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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.)

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.

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.)

The extraction process plays a crucial role in the production of Tibetan medicines. This study focused on assembling a set of online near-infrared (NIR) spectroscopy detection devices for the extraction of medicinal herbs. The original infrared device was transformed into an online detection system. After evaluating the stability of the system, we applied online NIR spectroscopy monitoring to the flavonoid contents (total flavonoids, quercetin-3-O-sophoroside, and luteolin) of Meconopsis quintuplinervia Regel. during the ultrasonic extraction process and determined the extraction endpoint. Nine batches of samples were employed to construct quantitative and discriminant models, half of the remaining two batches of samples are used for external verification. Our research shows that the residual predictive deviation (RPD) values of total flavonoids, quercetin-3-O-sophoroside and luteolin models exceeded 2.5. The R values for external verification of the three ingredients were above 0.9, with RPD values generally exceeding 2 and RSEP values within 10 %, demonstrating the model's strong predictive performance. Most of the extraction endpoints of the flavonoid components in M. quintuplinervia ranged from 18 to 58 min, with high consistency between the predicted extraction endpoints of the external validation, suggesting accurate determination of extraction endpoints based on predicted values. This study can provide a reference for the online NIR spectroscopy quality monitoring of the extraction process of Chinese and Tibetan herbs., 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 thiophene- and pyrrole-fused heterocyclic compounds have garnered significant interest for their distinctive electron-rich characteristics and notable optoelectronic properties. However, the construction of high-performance systems within this class is of great challenge. Herein, we develop a series of novel dithieno[3,2-b:2',3'-d] pyrrole (DTP) and tetrathieno[3,2-b:2',3'-d] pyrrole (TTP) bridged arylamine compounds (DTP-C 4 , DTP-C 12 , DTP-C 4 -Fc, TTP-C 4 -OMe, TTP-C 4 , and TTP-C 12 ) with varying carbon chain lengths. The pertinent experimental results reveal that this series of compounds undergo completely reversible multistep redox processes. Notably, TTP-bridged compounds TTP-C 4 and TTP-C 12 exhibit impressive multistep near-infrared (NIR) absorption alterations with notable color changes and electroluminescent behaviors, which are mainly attributed to the charge transfer transitions from terminal arylamine units to central bridges, as supported by theoretical calculations. Additionally, compound DTP-C 4 demonstrates the ability to visually identify gram-positive and gram-negative bacteria. Therefore, this work suggests the promising electroresponsive nature of compounds TTP-C 4 and TTP-C 12 , positioning them as excellent materials for various applications. It also provides a facile approach to constructing high-performance multifunctional luminescent materials, particularly those with strong and long-wavelength NIR absorption capabilities., 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.)

Breath analysis enables rapid, noninvasive diagnosis of human health by identifying and quantifying exhaled biomarker. Here, we demonstrated an exhaled breath sensing method using the near-infrared laser spectroscopy, and sub parts-per-million (ppm) level ammonia detection inside the exhaled gas was achieved employing a distributed feedback laser centered at 1512 nm and Kalman filtering algorithm. Integration of the ammonia sensor was realized for exhaled breath analysis of kidney patients, and a dual operation mechanism with static and dynamic modes was proposed to make this method applicable for real-time and comprehensive pre-diagnosis of kidney disease., 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 healthy benefits of seaweed have increased its market demand in recent times. Quality control is crucial for seaweed to ensure the customers' interest and the sustainable development of seaweed farming industry. This study developed a quality control method for seaweed Sargassum fusiforme, rapid and simple, using near-infrared spectroscopy (NIR) and chemometrics for the prediction of antioxidant capacity of S. fusiforme from different growth stages, S. fusiforme was distinguished according to growth stage by partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The antioxidant properties including 2,2'-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) scavenging capacity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity, and ferric reducing antioxidant power (FRAP) were quantified using competitive adaptive reweighted sampling (CARS)-PLS model. Based on the spectra data preprocessed by multiplicative scatter and standard normal variate methods, the PSO-SVM models can accurately identify the growth stage of all S. fusiforme samples. The CARS-PLS models exhibited good performance in predicting the antioxidant capacity of S. fusiforme, with coefficient of determination (R P 2 ) and root mean square error (RMSEP) values in the independent prediction sets reaching 0.9778 and 0.4018 % for ABTS, 0.9414 and 2.0795 % for DPPH, and 0.9763 and 2.4386 μmol L -1 for FRAP, respectively. The quality and market price of S. fusiforme should increase in the order of maturation < growth < seedling regarding the antioxidant property. The overall results indicated that the NIR spectroscopy accompanied by chemometrics can assist for the quality control of S. fusiforme in a more rapid and simple manner. This study also provided a customer-oriented concept of seaweed quality grading based on deep insight into the antioxidant capability of S. fusiforme at different growth stages, which is highly valuable for precise quality control and standardization of seaweed market., 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.)

In industrial production, the timely assessment of ginseng-derived ingredients is crucial and requires nondestructive techniques for identifying and analyzing composition. Hyperspectral imaging (HSI) effectively visualizes the three-dimensional spatial distribution of phytochemicals in dried ginseng. This study explores the in-situ prediction and visualization of moisture content (MC) and ginsenoside content (GC) in thermally processed ginseng using dual-band HSI. We collected hyperspectral images from 216 raw ginseng samples, which underwent dimensionality reduction, noise reduction, and feature enhancement via Principal Component Analysis (PCA) and Minimum Noise Separation (MNF). Linear regression models were developed following these pretreatments and evaluated using a validation set. The PCA-based models demonstrated superior performance over those based on MNF, especially in predicting GC in the near-infrared (NIR) spectrum. Similarly, models predicting MC in the visible spectrum showed favorable results. HSI enables rapid generation of distribution maps, facilitating real-time imaging for commercial applications. Repeated drying cycles and increased duration primarily affect the textural characteristics and visible color of the ginseng surface, without significantly altering its intrinsic properties. The deployment of this predictive model alongside real-time content inversion using HSI technology holds promise for integrating visual and intelligent quality monitoring in the trade of valuable herbal commodities., 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.)

