertility and sex (male and female) traits in-ovo. The NIR reflectance spectra of the egg samples were collected on days 0, 7, 14 and 18 of incubation and the data was analysed using principal component analysis (PCA), linear discriminant analysis (LDA) and support vector machines classification (SVM). The overall classification rates for the prediction of fertile and infertile egg samples ranged from 73 % to 84 % and between 93 % to 95 % using LDA and SVM classification, respectively. The highest classification rate was obtained on day 7 of incubation. The classification between male and female embryos achieved lower classification rates, between 62 % and 68 % using LDA and SVM classification, respectively. Although the classification rates for in-ovo sexing obtained in this study are higher than those obtained by chance (50 %), the classification results are currently not sufficient for industrial in-ovo sexing of chicken eggs. These results demonstrated that short wavelengths in the NIR range may be useful to distinguish between fertile and infertile egg samples at days 7 and 14 during incubation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Neurofeedback allows individuals to monitor and self-regulate their brain activity, potentially improving human brain function. Beyond the traditional electrophysiological approach using primarily electroencephalography, brain haemodynamics measured with functional magnetic resonance imaging (fMRI) and more recently, functional near-infrared spectroscopy (fNIRS) have been used (haemodynamic-based neurofeedback), particularly to improve the spatial specificity of neurofeedback. Over recent years, especially fNIRS has attracted great attention because it offers several advantages over fMRI such as increased user accessibility, cost-effectiveness and mobility-the latter being the most distinct feature of fNIRS. The next logical step would be to transfer haemodynamic-based neurofeedback protocols that have already been proven and validated by fMRI to mobile fNIRS. However, this undertaking is not always easy, especially since fNIRS novices may miss important fNIRS-specific methodological challenges. This review is aimed at researchers from different fields who seek to exploit the unique capabilities of fNIRS for neurofeedback. It carefully addresses fNIRS-specific challenges and offers suggestions for possible solutions. If the challenges raised are addressed and further developed, fNIRS could emerge as a useful neurofeedback technique with its own unique application potential-the targeted training of brain activity in real-world environments, thereby significantly expanding the scope and scalability of haemodynamic-based neurofeedback applications.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Lablab bean (Lablab purpureus L.), known for its higher protein content provides a promising alternative to reduce reliance on animal-based proteins and support sustainable agriculture. Nowadays, traditional methods for nutritional profiling have been outperformed by Convolutional Neural Networks (CNNs) integrated with Near-Infrared Reflectance Spectroscopy (NIRS). However, an advanced technique called Transformers, despite their potential, remains underexplored for prediction of key quality traits of crops. Thus, in the present study, we aimed to predict lablab bean protein content using a Transformer-based model (termed ProTformer) coupled with NIRS. The Dumas combustion method was used to determine protein content from 112 lablab bean genotypes. The performance of this model was compared with 1D CNN and Modified Partial Least Squares (MPLS)-based models. These models were evaluated using the Coefficient of Determination (RSQ), Residual Prediction Deviation (RPD), bias, and Corrected Standard Error of Prediction (SEP(C)). Our findings show that the Transformer-based model achieved the highest predictive accuracy (RSQ = 0.977, RPD = 13.92), surpassing the 1D CNN (RSQ = 0.956, RPD = 11.33) and MPLS (RSQ = 0.943, RPD = 10.53)- based models by 22.86% and 32.21% in RPD, respectively. The multi-head self-attention mechanism present in the Transformer-based model enables it to concurrently focus on different spectral regions, resulting in superior predictive performance. This study shows the potential of using Transformer-based models coupled with NIRS for nutritional profiling which could be effectively adapted for other crops for rapid screening of large germplasm available at global repositories., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Changes in the freshness of pork due to microbial action during complex transportation and storage indicate an urgent need for in-situ, real-time monitoring techniques for chemical spoilage of meat. This study reported the use of a portable detection device based on a miniaturized visible/near-infrared spectrometer, combined with data noise reduction and machine learning methods, to predict the total viable count (TVC) in pork samples. A rapid detection device for pork TVC was designed based on the miniaturized spectrometer; a spectral preprocessing method based on the resolution of the spectrometer was proposed; the effects of different preprocessing methods on the full-wavelength modeling effect were compared; and four different feature wavelength extraction algorithms were utilized for the feature wavelengths of pork TVC. The modeling effects of different simplified models were compared. The results showed that resolution interval correction combined with standard normal variation was the most effective in full-wavelength modeling, with correlation coefficients of prediction set (R P ), root mean square errors in prediction set (RMSEP), and relative percent deviation (RPD) of 0.918, 0.464 (lg CFU/g), and 2.508, respectively; interval random frog - partial least squares regression (iRF-PLSR) had the best predictive ability among all simplified models, the number of wavelengths used in the simplified model was reduced by 85.45% compared with the full-wavelength model. In contrast, the model performance was improved with R P , RMSEP, and RPD of 0.948, 0.392 (lg CFU/g) and 2.970, respectively. The combination of a rational spectral acquisition setup and a data processing methodology, the miniaturized spectrometer showed competitive results with the complex detection system in predicting meat TVC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Concealed information test (CIT) has been utilized for long to perform single measurements. The combination of multiple measures outperforms single measures because of the diverse cognitive processes they reflect and the reduction in random errors facilitated by multiple measures. To further explore the performance of the CIT with multiple measurements, 57 participants were recruited and randomly assigned into guilty and innocent groups. Subsequently, simultaneously recorded reaction time (RT), skin conductance responses (SCRs), heart rate (HR), and neuroimaging data were collected from functional near-infrared spectroscopy (fNIRS) to detect participants' concealed information in a standard CIT. The results demonstrated that all indicators including RT (area under the curve (AUC) = 0.87), SCRs (AUC = 0.79), HR (AUC = 0.78), and fNIRS (channel 8, AUC = 0.85) could differentiate guilty and innocent groups. Importantly, the use of multiple indicators achieved higher detection efficiency (AUC = 0.96) compared to the use of any single indicator. These results illustrate the effectiveness and feasibility of integrating multiple indicators for concealed information detection in CIT., (© 2024 Society for Psychophysiological Research.)

