Feasibility Of Near-infrared Spectroscopy Research Articles

Feasibility of Near-Infrared Spectroscopy and Peripheral Arterial Disease Risk in CKD

Feasibility of Near-Infrared Spectroscopy and Peripheral Arterial Disease Risk in CKD

Feasibility of Near-Infrared Spectroscopy in the Classification of Pig Lung Lesions.

Respiratory diseases significantly affect intensive pig farming, causing production losses and increased antimicrobial use. Accurate classification of lung lesions is crucial for effective diagnostics and disease management. The integration of non-destructive and rapid techniques would be beneficial to enhance overall efficiency in addressing these challenges. This study investigates the potential of near-infrared (NIR) spectroscopy in classifying pig lung tissues. The NIR spectra (908-1676 nm) of 101 lungs from weaned pigs were analyzed using a portable instrument and subjected to multivariate analysis. Two distinct discriminant models were developed to differentiate normal (N), congested (C), and pathological (P) lung tissues, as well as catarrhal bronchopneumonia (CBP), fibrinous pleuropneumonia (FPP), and interstitial pneumonia (IP) patterns. Overall, the model tailored for discriminating among pathological lesions demonstrated superior classification performances. Major challenges arose in categorizing C lungs, which exhibited a misclassification rate of 30% with N and P tissues, and FPP samples, with 30% incorrectly recognized as CBP samples. Conversely, IP and CBP lungs were all identified with accuracy, precision, and sensitivity higher than 90%. In conclusion, this study provides a promising proof of concept for using NIR spectroscopy to recognize and categorize pig lungs with different pathological lesions, offering prospects for efficient diagnostic strategies.

Rapid determination of the main components of corn based on near-infrared spectroscopy and a BiPLS-PCA-ELM model.

To evaluate corn quality quickly, the feasibility of near-infrared spectroscopy (NIRS) coupled with chemometrics was analyzed to detect the moisture, oil, protein, and starch content in corn. A backward interval partial least squares (BiPLS)-principal component analysis (PCA)-extreme learning machine (ELM) quantitative analysis model was constructed based on BiPLS in conjunction with PCA and the ELM. The selection of characteristic spectral intervals was accomplished by BiPLS. The best principal components were determined by the prediction residual error sum of squares of Monte Carlo cross validation. In addition, a genetic simulated annealing algorithm was utilized to optimize the parameters of the ELM regression model. The established regression models for moisture, oil, protein, and starch can meet the demand for corn component detection with the prediction determination coefficients of 0.996, 0.990, 0.974, and 0.976; the prediction root means square errors of 0.018, 0.016, 0.067, and 0.109; and the residual prediction deviations of 15.704, 9.741, 6.330, and 6.236, respectively. The results show that the NIRS rapid detection model has higher robustness and accuracy based on the selection of characteristic spectral intervals in conjunction with spectral data dimensionality reduction and nonlinear modeling and can be used as an alternative strategy to detect multiple components in corn rapidly.

Feasibility of near-infrared spectroscopy and chemometrics analysis for discrimination of Cymbopogon nardus from Cymbopogon citratus

The authenticity of essential oils has become an important issue in supplying essential oil raw materials for the pharmaceutical, perfume, and cosmetic industries. Citronella oil is one of the essential oils used in those industries. Cymbopogon nardus is one of the lemongrass species that can produce citronella oil. However, with the high price of citronella oil from C. nardus, there is a possibility of being substituted or adulterated with closely related plants, namely Cymbopogon citratus. This paper described the feasibility of near-infrared (NIR) spectroscopy combined with chemometrics analysis for rapid identification and authentication of C. nardus from C. citratus essential oil. NIR spectra of both essential oils and their mixture (10 % and 25 % v/v of C. citratus in C. nardus) showed a similar spectral profile, so we cannot easily discriminate them and need help from chemometrics analysis. For chemometrics analysis, we used absorbance data from the preprocessed NIR spectra at wavenumbers 4000–6500 cm−1. Using PCA, we could separate each essential oil from C. nardus and C. citratus but cannot discriminate between 10 % and 25 % of CC in CN. While using OPLS-DA with R2X(cum) = 0.88, R2Y(cum) = 0.859 and Q2(cum) = 0.723, we could group each sample. The OPLS-DA score plot clearly shows the difference between C. nardus and C. citratus essential oils and their mixtures. The combination of NIR and OPLS-DA could provide a suitable method for identifying and authenticating C. nardus from C. citratus essential oil.