One of the most prevalent psychiatric disorders is major depressive disorder (MDD), which increases the probability of suicidal ideation or untimely demise. Abnormal frontal hemodynamic changes detected by functional near-infrared spectroscopy (fNIRS) during verbal fluency task (VFT) have the potential to be used as an objective indicator for assessing clinical symptoms. However, comprehensive quantitative and objective assessment instruments for individuals who exhibit symptoms suggestive of depression remain undeveloped. Drawing from a total of 467 samples in a large-scale dataset comprising 289 MDD patients and 178 healthy controls, fNIRS measurements were obtained throughout the VFT. To identify unique MDD biomarkers, this research introduced a data representation approach for extracting spatiotemporal features from fNIRS signals, which were subsequently utilized as potential predictors. Machine learning classifiers (e.g., Gradient Boosted Decision Trees (GBDT) and Multilayer Perceptron) were implemented to assess the ability to predict selected features. The mean and standard deviation of the cross-validation indicated that the GBDT model, when combined with the 180-feature pattern, distinguishes patients with MDD from healthy controls in the most effective manner. The accuracy of correct classification for the test set was 0.829 ± 0.053, with an AUC of 0.895 (95 % CI: 0.864-0.925) and a sensitivity of 0.914 ± 0.051. Channels that made the most important contribution to the identification of MDD were identified using Shapley Additive Explanations method, located in the frontopolar area and the dorsolateral prefrontal cortex, as well as pars triangularis Broca's area. Assessment of abnormal prefrontal activity during the VFT in MDD serves as an objectively measurable biomarker that could be utilized to evaluate cognitive deficits and facilitate early screening for MDD. The model suggested in this research could be applied to large-scale case-control fNIRS datasets to detect unique characteristics of MDD and offer clinicians an objective biomarker-based analytical instrument to assist in the evaluation of suspicious cases., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Hydrogen sulfide (H 2 S) is a key factor in various biological processes such as plant grow and its response to environmental stress. Here, we develop a novel near-infrared (NIR) fluorescent probe for detecting hydrogen sulfide based on the regulatory NIR dye pKa values. After triggering the H 2 S substitution response, probe A with introducing the cyano moiety not only exhibits a significant near-infrared emission (Emax: 724 nm) response in physiological environments, but also shows a fast response, high selectivity, and sensitivity (LOD as 0.52 µM). In addition, probe A with low biological cytotoxicity is successfully used for imaging detection of cellular exogenous and endogenous hydrogen sulfide. More importantly, in situ imaging of probe A tracks the H 2 S fluctuations in the rice root system and its response to environmental stress. Hence, this work offers a new NIR fluorescence imaging monitoring tool for hydrogen sulfide 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 Elsevier B.V. All rights reserved.)

This study developed a rapid, accurate, objective and economic method to identify and evaluate the quality of Alismatis Rhizoma (AR) commodities. Traditionally, the identification of plant species and geographical origins of AR commodities mainly relied on experienced staff. However, the subjectivity and inaccuracy of human identification negatively impacted the trade of AR. Besides, liquid chromatographic methods such as ultra-high-performance liquid chromatography (UPLC) and high-performance liquid chromatography (HPLC), the major approach for the determination of triterpenoid contents in AR was time-consuming, expensive, and highly demanded in manoeuvre specialists. In this study, the combination of near-infrared (NIR) spectroscopy and chemometrics as the method was developed and utilised to address the two common issues of identifying the quality of AR commodities. Through the discriminant analysis (DA), the raw NIR spectroscopy data on 119 batches samples from two species and four origins in China were processed to the best pre-processed data. Subsequently, orthogonal partial least squares-discriminant analysis (OPLS-DA) and random forest (RF) as the major chemometrics were used to analyse the best pre-processed data. The accuracy rates by OPLS-DA and RF were respectively 100% and 97.2% for the two species of AR, and respectively100% and 94.4% for the four origins of AR. Meanwhile, a quantitative correction model was established to rapidly and economically predict the seven triterpenoid contents of AR through combining the partial least squares (PLS) method and NIR spectroscopy, and taking the triterpenoid contents measured by UPLC as the reference value, and carry out spectral pre-processing methods and band selection. The final quantitative model correlation coefficients of the seven triterpenoid contents of AR ranged from 0.9000 to 0.9999, indicating that prediction ability of this model had good stability and applicability., 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.)

The high catechin content in summer-to-autumn tea leaves often results in strong, unpleasant tastes, leading to significant resource waste and economic losses due to lignification of unpicked leaves. This study aims to improve the taste quality of summer-to-autumn green teas by combining fine manipulation techniques with hyperspectral observation. Fine manipulation notably enhanced infusion taste quality, particularly in astringency and its aftertaste (aftertasteA). Using Partial Least Squares Discriminant Analysis (PLSDA) on hyperspectral data, 100% prediction accuracy was achieved for dry tea appearance in the near-infrared spectrum. Astringency and aftertasteA correlated with hyperspectral data, allowing precise estimation with over 90% accuracy in both visible and near-infrared spectrums. Epicatechin gallate (ECG) emerged as a key taste compound, enabling non-invasive taste prediction. Practical applications in processing and quality control are demonstrated by the derived equations (Astringency = -0.88 × ECG + 45.401, AftertasteA = -0.353 × ECG + 18.609), highlighting ECG's role in shaping green tea taste profiles., 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.)

Peanut oil, prized for its unique taste and nutritional value, grapples with the pressing issue of adulteration by cost-cutting vendors seeking higher profits. In response, we introduce a novel approach using near-infrared spectroscopy to non-invasively and cost-effectively identify adulteration in peanut oil. Our study, analyzing spectral data of both authentic and intentionally adulterated peanut oil, successfully distinguished high-quality pure peanut oil (PPEO) from adulterated oil (AO) through rigorous analysis. By combining near-infrared spectroscopy with factor analysis (FA) and partial least squares regression (PLS), we achieved discriminant accuracies exceeding 92 % (S > 2) and 89 % (S > 1) for FA models 1 and 2, respectively. The PLS model demonstrated strong predictive capabilities, with a prediction coefficient (R 2 ) surpassing 93.11 and a root mean square error (RMSECV) below 4.43. These results highlight the effectiveness of NIR spectroscopy in confirming the authenticity of peanut oil and detecting adulteration in its composition., 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.)

Devising carbon dots with long wavelength emission (red light or near infrared), high selectivity and good bio-compatibility is critical in fluorescence detection and imaging, but achieving this goal remains a great challenge. Herein, near-infrared emissive carbon dots (NIR-CDs) with obvious emission characteristic of 653 nm were synthesized through hydrothermally treatment of toluidine bule and gallic acid. Noticeably, the NIR-CDs exhibited excellent selectivity and sensitivity to hypochlorite (ClO - ), and the limit of detection is as low as 42.7 nM. The selective recognition reaction between ClO - and the surface functional groups of NIR-CDs inhibits the fluorescence from NIR-CDs. The quenching mechanism was confirmed by fluorescence lifetime decays, FT-IR spectroscopy and UV-vis absorption spectra. More remarkably, the NIR-CDs have rich hydrophilic groups showed lower cytotoxicity, excellent bio-compatibility and specific cell membrane localization ability. The established spectrofluorometric method based on NIR-CDs has been used to determination of ClO - level in tap water sample, the recoveries were 97.7 %-103.3 %. In addition, the NIR-CDs also has been successfully applied for the imaging of cell membrane. The study provides a novel idea for developing NIR ClO - probe as well as cell membrane localization probe based on CDs, which present bright prospects in real water samples monitoring and cell membrane imaging., 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.)

