Performance Of Near-infrared Spectroscopy Research Articles

Performance of Near-Infrared Spectroscopy in Detecting Acute Tourniquet-Induced Upper-Extremity Ischemia Across Different Skin Phenotypes

Diagnosing acute tissue ischemia is challenging, particularly in patients with higher skin melanin content. We investigated whether near-infrared spectroscopy (NIRS) is effective and consistent in detecting upper extremity ischemia across various skin phenotypes. Volunteers underwent tourniquet-induced upper extremity ischemia. Skin color was evaluated by the Fitzpatrick scale (FP, range: I-VI) and the Von Luschan scale (vL, range: 1-36). A NIRS probe was placed on one finger. The tourniquet was inflated to 250 mmHg and perfusion was restricted for 7 minutes, followed by a 10-minute monitored reperfusion period. The percent tissue oxygenation (StO2) was recorded. A total of 55 volunteers were enrolled (22 self-identified as Caucasian, 21 African American, 7 Asian, 2 Latinx, and 2 Biracial). Average starting and ending StO2 for the cohort was 72.2% and 45.9%, respectively. However, there was variability based on skin melanin content. Increasing vL correlated with lower starting StO2, smaller StO2 decrease, and shorter time to reach ischemic steady state. High skin melanin (FP scale IV-VI) was associated with significantly lower starting StO2 (-7.1%) and shorter time to reach ischemic steady state (-0.3 mins). African Americans had lower starting StO2 (-8.6%) and 7.8% lesser total StO2 decrease than other groups. NIRS can rapidly detect acute onset tissue ischemia in the upper extremity. However, given the lower starting StO2 and smaller total StO2 decrease after tourniquet-induced ischemia for patients with higher skin melanin, using NIRS for clinical detection of acute ischemia may be more challenging in these patients. These inconsistencies may limit use of NIRS clinically for spot identification of ischemia. Although NIRS has utility in tracking tissue oxygenation, variable performance with different skin melanin content raises concerns as to whether different cutoff/threshold levels are needed for different groups, and whether NIRS is reliable for spot checks in acute events.

Performance of near infrared spectroscopy of a solid cattle and poultry manure database depends on the sample preparation and regression method used

Determining the chemical composition of animal manure rapidly is essential to manage fertilisation and decrease environmental pollution. Near infrared (NIR) spectroscopy is a non-destructive, inexpensive and rapid method to determine several components of manure simultaneously. This study investigated the ability of NIR spectroscopy to analyse the dry matter, total and ammonium nitrogen, phosphorus, calcium, potassium and magnesium contents in a database of heterogeneous cattle and poultry solid manures. The accuracy of calibration models obtained from different sample preparation methods (dried ground vs. fresh homogenized) and multivariate regression methods (partial least squares vs. local regression) were compared. The results showed that using local regression with NIR spectra of fresh homogenized manure could predict dry matter (R2=0.99, RMSEV = 1.64%, RPD = 13.31), total (R2=0.98, RMSEV = 0.16%, RPD = 7.11) and ammonium nitrogen (R2=0.97, RMSEV = 0.042%, RPD = 5.57) and phosphorus (R2=0.95, RMSEV = 0.10%, RPD = 5.56) contents accurately.

Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interfaces Using Transcranial Near-Infrared Photobiomodulation With the Same Device

Transcranial near-infrared photobiomodulation (tNIR-PBM) can modulate physiological characteristics of the human brain, such as the cerebral blood flow and oxidative metabolism. Here, we investigated whether the performance of near-infrared spectroscopy (NIRS)-based brain-computer interfaces (BCIs) can be improved by tNIR-PBM applied to the prefrontal cortex with the same NIRS device. A total of 14 healthy individuals participated in the NIRS-based BCI study where the aim was to distinguish the mental arithmetic task from the idle state (IS) task either after tNIR-PBM or after sham stimulation, with the two experiments being conducted at least two days apart. The tNIR-PBM was applied by simply turning on the NIRS recording equipment for 20 min. To evaluate the degree of performance improvement obtained after tNIR-PBM, the average BCI classification accuracy obtained under the tNIR-PBM condition was compared with that obtained under the sham stimulation condition. The classification accuracy of NIRS-based BCI was significantly improved upon conduction of tNIR-PBM (82.74%) as compared to that in the sham stimulation condition (76.07%, p < 0.005). Thus, our results suggest that simply turning on the NIRS recording equipment before the BCI experiment can improve the performance of the NIRS-based BCI system.