Background: There is a clinical need for improved intraoperative detection of endometriosis, and the use of Indocyanine Green with Near-Infrared Imaging (NIR-ICG) is a novel technique for this purpose. The aim of this review is to determine whether NIR-ICG is an effective tool for endometriosis detection and establish an evidence-based methodology for its use. Methods: This review searches Ovid MEDLINE and Embase through July 2023 and considers primary literature published in English describing the use of NIR-ICG to detect endometriosis intraoperatively. Case studies, video demonstrations and articles describing NIR-ICG used for other surgical roles were not considered. Identified studies were screened independently by two authors, and data was extracted by a single author. Results: NIR-ICG was found to enhance the detection of endometriosis in six out of the nine included studies with additional lesion identification, and to have an unchanged or reduced efficacy compared to current standards in the remaining three. Across all studies there were lesions missed by NIR-ICG which were detected by conventional imaging. A greater duration of time between dye administration and visualisation of lesions was found to be more effective for detection. The ideal ICG protocol proposed from this review is a fixed amount of dye proportional to patient weight prior to surgery (0.25-0.3 mg/kg) followed by a longer waiting time before imaging (10-30 min). Conclusion: NIR-ICG has a possible role to enhance the identification of endometriosis intraoperatively as an adjunct to conventional white light imaging, particularly deeper infiltrating disease. However, substantial further research is required in this field., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose of Review: Critically ill children are at risk of neurologic dysfunction and acquiring primary and secondary brain injury. Close monitoring of cerebral function is crucial to prevent, detect, and treat these complications., Recent Findings: A variety of neuromonitoring modalities are currently used in pediatric and neonatal ICUs. These include noninvasive modalities, such as electroencephalography, transcranial Doppler, and near-infrared spectroscopy, as well as invasive methods including intracranial pressure monitoring, brain tissue oxygen measurement, and cerebral microdialysis. Each modality offers unique insights into neurologic function, cerebral circulation, or metabolism to support individualized neurologic care based on a patient's own physiology. Utilization of these modalities in ICUs results in reduced neurologic injury and mortality and improved neurodevelopmental outcomes., Summary: Monitoring of neurologic function can significantly improve care of critically ill children. Additional research is needed to establish normative values in pediatric patients and to standardize the use of these modalities., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

While individuals tend to display poorer inhibitory control toward food-related than neutral stimuli, it is unclear whether this challenge is specific to food or extends to other pleasant stimuli. Uncertainty also remains regarding the general impact of hunger on inhibition. To address these questions, we used a within-subjects design whereby 44 healthy adults completed two go/no-go tasks including no-go images of highly palatable foods and no-go images of animals matched for valence and physical properties. Both tasks were completed one week apart in either a fed or a fasted state. Prefrontal cortex activity was measured using functional near-infrared spectroscopy. Poorer behavioral inhibition was observed when participants needed to withhold their response to images of food compared to animals, regardless of hunger state. In addition, more commission errors were made in the fasted compared to the fed condition, regardless of the type of image to avoid responding to. Responses to go trials were slower when these trials were interspersed with food compared to animal no-go trials. However, hunger did not influence go response time. Greater activation was observed in the dorsolateral prefrontal cortex during blocks of trials with (vs. without) no-go images, but brain activity did not differ according to the type of no-go image. The effect of hunger on prefrontal brain activity was also not significant. Exploratory correlations showed that food-related inhibition deficits were positively related to self-reported impulsivity, but unrelated to body mass index. This study suggests that even among healthy adults, food-related inhibitory control may have a unique behavioral signature beyond general inhibition toward pleasant stimuli. Hunger also exerts an independent influence on general inhibitory capabilities, highlighting the importance of carefully controlling hunger levels in inhibition studies., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The emotional and cognitive cerebellum has been explored by several studies in the past years. Recent evidence suggested the possible contribution of the cerebellum in processing emotional prosody, namely the ability to comprehend the emotional content of a given vocal utterance, likely mediated by anatomical and functional cerebello-prefrontal connections. In the present study, the involvement of a functional cerebello-prefrontal network in recognising emotional prosody was assessed by combining non-invasive anodal transcranial direct current stimulation (tDCS) over the right or the left cerebellum and functional Near Infrared Spectroscopy of the prefrontal cortex, in a double-blind within-subject experimental design on healthy participants. The results showed that right and, to a less extent, left cerebellar tDCS (as compared to sham stimulation) reduced neural activation in the prefrontal cortex while accuracy and reaction times at the vocal recognition task remained unchanged. These findings highlight functional properties of the cerebello-frontal connections and the psychophysiological effects of cerebellar brain stimulation, with possible clinical applications in psychiatric and neurological conditions., Competing Interests: Declarations. Ethics approval: The study was approved by the Institutional Ethic Committee and were in accordance with the 1964 Helsinki Declaration and its later amendments. Consent to participate: All participants gave their written informed consent to participate. Consent for publication: All authors consent to publication; all participants gave their written informed consent to publication. Conflicts of interest: The authors declare no competing interests. Open practice statement: Data or materials for the experiments are available upon request, and none of the experiments was preregistered., (© 2024. The Author(s).)