Feasibility of near infrared spectroscopy for estimating suppressiveness of carnation (Dianthus cariophyllus L.) fusarium wilt in different plant growth media

Fusarium wilt is one of the most widespread diseases in carnation crops in a large number of countries. Plant protection products commonly used to remedy the disease have been considered ineffective and environmentally unsafe for commercial use. As an alternative, the use of suppressive growth media has been proposed. The aim of this study was to evaluate the use of a rapid method such as near infrared spectroscopy (NIRS) to monitor and evaluate suppressive media potential. The NIR spectra were collected from 6 plant growth media used in a series of trials to evaluate suppressiveness to carnation Fusarium wilt namely grape marc compost, cork compost, olive oil husk + cotton gin trash composted and mixed with rice husk, spent mushroom composted and mixed with peat, coir fiber and light peat.The NIR calibration models showed promising results for estimating pH, β-glucosidase activity, disease severity (AUDPC and RLSBX) in the growth media evaluated, with coefficients of determination of 0.99, 0.98, 0.98 and 0.90; SECV of 0.09, 11.63, 0.05 and 0.10; and RPD values of 13.86, 6.62, 7.19 and 3.24; respectively.NIR spectroscopy could become a useful non-destructive and fast analytical tool for the identification of Fusarium wilt suppressive composts, avoiding the use of reagents.

Determination of nutritional health indexes of fresh bovine milk using near infrared spectroscopy

Bovine milk is one of the most complete foods that exist. During the last decades, milk FA have shown to improve human health due to the reduction in risk of cardiovascular disease and related pathologies. The aim of this study was to evaluate the feasibility of near infrared spectroscopy (NIRS) reflectance analysis to predict the nutritional value, fatty acid (FA) composition, and health index of fresh milk from dairy cows of pastoral systems. The prediction of Atherogenicity and Thrombogenicity indexes, along with other FA ratios in fresh milk samples by NIRS were precise and accurate. In addition, the calibration model obtained by NIRS provides an opportunity for the routine quantification of milk’s healthy FA such as omega-3 and conjugated linoleic acid (CLA), with applications in the dairy industry for food labeling, and at the farm level for management of the dairy cow’s diet.

Feasibility of near infrared spectroscopy to classify lamb hamburgers according to the presence and percentage of cherry as a natural ingredient

This study assesses the feasibility of Near Infrared Spectroscopy in combination with various spectral treatments and chemometric approaches, i.e. the Partial Least Squares-Discriminant Analysis and the Soft Independent Modelling of Class Analogies, for the onsite classification of lamb hamburgers according to the presence of cherry (presence/absence) as well as the percentage of cherry (2%, 6% and 10%) in cherry lamb hamburgers. The models developed using the Partial Least Squares-Discriminant Analysis offered high classificatory suitability for classification of lamb hamburgers according to the presence and the percentage of cherry, yielding an accuracy higher than 95% and 84%, respectively. Furthermore, the models proved their reliability after external validation, as the accuracy values remained above 81% and 90% respectively. Furthermore, the models proved their reliability after external validation. Such results might help support onsite quality control and simplify the management of stock, traceability and logistics operations applied to processed meat products.

Rapid detection of cellulose and hemicellulose contents of corn stover based on near-infrared spectroscopy combined with chemometrics.

