Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics.

A rapid method was developed for determining the total flavonoid and protein content in Tartary buckwheat by employing near-infrared spectroscopy (NIRS) and various machine learning algorithms, including partial least squares regression (PLSR), support vector regression (SVR), and backpropagation neural network (BPNN). The RAW-SPA-CV-SVR model exhibited superior predictive accuracy for both Tartary and common buckwheat, with a high coefficient of determination (R2p = 0.9811) and a root mean squared error of prediction (RMSEP = 0.1071) for flavonoids, outperforming both PLSR and BPNN models. Additionally, the MMN-SPA-PSO-SVR model demonstrated exceptional performance in predicting protein content (R2p = 0.9247, RMSEP = 0.3906), enhancing the effectiveness of the MMN preprocessing technique for preserving the original data distribution. These findings indicate that the proposed methodology could efficiently assess buckwheat adulteration analysis. It can also provide new insights for the development of a promising method for quantifying food adulteration and controlling food quality.

An integrated and rapid evaluation of Curcumae Radix from different botanical origins based on chemical components, antiplatelet aggregation effect and Fourier transform near-infrared spectroscopy.

Curcumae Radix (CR) is a widely used traditional Chinese medicine with significant pharmaceutical importance, including enhancing blood circulation and addressing blood stasis. This study aims to establish an integrated and rapid quality assessment method for CR from various botanical origins, based on chemical components, antiplatelet aggregation effects, and Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate algorithms. Firstly, ultra-performance liquid chromatography-photodiode array (UPLC-PDA) combined with chemometric analyses was used to examine variations in the chemical profiles of CR. Secondly, the activation effect on blood circulation of CR was assessed using an in vitro antiplatelet aggregation assay. The studies revealed significant variations in chemical profiles and antiplatelet aggregation effects among CR samples from different botanical origins, with constituents such as germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin, and curcumin showing a positive correlation with antiplatelet aggregation biopotency. Thirdly, FT-NIR spectroscopy was integrated with various machine learning algorithms, including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Subspace K-Nearest Neighbors (Subspace KNN), to classify CR samples from four distinct sources. The result showed that FT-NIR combined with KNN and SVM classification algorithms after SNV and MSC preprocessing successfully distinguished CR samples from four plant sources with an accuracy of 100%. Finally, Quantitative models for active constituents and antiplatelet aggregation bioactivity were developed by optimizing the partial least squares (PLS) model with interval combination optimization (ICO) and competitive adaptive reweighted sampling (CARS) techniques. The CARS-PLS model achieved the best predictive performance across all five components. The coefficient of determination (R2p) and root mean square error (RMSEP) in the independent test sets were 0.9708 and 0.2098, 0.8744 and 0.2065, 0.9511 and 0.0034, 0.9803 and 0.0066, 0.9567 and 0.0172 for germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin and curcumin, respectively. The ICO-PLS model demonstrated superior predictive capabilities for antiplatelet aggregation biotency, achieving an R2p of 0.9010, and an RMSEP of 0.5370. This study provides a valuable reference for the quality evaluation of CR in a more rapid and comprehensive manner.

Fusion of convolutional neural network with XGBoost feature extraction for predicting multi-constituents in corn using near infrared spectroscopy.

Near-infrared (NIR) spectroscopy has been widely utilized to predict multi-constituents of corn in agriculture. However, directly extracting constituent information from the NIR spectra is challenging due to many issues such as broad absorption band, overlapping and non-specific nature. To solve these problems and extract implicit features from the raw data of NIR spectra to improve performance of quantitative models, a one-dimensional shallow convolutional neural network (CNN) model based on an eXtreme Gradient Boosting (XGBoost) feature extraction method was proposed in this paper. The leaf node feature information in the XGBoost was encoded and reconstructed to obtain the implicit features of raw data in the NIR spectra. A two-parametric Swish (TSwish or TS) activation function was proposed to improve the performance of CNN, and the elastic net (EN) was also applied to avoid the overfitting problem of the CNN model. Performance of the developed XGBoost-CNN-TS-EN model was evaluated using two public NIR spectroscopy datasets of corn and soil, and the obtained determination coefficients (R2) for moisture, oil, protein, and starch of the corn on test set were 0.993, 0.991, 0.998, and 0.992, respectively, with that of the soil organic matter being 0.992. The XGBoost-CNN-TS-EN model exhibits superior stability, good prediction accuracy, and generalization ability, demonstrating its great potentials for quantitative analysis of multi-constituents in spectroscopic applications.