Near infrared (NIR) analysis of tissue perfusion via indocyanine green fluorescence assessment is performed clinically during surgery for a range of indications. Its usefulness can potentially be further enhanced through the application of interpretable artificial intelligence (AI) methods to improve dynamic interpretation accuracy in these and also open new applications. While its main use currently is for perfusion assessment as a tissue health check prior to performing an anastomosis, there is increasing interest in using fluorophores for cancer detection during surgical interventions with most research being based on the paradigm of static imaging for fluorophore uptake hours after preoperative dosing. Although some image boosting and relative estimation of fluorescence signals is already inbuilt into commercial NIR systems, fuller implementation of AI methods can enable actionable predictions especially when applied during the dynamic, early inflow-outflow phase that occurs seconds to minutes after ICG (or indeed other fluorophore) administration. Already research has shown that such methods can accurately differentiate cancer from benign tissue in the operating theatre in real time in principle based on their differential signalling and could be useful for tissue perfusion classification more generally. This can be achieved through the generation of fluorescence intensity curves from an intra-operative NIR video stream. These curves are processed to adjust for image disturbances and curve features known to be influential in tissue characterisation are extracted. Existing machine learning based classifiers can then use these features to classify the tissue in question according to prior training sets. The use of this interpretable methodology enables accurate classification algorithms to be built with modest training sets in comparison to those required for deep learning modelling in addition to achieving compliance with medical device regulations. Integration of the multiple algorithms required to achieve this classification into a desktop application or medical device could make the use of this method accessible and useful to (as well as useable by) surgeons without prior training in computer technology. This document details some technical and functional design considerations underlying such a novel recommender system to advance the foundational concept and methodology as software as medical device for in situ cancer characterisation with relevance more broadly also to other tissue perfusion applications., Competing Interests: Declaration of competing interest A Moynihan receives support for travel and attendance at meetings from Horizon Europe and the Irish Government Disruptive Technologies & Innovation Fund (DTIF). P Boland and P McEntee receive salaries from UCD funded by Horizon Europe, in addition to support for travel and attendance at meetings from Horizon Europe. J Cucek, S Erzen and J Rojc are employed by Arctur through funding from Horizon Europe. N Hardy previously received a salary funded by the Irish Government Disruptive Technologies and Innovation Fund (DTIF) via UCD in addition to support for travel and attendance at meetings funded through the DTIF. R Cahill receives speaker fees from Stryker Corp, Ethicon/Johnson & Johnson and Olympus, research funding from Intuitive, Irish Government: Disruptive Technologies & Innovation Fund in collaboration with IBM Research and Deciphex, and the EU: Horizon Europe programme in collaboration with Palliare, Steripak and Arctur, and consultancy fees from Arthrex, Medtronic, Distalmotion, (© 2024 Published by Elsevier Ltd.)

Orthostatic hypotension (OH) is prevalent in older adults and can cause falls and hospitalization. Diagnostic intermittent blood pressure (BP) measurements are only a proxy for cerebral perfusion and do not reflect daily-life BP fluctuations. Near-infrared spectroscopy (NIRS)-measured cerebral oxygenation potentially overcomes these drawbacks. This study aimed to determine feasibility, face validity, and reliability of NIRS in the home environment. Ten participants with OH (2 female, mean age 77, SD 3.7) and 11 without OH (5 female, mean age 78, SD 6.7) wore a NIRS sensor at home on two different days for 10-11 h per day. Preceded by a laboratory-situated test, cerebral oxygenation was measured during three standardized supine-stand tests per day and during unsupervised daily life activities. Data availability, quality, and user experience were assessed (feasibility), as well as differences in posture-related oxygenation responses between participants with and without OH and between symptomatic (dizziness, light-headedness, blurred vision) and asymptomatic postural changes (face validity). Reliability was assessed through repetitive supine-stand tests. Up to 80% of the standardized home-based supine-stand tests could be analyzed. Oxygenation recovery values were lower for participants with OH (p = 0 .03-0.15); in those with OH, oxygenation showed a deeper maximum drop for symptomatic than asymptomatic postural changes (p = 0.04). Intra-class correlation coefficients varied from 0.07 to 0.40, with no consistent differences over measurements. This proof-of-concept study shows feasibility and face validity of at-home oxygenation monitoring using NIRS, confirming its potential value for diagnosis and monitoring in OH and OH-related symptoms. Further data are needed for conclusions about reliability., (© 2024. The Author(s).)