Measuring the quality of life of an animal in a production system is difficult, time-consuming, and expensive. We tested the ability of both NIR and MIR spectroscopy, each combined with machine learning, to predict the prior exposure of pigs to long- and short-term life challenges when they are raised in an intensive system. Samples were obtained post-mortem from two locations in the prefrontal cortex. The analysis showed a clear separation between the gray and white matter from the prefrontal cortex with MIR spectroscopy. Exposure to long-term challenge was poorly predicted by the MIR or NIR spectra (< 45% correct classifications). By contrast, the correct classification of samples according to the exposure to a short-tern challenge before death was higher than 65%. These rates of classification, considering the complexity of the stimulus and the small sample size, support that vibrational spectroscopy could be used to assess the exposure to challenging events of an animal on post-mortem brain tissue., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

In vivo imaging of bacterial infection models enables noninvasive and temporal analysis of individuals, enhancing our understanding of infectious disease pathogenesis. Conventional in vivo imaging methods for bacterial infection models involve the insertion of the bacterial luciferase LuxCDABE into the bacterial genome, followed by imaging using an expensive ultrasensitive charge-coupled device (CCD) camera. However, issues such as limited light penetration into the body and lack of versatility have been encountered. We focused on near-infrared (NIR) light, which penetrates the body effectively, and attempted to establish an in vivo imaging method to evaluate the number of lung-colonizing bacteria during the course of bacterial pneumonia. This was achieved by employing a novel versatile system that combines plasmid-expressing firefly luciferase bacteria, NIR substrate, and an inexpensive, scientific complementary metal-oxide semiconductor (sCMOS) camera. The D-luciferin derivative "TokeOni," capable of emitting NIR bioluminescence, was utilized in a mouse lung infection model of Acinetobacter baumannii , an opportunistic pathogen that causes pneumonia and is a concern due to drug resistance. TokeOni exhibited the highest sensitivity in detecting bacteria colonizing the mouse lungs compared with other detection systems such as LuxCDABE, enabling the monitoring of changes in bacterial numbers over time and the assessment of antimicrobial agent efficacy. Additionally, it was effective in detecting A. baumannii clinical isolates and Klebsiella pneumoniae . The results of this study are expected to be used in the analysis of animal models of infectious diseases for assessing the efficacy of therapeutic agents and understanding disease pathogenesis., Importance: Conventional animal models of infectious diseases have traditionally relied upon average assessments involving numerous individuals, meaning they do not directly reflect changes in the pathology of an individual. Moreover, in recent years, ethical concerns have resulted in the demand to reduce the number of animals used in such models. Although in vivo imaging offers an effective approach for longitudinally evaluating the pathogenesis of infectious diseases in individual animals, a standardized method has not yet been established. To our knowledge, this study is the first to develop a highly versatile in vivo pulmonary bacterial quantification system utilizing near-infrared luminescence, plasmid-mediated expression of firefly luciferase in bacteria, and a scientific complementary metal-oxide semiconductor camera. Our research holds promise as a useful tool for assessing the efficacy of therapeutic drugs and pathogenesis of infectious diseases., Competing Interests: The authors declare no conflict of interest.

Unfertilized duck eggs not removed prior to incubation will deteriorate quickly, posing a risk of contaminating the normally fertilized duck eggs. Thus, detecting the fertilization status of breeding duck eggs as early as possible is a meaningful and challenging task. Most existing work usually focus on the characteristics of chicken eggs during mid-term hatching. However, little attention has been paid to the detection for duck eggs prior to incubation. In this paper, we present a novel hybrid deep learning detection framework for the fertilization status of pre-incubation duck eggs, termed CVAE-DF, based on visible/near-infrared (VIS/NIR) transmittance spectroscopy. The framework comprises the encoder of a convolutional variational autoencoder (CVAE) and an improved deep forest (DF) model. More specifically, we first collected transmittance spectral data (400-1000 nm) of 255 duck eggs before hatching. The multiplicative scatter correction (MSC) method was then used to eliminate noise and extraneous information of the raw spectral data. Two efficient data augmentation methods were adopted to provide sufficient data. After that, CVAE was applied to extract representative features and reduce the feature dimension for the detection task. Finally, an improved DF model was employed to build the classification model on the enhanced feature set. The CVAE-DF model achieved an overall accuracy of 95.94 % on the test dataset. These experimental results in terms of four metrics demonstrate that our CVAE-DF method outperforms the traditional methods by a significant margin. Furthermore, the results also indicate that CVAE holds great promise as a novel feature extraction method for the VIS/NIR spectral analysis of other agricultural products. It is extremely beneficial to practical engineering., 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.)

Among the severe foodborne illnesses, listeriosis resulting from the pathogen Listeria monocytogenes exhibits one of the highest fatality rates. This study investigated the application of near infrared hyperspectral imaging (NIR-HSI) for the classification of three L. monocytogenes serotypes namely serotype 4b, 1/2a and 1/2c. The bacteria were cultured on Brain Heart Infusion agar, and NIR hyperspectral images were captured in the spectral range 900-2500 nm. Different pre-processing methods were applied to the raw spectra and principal component analysis was used for data exploration. Classification was achieved with partial least squares discriminant analysis (PLS-DA). The PLS-DA results revealed classification accuracies exceeding 80 % for all the bacterial serotypes for both training and test set data. Based on validation data, sensitivity values for L. monocytogenes serotype 4b, 1/2a and 1/2c were 0.69, 0.80 and 0.98, respectively when using full wavelength data. The reduced wavelength model had sensitivity values of 0.65, 0.85 and 0.98 for serotype 4b, 1/2a and 1/2c, respectively. The most relevant bands for serotype discrimination were identified to be around 1490 nm and 1580-1690 nm based on both principal component loadings and variable importance in projection scores. The outcomes of this study demonstrate the feasibility of utilizing NIR-HSI for detecting and classifying L. monocytogenes serotypes on growth media., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Paul James Williams reports financial support was provided by National Research Foundation of South Africa. Rumbidzai T Matenda reports financial support and travel were provided by South Africa Department of Science and Innovation. 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 Author(s). Published by Elsevier B.V. All rights reserved.)

The quality of the grains during the fumigation process can significantly affect the flavour and nutritional value of Shanxi aged vinegar (SAV). Hyperspectral imaging (HSI) was used to monitor the extent of fumigated grains, and it was combined with chemometrics to quantitatively predict three key physicochemical constituents: moisture content (MC), total acid (TA) and amino acid nitrogen (AAN). The noise reduction effects of five spectral preprocessing methods were compared, followed by the screening of optimal wavelengths using competitive adaptive reweighted sampling. Support vector machine classification was employed to establish a model for discriminating fumigated grains, and the best recognition accuracy reached 100%. Furthermore, the results of partial least squares regression slightly outperformed support vector machine regression, with correlation coefficient for prediction (Rp) of 0.9697, 0.9716, and 0.9098 for MC, TA, and AAN, respectively. The study demonstrates that HSI can be employed for rapid non-destructive monitoring and quality assessment of the fumigation process in SAV., 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.)

Hydrogen sulfide (H 2 S) is a pungent gas that is one of the key mediators of signal transduction in biological systems, and its presence is related to the freshness of some protein foods. Using phenothiazine derivatives as fluorophores and 2, 4-dinitrobenzene sulfonate (DNBS) fragments as reaction groups, a near-infrared (NIR) probe WX-HS for H 2 S identification was designed. With the addition of H 2 S, WX-HS appeared a strong fluorescence signal at 660 nm with short reaction time (90 s) and high sensitivity, and fluorescence state change from non-fluorescent to orange-red. In addition, WX-HS could effectively detect H 2 S produced during food oxidation. Based on its low cytotoxicity, the WX-HS probe further enabled the detection and imaging of H 2 S in A549 cells., 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.)