Authentication of Argentinean extra-virgin olive oils using three-way fluorescence and two-way near-infrared data fused with multi-block DD-SIMCA.

A trending problem of Extra Virgin Olive Oil (EVOO) adulteration is investigated using two analytical platforms, involving: (1) Near Infrared (NIR) spectroscopy, resulting in a two-way data set, and (2) Fluorescence Excitation-Emission Matrix (EEFM) spectroscopy, producing three-way data. The related instruments were employed to study genuine and adulterated samples. Each data set was first separately analyzed using the Data Driven-Soft Independent Modeling of Class Analogies (DD-SIMCA) method, based on Principal Component Analysis (for the two-way NIR data) and PARallel FACtor analysis (for the three-way EEFM data). The data sets were then processed together using the multi-block fusion method, based on the concept of Cumulative Analytical Signal (CAS). A comparison of the data processing methods in terms of sensitivity, specificity and selectivity showed the following order of excellence: NIR < EEFM < NIR + EEFM. This finding confirms the effectiveness of multi-block data fusion, which cumulatively improves the model performance.

A three-stage wavelength selection algorithm for near-infrared spectroscopy calibration.

The near-infrared spectral data is highly high dimensional and contains redundant information, it is necessary to identify the most representative characteristic wavelengths before modeling to improve model accuracy and reliability. At present, there are many methods for selecting the characteristic wavelengths of NIR spectroscopy, but the collinearity among wavelengths is still a main issue that leads to poor model effects. Therefore, this study proposes a three-stage wavelength selection algorithm (Stage III) to reduce redundancy in NIR spectral data and collinearity between wavelength variables, resulting in a simpler and more accurate predictive model. The research uses a public NIR data set of corn samples as its subject. Initially, the wavelengths with the higher correlation coefficients are chosen after calculating the relationship coefficients between every wavelength vector and the concentration vector. On this basis, the correlation coefficients between the vectors of each wavelength point are calculated, and those wavelength points with smaller correlation coefficients with other wavelength points are selected. Ultimately, the stepwise regression analysis selects the wavelengths that provide substantial value to the model as the variables for modeling, leading to the development of a multiple linear regression model. The results show that the model using the three-stage wavelength selection algorithm outperforms those using the full spectrum, Stages I and Stage II, and the coefficient of determination of the test set of the Stage III-MLR model achieved an accuracy of 0.9360. Instead of the successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS), Stage III is better in the model prediction accuracy. Therefore, the three-stage wavelength selection algorithm is an effective wavelength selection algorithm that can effectively model NIR spectroscopy, reduce the collinearity between the wavelength variables, simplify the complexity of the model, and improve the prediction precision of the model.

Quantifying neurovascular coupling through a concurrent assessment of arterial, capillary, and neuronal activation in humans: A multimodal EEG-fNIRS-TCD investigation.

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).

Atypical prefrontal neural activity during an emotional interference control task in adolescents with autism spectrum disorder: A functional near-infrared spectroscopy study.

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 O2Hb 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.

Diminished negative emotion regulation through affect labeling and reappraisal: insights from functional near infrared spectroscopy on lateral prefrontal cortex activation.

