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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI, USA (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Near-Infrared Spectroscopy studies on TBI patients with Modified Multiscale Entropy analysis
摘要： Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive functional brain imaging technique, through detecting the changes of hemoglobin concentrations to investigate brain activities in various tasks. The aim of this study is to investigate the complexity of near-infrared spectroscopy signals during resting state and upper limb movements. Experimental study was designed by applying NIRS to collect the data especially for both healthy subjects and traumatic brain injury (TBI) patients. The modified multiscale entropy (MMSE) algorithm was employed to assess the complexity of fNIRS signals which may reflect the changes of brain activity when people underwent brain injury. The results that the mean MMSE of oxyhemoglobin values was lower in TBI patients compared to healthy subjects, indicated that MMSE was feasible to measure complexity of cerebral near-infrared spectroscopy signals in TBI patients, and that brain injury was associated with the decreased complexity of cerebrovascular reactivity. Moreover, measurement of complexity of brain signals has potential to provide significant guidance for rehabilitation.
关键词： cerebrovascular reactivity,modified multiscale entropy (MMSE),traumatic brain injury (TBI),brain activity,Functional near-infrared spectroscopy (fNIRS)
更新于2025-09-10 09:29:36
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EXPRESS: Bulk Protein and Oil Prediction in Soybean Using Transmission Raman Spectroscopy: A Comparison of Approaches to Optimize Accuracy
摘要： Rapid measurements of protein and oil content are important for a variety of uses, from sorting of soybeans at the point of harvest to feedback during soybean meal production. In this study, our goal is to develop a simple protocol to permit rapid and robust quantitative prediction of soybean constituents using transmission Raman spectroscopy (TRS). To develop this approach, we systematically varied the various elements of the measurement process to provide a diverse test bed. First, we utilized an in-house-built benchtop TRS instrument such that suitable optical configurations could be rapidly deployed and analyzed for experimental data collection for individual soybean grains. Second, we also utilized three different soybean varieties with relatively low (33.97%), medium (36.98%), and high protein (41.23%) contents to test the development process. Third, samples from each variety were prepared using whole bean and three different sample treatments (i.e., ground bean, whole meal, and ground meal). In each case, we modeled the data obtained using partial least squares (PLS) regression and assessed spectral metric-based multiple linear regression (metric-MLR) approaches to build robust prediction models. The metric-MLR models showed lower root mean square errors (RMSEPs), and hence better prediction, compared to corresponding classical PLS regression models for both bulk protein and oil for all treatment types. Comparing different sample preparation approaches, a lower RMSEPs was observed for whole meal treatment and thus the metric-MLR modeling with ground meal treatment was considered to be optimal protocol for bulk protein and oil prediction in soybean, with RMSEP values of 1.15±0.04 (R2= 0.87) and 0.80±0.02 (R2= 0.87) for bulk protein and oil, respectively. These predictions were nearly two- to three-fold better (i.e., lower RMSEPs) than the corresponding NIR spectroscopy measurements (i.e., secondary gold standards in grain industry). For content prediction in whole soybean, incorporating physical attributes of individual grains in metric-MLR approach show up to 22% improvement in bulk protein and a relatively mild (up to ~ 5%) improvement in bulk oil prediction. The unique combination of metric-MLR modeling approach (which is rare in the field of grain analysis) and sample treatments resulted in improved prediction models, and using the physical attributes of individual grains is suggested as a novel measure for improving accuracy in prediction.