Hyperspectral imaging (HSI) provides opportunity for non-destructively detecting bioactive compounds contents of tea leaves and high detection accuracy require extracting effective features from the complex hyperspectral data. In this paper, we proposed a feature wavelength refinement method called interval band selecting-competitive adaptive reweighted sampling-fusing (IBS-CARS-Fusing) to extract feature wavelengths from visible-near-infrared (VNIR) and short-wave-near-infrared (SWIR) hyperspectral images. Combined with the proposed IBS-CARS-Fusing method, a kernel ridge regression (KRR) model was established to predict the contents of bioactive compounds including chlorophyll a, chlorophyll b, carotenoids, tea polyphenols, and amino acids in Dancong tea. It was revealed that the IBS-CARS-Fusing method can improve R p 2 of KRR model for these bioactive compounds by 4.77%, 4.60%, 6.74%, 15.52%, and 13.10%, respectively, and R p 2 of the model reached high values of 0.9500, 0.9481, 0.8946, 0.8882, and 0.8622. Additionally, a leaf compound mass per area thermal map was used to visualize the spatial distribution of the compounds., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In order to achieve rapid and effective identification of Hebei yam, a qualitative discrimination model was constructed based on near infrared (NIR), mid infrared (MIR), and microscopic Raman spectra in combination with individual spectra and multispectral data fusion strategies. The results showed that the gray wolf optimizer-support vector machine (GWO-SVM) model constructed by mid-level fusion using the three feature spectra performed the best in distinguishing the geographic origin of the yam, with a prediction accuracy of 100.00% in both the training set and the test set, and an F1 score of 1.00. The results indicated that due to spectral complementarity, NIR, MIR and Raman combined with feature-level fusion can be used as a powerful, non-destructive, fast and feasible tool for geographic origin classification and brand protection of Hebei yam. This work is expected to be a potential method for origin identification analysis and quality monitoring in the food and pharmaceutical industries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Acute respiratory distress syndrome (ARDS) is a serious complication following cardiac surgery mainly associated with the use of cardiopulmonary bypass (CPB), which could increase the risk of mortality and morbidity. This study investigated the association of regional oxygen saturation (rSO 2 ) during CPB with postoperative outcomes, including respiratory function. Patients who underwent cardiac surgery with CPB from 2015 to 2019 were included. Near-infrared spectroscopy was used to monitor rSO 2 at the forehead, abdomen, and thighs throughout the surgery. Postoperative markers associated with CPB were assessed for correlations with PaO 2 /FiO 2 (P/F) ratios at intensive care unit (ICU) admission. Postoperative lung injury (LI) was defined as moderate or severe ARDS based on the Berlin criteria, and its incidence was 29.9% (20/67). On multiple regression analysis, the following were associated with P/F ratios at ICU admission: vasoactive-inotropic scores at CPB induction (P = 0.03), thigh rSO 2 values during CPB (P = 0.04), and body surface area (P < 0.001). A thigh rSO 2 of 71% during CPB was significantly predictive of postoperative LI with an area under the curve of 0.71 (P = 0.03), sensitivity of 0.70, and specificity of 0.68. Patients with postoperative LI had longer ventilation time and ICU stays. Thigh rSO 2 values during CPB were a potential predictor of postoperative pulmonary outcomes., Competing Interests: Declarations. Conflict of interest: The authors have no competing interests to declare that are relevant to the content of this article. Ethical approval: This retrospective study was approved by the Institutional Review Board (IRB) of Kitasato University Hospital on October 23, 2019 (approval no. B19-112). Consent to participate: Informed consent was obtained using an opt-out approach on the websites approved by the IRB., (© 2024. The Japanese Society for Artificial Organs.)