BACKGROUND: Reappraisal, an emotion regulation strategy, includes reinterpretation and affect labeling involving verbalizing emotions. In general, reappraisal is supported by lateral prefrontal cortical regions, which are also known to underlie cognitive regulation. Other research has shown that affect labeling combined with reappraisal of negative emotions increases lateral prefrontal cortex activity more than reappraisal alone does, suggesting that affect labeling facilitates emotional regulation. However, the influence of affect labeling on the efficacy of reappraisal in reducing subjective negative emotions has not been determined. METHODS: In the experiment, 35 participants (mean age = 28.2 years (SD = 9.63); 12 women and 23 men) viewed vignettes that aroused negative emotion. Then, they rated subjective negative emotions as baseline values. Following the baseline rating, the task branched into four conditions, combining affect labeling and emotion regulation factors. In the affect-labeling factor, participants selected emotional labels consistent with their own emotions or not. Regarding the emotion regulation factor, participants engaged in reappraisal to regulate their negative emotions. Throughout the experiment, the intensity of negative emotions was measured three times, mirroring the baseline measurement. Oxyhemoglobin (OxyHb) signal values in prefrontal cortex regions during tasks were measured by functional near-infrared spectroscopy. RESULTS: Differences between the subjective negative emotion ratings at baseline and after reappraisal indicated that reappraisal significantly reduced negative emotion with and without affect labeling (t (1173.05) = 29.97, p < 0.001), and the combination of affect labeling and reappraisal was less effective in regulating negative emotions at the subjective level than reappraisal without affect labeling (t (1172.03) = 3.15, p < 0.01). Additionally, there was an increase in OxyHb signal in the bilateral dorsolateral prefrontal and right ventral prefrontal cortices while participants performed reappraisal with affect labeling. CONCLUSION: Our findings suggest that affect labeling, when performed prior to cognitive reappraisal, may influence the process of negative emotion regulation in complex ways. The interaction between affect labeling and reappraisal appears to modulate prefrontal cortex activity, potentially reflecting changes in cognitive processing during emotion regulation attempts. These results highlight the need for further investigation into the intricate relationship between different emotion regulation strategies.

Executive functions and theory of mind development in preschoolers: Insights from NIRS data.

Numerous studies have highlighted the importance of executive functions (EFs) in the development of Theory of Mind (ToM) in preschoolers. However, research focusing on young children at the neural level has been limited. This study examined the relationship between EFs and ToM in twenty-nine healthy Japanese preschoolers aged 5-7 years, focusing on neural responses during EF and ToM tasks using near-infrared spectroscopy (NIRS) to monitor prefrontal cortex (PFC) activity. The study utilized EF tasks and the Sally-Anne scenario to assess false- and true-belief understanding, aiming to provide a comprehensive analysis of ToM capabilities. Results indicated that despite advanced EF capabilities and a ceiling effect across all EF tasks, there were no significant correlations between EF performance or verbal ability and ToM task performance. NIRS data revealed no PFC activation during the Stroop task. However, activation was observed in the left and right lateral PFC in the control false belief condition, the left lateral PFC in the false belief condition, and across all PFC regions in the true belief condition during ToM tasks. Significant relationships were found between behavioral performance in ToM tasks and neural activity in key brain regions. The study also identified a complex relationship between false and true belief reasoning, suggesting a nuanced developmental trajectory for ToM. These findings underscore the crucial role of early childhood in the development of ToM and the complex interplay between cognitive functions and neural efficiency in understanding others' mental states.

The DLPFC is centrally involved in resolving Stroop conflicts, suppressing distracting sensory input within the auditory and visual system.