关键词： near-infrared spectroscopy,Soybean,NIR spectroscopy,MLR,transmission Raman spectroscopy,multiple liner regression,PLS regression
更新于2025-09-09 09:28:46
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Temperature Compensation on Sugar Content Prediction of Molasses by Near-Infrared Spectroscopy (NIR)
摘要： The rapid, nondestructive, cost-effective NIR measurement method was used for final molasses quality monitoring to determine fermentable sugar content to optimize ethanol yield. Molasses is stored in temperature-controlled tanks during the cane crushing and remelt seasons to ensure molasses quality and availability. However, there is variation in molasses temperature during storage. The impacts of temperature variation on molasses NIR spectra and calibration performance were studied. About one hundred molasses samples were collected for spectral profiling (400–2500 nm) at three different temperatures (25, 35 and 45 °C) using a FOSS NIR DS2500 spectrometer. A partial least squares regression (PLSR) model was developed using full cross-validation. The predictive models were developed using molasses spectra at 25, 35 and 45 °C and used to determine sucrose, glucose, fructose (fermentable sugars) concentrations in the molasses. External validation was achieved using thirty percent of calibration samples for each validation set, 25, 35, and 45 °C. Variation of the sample spectra was observed for the visible region and NIR region (1450 and 1970 nm), due to O–H bonding. The root means squared standard error of cross-validation obtained varied depending on sample temperature. Root means squared standard error of prediction results for external validation samples tended to increase with increasing temperature. Predicted values were not statistically different (p > 0.05) to reference values using different temperatures of models and validation. Calibration models including three temperature spectra showed potential of fermentable sugar analysis in molasses without temperature compensation.
关键词： Molasses,Non-destructive,Temperature compensation,Rapid,Near Infrared Spectroscopy
更新于2025-09-09 09:28:46
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Direct Determination of Ni2+-Capacity of IMAC Materials Using Near-Infrared Spectroscopy
摘要： The present paper reports a new method for the quanti?cation of the Ni2+-capacity of an immobilized metal af?nity chromatography (IMAC) material using near-infrared spectroscopy (NIRS). Conventional analyses using UV absorption spectroscopy or atomic absorption spectrometry (AAS) need to dissolve the silica-based metal chelate sorbent as sample pretreatment. In the ?rst step, those methods were validated on the basis of an ideal homogenous NiSO4-solution and unveiled that UV with an intermediate precision of 2.6% relative standard deviation (RSD) had an advantage over AAS with an intermediate precision of 6.5% RSD. Therefore, UV analysis was chosen as reference method for the newly established NIRS model which has the advantage of being able to measure the material directly in diffuse re?ection mode. Partial least squares regression (PLSR) analysis was used as multivariate data analysis tool for quanti?cation. The best PLSR result obtained was: coef?cient of determination (R2) = 0.88, factor = 2, root mean square error of prediction (RMSEP) = 22 μmol/g (test-set validation) or 7.5% RSDPLSR. Validation of the Ni2+-capacity using UV absorption spectroscopy resulted in an intermediate precision of ±18 μmol/g or 5.0% RSD. Therefore, NIRS provides a fast alternative analysis method without the need of sample preparation.
关键词： Ni2+-capacity,partial least squares regression,IMAC,near-infrared spectroscopy,method validation
更新于2025-09-09 09:28:46
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Study of memory deficit in Alzheimer’s disease by means of complexity analysis of fNIRS signal
摘要： Working memory deficit is a signature of Alzheimer’s disease (AD). The free and cued selective reminding test (FCSRT) is a clinical test that quantifies memory deficit for AD diagnosis. However, the diagnostic accuracy of FCSRT may be increased by accompanying it with neuroimaging. Since the test requires doctor–patient interaction, brain monitoring is challenging. Functional near-infrared spectroscopy (fNIRS) could be suited for such a purpose because of the fNIRS flexibility. We investigated whether the complexity, based on sample entropy and multiscale entropy metrics, of the fNIRS signal during FCSRT was correlated with memory deficit in early AD. fNIRS signals were recorded over the prefrontal cortex of healthy and early AD participants. Group differences were tested through Wilcoxon–Mann–Whitney test (p < 0.05). At group level, we found significant differences for Brodmann areas 9 and 46. The results, although preliminary, demonstrate the feasibility of performing ecological studies on early AD with fNIRS. This approach may provide a potential neuroimaging-based method for diagnosis of early AD, viable at the doctor’s office level, improving test-based diagnosis. The increased entropy of the fNIRS signal in early AD suggests the opportunity for further research on the neurophysiological status in AD and its relevance for clinical symptoms.