Long-term studies have shown a bias drift over time in the prediction performance of near-infrared spectroscopy measurement systems. This bias drift generally requires extra laboratory reference measurements to detect and correct for this bias. Since these reference measurements are expensive and time consuming, there is a need for advanced methodologies for bias drift monitoring and correction without the need for taking extra samples. In this study, we propose and validate a method to monitor the bias drift and two methods to tackle it. The first method requires no extra measurements and uses a modified version of Partial Least Squares Regression to estimate and correct the bias. This method is based on the assumption that the mean concentration of the predicted component remains constant over time. The second method uses regular bulk milk measurements as a reference for bias correction. This method compares the measured concentrations of the bulk milk to the volume-weighted average concentrations of individual milk samples predicted by the sensor. Any difference between the actual and calculated bulk milk composition is then used to perform a bias correction on the predictions by the sensor system. The effectiveness of these methods to improve the component prediction was evaluated on data originating from a custom-built sensor that automatically measures the NIR reflectance and transmittance spectra of raw milk on the farm. We evaluate the practical use case where models for predicting the milk composition are trained upon installation of the sensor at the farm, and later used to predict the composition of subsequent samples over a period of more than 6 months. The effectiveness of the fully unsupervised method was confirmed when the mean concentration of the milk samples remained constant, while the effectiveness reduced when this was not the case. The bulk milk correction method was effective when all relevant samples for the component were measured by the sensor and included in the analyzed bulk milk, but is less effective when samples included in the bulk which are not measured by the sensor system. When the necessary conditions are met, these methods can be used to extend the lifetime of deployed prediction models by significantly reducing the bias on the predicted values., 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.)

As a crucial endogenous reactive oxygen species, hypochlorous acid (HClO) plays an indispensable role in numerous physiological and pathological processes. Additionally, it serves as a biomarker closely associated with inflammation and liver injury. The utilization of near-infrared fluorescence probes has surged in recent years for live biological imaging, owing to their minimal tissue damage and potent tissue penetration capabilities. In this work, a novel near-infrared fluorescence probe MB-HPD was synthesized to sensitively detect HClO. Probe MB-HPD exhibits remarkable selectivity, high sensitivity (14.3 nM), and rapid response towards HClO (20 s). Probe MB-HPD has demonstrated successful application in the imaging of HClO within cells and zebrafish. Remarkably, it has proven to be effective for detecting HClO within environmental samples, as well as imaging HClO in mice models of arthritis and APAP-induced liver injury. These findings indicate the broad applicability of probe MB-HPD, offering a promising avenue for designing highly selective near-infrared fluorescence probes suitable for real-time HClO monitoring., 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.)

Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant's nutritional status. This is limited by the fact that it requires extensive leaf sampling and chemical analysis when it is to be done over more extensive areas like field- or landscape scale. Thus, rapid, reliable, and repeatable means of nutrient estimations are needed. In this context, lab-based remote sensing or spectroscopy has been explored in the current study to predict the foliar nutritional status of the cashew crop. Novel spectral indices (normalized difference and simple ratio), chemometric modeling, and partial least square regression (PLSR) combined machine learning modeling of the visible near-infrared hyperspectral data were employed to predict macro- and micronutrients content of the cashew leaves. The full dataset was divided into calibration (70 % of the full dataset) and validation (30 % of the full dataset) datasets. An independent validation dataset was used for the validation of the algorithms tested. The approach of spectral indices yielded very poor and unreliable predictions for all eleven nutrients. Among the chemometric models tested, the performance of the PLSR was the best, but still, the predictions were not acceptable. The PLSR combined machine learning modeling approach yielded acceptable to excellent predictions for all the nutrients except sulphur and copper. The best predictions were observed when PLSR was combined with Cubist for nitrogen, phosphorus, potassium, manganese, and zinc; support vector machine regression for calcium, magnesium, iron, copper, and boron; elastic net for sulphur. The current study showed hyperspectral remote sensing-based models could be employed for non-destructive and rapid estimation of cashew leaf macro- and micro-nutrients. The developed approach is suggested to employ within the operational workflows for site-specific and precision nutrient management of the cashew orchards., 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 one of endogenous reactive oxygen species (ROS), peroxynitrite (ONOO - ) performs various functions in both pathological and physiological mechanisms. In this work, an optical and near-infrared (NIR) fluorescent probe (NX), which based on 3-dihydro-1H-xanthene and 2-dicyanomethylene-3-cyano-4,5,5-trimethyl-2,5-dihydrofuran (TCF) group was designed and prepared to detect ONOO - . This probe revealed an obvious optical and a fluorescent response when ONOO - was present and it exhibited higher selectivity over other ROS. Especially, the dual NIR fluorescence changes at 660 and 800 nm allowed quantitative detection of ONOO - in the range of 15-40 μM, and the detection limit was 82 nM. Finally, the probe was effectively employed to visualize exogenous and endogenous ONOO - in HepG2 cells and zebrafish, respectively. All the results indicated the dual NIR-channel probe could serve as a potent detecting tools in studying ONOO - in vitro and in vivo., 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.)

A data fusion strategy based on near-infrared (NIR) and mid-infrared (MIR) spectroscopy techniques were developed for rapid origin identification and quality evaluation of Lonicerae japonicae flos (LJF). A high-level data fusion for origin identification was formed using the soft voting method. This data fusion model achieved accuracy, log-loss value and Kappa value of 95.5%, 0.347 and 0.910 on the prediction set. The spectral data were converted to liquid chromatography data using a data fusion model constructed by the weighted average algorithm. The Euclidean distance and adjusted cosine similarity were used to evaluate the similarity between the converted and the real chromatographic data, with results of 247.990 and 0.996, respectively. The data fusion models all performed better than the models constructed using single data. This indicates that multispectral data fusion techniques have a wide range of application prospects and practical value in the quality control of natural products such as LJF., 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.)

Real-time and non-invasive measurements of tissue concentrations of oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR) are invaluable for research and clinical use. Frequency-domain near-infrared spectroscopy (FD-NIRS) enables non-invasive measurement of these chromophore concentrations in human tissue. We present a small form factor, dual-wavelength, miniaturized FD-NIRS instrument for absolute optical measurements, built around a custom application-specific integrated circuit and a dual-slope/self-calibrating (DS/SC) probe. The modulation frequency is 55 MHz, and the heterodyning technique was used for intensity and phase readout, with an acquisition rate of 0.7 Hz. The instrument consists of a 14 × 17 cm2 printed circuit board (PCB), a Raspberry Pi 4, an STM32G491 microcontroller, and the DS/SC probe. The DS/SC approach enables this instrument to be selective to deeper tissue and conduct absolute measurements without calibration. The instrument was initially validated using a tissue-mimicking solid phantom, and upon confirming its suitability for in vivo, a vascular occlusion experiment on a human subject was conducted. For the phantom experiments, an average of 0.08° phase noise and 0.10% standard deviation over the mean for the intensities was measured at a source-detector distance of 35 mm. The absorption and reduced scattering coefficients had average precisions (variation of measurement over time) of 0.5% and 0.9%, respectively, on a window of ten frames. Results from the in vivo experiment yielded the expected increase in HbO2 and HbR concentration for all measurement types tested, namely SC, DS intensity, and DS phase., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

The identification of fragrant rice varieties using near-infrared reflectance spectroscopy (NIRS) models has attracted extensive attention from regulatory authorities worldwide. In this study, 138 fragrant and 54 nonfragrant rice varieties were planted in the same region and distinguished using sensory evaluation, gas chromatography-mass spectrometry analysis, and betaine aldehyde dehydrogenase 2 (Badh2) genotyping. Then, the 2-acetyl-1-pyrroline (2-AP) content was assessed based on partial least-squares discriminant (PLS-DA) models generated after 2nd individually or combined with SNV/MSC/smoothing preprocessing successfully classified fragrant rice both in the calibration and predictive sets. Moreover, design of experiments (DoE)-based preprocessing selection was employed as an effective strategy to optimize the calibration models compared with the one variable at a time (OVAT) method. Further, Badh2 genotype sample screening assisted with identifying authentic fragrant rice and guaranteed the NIRS model's prediction accuracy in identifying fragrant rice. In conclusion, the high throughput PLS-DA multivariate method coupled with NIRS data was applied to identify fragrant rice varieties in routine monitoring and was effective, accurate, and rapid., 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.)