Short communication: Long term performance of near infrared spectroscopy to predict intramuscular fat content in New Zealand lamb

This study investigates the performance of a partial least squares regression model to predict intramuscular fat (IMF) in lamb M. longissimus lumborum developed using near infrared (NIR) data collected under a range of different conditions. A total of 26 independent NIR datasets were collected across 7 years, including 14 flocks, four devices and several measurement conditions. A model is developed and its performance is tested using a total of n = 3201 NIR spectra and intramuscular fat percentage measurements by wet chemistry. The model had a coefficient of determination by cross-validation of 0.52, which agrees with previous results using smaller numbers of animals. Overall the results show that near infrared models can be robust across many varying conditions. These models could potentially be implemented in an automated meat quality monitoring system.

Performance of near-infrared spectroscopy in pork shoulder as a predictor for pork belly softness

Pork belly quality indicators are economically relevant in the pork industry. Near-infrared (NIR) spectroscopy of the pork shoulder outer subcutaneous fat layer, belly flop angle, and subjective softness scores of the pork belly were measured (N = 144) to determine the accuracy of pork shoulder NIR spectroscopy as a predictor of pork belly softness. The NIR spectra hot carcass estimates explained over 80.0% variability in pork belly softness (80.5%–90.8%), with low prediction error, suggesting that NIR spectroscopy measured in the pork shoulder is an efficient and accurate indicator of pork belly softness to classify pork bellies for specific market demands.

Near-Infrared Spectroscopy as a classification tool for agraz (Vaccinium meridionale Swartz)

The importance of the selection and classification processes in the industry of agricultural products and the increase in the production of fruits make necessary the development and implementation of new techniques to efficiently perform these tasks. Techniques such as NIR spectroscopy have proved to have potential to accomplish this purpose. The aim of this research was to evaluate the performance of near infrared spectroscopy as a classification tool for agraz (Vaccinium meridionale Swartz), according to its state of maturity. In order to obtainthe classification models, the PCA and SIMCA methods were used. Results were obtained close to 100% accuracy in the classification for maturity stages 4 and 5 and between 81 and 90% for maturity stage 3. The NIR spectroscopy appears as a suitable technique for the classification of fruits of agraz according to their state of maturity.

Performance Prediction for a Near-Infrared Spectroscopy-Brain-Computer Interface Using Resting-State Functional Connectivity of the Prefrontal Cortex.

One of the most important issues in current brain-computer interface (BCI) research is the prediction of a user's BCI performance prior to the main BCI session because it would be useful to reduce the time required to determine the BCI paradigm best suited to that user. In electroencephalography (EEG)-BCI research, whether a user has low BCI performance toward a specific BCI paradigm has been estimated using a variety of resting-state EEG features. However, no previous study has attempted to predict the performance of near-infrared spectroscopy (NIRS)-BCI using resting-state NIRS data recorded before the main BCI experiment. In this study, we investigated whether the performance of an NIRS-BCI discriminating a mental arithmetic task from the baseline state could be predicted using resting-state functional connectivity (RSFC) of the prefrontal cortex. The investigation of NIRS signals recorded from 29 participants revealed that the RSFC between bilateral channels in the prefrontal area was negatively correlated with subsequent BCI performance (e.g. a fitted line for the RSFC between L2 and R2 channels explains 41% of BCI performance variation). We expect that our indicator can be used to predict BCI performance of an individual user prior to the main NIRS-BCI experiments, thereby facilitating implementation of more efficient NIRS-BCI systems.