The feasibility of near-infrared spectroscopy (NIRS) combined with chemometrics for the rapid detection of the cellulose and hemicellulose contents in corn stover is discussed. Competitive adaptive reweighted sampling (CARS) and genetic simulated annealing algorithm (GSA) were combined (CARS-GSA) to select the characteristic wavelengths of cellulose and hemicellulose and to reduce the dimensionality and multicollinearity of the NIRS data. The whole spectra contained 1845 wavelength variables. After CARS-GSA optimization, the number of characteristic wavelengths of cellulose (hemicellulose) was reduced to 152 (260), accounting for 8.24% (14.09%) of all wavelengths. The coefficients of determination of the regression models for predicting the cellulose and hemicellulose contents were 0.968 and 0.996, the root mean square errors of prediction (RMSEPs) were 0.683 and 0.648, and the residual predictive deviations (RPDs) were 5.213 and 16.499, respectively. The RMSEP of the cellulose and hemicellulose regression models was 0.152 and 0.190 lower for CARS-GSA than for the full-spectrum, and the RPD was increased by 0.949 and 3.47, respectively. The results showed that the CARS-GSA model substantially reduced the number of characteristic wavelengths and significantly improved the predictive ability of the regression model.

Feasibility of near-infrared spectroscopy as a tool for anatomical mapping of the human epicardium.

Epicardial ablation is necessary for the treatment of ventricular tachycardias refractory to endocardial ablation due to arrhythmic substrates involving the epicardium. The human epicardium is composed of adipose tissue and coronary vasculature embedded on the surface and within the myocardium, which can complicate electroanatomical mapping, electrogram interpretation and ablation delivery. We propose using near-infrared spectroscopy (NIRS) to decipher adipose tissue from myocardial tissue within human hearts ex vivo. Histological measurement of epicardial adipose thickness direct correlated (R = 0.884) with the adipose contrast index. These results demonstrate the potential of NIRS integrated catheters for mapping the spatial distribution of epicardial substrates and could aid in improving guidance during epicardial ablation interventions.

Feasibility of Near-infrared Spectroscopy to Measure Cortical Pain Pathway (Brain) Activation During Dry Needling

Feasibility of Near-infrared Spectroscopy to Measure Cortical Pain Pathway (Brain) Activation During Dry Needling

Rapid Determination of Chlorogenic Acid, Luteoloside and 3,5-O-dicaffeoylquinic Acid in Chrysanthemum Using Near-Infrared Spectroscopy.

The feasibility of near-infrared spectroscopy (NIR) to detect chlorogenic acid, luteoloside and 3,5-O-dicaffeoylquinic acid in Chrysanthemum was investigated. An NIR spectroradiometer was applied for data acquisition. The reference values of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid of the samples were determined by high-performance liquid chromatography (HPLC) and were used for model calibration. The results of six preprocessing methods were compared. To reduce input variables and collinearity problems, three methods for variable selection were compared, including successive projections algorithm (SPA), genetic algorithm-partial least squares regression (GA-PLS), and competitive adaptive reweighted sampling (CARS). The selected variables were employed as the inputs of partial least square (PLS), back propagation-artificial neural networks (BP-ANN), and extreme learning machine (ELM) models. The best performance was achieved by BP-ANN models based on variables selected by CARS for all three chemical constituents. The values of rp2 (correlation coefficient of prediction) were 0.924, 0.927, 0.933, the values of RMSEP were 0.033, 0.018, 0.064 and the values of RPD were 3.667, 3.667, 2.891 for chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid, respectively. The results indicated that NIR spectroscopy combined with variables selection and multivariate calibration methods could be considered as a useful tool for rapid determination of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid in Chrysanthemum.