Objective: This study undertook neuropharmacological research on the clinical course of controlled medication discontinuation to guide practitioners who are considering stopping medications for youths with attention-deficit hyperactivity disorder (ADHD)., Methods: This study analyzed the data for 14 ADHD children (12 male and 2 female) in two datasets: The children prescribed methylphenidate (MPH) were at an initial mean age of 7.5 years (SD = 1.70, range: 6-11) with a mean ADHD-Rating Score (ADHD-RS) of 26.6 (SD = 8.64, range 15-40). The children who discontinued MPH based on clinical judgment were at a mean age of 12.21 years (SD = 2.12, range: 8-15) with a mean ADHD-RS of 15.9 (SD = 6.86, range 5-27). The go/no-go task was used to assess response inhibition, while functional near-infrared spectroscopy (fNIRS) was used to measure cerebral hemodynamics. Oxygenated hemoglobin (Oxy-Hb) values from fNIRS data were analyzed for each subject, focusing on past and current measurements. Baseline was set at 10 s pre-task, with interval means from 4 to 24 s analyzed. One-sample t-tests were used to evaluate brain activity magnitude., Results: The results of the study demonstrate that the children who had discontinued the medication exhibited activation in specific brain regions including the frontopolar cortex and the right ventrolateral prefrontal cortex. Activation (t = 2.363, p = 0.034, Cohen's d = 0.632) was found especially in the right dorsolateral prefrontal cortex during the performance of the go/no-go task. These activated areas were consistent with those observed in a previous study comparing brain activity during a go/no-go task between children with ADHD and healthy children., Conclusion: The present study showed differences in cerebral hemodynamics before and after discontinuation of MPH in ADHD children whose ADHD symptoms did not recur after MPH was discontinued. In the near future, further investigations that include control groups will be conducted to demonstrate the effects of MPH prior to discontinuation based on the changes in cerebral blood flow in the right prefrontal cortex, which is involved in behavioral inhibition, as observed in this study. This and future research will facilitate the development of criteria for discontinuing treatment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced or appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Background: Parathyroid gland (PG) surgery is often challenging due to the small size and indistinct nature of these glands. The introduction of intraoperative near-infrared autofluorescence (NIRAF) has shown promise in localizing parathyroid tissue. However, the NIRAF features of parathyroid adenomas remain unclear. The aim of this study is to assess the NIRAF pattern of parathyroid adenomas., Methods: Patients who underwent surgery for primary hyperparathyroidism at the University Hospital of Pisa, Endocrine Surgery Unit, between December 2021 and February 2022 were enrolled in this study. Intraoperative NIRAF patterns of suspected parathyroid adenomas were evaluated, with particular attention given to the presence of a bright cap., Results: A retrospective study was conducted on 11 patients with primary hyperparathyroidism who underwent parathyroidectomy at our institution. Histopathological examination of the 15 resected specimens confirmed 14 parathyroid adenomas (12 chief cell parathyroid adenomas, 1 oxyphil cell parathyroid adenoma, and 1 mixed cell parathyroid adenoma) and one schwannoma. All adenomas exhibited a heterogeneous NIRAF pattern, distinct from the homogeneous pattern observed in the schwannoma. A bright cap was identified in 9 out of 14 (64.3%) parathyroid adenomas (all chief cell adenomas). On the contrary, all 9 macroscopically normal PGs identified during surgery presented an homogeneous pattern., Conclusion: Our findings support the integration of NIRAF into parathyroid surgical procedures. The heterogeneous NIRAF pattern observed in parathyroid adenomas, often accompanied by a bright cap, offers a promising intraoperative diagnostic tool to differentiate hyperfunctioning from normal parathyroid tissue. Larger-scale randomized trials are warranted to further validate these findings., (© 2024. The Author(s).)

Peaches are highly valued for their rich nutritional content. Traditional fruit quality accessing methods (i.e., manual squeezing the fruit for firmness) are both subjective and destructive, which tend to diminish the integrity of fruit samples, consequently undermining their market value. Compared to traditional detection methods, nondestructive technology offers efficient and noninvasive solutions for rapidly and accurately assessing internal external quality of peaches. This can significantly enhance product classification and quality assurance while reducing the need for extensive human resources and minimizing potential physical damage to peaches. This review provided a comprehensive overview of nondestructive techniques for peach quality evaluation, including visible/near-infrared spectroscopy, machine vision technology, hyperspectral imaging, dielectric and optical properties, fluorescence spectroscopy, electronic nose/tongue, and acoustic vibration methods. It also evaluates the effectiveness of each technique in assessing internal quality, maturity, and disease detection of peaches. The advantages and limitations of each method were also summarized. This study focuses specifically on peaches and encompasses all existing nondestructive testing methods, providing valuable insights and references for future studies in the field of peach quality analysis using nondestructive testing methods., (© 2024 Institute of Food Technologists.)

Objective: This study aims to explores the physiological and psychological mechanisms of exercise-induced hypoalgesia (EIH) by combining the behavioral results with neuroimaging data on changes oxy-hemoglobin (HbO) in prefrontal cortex (PFC)., Methods: A total of 97 healthy participants were recruited and randomly divided into three groups: a single dance movement therapy (DMT) group, a double DMT group, and control group. Evaluation indicators included the pressure pain threshold (PPT) test, the color-word stroop task (CWST) for wearing functional near-infrared spectroscopy (fNIRS), and the self-assessment manikin (SAM). The testing time is before intervention, after intervention, and one hour of sit rest after intervention., Results: 1) Repeated measures ANOVA revealed that, there is a time * group effect on the PPT values of the three groups of participants at three time points. After 30 min of acute dance intervention, an increase in the PPT values of 10 test points occurred in the entire body of the participants in the experimental group with a significant difference than the control group. 2) In terms of fNIRS signals, bilateral DLPFC and left VLPFC channels were significantly activated in the experimental group. 3) DMT significantly awakened participants and brought about pleasant emotions, but cognitive improvement was insignificant. 4) Mediation effect analysis found that the change in HbO concentration in DLPFC may be a mediator in predicting the degree of improvement in pressure pain threshold through dance intervention (total effect β = 0.7140)., Conclusion: In healthy adults, DMT can produce a diffuse EIH effect on improving pressure pain threshold, emotional experience but only showing an improvement trend in cognitive performance. Dance intervention significantly activates the left ventrolateral and bilateral dorsolateral prefrontal cortex. This study explores the central nervous system mechanism of EIH from a physiological and psychological perspective., Competing Interests: Declaration of competing interest There are no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Most existing studies have focused on identifying the origin of species with protected geographical indications while neglecting to determine the proximate geographical origin of different species. In this study, we investigated the feasibility of using near- and mid-infrared spectroscopy to identify the origin of 156 Polygonatum kingianum samples from six regions in Yunnan, China. In this work, spectral images of different modes reveal more information about the P. kingianum. Comparing the performance of traditional machine learning models according to single spectrum and data fusion, the middle-level data fusion-principal component model has the best performance, and its sensitivity, specificity, and accuracy are all 1, and the model has the least number of variables. The residual convolutional neural network (ResNet) model constructed in the 1050-850 cm -1 band confirms that fewer variables are beneficial in improving the accuracy of the model. In conclusion, this study verifies the feasibility of the proposed strategy and establishes a practical model to determine the source of P. kingianum., (© 2024 Institute of Food Technologists.)