Introduction: Cognitive control is a prerequisite for successful, goal-oriented behavior. The dorsolateral prefrontal cortex (DLPFC) is assumed to be a key player in applying cognitive control; however, the neural mechanisms by which this process is accomplished are still unclear. Methods: To further address this question, an audiovisual Stroop task was used, comprising simultaneously presented pictures and spoken names of actors and politicians. Depending on the task block, participants had to indicate whether they saw the face or heard the name of a politician or an actor (visual vs. auditory blocks). In congruent trials, both stimuli (visual and auditory) belonged to the same response category (actor or politician); in incongruent trials, they belonged to different categories. During this task, activity in sensory target regions was measured via functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), respectively. Specifically, fNIRS was used to monitor activity levels within the auditory cortex, while the EEG-based event-related potential of the N170 was considered as a marker of FFA (fusiform face area) involvement. Additionally, we assessed the effects of inhibitory theta-burst stimulation-a specific protocol based on repetitive transcranial magnetic stimulation (rTMS)-over the right DLPFC. Non-invasive brain stimulation is one of the few means to draw causal conclusions in human neuroscience. In this case, rTMS was used to temporarily inhibit the right DLPFC as a presumed key player in solving Stroop conflicts in one of two measurement sessions; then, effects were examined on behavioral measures as well as neurophysiological signals reflecting task-related activity in the frontal lobes and sensory cortices. Results: The results indicate a central role of the DLPFC in the implementation of cognitive control in terms of a suppression of distracting sensory input in both the auditory cortex and visual system (FFA) in high-conflict situations. Behavioral data confirm a reduced Stroop effect following previous incongruent trials ("Gratton effect") that was only accomplished with an intact DLPFC (i.e., following placebo stimulation). Discussion: Because non-invasive brain stimulation is uniquely suited to causally test neuroscientific hypotheses in humans, these data give important insights into some of the mechanisms by which the DLPFC establishes conflict resolution across different sensory modalities.

Gender differences in the use of an upper-extremity exoskeleton during physically and cognitively demanding tasks- a study protocol for a randomized experimental trial.

Background: Upper limb exoskeletons are recommended to alleviate muscle fatigue, particularly in working conditions inducing musculoskeletal discomfort like overhead work. However, wearing an exoskeleton might introduce cognitive-motor interference, affecting performance. Understanding its neural impact and potential gender differences in design effects is crucial. Therefore, the aim of this study is to examine exoskeleton effects addressing cross-gender comparisons, and exploring the impact on cognitive and physical workload in real-world scenarios. The research questions address the impact of exoskeleton use on muscle synergies, upper body posture, cognitive resources, comfort/discomfort, acceptance and usability. Methods: The cross-sectional study integrates a multifactorial mixed-measure design. Participants are grouped by gender (female vs. male) and working condition (with vs. without exoskeleton). Motor performance and underlying neuronal correlates (fNIRS) will be analyzed. Based on an a priori sample size calculation, 80 participants (40 female/40 male) will be recruited. Working performance will be assessed by 1. Physical Performance Task (PILE task) and 2. Precision Task (following the Fitts paradigm), while body postures will be monitored with an Xsens motion capture system. Brain activation will be captured with an fNIRS system comprising 32 active optodes. Postural comfort/discomfort, acceptance, and usability will be reported via standardized questionnaires. Discussion: The study will gain insights into potential gender differences in exoskeleton use and will contribute to designing and optimizing the implementation of exoskeletons by considering muscle synergies, movement variability and cognitive resource allocation. Additionally, the study also highlights user discomfort, a crucial factor that could impede widespread adoption, particularly among females, in real-world scenarios.

Fundamental effects of array density and modulation frequency on image quality of diffuse optical tomography.

BACKGROUND: Diffuse optical tomography (DOT) provides three-dimensional image reconstruction of chromophore perturbations within a turbid volume. Two leading strategies to optimize DOT image quality include, (i) arrays of regular, interlacing, high-density (HD) grids of sources and detectors with closest spacing less than 15 mm, or (ii) source modulated light of order ∼100 MHz. PURPOSE: However, the general principles for how these crucial design parameters of array density and modulation frequency may interact to provide an optimal system design have yet to be elucidated. METHODS: Herein, we systematically evaluated how these design parameters effect image quality via multiple key metrics. Specifically, we simulated 32 system designs with realistic measurement noise and quantified localization error, spatial resolution, signal-to-noise, and localization depth of field for each of ∼85 000 point spread functions in each model. RESULTS: We found that array density had a far stronger effect on image quality metrics than modulation frequency. Additionally, model fits for image quality metrics revealed that potential improvements diminish with regular arrays denser than 9 mm closest spacing. Further, for a given array density, 300 MHz source modulation provided the deepest reliable imaging compared to other frequencies. CONCLUSIONS: Our results indicate that both array density and modulation frequency affect the spatial sampling of tissue, which asymptotically saturates due to photon diffusivity within a turbid volume. In summary, our results provide comprehensive perspectives for optimizing future DOT system designs in applications from wearable functional brain imaging to breast tumor detection.