Multivariate and machine learning approaches for honey botanical origin authentication using near infrared spectroscopy

In this work the feasibility of near infrared spectroscopy was evaluated combined with chemometric approaches, as a tool for the botanical origin prediction of 119 honey samples. Four varieties related to polyfloral, acacia, chestnut, and linden were first characterized by their physical–chemical parameters and then analyzed in triplicate using a near infrared spectrophotometer equipped with an optical path gold reflector. Three different classifiers were built on distinct multivariate and machine learning approaches for honey botanical classification. A partial least squares discriminant analysis was used as a first approach to build a predictive model for honey classification. Spectra pretreatments named autoscale, standard normal variate, detrending, first derivative, and smoothing were applied for the reduction of scattering related to the presence of particle size, like glucose crystals. The values of the descriptive statistics of the partial least squares discriminant analysis model allowed a sufficient floral group prediction for the acacia and polyfloral honeys but not in the cases of chestnut and linden. The second classifier, based on a support vector machine, allowed a better classification of acacia and polyfloral and also achieved the classification of chestnut. The linden samples instead remained unclassified. A further investigation, aimed to improve the botanical discrimination, exploited a feature selection algorithm named Boruta, which assigned a pool of 39 informative averaged near infrared spectral variables on which a canonical discriminant analysis was assessed. The canonical discriminant analysis accounted a better separation of samples according to the botanical origin than the partial least squares discriminant analysis. The approach used has permitted to achieve a complete authentication of the acacia honeys but not a precise segregation of polyfloral ones. The comparison between the variables important in projection and the Boruta pool showed that the informative wavelengths are partially shared especially in the middle and far band of the near infrared spectral range.

Near Infrared Spectroscopic Evaluation of Ligament and Tendon Biomechanical Properties.

Knee ligaments and tendons are collagen-rich viscoelastic connective tissues that provide vital mechanical stabilization and support to the knee joint. Deterioration of ligaments has an adverse effect on the health of the knee and can eventually lead to ligament rupture and osteoarthritis. In this study, the feasibility of near infrared spectroscopy (NIRS) was, for the first time, tested for evaluation of ligament and tendon mechanical properties by performing measurements on bovine stifle joint ligament (N = 40) and patellar tendon (N = 10) samples. The mechanical properties of the samples were determined using a uniaxial tensile testing protocol. Partial least squares regression models were then developed to determine if morphological, viscoelastic, and quasi-static properties of the samples could be predicted from the NIR spectra. Best performance of NIRS in predicting mechanical properties was observed for toughness at yield point (median [Formula: see text], median normalized [Formula: see text]), toughness at failure point (median [Formula: see text], median normalized [Formula: see text]), and the ultimate strength of the ligament/tendon (median [Formula: see text], median normalized [Formula: see text]). Thus, we show that NIRS is capable of estimating ligament and tendon biomechanical properties, especially in parameters related to tissue failure. We believe this method could substantially enhance the currently limited arthroscopic evaluation of ligaments and tendons.

Determination of geographical origin and icariin content of Herba Epimedii using near infrared spectroscopy and chemometrics

Near infrared (NIR) spectroscopy coupled with chemometrics was used to discriminate the geographical origin of Herba Epimedii in this work. Four different classification models, namely discriminant analysis (DA), back propagation neural network (BPNN), K-nearest neighbor (KNN), and support vector machine (SVM), were constructed, and their performances in terms of recognition accuracy were compared. The results indicated that the SVM model was superior over the other models in the geographical origin identification of Herba Epimedii. The recognition rates of the optimum SVM model were up to 100% for the calibration set and 94.44% for the prediction set, respectively. In addition, the feasibility of NIR spectroscopy with the CARS-PLSR calibration model in prediction of icariin content of Herba Epimedii was also investigated. The determination coefficient (RP2) and root-mean-square error (RMSEP) for prediction set were 0.9269 and 0.0480, respectively. It can be concluded that the NIR spectroscopy technique in combination with chemometrics has great potential in determination of geographical origin and icariin content of Herba Epimedii. This study can provide a valuable reference for rapid quality control of food products.