Cochlear implants (CIs) are the most successful neural prostheses, enabling individuals with severe to profound hearing loss to access sounds and understand speech. While CI has demonstrated success, speech perception outcomes vary largely among CI listeners, with significantly reduced performance in noise. This review paper summarizes prior findings on speech-evoked cortical activities in adult CI listeners using functional near-infrared spectroscopy (fNIRS) to understand (a) speech-evoked cortical processing in CI listeners compared to normal-hearing (NH) individuals, (b) the relationship between these activities and behavioral speech recognition scores, (c) the extent to which current fNIRS-measured speech-evoked cortical activities in CI listeners account for their differences in speech perception, and (d) challenges in using fNIRS for CI research. Compared to NH listeners, CI listeners had diminished speech-evoked activation in the middle temporal gyrus (MTG) and in the superior temporal gyrus (STG), except one study reporting an opposite pattern for STG. NH listeners exhibited higher inferior frontal gyrus (IFG) activity when listening to CI-simulated speech compared to natural speech. Among CI listeners, higher speech recognition scores correlated with lower speech-evoked activation in the STG, higher activation in the left IFG and left fusiform gyrus, with mixed findings in the MTG. fNIRS shows promise for enhancing our understanding of cortical processing of speech in CI listeners, though findings are mixed. Challenges include test-retest reliability, managing noise, replicating natural conditions, optimizing montage design, and standardizing methods to establish a strong predictive relationship between fNIRS-based cortical activities and speech perception in CI listeners., (© 2024. The Author(s).)

Background: Olfactory deterioration is suggested to be a predictor of some neurodegenerative diseases. Recent studies indicate that physical exercise has a positive relationship with olfactory performance, and a subregion in the prefrontal cortex (PFC) may play an important role in olfactory processing. The PFC is not only related to olfactory function but it also engages in complex functions such as cognition and emotional processing. Methodology: Our study compared the functional connectivity between the olfactory cortex and the PFC in healthy individuals who exercised regularly and healthy persons who did not. Those who exercised more than three times/week for at least 30 min each time were considered the exercise group, and those who did not meet this exercise criteria were considered the nonexercise group. We also assessed their odor threshold. Participants were aged 55 years or older, and the two groups were balanced for age, sex, body mass index, and educational level. Results: We found that compared with individuals who did not exercise, exercisers had a significantly lower threshold for detecting odors. In addition, the olfactory cortex had stronger connectivity with the PFC in exercisers than in nonexercisers. More specifically, when the PFC was grouped into three subregions, namely, the ventrolateral prefrontal cortex (VLPFC), dorsolateral prefrontal cortex (DLPFC), and frontopolar cortex (FPA), Pearson correlation analysis revealed stronger connectivity between the VLPFC and the orbitofrontal cortex (OFC), between the OFC and the FPA, and between the left and right OFC hemispheres in the exercisers. In addition, Granger causality indicated higher directional connectivity from the DLPFC to the OFC in exercisers than in nonexercisers. Conclusion: Our findings indicated that the exercise group not only had better olfactory performance but also had stronger functional connectivity between the olfactory cortex and the PFC than nonexercise group.

Background: The neural mechanisms underlying differences in the performance of simulated arthroscopic skills across various skill levels remain unclear. Our primary objective is to investigate the learning mechanisms of simulated arthroscopic skills using functional near-infrared spectroscopy (fNIRS)., Methods: We recruited 27 participants, divided into three groups: novices (n = 9), intermediates (n = 9), and experts (n = 9). Participants completed seven arthroscopic tasks on a simulator, including diagnostic navigation, triangulation, grasping stars, diagnostic exploration, meniscectomy, synovial membrane cleaning, and loose body removal. All tasks were videotaped and assessed via the simulator system and the Arthroscopic Surgical Skill Evaluation Tool (ASSET), while cortical activation data were collected using fNIRS. Simulator scores and ASSET scores were analyzed to identify different level of performance of all participants. Brain region activation and functional connectivity (FC) of different types of participants were analyzed from fNIRS data., Results: Both the expert and intermediate groups scored significantly higher than the novice group (p < 0.001). There were significant differences in ASSET scores between experts and intermediates, experts and novices, and intermediates and novices (p = 0.0047, p < 0.0001, p < 0.0001), with the trend being experts > intermediates > novices. The intermediate group exhibited significantly greater activation in the left primary motor cortex (LPMC) and left prefrontal cortex (LPFC) compared to the novice group (p = 0.0152, p = 0.0021). Compared to experts, the intermediate group demonstrated significantly increased FC between the presupplementary motor area (preSMA) and the right prefrontal cortex (RPFC; p < 0.001). Additionally, the intermediate group showed significantly increased FC between the preSMA and LPFC, RPFC and LPFC, and LPMC and LPFC compared to novices (p = 0.0077, p = 0.0285, p = 0.0446)., Conclusion: Cortical activation and functional connectivity reveal varying levels of activation intensity in the PFC, PMC, and preSMA among novices, intermediates, and experts. The intermediate group exhibited the highest activation intensity., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Preserving parathyroid function during thyroidectomy is crucial, but remains challenging. Real-time near-infrared autofluorescence (NIRAF) aids surgeons in intraoperative parathyroid gland (PTG) identification. However, its role in detecting PTGs unintentionally removed during surgery is unclear., Study Design: This prospective study included adult patients undergoing endoscopic thyroidectomy. Surgeons identified and documented PTGs visually. Excised specimens underwent visual inspection and NIRAF imaging (PDE-Neo II). All fluorescent tissues were dissected and pathologically evaluated (reference standard). One scanned image per lobe was chosen to quantify autofluorescence (AF) intensity., Results: Overall, 95 patients underwent endoscopic thyroidectomies, with NIRAF imaging applied to 152 excised lobes. Of these, 19 lobes displayed a total of 23 spots with increased intensity. 175 specimens were sent for pathological evaluation, and 7 were confirmed to be parathyroid tissue. NIRAF demonstrated 100.0 % sensitivity and 90.5 % specificity for predicting parathyroid tissue, with 30.4 % positive predictive value, 100.0 % negative predictive value of and 90.9 % accuracy. Quantitatively normalized, the AF signal intensity was significantly higher in NIRAF-positive tissues than negative (4.3 vs 1.2 times, p < 0.0001). Additionally, the AF signal intensity in regions pathologically confirmed of parathyroid tissue was higher than non-parathyroid tissue (9.1 vs 2.1 times, p < 0.0001)., Conclusion: This study suggests that NIRAF has high sensitivity and specificity for detecting inadvertently resected PTGs after endoscopic thyroidectomy, contributing to preservation efforts. However, NIRAF-positive tissues still require additional confirmation through multiple methods, emphasizing other examinations to verify that they are indeed parathyroid tissues. Further research is warranted to refine NIRAF imaging parameters., (Copyright © 2024 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.)