In order to guide the formulation of post-stroke treatment strategy in time, it is necessary to have real-time feedback on collateral circulation and revascularization. Currently used near-infrared II (NIR-II) probes have inherent binding with endogenous albumin, resulting in significant background signals and uncontrollable pharmacokinetics. Therefore, the albumin-escaping properties of the new probe, IR-808AC, was designed, which achieved timely excretion and low background signal, enabling the short-term repeatable injection for visualization of cerebral vessels and perfusion. We further achieved continuous observation of changes in collateral vessels and perfusion during the 7-d period in middle cerebral artery occlusion mice using IR-808AC in vivo. Furthermore, using IR-808AC, we confirmed that remote ischemic conditioning could promote collateral vessels and perfusion. Finally, we evaluated the revascularization after thrombolysis on time in embolic stroke mice using IR-808AC. Overall, our study introduces a novel methodology for safe, non-invasive, and repeatable assessment of collateral circulation and revascularization in real-time that is crucial for the optimization of treatment strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The digestion rate of steamed soybean (DRSS), protease activity of koji (PAK) and formaldehyde nitrogen content of moromi (FNCM) are key indicators to monitor soy sauce production. Currently, monitoring these indicators relies on workers' experience, which can sometimes lead to low material utilization rates and even fermentation failures. Near-infrared spectra were collected during soybean steaming, as well as koji and moromi fermentation, using miniature fiber spectrometers. These spectra were optimized using four pretreatment methods, and regression models were constructed using PLS, iPLS, and Si-PLS. The evaluation of models in prediction sets was based on the correlation coefficient (Rp) and root mean square error (RMSEP). Results indicated that Rp = 0.9327, RMSEP = 4.37% for DRSS, Rp = 0.9364, RMSEP = 228 U/g for PAK, and Rp = 0.9237, RMSEP =0.148 g/100 mL for FNCM were obtained. The above results coupling with validation experiments demonstrated that the developed in-situ and real-time spectroscopy system could ensure high-quality soy sauce production., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

An anti-interference colorimetric sensor array (CSA) technique was developed for the qualitative and quantitative detection of target heavy metals in corn oil. This method involves a binding mechanism that triggers changes in atomic energy levels and visible color changes. A custom-built olfactory visualization device was employed to gather spectral data, revealing distinct CSA color difference patterns. Subsequently, three pattern recognition algorithms were used to create an identification model for the target heavy metals. The results showed that the ACO-KNN (Ant Colony Optimization-K-Nearest Neighbor) model outperformed the other models, achieving accuracy rates of 90.28% and 89.58% for the calibration and prediction sets, respectively. The ACO-PLS (Partial Least Square) model was more stable with the lowest root mean square error of prediction (RMSEP), which were 0.1730 and 0.1180, respectively. The limit of detection (LOD) and quantification (LOQ) of Pb and Hg were (0.3, 0.6, 1.1 and 2.2) x 10 -3  mg/L, respectively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

Background: Blood-brain barrier (BBB) disruption after stroke is closely associated with brain tissue edema and neuronal injury, which requires accurate assessment. However, there is a lack of appropriate BBB imaging modality in vivo. As albumin in the blood could cross the damaged BBB into brain tissue after stroke, it serves as a biomarker for BBB disruption. Therefore, we aimed to develop an albumin-seeking near-infrared (NIR) probe to assess BBB disruption in stroke., Results: We proposed a chemoselective strategy for seeking albumin with NIR dyes and identified an optimal probe to evaluate BBB disruption in stroke. The probe combined a NIR fluorescent dye with inherent albumin-targeting moieties and exhibited high affinity and selectivity for binding to albumin. Using a mouse stroke model, the probe displayed a high-resolution visualization of the location and extent of BBB disruption in vivo and correlated well with BBB leakage measured by Evans blue ex vivo. A dual-channel NIR-II imaging was successfully used to simultaneously assess BBB disruption and cerebral perfusion after stroke. Furthermore, we applied this method to dynamically evaluate the BBB disruption process and reperfusion of thrombolytic therapy in a stroke model in real time, which showed excellent application value., Conclusions: We developed an albumin-seeking NIR probe that accurately evaluated BBB disruption in a safe, non-invasive and real-time manner in various stroke models, and has a great potential guiding stroke treatment in a real-time manner., Competing Interests: Declarations. Ethics approval and consent to participate: This study was approved by the Animal Ethical Committee of The First Hospital of Jilin University (20210642) and conforms to all the relevant guidelines and regulations. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Transcutaneous electrical nerve stimulation (TENS) has been used to treat chronic pain. However, the potential efficacy and mechanism of the effect of applying TENS for a short time in chronic pain patients remains unclear. To identify the effect of short-term TENS on chronic pain patients and to clarify the mechanism of the effect, we investigated abnormalities of functional connectivity (FC) within the prefrontal cortex (PFC) using resting-state functional near-infrared spectroscopy (rs-fNIRS). Fifteen patients (56.8 ± 17.4 years, nine females) with chronic pain participated in this rs-fNIRS study. The fNIRS scans included two parts: a 5-minute resting-state scan followed by a 5-minute scan during TENS (150 Hz) application. The pain intensity was measured using a Visual Analog Scale (VAS) and Pittsburgh Sleep Quality Index (PSQI). The spontaneous brain activity of the PFC and resting-state functional connectivity (rsFC) in the PFC were examined during TENS and compared to before TENS. The results showed that Pain intensity significantly decreased after TENS (p < 0.001). During TENS, fALFF values were significantly lower in BA46 (**p = 0.0025) and BA45 (**p = 0.0056). rsFC strength increased during TENS compared to before, with significant group-level increases in BA10, BA9, BA46, and BA44/45 (p < 0.05). Notably, the variation between BA10 and BA44/45 was highly significant (***p < 0.001). These findings suggest that FC between BA10 and BA44/45 was associated with analgesia of TENS in patients with chronic pain, indicating the potential role of FC as a novel objective parameter to predict the outcome of clinical use of TENS for pain relief in chronic pain patients., Competing Interests: Declarations. Ethical approval: This study was approved by the Ethics Committees of Yueyang Hospital of Integrative Medicine of Shanghai University of Traditional Chinese Medicine and Changhai Hospital of Naval Medical University, and by the China Clinical Trial Registry (ChiCTR2200056394). Informed consent: Verbal informed consent was obtained from patients to publish their anonymized information in this article. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure-functional relationship has yet to be explored. In our study, using simultaneous Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects, we characterize global and local structure-function coupling using source-reconstructed EEG and fNIRS signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored. Employing the mathematical framework of graph signal processing, we investigate how this relationship varies across electrical and hemodynamic networks and different brain states. Results show that fNIRS structure-function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, with greater coupling in the sensory cortex and increased decoupling in the association cortex, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure-function relationship and brain organization when using affordable functional brain imaging., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Near-infrared spectroscopy (NIRS) has become a popular tool for investigating phenotypic variability in plants. We developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits, including classical functional traits as well as a large variety of commonly measured chemical compounds, based from near-infrared spectroscopy values based on deep learning. It is freely accessible at the following URL: https://shiny.cefe.cnrs.fr/NirsPredict/ . NIRSpredict has three main functionalities. First, it allows users to submit their spectrum values to get the predictions of plant traits from models built with the hosted A. thaliana database. Second, users have access to the database of traits used for model calibration. Data can be filtered and extracted on user's choice and visualized in a global context. Third, a user can submit his own dataset to extend the database and get part of the application development. NIRSpredict provides an easy-to-use and efficient method for trait prediction and an access to a large dataset of A. thaliana trait values. In addition to covering many of functional traits it also allows to predict a large variety of commonly measured chemical compounds. As a reliable way of characterizing plant populations across geographical ranges, NIRSpredict can facilitate the adoption of phenomics in functional and evolutionary ecology., Competing Interests: Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Objective. Methods that can detect brain activities accurately are crucial owing to the increasing prevalence of neurological disorders. In this context, a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offers a powerful approach to understanding normal and pathological brain functions, thereby overcoming the limitations of each modality, such as susceptibility to artifacts of EEG and limited temporal resolution of fNIRS. However, challenges such as class imbalance and inter-class variability within multisubject data hinder their full potential. Approach. To address this issue, we propose a novel temporal attention fusion network (TAFN) with a custom loss function. The TAFN model incorporates attention mechanisms to its long short-term memory and temporal convolutional layers to accurately capture spatial and temporal dependencies in the EEG-fNIRS data. The custom loss function combines class weights and asymmetric loss terms to ensure the precise classification of cognitive and motor intentions, along with addressing class imbalance issues. Main results. Rigorous testing demonstrated the exceptional cross-subject accuracy of the TAFN, exceeding 99% for cognitive tasks and 97% for motor imagery (MI) tasks. Additionally, the ability of the model to detect subtle differences in epilepsy was analyzed using scalp topography in MI tasks. Significance. This study presents a technique that outperforms traditional methods for detecting high-precision brain activity with subtle differences in the associated patterns. This makes it a promising tool for applications such as epilepsy and seizure detection, in which discerning subtle pattern differences is of paramount importance., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Singing plays a critical role in enhancing musicality, sound discrimination, and attention, and proves advantageous for speech rehabilitation in children with hearing impairments. Computer-based training games are well-suited to the learning behaviors of children, with substantial evidence suggesting that music training augments speech training capabilities in this demographic. Despite this, there is a lack of detailed exploration into the design of interactive online music training interfaces tailored for these needs. This study investigates brain activation changes using two visual feedback singing games, analyzed through functional near-infrared spectroscopy: a serious game (SG) and an entertainment game (EG) with visually enhanced feedback. It also assesses the efficacy of home-based music training software for speech rehabilitation. Methods involved recording oxygenated hemoglobin concentration (Delta [HbO]) signals from the prefrontal cortex, motor cortex, occipital lobe, and temporal lobe in 21 children (average age: 9.3 ± 1.9 years) during two singing interface experiments. Subjects also completed the Intrinsic Motivation Inventory (IMI) questionnaire post-experiment. Main results showed that brain regions, particularly the temporal lobe, exhibited stronger and more pronounced activation signals with the SG interface compared to the EG, suggesting that SG is more effective for speech system rehabilitation. The Intrinsic Motivation Scale results revealed higher acceptability for SG than for EG. This study provides insights into designing online speech rehabilitation products for children with hearing impairment, advocating for better interactive training methods from a neuroscience perspective., (Copyright © 2024 Elsevier B.V. All rights reserved.)

We have designed and built an improved system for combined Time-Domain Near-Infrared Spectroscopy (TD NIRS) and Diffuse Correlation Spectroscopy (DCS) measurements. The system features two independent channels, enabling TD NIRS and DCS acquisition at short and long source-detector distances to enhance depth sensitivity in layered tissues. Moreover, the device can operate at fast acquisition rates (up to 50 Hz) to monitor hemodynamic oscillations in biological tissues. An OEM (Original Equipment Manufacturer) TD NIRS device enables stable and robust acquisition of photon distribution of time-of-flight. For the DCS signals, the use of a time tagger and a software correlator allows us flexibility in post-processing. A user-friendly GUI controls TD NIRS data acquisition and online data analysis. We present results for the system characterization on calibrated tissue phantoms according to standardized protocols for performance assessment of TD NIRS and DCS devices. In-vivo measurements during rest and during vascular occlusions are also reported to validate the system in real settings.