Rationalizing iron-gall ink recipes from antiquity until the 19th century. A kinetic study.

Iron-gall inks, a vital part of our written cultural heritage, are at risk of complete loss due to degradation, a potential loss that we must urgently address. These inks are based on Fe3+-complexes with phenolic compounds, which grow to form a complex network of iron oxyhydroxides. Over time, these black inks turn into brownish tones, with extensive degradation in paper support leading to extensive breaking. The kinetics of iron-gall ink preparation explains the use of iron sulfate, FeSO4, in all ancient recipes to obtain a stable amorphous ink. The novelty of this work shows that a low ratio of Fe3+/polyphenol is a crucial factor in allowing the ink's growth without its degradation. This ratio also prevents the formation of superoxide. This was achieved through a comprehensive research methodology involving spectroscopic techniques in the visible and the near-infrared regions, stopped-flow spectrometry and electrochemical studies.

Staged strategy of combined rotational atherectomy and intravascular lithotripsy for severely calcified lesions: an evaluation using multimodality intracoronary imaging-a case report.

Background: Severely calcified lesions are the most significant challenge for percutaneous coronary intervention, exhibiting poor clinical outcomes. Some severely calcified lesions remain untreatable with conventional balloons or even atherectomy devices. Intravascular lithotripsy is a new option for treating severe calcification. Case summary: Herein, we describe a case of ischaemic cardiomyopathy with a thick, circumferential calcified lesion in the proximal and mid-segments of the left anterior descending coronary artery. In the first session, high-pressure balloons, cutting balloons, and rotational atherectomy failed to disrupt the calcification. In the staged additional treatment that was subsequently planned, eight cycles of intravascular lithotripsy created multiple fractures in the deep calcification, resulting in successful stent deployment. The effect of intravascular lithotripsy was observed mainly in calcified areas with lipid components detected using near-infrared spectroscopy-intravascular ultrasound. Discussion: Our report suggests the efficacy of employing a combined strategy of rotational atherectomy with small burrs and intravascular lithotripsy in the treatment of severe calcification with a minimal risk of complications. Our study introduces a novel aspect by utilizing near-infrared spectroscopy-intravascular ultrasound to evaluate calcified lesions before performing intravascular lithotripsy. To our knowledge, there have been no similar reports to date. The effect of intravascular lithotripsy on calcified lesions may be related to the distribution of lipid components and/or heterogeneity within the calcification.

Portable NIR Spectroscopy to Simultaneously Trace Honey Botanical and Geographical Origins and Detect Syrup Adulteration.

Fraudulent practices concerning honey are growing fast and involve misrepresentation of origin and adulteration. Simple and feasible methods for honey authentication are needed to ascertain honey compliance and quality. Working on a robust dataset and simultaneously investigating honey traceability and adulterant detection, this study proposed a portable FTNIR fingerprinting approach combined with chemometrics. Multifloral and unifloral honey samples (n = 244) from Spain and Sardinia (Italy) were discriminated by botanical and geographical origin. Qualitative and quantitative methods were developed using linear discriminant analysis (LDA) and partial least squares (PLS) regression to detect adulterated honey with two syrups, consisting of glucose, fructose, and maltose. Botanical and geographical origins were predicted with 90% and 95% accuracy, respectively. LDA models discriminated pure and adulterated honey samples with an accuracy of over 92%, whereas PLS allows for the accurate quantification of over 10% of adulterants in unifloral and 20% in multifloral honey.

Rapid Identification of the Geographical Origin of the Chinese Mitten Crab (Eriocheir sinensis) Using Near-Infrared Spectroscopy.