Functional near-infrared spectroscopy was used to record spontaneous hemodynamic fluctuations form the bilateral temporal lobes in 25 children with autism spectrum disorder (ASD) and 22 typically developing (TD) children. The coupling between oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) was calculated by Pearson correlation coefficient, showing significant difference between ASD and TD, thus the coupling could be a characteristic feature for ASD. To evaluate the discrimination ability of the feature obtained in different acquisition times, the receiver operating characteristic curve (ROC) was constructed and the area under curve (AUC) was calculated. The results showed AUC > 0.8 when the time duration was longer than 1.5 min, but longer than 4 min, AUC value (~0.87) hardly varied, implying the maximal discrimination ability reached. This study demonstrated the coupling could be one of characteristic features for ASD even acquired in a short measurement time., (© 2024 Wiley‐VCH GmbH.)

The continuous feeding-mixing system ensures the composition uniformity down to the tableting continuous manufacturing line so that a quality end-product is consistently delivered. Near-infrared spectroscopy (NIRS) enables in-line assessment of the blend's critical quality attributes in real-time. In this study, the effect of total feed rate and impeller speed on the continuous blending process monitored in-line by NIRS was examined by principal component analysis (PCA), ANOVA simultaneous component analysis (ASCA) and partial least squares (PLS) regression. Process data were generated by a factorial experimental design with process parameters and a constant formulation comprised of: 30 % (wt/wt) ibuprofen, 67.5 % (wt/wt) microcrystalline cellulose, 2 % (wt/wt) of sodium starch glycolate and 0.5 % (wt/wt) of magnesium stearate. The PCA hinted at the prevalence of impeller speed effect on ibuprofen concentration due to path length variation of the NIR light caused by the fluidized behaviour in the powder blend as a result of high speed ranges (>300 rpm). The ASCA model indicated that while both impeller speed and total feed rate effects were statistically significant (p-value=0.004), the impeller speed was the factor that contributed the most to the spectral variance (55.5 %). The PLS regression model for the ibuprofen content resulted in a RMSECV of 1.3 % (wt/wt) and showed that impeller speed was yet again the factor that exerted the major influence on spectral variance, owing to its wavelength-dependent effect that prevents common pre-processing techniques from eliminating it across the entire NIR range. The best sample presentation to the NIR probe was achieved at low impeller speed ranges (<600 rpm) and low total feed rates (<15 kg/h), such that it enhanced the PLS model ability to predict the ibuprofen concentration in the blend., (Copyright © 2024. Published by Elsevier B.V.)

Objectives: The objective of this systematic review was to clarify the status of near-infrared spectroscopy (NIRS) in monitoring perioperative renal regional tissue oxygen saturation (rSO 2 ) and determine whether there is evidence supporting its use in predicting postoperative acute kidney injury (AKI)., Design: A systematic search of electronic databases was conducted to identify all clinical studies that utilized NIRS to monitor renal rSO 2 during the perioperative period to observe postoperative AKI., Setting: Studies published online as of May 31, 2024, were included in the review., Participants: Studies involving human participants undergoing surgery with a predefined outcome of AKI were included., Interventions: Regional tissue oxygen saturation was measured using NIRS., Measurements and Main Results: A total of 144 records were identified in the primary search after removing duplicates. After screening, 18 studies were included in the analysis, consisting of 3 case-control studies and 15 prospective cohort studies. Thirteen reports focused on pediatric surgery, whereas five reports focused on adult surgery. Sixteen studies involved cardiovascular surgery with cardiopulmonary bypass, and two studies focused on liver surgery. All studies received a quality score of 7 or above. Significant heterogeneity and mostly short follow up periods were noted., Conclusion: Renal desaturation may indicate AKI in patients; however, further studies are required to substantiate this relationship. Additional clinical trials are necessary to evaluate normal values and establish the exact threshold of renal rSO 2 that signifies a meaningful decline in renal function., Competing Interests: Declaration of competing interest The authors declare that the research was conducted without any commercial or financial relationship that could be construed as a potential conflict of interest., (Copyright © 2024. Published by Elsevier Inc.)

Conventionally, soil cadmium (Cd) measurements in the laboratory are expensive and time-consuming, involving complex processes of sample preparation and chemical analysis. This study aimed to identify the feasibility of using sensor data of visible near-infrared reflectance (Vis-NIR) spectroscopy and portable X-ray fluorescence spectrometry (PXRF) to estimate regional soil Cd concentration in a time- and cost-saving manner. The sensor data of Vis-NIR and PXRF, and Cd concentrations of 128 surface soils from Yunnan Province, China, were measured. Outer-product analysis (OPA) was used for synthesizing the sensor data and Granger-Ramanathan averaging (GRA) was applied to fuse the model results. Artificial neural network (ANN) models were built using Vis-NIR data, PXRF data, and OPA data, respectively. Results showed that: (1) ANN model based on PXRF data performed better than that based on Vis-NIR data for soil Cd estimation; (2) Fusion methods of both OPA and GRA had higher predictive power (R 2 ) = 0.89, ratios of performance to interquartile range (RPIQ) = 4.14, and lower root mean squared error (RMSE) = 0.06, in ANN model based on OPA fusion; R 2 = 0.88, RMSE = 0.06, and RPIQ = 3.53 in GRA model) than those based on either Vis-NIR data or PXRF data. In conclusion, there exists a great potential for the combination of OPA fusion and ANN to estimate soil Cd concentration rapidly and accurately., 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 B.V.)

Background: Functional electrical stimulation (FES) cycling has been reported to enhance muscle strength and improve muscle fatigue resistance after spinal cord injury (SCI). Despite its proposed benefits, the quantification of muscle fatigue during FES cycling remains poorly documented. This study sought to quantify the relationship between the vibrational performance of electrically-evoked muscles measured through mechanomyography (MMG) and its oxidative metabolism through near-infrared spectroscopy (NIRS) characteristics during FES cycling in fatiguing paralyzed muscles in individuals with SCI., Methods: Six individuals with SCI participated in the study. They performed 30 min of FES cycling with MMG and NIRS sensors on their quadriceps throughout the cycling, and the signals were analyzed., Results: A moderate negative correlation was found between MMG root mean square (RMS) and oxyhaemoglobin (O 2 Hb) [r = -0.38, p = 0.003], and between MMG RMS and total hemoglobin (tHb) saturation [r = -0.31, p = 0.017]. Statistically significant differences in MMG RMS, O 2 Hb, and tHb saturation occurred during pre- and post-fatigue of FES cycling (p < 0.05)., Conclusions: MMG RMS was negatively associated with O 2 Hb and muscle oxygen derived from NIRS. MMG and NIRS sensors showed good inter-correlations, suggesting a promising use of MMG for characterizing metabolic fatigue at the muscle oxygenation level during FES cycling in individuals with SCI., (© 2024 International Center for Artificial Organ and Transplantation (ICAOT) and Wiley Periodicals LLC.)