The rapid and accurate identification of pathogenic bacteria is crucial for combating the growing threat of antibiotic resistance, nosocomial infections, and food safety concerns. This study presents a novel and comprehensive comparison of two vibrational spectroscopic techniques - attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and a low-cost miniature near-infrared (NIR) spectrometer - for distinguishing Gram-positive and Gram-negative bacterial samples grown using the same stock media solution. This is the first report of NIR spectroscopy being applied to differentiate Gram-positive and Gram-negative bacteria, as well as the first direct comparison of ATR-FTIR and NIR for the combined multimodal analysis of clinical bacterial isolates. Using a data set of five Gram-positive and seven Gram-negative species and recording spectra in triplicate, the study employed advanced data fusion and multivariate analysis techniques to classify the spectra and facilitate NIR band assignment. 2D correlation analysis revealed strong positive correlations between key spectral markers identified in both modalities. Partial least-squares- and support vector machine discriminant analysis models were validated using a methodology based on 100 repeated random sampling of calibration and test sets. Models demonstrated that both the standalone ATR-FTIR and the combined ATR-FTIR/NIR approach achieved exceptional classification accuracy (>98%) in differentiating the two bacterial groups. Differences observed in the spectra were attributed to the distinct cell wall compositions of Gram-Positive and Gram-negative bacteria. Notably, the low-cost NIR technique also showed promising performance, with classification accuracy values above 90%. The findings highlight the potential of these rapid, noninvasive, and cost-effective vibrational spectroscopic techniques, particularly the NIR method, for point-of-care applications in clinical microbiology and food safety monitoring. The combination of ATR-FTIR and NIR data further enhances the robustness and reliability of bacterial identification, paving the way for broader adoption of these advanced analytical tools in various healthcare and food safety settings.

Organic molecular fluorophores have been extensively utilized for biological imaging in the visible and the first near-infrared windows. However, their applications in the second near-infrared (NIR-II) window remain constrained, primarily due to the insufficient fluorescence brightness. Herein, we employ a theoretical protocol combining the thermal vibration correlation function with the time-dependent density functional theory method to investigate the mechanism of the planar-twisted strategy for developing fluorophores with balanced NIR-II emission and fluorescence brightness. Based on a planar donor-acceptor-donor molecular skeleton, various ortho-positioned alkyl side chains with steric hindrances are tactfully incorporated into the backbone to construct a series of twisted fluorophores. Photophysical characterizations of the studied fluorophores demonstrate that the emission spectra located in the NIR-II region exhibited a hypsochromic shift with the structural distortion. Notably, conformational twisting significantly accelerated the radiative decay rate while simultaneously suppressing the nonradiative decay rate, resulting in an improved fluorescence quantum efficiency (FQE). This enhancement can be mainly attributed to both the enlarged adiabatic excitation energy and reduced nonadiabatic electronic coupling between the first excited state and the ground state. Compared with the planar fluorophore, the twisted structures possessed a more than fivefold increase in FQE. In particular, the optimal twisted fluorophore BBTD-4 demonstrated a desirable fluorescence brightness (16.59 M -1 cm -1 ) on the premise of typical NIR-II emission (980 nm), making it a promising candidate for NIR-II fluorescence imaging in biomedical applications. The findings in this study elucidate the available experimental observations on the analogues, highlighting a feasible approach to modulating the photophysical performances of NIR-II chromophores for developing more highly efficient fluorophores toward optical imaging applications.

As the fundamental dispositional determinant of social motivation, social value orientation (SVO) may modulate individuals' response patterns in interpersonal coordination contexts. Adopting fNIRS-based hyperscanning approach, the present investigation uncovered the hyper-brain network topological dynamics underlying the effect of the dyadic similarity in the social value orientation on interpersonal coordination. Our findings indicated that the dyads in proself group exhibited the higher degree of competitive intensity during the competitive coordination block, and the dyads in the prosocial group exhibited a higher degree of cooperative coordination during the cooperative coordination block. Distinct hyper-brain functional connectivity patterns and network topological characteristics were identified during the competitive and cooperative coordination blocks in the proself and prosocial groups. The nodal-network global efficiency at the right frontopolar area further mediated the effect of the dyadic deviation in social value orientation similarity on effective adjustments after the negative feedback during the cooperative coordination block in the prosocial group. Our findings manifested distinct behavioral performances and hyper-brain functional connectivity patterns underlying the effect of the dyadic similarity in social value orientation on interpersonal coordination in the real-time mode., Competing Interests: Declarations Ethics Approval and Consent to Participate The current study was authorized by the Academic Committee of Key Laboratory of Brain-Machine Intelligence for Information Behavior at Shanghai International Studies University in China. All participants recruited in the current investigation signed the informed consent forms. Consent for Publication Not applicable. Competing Interests The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Fast and accurate assessment of skin mechanics holds great promise in diagnosing various epidermal diseases, yet substantial challenges remain in developing simple and wearable strategies for continuous monitoring. Here, we present a design concept, named active near-infrared spectroscopy patch (ANIRP) for continuously mapping skin mechanics. ANIRP addresses these challenges by integrating near-infrared (NIR) sensing with mechanical actuators, enabling rapid measurement (<1 s) of Young's modulus, high spatial sensing density (~1 cm 2 ), and high spatial sensitivity (<1 mm). Unlike conventional electromechanical sensors, NIR sensors precisely capture vibrational frequencies propagated from the actuators without needing ultraclose contact, enhancing wearing comfort. Demonstrated examples include ANIRPs for comprehensively moduli mapping of artificial tissues with varied mechanical properties emulating tumorous fibrosis. On-body validation of the ANIRP across skin locations confirms its practical utility for clinical monitoring of epidermal mechanics, promising considerable advancements in real-time, noninvasive skin diagnostics and continuous health monitoring.