P 16 The implementation of Near-Infrared-Spectroscopy (NIRS) in sports science – a verification of suitability using the compound movement squat

Introduction Near - Infrared Spectroscopy (NIRS) serves as a promising tool to examine cortical activity in a sports-scientific context. The associated feasibility of said method remains to be validated as exercise - induced confounders such as increases in extracerebral blood flow have been postulated ( Tachtsidis and Scholkmann, 2016 ). Additionally, the relationship between force and cortical activity is yet to be investigated in compound movements. Both parameters show a linear relationship in isolated movements ( Shibuya et al., 2014 ) yet it remains unclear whether this relationship applies to compound movements. Here we assessed the feasibility of NIRS for the compound movement barbell squat (BS). Complementarily the relationship between force levels and cortical activity during the squat was examined. We hypothesized that the extracerebral blood flow superimposes the signal and can be eliminated. Furthermore, we hypothesized a linear relationship between force during BS and cortical activity in the leg area of bilateral primary motor cortex (M1). Methods Ten healthy young adults participated in this study (n = 10; age: 25.7 ± 2.214 years). All subjects performed the BS task, five 90° squats per activity block, on three separate days with varying NIRS - configurations to control for and eliminate extracerebral effects. The experimental setup corresponds to a block design with varying force levels (L0%, L20%, L40%) which were individually adjusted in succession to Maximum Strength Tests. Our standard NIRS - configuration (A) covered the cortical motor system. The second configuration (B) additionally covered bilateral auditory cortices, frontopolar areas, as well as primary visual cortex. A third configuration incorporated a short - distance - channel to use this channel in a short separation regression (C). Cortical activation was assumed to be reflected by increased oxyhemoglobin (HbO2) and a corresponding decline or unchanged deoxyhemoglobin (HHb). Results For (A) and (B), HbO2showed a significant increase in the entire detection range when comparing the varying force levels during BS ( p 0.0023 ). For (C), HbO2 showed a significant increase in bilateral superior parietal lobule (SPL) (L20%, L40%) when compared to L0% ( p 0.01 ). HbO2 showed no significant increase when comparing the different force levels to baseline values. HHb showed no regionally consistent, significant decline for all comparisons. Discussion Short separation regression potentially enables the disentanglement of extracerebral blood flow from cerebral blood flow. There is a relationship between force and cortical activity in the squat when comparing force levels, although in bilateral SPL as opposed to M1 as seen in isolated movements. This implies a different modulator for compound movements compared to isolated movements.

Useful near infrared spectroscopy model calibrations on solid wood samples of Pterocarpus erinaceus (Poir.) for physical, mechanical and colour properties

Pterocarpus erinaceus (Poir.) (local name: Toti, Tem) is a species endemic to the Guinea-Sudan and Sudan-Sahel zones. It is over-exploited for its wood, which is sold in international trade to China and Indian markets. The selection of high performance wood based on wood properties and their vegetative reproduction represents the best option for the establishment of plantations with the aim of promoting the restoration of degraded natural resources. However, measurements of these characters are lengthy, time consuming and destructive, and difficult to perform in Togo because of the lack of equipment. An alternative to those methods is near infrared (NIR) spectroscopy. The aim of this study was to assess the efficiency and feasibility of near infrared spectroscopy for predicting the physical and mechanical properties and the colour parameters of P. erinaceus wood from Burkina Faso, Niger and Togo. The properties were determined by conventional method and correlated with near infrared spectra using partial least square regression. The partial least square regression models were tested by cross-validation and by external validation. The results of this study showed the possible use of near infrared spectroscopy to estimate the physical and mechanical properties and the colour parameters of P. erinaceus wood. In fact, when validating prediction models for radial and tangential shrinkage, the fibre saturation point (FSP) showed R2 values of 0.70, 0.71, and 0.92, respectively, with ratio performance deviation values of 1.84, 1.87 and 3.57. For the colour parameters L*, a* and b*, the R2 values were 0.83, 0.74 and 0.67, respectively, with ratio performance deviation values of 2.40, 2 and 1.70. The results showed that the basic density and fibre saturation point models were classed as good to very good, respectively. Near infrared spectroscopy can therefore be used to predict efficiently the properties of P. erinaceus wood for a large number of samples, enabling the incorporation of those traits in breeding criteria.