Functional near-infrared spectroscopy (fNIRS) stands poised to revolutionize our understanding of auditory detection, speech perception, and language development in infants. In this study, we conducted a meticulous integrative review across Medline, Scopus, and LILACS databases, targeting investigations utilizing fNIRS to explore language-related features and cortical activation during auditory stimuli in typical infants. We included studies that used the NIRS technique to study language and cortical activation in response to auditory stimuli in typical infants between 0 and 3 years old. We used the ROBINS-I tool to assess the quality and the risk of bias in the studies. Our analysis, encompassing 66 manuscripts, is presented in standardized tables for streamlined data extraction. We meticulously correlated findings with children's developmental stages, delineating crucial insights into brain development and its intricate interplay with language outcomes. Although most studies have a high risk for overall bias, especially due to the high loss of data in NIRS studies, the low risk in the other domains is predominant and homogeneous among the studies. Highlighted are the unique advantages of fNIRS for pediatric studies, underscored by its innate suitability for use in children. This review accentuates fNIRS' capacity to elucidate the neural correlates of language processing and the sequential steps of language acquisition. From birth, infants exhibit abilities that lay the foundation for language development. The progression from diffuse to specific neural activation patterns is extremely influenced by exposure to languages, social interaction, and prosodic features and, reflects the maturation of brain networks involved in language processing. In conclusion, fNIRS emerges as an indispensable functional imaging modality, providing insights into the temporal dynamics of language acquisition and associated developmental milestones. This synthesis presents the pivotal role of fNIRS in advancing our comprehension of early language development and paves the way for future research endeavors in this domain., (© 2024 International Society for Developmental Neuroscience.)

Objective This study aimed to explore whether behavioral synchrony (BS) and inter-brain synchrony (IBS) could serve as potential biomarkers for alliance quality or outcomes among clients with different adult attachment styles. Method: We assessed the clients' self-report working alliance and clinical outcomes as well as simultaneously measured BS using motion energy analysis (MEA) and IBS with functional near-infrared spectroscopy (fNIRS) among 37 secure ( N  = 21) or dismissing ( N  = 16) clients with their counselors during the first psychological counseling meeting. Results: Dismissing dyads manifested significantly higher late-stage counselor-led and client-led IBS ( p  = .018) than secure dyads. Adult attachment style served as the moderators in the correlation of both whole-stage client-led BS with bond dimension of alliance ( p  = .015) as well as in the correlation of both whole-stage no-lag IBS with CORE-10 score changes ( p  = .022). Moreover, increases in the whole-stage client-led BS were significantly associated with decreases in early-stage, late-stage and whole-stage no-lag IBS (all p s ≤ 0.01). Conclusion: These findings revealed the potentially impeding role of interpersonal synchrony in alliance quality for dismissing clients, at least during the first psychological counseling meetings. They also might partially validate the relationship between different modalities of interpersonal synchrony.

Plant stress diagnosis is essential for efficient crop management and productivity increase. Under stress, plants undergo physiological and compositional changes. Vegetation indices obtained from leaf reflectance spectra and bioimpedance spectroscopy provide information about the external and internal aspects of plant responses, respectively. In this study, bioimpedance and vegetation indices were noninvasively acquired from sweet basil (Ocimum basilicum L.) leaves exposed to three types of stress (drought, salinity, and chilling). Integrating the vegetation index, a novel approach, contains information about the surface of plants and bioimpedance data, which indicates the internal changes of plants. The fusion of these two datasets was examined to classify the types and severity of stress. Among the eight supervised machine learning models (three linear and five non-linear), the support vector machine (SVM) exhibited the highest accuracy in classifying stress types. Bioimpedance spectroscopy alone exhibited an accuracy of 0.86 and improved to 0.90 when fused with vegetation indices. Additionally, for drought and salinity stresses, it was possible to classify the early stage of stress with accuracies of 0.95 and 0.93, respectively. This study will allow us to classify the different types and severity of plant stress, prescribe appropriate treatment methods for efficient cost and time management of crop production, and potentially apply them to low-cost field measurement systems., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Soo Chung reports financial support was provided by Electronics and Telecommunications Research Institute. Daesik Son reports financial support was provided by Electronics and Telecommunications Research Institute. Junyoung Park reports financial support was provided by Electronics and Telecommunications Research Institute. Siun Lee reports financial support was provided by Electronics and Telecommunications Research Institute. Jae Joon Kim reports financial support was provided by Institute of Information & communications Technology Planning & Evaluation. 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. Published by Elsevier B.V.)

This study proposed a novel approach to automatically select the preprocessing methods and hyperparameters of machine learning (ML) algorithms based on their best performance in cross-validation for near-infrared (NIR) spectroscopy data. The proposed method simultaneously incorporates single or multiple-preprocessing steps and tunes hyperparameters to determine the best model performance for FT-NIR and Micro-NIR spectral data of coconut milk adulteration with distilled water and mature coconut water in the range of 0%-50%. Computational experiments were conducted using nine single preprocessing types, three types of ML classifier (linear discriminant analysis (LDA), k-nearest neighbour (KNN), multilayer perceptron (MLP)) and three types of ML regressor (partial least squares (PLS), KNN, MLP). The proposed performance strategy effectively addressed and produced satisfactory outcomes for classification and regression challenges in coconut milk adulteration. Finally, the results demonstrated that the proposed approach can more accurately determine the best model, particularly for NIR spectroscopy of coconut milk adulteration., 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.)

Functional near-infrared spectroscopy (fNIRS), a non-invasive optical neuroimaging technique that is portable and acoustically silent, has become a promising tool for evaluating auditory brain functions in hearing-vulnerable individuals. This study, for the first time, used fNIRS to evaluate neuroplasticity of speech-in-noise processing in older adults. Ten older adults, most of whom had moderate-to-mild hearing loss, participated in a 4-week speech-in-noise training. Their speech-in-noise performances and fNIRS brain responses to speech (auditory sentences in noise), non-speech (spectrally-rotated speech in noise) and visual (flashing chequerboards) stimuli were evaluated pre- (T0) and post-training (immediately after training, T1; and after a 4-week retention, T2). Behaviourally, speech-in-noise performances were improved after retention (T2 vs. T0) but not immediately after training (T1 vs. T0). Neurally, we intriguingly found brain responses to speech vs. non-speech decreased significantly in the left auditory cortex after retention (T2 vs. T0 and T2 vs. T1) for which we interpret as suppressed processing of background noise during speech listening alongside the significant behavioural improvements. Meanwhile, functional connectivity within and between multiple regions of temporal, parietal and frontal lobes was significantly enhanced in the speech condition after retention (T2 vs. T0). We also found neural changes before the emergence of significant behavioural improvements. Compared to pre-training, responses to speech vs. non-speech in the left frontal/prefrontal cortex were decreased significantly both immediately after training (T1 vs. T0) and retention (T2 vs. T0), reflecting possible alleviation of listening efforts. Finally, connectivity was significantly decreased between auditory and higher-level non-auditory (parietal and frontal) cortices in response to visual stimuli immediately after training (T1 vs. T0), indicating decreased cross-modal takeover of speech-related regions during visual processing. The results thus showed that neuroplasticity can be observed not only at the same time with, but also before, behavioural changes in speech-in-noise perception. To our knowledge, this is the first fNIRS study to evaluate speech-based auditory neuroplasticity in older adults. It thus provides important implications for current research by illustrating the promises of detecting neuroplasticity using fNIRS in hearing-vulnerable individuals., (© 2024. The Author(s).)