Autism spectrum disorder (ASD) is typically characterized by impairments in social interaction and communication, which may be associated with a failure to naturally orient to social stimuli, particularly in recognizing and responding to facial emotions. As most previous studies have used nonsocial stimuli to investigate inhibitory control in children and adults with ASD, little is known about the behavioral and neural activation patterns of emotional inhibitory control in adolescent with ASD. Functional neuroimaging studies have underscored the key role of the prefrontal cortex (PFC) in inhibitory control and emotional face processing. Thus, this study aimed to examine whether adolescent with ASD exhibited altered PFC processing during an emotional Flanker task by using non-invasive functional near-infrared spectroscopy (fNIRS). Twenty-one adolescents with high-functioning ASD and 26 typically developing (TD) adolescents aged 13-16 years were recruited. All participants underwent an emotional Flanker task, which required to decide whether the centrally positioned facial emotion is consistent with the laterally positioned facial emotion. TD adolescents exhibited larger RT and mean O 2 Hb level in the incongruent condition than the congruent condition, evoking cortical activations primarily in right PFC regions in response to the emotional Flanker effect. In contrast, ASD adolescents failed to exhibit the processing advantage for congruent versus incongruent emotional face in terms of RT, but showed cortical activations primarily in left PFC regions in response to the emotional Flanker effect. These findings suggest that adolescents with ASD rely on different neural strategies to mobilize PFC neural resources to address the difficulties they experience when inhibiting the emotional face., Competing Interests: Declaration of competing interest The authors declare no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Background: This study explored a novel multimodal neuroimaging approach to assess neurovascular coupling (NVC) in humans using electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and transcranial Doppler ultrasound (TCD)., Methods: Fifteen participants (nine females; age 19-32) completed concurrent EEG-fNIRS-TCD imaging during motor (finger tapping) and visual ("Where's Waldo?") tasks, with synchronized monitoring of blood pressure, capnography, and heart rate. fNIRS assessed microvascular oxygenation within the frontal, motor, parietal, and occipital cortices, while the middle and posterior cerebral arteries (MCA/PCA) were insonated using TCD. A 16-channel EEG set-up was placed according to the 10-20 system. Wilcoxon signed-rank tests were used to compare physiological responses between the active and resting phases of the tasks, while cross-correlations with zero legs compared cerebral and systemic hemodynamic responses across both tasks., Results: Time-frequency analysis demonstrated a reduction in alpha and low beta band power in electrodes C3/C4 during finger tapping (p<0.045) and all electrodes during the Waldo task (all p<0.001). During Waldo, cross-correlation analysis demonstrated the change in oxygenated hemoglobin and cerebral blood velocity had a moderate-to-strong negative correlation with systemic physiological influences, highlighting the measured change resulted from neuronal input. Deoxygenated hemoglobin displayed the greatest negative cross-correlation with the MCA/PCA within the motor cortices and visual during the motor and visual tasks, respectively (range:0.54, -0.82)., Conclusions: This investigation demonstrated the feasibility of the proposed EEG-fNIRS-TCD response to comprehensively assess the NVC response within human, specifically quantifying the real-time temporal synchrony between neuronal activation (EEG), microvascular oxygenation changes (fNIRS), and conduit artery velocity alterations (TCD)., Competing Interests: Declaration of competing interest The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (Copyright © 2024. Published by Elsevier Inc.)

Background: Autism spectrum disorder (ASD) has been associated with disrupted brain connectivity, yet a comprehensive understanding of the dynamic neural underpinnings remains lacking. This study employed concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques to investigate dynamic functional connectivity (dFC) patterns and neurovascular characteristics in children with ASD. We also explored associations between neurovascular characteristics and the developmental trajectory of adaptive behavior in individuals with ASD., Methods: Resting-state EEG and fNIRS data were simultaneously recorded from 58 ASD and 63 TD children. We implemented a k-means clustering approach to extract the dFC states for each modality. In addition, a multimodal covariance network (MCN) was constructed from the EEG and fNIRS dFC features to capture the neurovascular characteristics linked to ASD., Results: EEG analyses revealed atypical properties of dFC states in the beta and gamma bands in children with ASD compared to TD children. For fNIRS, the ASD group exhibited atypical properties of dFC states such as duration and transitions relative to the TD group. The MCN analysis revealed significantly suppressed functional covariance between right superior temporal and left Broca's areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, we found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD., Conclusion: The multimodal investigation revealed distinct dFC patterns and neurovascular characteristics associated with ASD, elucidating potential neural mechanisms underlying core symptoms and their developmental trajectories. Our study highlights that integrating complementary neuroimaging modalities may aid in unraveling the complex neurobiology of ASD., Competing Interests: Declaration of competing interest None of the authors has any conflict of interest to disclose. No financial or non-financial benefits were received or will be received from any party directly or indirectly related to the subject of this article. We confirm that we have read the journal's position regarding issues involving ethical publication and affirm that this report is consistent with those guidelines., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)