Near infrared spectroscopy as an easy and precise method to estimate soil texture

Soil texture is beside soil organic matter the most fundamental soil parameter affecting most biological, chemical, physical soil properties. However, conventional laboratory analyses of soil texture are time-consuming and expensive, calling for alternative analytical methods. In this study, we tested the performance of near infrared spectroscopy (NIRS) derived texture estimates for a national-scale soil dataset of the German Agricultural Soil Inventory, using memory-based learning and log-ratio transformation. The developed NIRS models had a root mean square error of prediction of 66.1, 55.5, and 18.5 g kg−1 for sand, silt, and clay content, respectively. The lowest relative error (7.5%) was found for clay content, while the relative error for silt content was 11.2% and for sand content 11.1%. Ratio of performance to deviation varied between 4.8 and 6.2 in all cases, indicating excellent model fit. The key to excellent model performance was log-ratio transformation, which allowed all three particle size fractions to be modeled simultaneously while meeting the constraint that all size fractions should add up to 100%. Our NIRS based soil texture estimates outperformed the texture-by-feel method applied to the same set of soils (Vos et al. 2016). NIRS is thus a suitable low-cost analytical method for texture analysis that can be used for large-scale datasets.

Noninvasive Measurement of Glucose in Artificial Plasma with Near-Infrared and Raman Spectroscopy

The goal of this research was to develop a method for noninvasive blood glucose assay. Near-infrared (NIR) spectroscopy and Raman spectroscopy, two more promising techniques compared to other methods, were investigated in two kinds of artificial plasma (AP). Calibration models were generated by performing partial least squares (PLS) regression and optimized individually by considering spectral range, spectral pretreatment methods, and number of model factors. The two spectroscopic models were validated for the determination of glucose, and the results show that the two spectroscopic models established are robust, accurate, and repeatable. Compared to Raman spectroscopy, the performance of NIR spectroscopy was much better, with lower root mean square errors of cross-validation (RMSECV) of 0.128 and 0.094 mg/ml, lower root mean square errors of validation (RMSEP) of 0.061 and 0.046 mg/ml, higher correlation coefficients (R) of 99.15% and 99.55%, and higher residual predictive deviations (RPD) of 10.8 and 15.0 for artificial plasma I and II, respectively.

Use of Near Infrared Spectroscopy as an Alternative to Videourodynamics to Detect Detrusor Overactivity in Women With the Overactive Bladder Syndrome

To evaluate near infrared spectroscopy as a noninvasive alternative to cystometry for detecting detrusor overactivity in women with overactive bladder (OAB). Although cystometry is considered the "gold standard" investigation for the lower urinary tract, it is invasive. Recently, a noninvasive form of assessment of the lower urinary tract has been introduced using near infrared spectroscopy. This was a prospective pilot study. Women with symptoms of OAB, referred to a tertiary referral one-stop urodynamics clinic were studied. A urodynamic diagnosis was made according to the International Continence Society guidelines. The near infrared spectroscopy monitoring results were analyzed by an independent near infrared spectroscopy Clinical Research Assessor. Both the urodynamics and near infrared spectroscopy assessors reported whether detrusor overactivity was present. Primary outcome measurement was the performance of near infrared spectroscopy as a new diagnostic test. We evaluated the performance of this by calculating the sensitivity and specificity. The clinical usefulness of near infrared spectroscopy was evaluated using positive and negative predictive values. One hundred patients were recruited of whom 95 had traces that could be interpreted. Thirty-one patients were found to have detrusor overactivity on cystometry. Twenty-five of these patients (26%) had detrusor overactivity on near infrared spectroscopy analysis. In 6% of these cases, no near infrared spectroscopy changes identified as suggestive of detrusor overactivity were seen. No detrusor overactivity was detected by cystometry in 64 patients, and in 19% of cases by near infrared spectroscopy. Forty-six patients (48%) had near infrared spectroscopy monitoring events identified as detrusor overactivity but no cystometry changes diagnostic of detrusor overactivity. The results of our study suggest that near infrared spectroscopy is an unreliable method for detecting detrusor overactivity in women with OAB symptoms.

Monte Carlo study of global interference cancellation by multidistance measurement of near-infrared spectroscopy

The performance of near-infrared spectroscopy is sometimes degraded by the systemic physiological interference in the extracerebral layer. There is some systemic interference, which is highly correlated with the functional response evoked by a task execution. This kind of interference is difficult to remove by using ordinary techniques. A multidistance measurement method is one of the possible solutions for this problem. The multidistance measurement method requires estimation parameters derived from partial pathlength values of tissue layers to calculate an absorption coefficient change from a temporal absorbance change. Because partial path lengths are difficult to obtain, experimentally, we estimated them by a Monte Carlo simulation based on a five-layered slab model of a human adult head. Model parameters such as thickness and the transport scattering coefficient of each layer depend on a subject and a measurement position; thus, we assumed that these parameters obey normal distributions around standard parameter values. We determined the estimation parameters that provide a good separation performance in average for the model parameter distribution. The obtained weighting is robust to model parameter deviation and provides smaller errors on average compared to the parameters, which are determined without considering parameter distribution.