The Chinese mitten crab (Eriocheir sinensis) is highly valued by consumers for its delicious taste and high nutritional content, including proteins and trace elements, giving it significant economic value. However, variations in taste and nutritional value among crabs from different regions lead to considerable price differences, fueling the prevalence of counterfeit crabs in the market. Currently, there are no rapid detection methods to verify the origin of Chinese mitten crabs, making it crucial to develop fast and accurate detection techniques to protect consumer rights. This study focused on Chinese mitten crabs from different regions, specifically Hongze Lake, Tuo Lake, and Weishan Lake, by collecting near-infrared (NIR) diffuse reflectance spectral data from both the abdomen and carapace regions of the crabs. To eliminate noise from the spectral data, pretreatment was performed using Savitzky-Golay (SG) smoothing, Standard Normal Variate (SNV) transformation, and Multiplicative Scatter Correction (MSC). Key wavelengths reflecting the origin of Chinese mitten crabs were selected using Competitive Adaptive Reweighted Sampling (CARS), Bootstrap Soft Shrinkage (BOSS), and Uninformative Variable Elimination (UVE) algorithms. Finally, Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Back Propagation Neural Network (BP) models were developed for rapid detection of crab origin. The results demonstrated that MSC provided the best preprocessing performance for NIR spectral data from both the abdomen and back of the crabs. For abdomen data, the SVM model developed using feature wavelengths selected by the CARS algorithm after MSC preprocessing achieved the highest accuracy (Acc) of 90.00%, with precision (P), recall (R), and F1-score for crabs from Weishan Lake at 89.29%, 86.21%, and 87.72%, respectively; for crabs from Tuo Lake at 86.96%, 95.24%, and 90.91%; and for crabs from Hongze Lake at 90.00%, 93.10%, and 91.53%. For carapace data, the SVM model based on wavelengths selected by the BOSS algorithm after MSC pretreatment achieved the best performance, with an Acc of 87.50%, and P, R, and F1 for crabs from Weishan Lake at 77.14%, 93.10%, and 84.38%; for Tuo Lake crabs at 100%, 90.47%, and 95.00%; and for Hongze Lake crabs at 92.31%, 80.00%, and 85.71%. In conclusion, NIR spectroscopy can effectively detect the origin of Chinese mitten crabs, providing technical support for developing rapid detection instruments and thereby safeguarding consumer rights.

The Impact of Sample Quantity, Traceability Scale, and Shelf Life on the Determination of the Near-Infrared Origin Traceability of Mung Beans.

This study aims to address the gap in understanding of the impact of the sample quantity, traceability range, and shelf life on the accuracy of mung bean origin traceability models based on near-infrared spectroscopy. Mung beans from Baicheng City, Jilin Province, Dorbod Mongol Autonomous, Tailai County, Heilongjiang Province, and Sishui County, Shandong Province, China, were used. Through near-infrared spectral acquisition (12,000-4000 cm-1) and preprocessing (Standardization, Savitzky-Golay, Standard Normal Variate, and Multiplicative Scatter Correction) of the mung bean samples, the total cumulative variance contribution rate of the first three principal components was determined to be 98.16% by using principal component analysis, and the overall discriminatory correctness of its four origins combined with the K-nearest neighbor method was 98.67%. We further investigated how varying sample quantities, traceability ranges, and shelf lives influenced the discrimination accuracy. Our results indicated a 4% increase in the overall correct discrimination rate. Specifically, larger traceability ranges (Tailai-Sishui) improved the accuracy by over 2%, and multiple shelf lives (90-180-270-360 d) enhanced the accuracy by 7.85%. These findings underscore the critical role of sample quantity and diversity in traceability studies, suggesting that broader traceability ranges and comprehensive sample collections across different shelf lives can significantly improve the accuracy of origin discrimination models.

Quantitative Analysis of High-Price Rice Adulteration Based on Near-Infrared Spectroscopy Combined with Chemometrics.