关键词： entropy,functional near-infrared spectroscopy,Alzheimer’s disease,free and cued selective reminding test
更新于2025-09-09 09:28:46
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A Novel Method for Classifying Driver Mental Workload Under Naturalistic Conditions With Information From Near-Infrared Spectroscopy
摘要： Driver cognitive distraction is a critical factor in road safety, and its evaluation, especially under real conditions, presents challenges to researchers and engineers. In this study, we considered mental workload from a secondary task as a potential source of cognitive distraction and aimed to estimate the increased cognitive load on the driver with a four-channel near-infrared spectroscopy (NIRS) device by introducing a machine-learning method for hemodynamic data. To produce added cognitive workload in a driver beyond just driving, two levels of an auditory presentation n-back task were used. A total of 60 experimental data sets from the NIRS device during two driving tasks were obtained and analyzed by machine-learning algorithms. We used two techniques to prevent overfitting of the classification models: (1) k-fold cross-validation and principal-component analysis, and (2) retaining 25% of the data (testing data) for testing of the model after classification. Six types of classifier were trained and tested: decision tree, discriminant analysis, logistic regression, the support vector machine, the nearest neighbor classifier, and the ensemble classifier. Cognitive workload levels were well classified from the NIRS data in the cases of subject-dependent classification (the accuracy of classification increased from 81.30 to 95.40%, and the accuracy of prediction of the testing data was 82.18 to 96.08%), subject-independent classification (the accuracy of classification increased from 84.90 to 89.50%, and the accuracy of prediction of the testing data increased from 84.08 to 89.91%), and channel-independent classification (classification 82.90%, prediction 82.74%). NIRS data in conjunction with an artificial intelligence method can therefore be used to classify mental workload as a source of potential cognitive distraction in real time under naturalistic conditions; this information may be utilized in driver assistance systems to prevent road accidents.
关键词： mental workload,near-infrared spectroscopy,artificial intelligence,driver attention,cognitive distraction,classification
更新于2025-09-09 09:28:46
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Visible-Near-Infrared Spectroscopy Prediction of Soil Characteristics as Affected by Soil-Water Content
摘要： Soil physical characteristics are important drivers for soil functions and productivity. Field applications of near-infrared spectroscopy (NIRS) are already deployed for in situ mapping of soil characteristics and therefore, fast and precise in situ measurements of the basic soil physical characteristics are needed at any given water content. Visible-near-infrared spectroscopy (vis–NIRS) is a fast, low-cost technology for determination of basic soil properties. However, the predictive ability of vis–NIRS may be affected by soil-water content. This study was conducted to quantify the effects of six different soil-water contents (full saturation, pF 1, pF 1.5, pF 2.5, pF 3, and air-dry) on the vis–NIRS predictions of six soil physical properties: clay, silt, sand, water content at pF 3, organic carbon (OC), and the clay/OC ratio. The effect of soil-water content on the vis–NIR spectra was also assessed. Seventy soil samples were collected from five sites in Denmark and Germany with clay and OC contents ranging from 0.116 to 0.459 and 0.009 to 0.024 kg kg-1, respectively. The soil rings were saturated and successively drained/dried to obtain different soil–water potentials at which they were measured with vis–NIRS. Partial least squares regression (PLSR) with leave-one-out cross-validation was used for estimating the soil properties using vis–NIR spectra. Results showed that the effects of water on vis–NIR spectra were dependent on the soil–water retention characteristics. Contents of clay, silt, and sand, and the water content at pF 3 were well predicted at the different soil moisture levels. Predictions of OC and the clay/OC ratio were good at air-dry soil condition, but markedly weaker in wet soils, especially at saturation, at pF 1 and pF 1.5. The results suggest that in situ measurements of spectroscopy are precise when soil-water content is below field capacity.
关键词： Visible-Near-Infrared Spectroscopy,Soil Physical Properties,Soil Characteristics,Soil-Water Content,Partial Least Squares Regression
更新于2025-09-09 09:28:46
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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI, USA (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Cancellation Method of Signal Fluctuations in Brain Function Measurements Using Near-Infrared Spectroscopy
摘要： To estimate brain activity, it is important to improve the accuracy of brain function measurements by using near-infrared spectroscopy. The detection of signals is vital for correcting any disturbances or changes in the skin blood volume. We developed a cancellation method for brain probes placed on the scalp in the configuration of an equilateral triangle. In this configuration, 12 types of target signals were detected between the vertices, and 6 types of correction signals were detected between the vertices and the center of the triangle. We measured the changes in the blood volume resulting from the specific postural changes of the subject and applied the correction method using three calculation methods. The measured results showed that the correction signals were effective in reducing the disturbances. The correction was based on the cross-correlation coefficient and the amplitude ratio of signals.