Feasibility of Near Infra-Red Spectroscopy in Blood Pressure Management Following Thrombectomy for Acute Large Vessel Occlusion Ischemic Stroke (P5.045)

Feasibility of Near Infra-Red Spectroscopy in Blood Pressure Management Following Thrombectomy for Acute Large Vessel Occlusion Ischemic Stroke (P5.045)

Rapid monitoring the water extraction process of Radix Paeoniae Alba using near infrared spectroscopy

Near infrared (NIR) spectroscopy has been developed into one of the most important process analytical techniques (PAT) in a wide field of applications. The feasibility of NIR spectroscopy with partial least square regression (PLSR) to monitor the concentration of paeoniflorin, albiflorin, gallic acid, and benzoyl paeoniflorin during the water extraction process of Radix Paeoniae Alba was demonstrated and verified in this work. NIR spectra were collected in transmission mode and pretreated with smoothing and/or derivative, and then quantitative models were built up using PLSR. Interval partial least squares (iPLS) method was used for the selection of spectral variables. Determination coefficients ([Formula: see text] and [Formula: see text]), root mean squares error of prediction (RMSEP), root mean squares error of calibration (RMSEC), and residual predictive deviation (RPD) were applied to verify the performance of the models, and the corresponding values were 0.9873 and 0.9855, 0.0487[Formula: see text]mg/mL, 0.0545[Formula: see text]mg/mL and 8.4 for paeoniflorin; 0.9879, 0.9888, 0.0303[Formula: see text]mg/mL, 0.0321[Formula: see text]mg/mL and 9.1 for albiflorin; 0.9696, 0.9644, 0.0140[Formula: see text]mg/mL, 0.0145[Formula: see text]mg/mL and 5.1 for gallic acid; 0.9794, 0.9781, 0.00169[Formula: see text]mg/mL, 0.00171[Formula: see text]mg/mL and 6.9 for benzoyl paeoniflorin, respectively. The results turned out that this approach was very efficient and environmentally friendly for the quantitative monitoring of the water extraction process of Radix Paeoniae Alba.

Non-destructive evaluation of avocado fruit maturity using near infrared spectroscopy and PLS regression models

Harvest maturity of avocado fruit is currently determined using indices such as mesocarp oil content, dry matter (DM) or moisture content (MC), both measured destructively using representative samples of a batch in an orchard. Although useful, destructive techniques are time-consuming and results reflect properties of specific produce evaluated. In this study, the feasibility of near-infrared spectroscopy (NIRS) as a rapid non-destructive method for predicting maturity parameters of individual avocado fruit was evaluated. NIRS prediction results showed that MC and DM were predicted accurately, with residual predictive deviation (RPD) of 2.00 and 2.13 respectively. However, models for predicting oil content, though promising, were not accurate, with RPD value of less than 1.0. The good prediction statistics between NIRS predicted MC and DM content demonstrated the potential of this system for non-destructive evaluation of avocado fruit maturity status. The stability and accuracy of models developed over two seasons, 2013 and 2014, to predict maturity parameters of avocado fruit demonstrated model robustness. NIRS combined with PLS models developed in this study are recommended for non-destructive evaluation of individual avocado fruit maturity status.

Oxygenation of the Prefrontal Cortex during Sequential Planning in the Tower of London

To investigate the utility of neural theories for human planning, this study used near-infrared spectroscopy to investigate prefrontal (PFC) oxygenation for a well-established planning task: The Tower of London (TOL). Changes in prefrontal oxygenated hemoglobin from baseline were measured during task performance. Performing the Tower of London led to a significant increase in oxygenation in the left caudal region of the PFC in difficult trials moves relative to easier trials. The different degree of prefrontal oxygenation agrees with previous research and provides further evidence for a capacity utilization framework for measuring neurocognitive demand. Higher activity in the left rostral frontopolar region predicted better performance in the Tower of London, which agrees with a proposed rostral-caudal control hierarchy for the prefrontal cortex. Observed results support the feasibility of near-infrared spectroscopy to assess activity during tasks requiring planning ability and provide support for two neurocognitive models, capacity utilization and rostro-caudal control hierarchy.