Near-infrared spectroscopy (NIRS) holds significant promise in detecting food adulteration due to its non-destructive, simple, and user-friendly properties. This study employed NIRS in conjunction with chemometrics to estimate the content of low-price rice flours (Nanjing, Songjing, Jiangxi silk, Yunhui) blended with high-price rice (Wuchang and Thai fragrant). Partial least squares regression (PLSR), support vector regression (SVR), and back-propagation neural network (BPNN) models were deployed to analyze the spectral data of adulterated samples and assess the degree of contamination. Various preprocessing techniques, parameter optimization strategies, and wavelength selection methods were employed to enhance model accuracy. With correlation coefficients exceeding 87%, the BPNN models exhibited high accuracy in estimating adulteration levels in high-price rice. The SPXY-SG-BPNN, SPXY-MMN-BPNN, KS-SNV-BPNN, and SPXY-SG-BPNN models showcased exceptional performance in discerning mixed Wuchang japonica, Thai fragrant indica, and Thai fragrant Yunhui rice. As shown above, NIRS demonstrated its potential as a rapid, non-destructive method for detecting low-price rice in premium rice blends. Future studies should be performed to concentrate on enhancing the models' versatility and practical applicability.

Rapid Classification of Milk Using a Cost-Effective Near Infrared Spectroscopy Device and Variable Cluster-Support Vector Machine (VC-SVM) Hybrid Models.

Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly used technique for distinguishing pure milk from adulterated milk, even when it comes from different animal species. More recently, portable spectrometers have enabled in situ analysis with analytical performance comparable to that of benchtop instruments. Partial Least Square (PLS) analysis is the most popular tool for developing calibration models, although the increasing availability of portable near infrared spectroscopy (NIRS) has led to the use of alternative supervised techniques, including support vector machine (SVM). The aim of this study was to develop and implement a method based on the combination of a compact and low-cost Fourier Transform near infrared (FT-NIR) spectrometer and variable cluster-support vector machine (VC-SVM) hybrid model for the rapid classification of milk in accordance with EU Regulation EC No. 1308/2013 without any pre-treatment. The results obtained from the external validation of the VC-SVM hybrid model showed a perfect classification capacity (100% sensitivity, 100% specificity, MCC = 1) for the radial basis function (RBF) kernel when used to classify whole vs. not-whole and skimmed vs. not-skimmed milk samples. A strong classification capacity (94.4% sensitivity, 100% specificity, MCC = 0.95) was also achieved in discriminating semi-skimmed vs. not-semi-skimmed milk samples. This approach provides the dairy industry with a practical, simple and efficient solution to quickly identify skimmed, semi-skimmed and whole milk and detect potential fraud.

Classifying Learning Speed Using Brain Networks and Psychological States: Unraveling the Interdependence Between Learning Performance, Psychological States, and Brain Functions.

Introduction The progression of performance learning (PL) may have complex relationships beyond mere concurrent occurrences and may influence each other. This study aimed to classify the speed of PL using a random forest based on brain network and stress state information and to identify the factors necessary for PL. In addition, this study also aimed to clarify the complex interdependent relationships between PL, psychological state, and brain function through these factors, using covariance structure analysis. Methods A total of 20 healthy individuals participated in a choice reaction time task, and brain function was measured using near-infrared spectroscopy (NIRS). Participants were divided into high-PL and low-PL groups based on the median difference in correct responses. Results Random forest analysis identified the left orbitofrontal area, right premotor cortex, right frontal pole, left frontal pole, left dorsolateral prefrontal cortex, and depression and anxiety as key factors. Covariance structure analysis revealed that depression and anxiety affected PL through the frontal pole and prefrontal cortex, suggesting a complex interplay between psychological state, brain function, and learning. Conclusions These findings suggest that psychological states influence brain networks, thereby affecting learning performance. Tailoring rehabilitation programs to address psychological states and providing targeted feedback may improve learning outcomes. The study provides insights into the theoretical and practical applications of understanding the brain's role in PL, as well as the importance of addressing psychological factors to optimize learning and rehabilitation strategies.