关键词： near-infrared spectroscopy,cancellation method,brain function measurements,equilateral triangle configuration,cross-correlation coefficient,amplitude ratio
更新于2025-09-09 09:28:46
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Calibration modelling for non-destructive estimation of external and internal quality parameters of ‘Marsh’ grapefruit using Vis/NIR spectroscopy
摘要： Consumer preference for fruit without disorder influences purchase of fruit at both local and international markets. Recent trends in horticulture show that consumer preference is influenced by assurance that external appearance is linked with rewarding internal sensory quality. Therefore, the need for non-destructive evaluation of external and internal quality parameters is important. This study was conducted to develop and test calibration models for integrated prediction of external and internal quality of 'Marsh' grapefruit. Visible to near infrared (Vis/NIR) spectroscopy (Vis/NIRS) was used to acquire spectral information from 522 intact fruit. Reference quality parameters such as colour indices (luminosity (L*), greenness (a*) and yellowness (b*)), rind dry matter (DM), rind total phenolics concentration, BrimA, carbohydrates, sweetness index (SI) and total sweetness index (TSI) were obtained using conventional methods. Principal component analysis was applied to analyse spectral data to identify outliers. Savitzky-Golay second derivative with second order polynomial was employed as pre-processing method to correct light scattering properties of the spectra. The spectra were subjected to a test set validation by categorising the spectra into calibration (60%) and validation (40%) sets. Partial least square regression was used as chemometric tool to develop models for predicting each parameter. The model validation results showed that external and internal quality parameters of grapefruit could be predicted with satisfactory accuracy with R2 value of 0.99 for rind quality parameters (L*, a*, b*, DM) and 0.77, 0.99, 0.99 for BrimA, SI and TSI, respectively. The residual predictive deviation (RPD) results for L*, a*, b*, DM, BrimA, SI and TSI were 64.1, 61.4, 123.4, 12.9, 1.4, 9.0 and 13.9, respectively. Vis/NIR calibration and validation results demonstrated that quality parameters of 'Marsh' grapefruit could be predicted using Vis/NIRS.
关键词： citrus fruit,multivariate data analysis,rind,chemometrics,near infrared spectroscopy,'Marsh' grapefruit
更新于2025-09-09 09:28:46
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Early Detection of Alzheimer’s Disease Using Non-invasive Near-Infrared Spectroscopy
摘要： Mild cognitive impairment (MCI) is a cognitive disorder characterized by memory impairment, wherein patients have an increased likelihood of developing Alzheimer’s disease (AD). The classification of MCI and different AD stages is therefore fundamental for understanding and treating the disease. This study aimed to comprehensively investigate the hemodynamic response patterns among various subject groups. Functional near-infrared spectroscopy (fNIRS) was employed to measure signals from the frontal and bilateral parietal cortices of healthy controls (n = 8), patients with MCI (n = 9), mild (n = 6), and moderate/severe AD (n = 7) during a digit verbal span task (DVST). The concentration changes of oxygenated hemoglobin (HbO) in various subject groups were thoroughly explored and tested. Result revealed that abnormal patterns of hemodynamic response were observed across all subject groups. Greater and steeper reductions in HbO concentration were consistently observed across all regions of interest (ROIs) as disease severity developed from MCI to moderate/severe AD. Furthermore, all the fNIRS-derived indexes were found to be significantly and positively correlated to the clinical scores in all ROIs (R ≥ 0.4, P < 0.05). These findings demonstrate the feasibility of utilizing fNIRS for the early detection of AD, suggesting that fNIRS-based approaches hold great promise for exploring the mechanisms underlying the progression of AD.
关键词： oxygenated hemoglobin,functional near-infrared spectroscopy,Alzheimer’s disease,hemodynamic response,mild cognitive impairment
更新于2025-09-09 09:28:46