Non-Destructive Determination of Carbohydrate Content in Potatoes Using near Infrared Spectroscopy

The performance of near infrared spectroscopy as a simple technique for the non-destructive determination of carbohydrate content in potatoes was examined. An interactance method was adopted to measure near infrared spectra (700–1100 nm). A good calibration model with reasonable accuracy (correlation coefficient of 0.93 and standard error of prediction of 0.98%) was developed using partial least square (PLS) regression. The PLS calibration model utilised effectively two characteristic absorption bands of 990 nm and 916 nm, assigned to potato starch. The plots of the regression coefficients were thought to be useful for understanding the calibration model structure.

Influence of the procedure used to prepare the calibration sample set on the performance of near infrared spectroscopy in quantitative pharmaceutical analyses.

Calibrating near infrared diffuse reflectance spectroscopy (NIRS) methods usually involves preparing a set of samples with a view to expanding the analyte concentration range spanned by production samples. In this work, the performances of the two procedures most frequently used for this purpose in near infrared pharmaceutical analysis, viz., synthetic samples obtained by weighing of the pure constituents of the pharmaceutical and doped samples made by under- or overdosing previously powdered production samples, were compared. Both procedures were found to provide similar results in the quantification of the active compound in the pharmaceutical, which was determined with a relative standard error of prediction (RSEP) of < 1.6%. However, the two types of sample preparation provide different spectra, which precludes the accurate quantification of synthetic samples from calibrations obtained with doped samples and vice versa. None of the mathematical pre-treatments tested with a view to reducing this different scattering (viz., second derivative, standard normal variate and orthogonal signal correction) could effectively solve this problem. This hinders accurate validation of the linearity of the procedure and makes it advisable to use doped samples which are markedly less different to production samples.

The utility and performance of near-infra red spectroscopy in simultaneous monitoring of multiple components in a high cell density recombinant Escherichiacoli production process

Bioprocess optimisation is often limited by an inability to measure biomass, nutrient and by-product concentrations in a time frame which allows process adjustments. Near-infrared (near-IR) spectroscopy can potentially be used to measure each of these components within 2 minutes of sampling, using an unprocessed whole broth sample. In the present study the use of near-IR spectroscopy for at-line (rapid off-line) monitoring of biomass, glycerol, ammonium, and acetate in a recombinant Escherichia coli fed batch process was investigated. The following robust correlation models were developed for these analytes using multiple least squares linear regression (MLR): [Glycerol],gl−1 =15.957 − 2219.270* A(λ2274)−1705.041* A(λ2172); [Acetate],gl−1=27.683 −1757.258* A(λ2254)+296.903* A(λ2340)−21.325* A (λ620); [Ammonium],gl−1=−1310.502−47912.960* A (λ2148)−135149.300* A(λ1782)−27636.200* A (λ830); and [Biomass],gl−1=14.034−3.548* A(λ602/λ1134) −4286.050* A(λ928). Using these models permitted rapid simultaneous analysis of all four analytes. This improved monitoring capability was used to develop a high cell density recombinant E. coli fed-batch process in which ammonium and acetate accumulation were minimised leading to higher cell densities. By manipulation of the C : N ratio in the complex feed, the toxic effects of ammonium accumulation upon the organism were minimised, thereby facilitating the application of a carbon limited feeding strategy. The effect of these C : N ratio medium changes, upon the near-IR measurement capability, was investigated. In this process, near-IR spectroscopy has been shown to be a powerful, accurate and precise method for simultaneously measuring several key process variables. Its accuracy, precision and utility for at-line measurement and control are evaluated, particularly in reference to processes where the initial medium composition may vary, leading to changes in the chemical matrix. The potential of near-infra red for online analysis and control is discussed.

The utility and performance of near-infra red spectroscopy in simultaneous monitoring of multiple components in a high cell density recombinant

The utility and performance of near-infra red spectroscopy in simultaneous monitoring of multiple components in a high cell density recombinant