Multivariate Partial Least-squares Research Articles

Simultaneous determination of tartrazine and auramine O using FTIR spectroscopy and partial least square

Nowadays, the use of colouring agents are needed to increase customers’ acceptability of foods. However, the development of food technology averts a big opportunity to use prohibited food colouring agents. Tartrazine (TAR) and Auramine O (AO) are two colouring agents may be used as food colorants. TAR is allowed, while AO is not allowed to be used in food colorants, respectively. For this study, the goal was to validate and apply Fourier transform infrared (FTIR) combined with multivariate calibrations for quantitative analysis of TAR and AO in beverages. An attenuated total reflectance FTIR scan of samples was performed at wavenumbers ranging from 4000-650 cm-1 . Quantitative analysis using FTIR spectroscopy was made by plotting the actual levels of TAR and AO obtained using HPLC and the prediction levels facilitated by partial least square (PLS) from the sample series. Internal cross-validation is performed on the model using the leave-one-out method. The root mean square error of prediction (RMSEP), the root mean square error of cross validation (RMSECV), the root mean square error of calibration (RMSEC), as well as the coefficient of determination (R2 ) become validation criteria. The results exhibited that calibration model was gained using variables of FTIR spectra at wavenumbers of 1730-1264 cm-1 . Correlation coefficients (r) of 0.9437 (TAR) and 0.9381 (FTIR) are present in the calibration model for the correlation of actual and predicted values. The method for analyzing tartrazine and auramine O in powdered beverages was validated using multivariate PLS FTIR calibration spectroscopy. FTIR spectroscopy could be further developed as an alternative technique for analysis of TAR and AO in beverages.

Just-In-Time Learning Based Functional Spectral Data Modeling for In-Situ Measurement of Slurry Component Concentrations via Infrared Spectroscopy

For using the infrared spectroscopy to conduct in-situ measurement of slurry component concentrations during batch crystallization or fermentation process in engineering practice, a novel just-in-time learning (JITL) based functional modelling method is proposed for spectral data analysis in this paper to improve on-line measurement accuracy, by addressing engineering issues of very limited assay samples, spectral absorption nonlinearity and high-dimensional spectral variables. The traditional univariate or multivariate partial least-squares (PLS) model is subtly extended to the corresponding functional form for describing the relationship between the measured spectra and slurry component concentrations, such that the salient problem of only a small number of slurry component concentration samples available in practice for spectral data modeling could be effectively solved. The wavelet functions are adopted to properly approximate the measured spectral curves owing to their multi-scale and orthogonal properties. Accordingly, a functional spectral data model is established by wavelet functional PLS for on-line measurement of single and multiple component concentrations, respectively. Meanwhile, a JITL strategy is introduced to select the most representative samples for such modeling, which could efficiently handle the disparity issue involved with real-time samples and guarantee the model validity. Experimental results show that root mean square error of model prediction on the solution concentration during L-glutamic acid cooling crystallization process and multiple componts of ethanol fermentation process, could be evidently reduced over 40% and 50%, respectively, compared with the traditional PLS and support vector regression methods.

Advanced Spectrophotometric Resolution Techniques for a Spectrally Overlapping Spasmolytic Binary Mixture along with Dichloroaniline as a Toxic Impurity: Application to Content Uniformity Testing.

Irritable bowel syndrome (IBS) is a common disorder leading to undesirable pain. Phloroglucinol (PHG) and trimethylphloroglucinol (TMG) are co-formulated as spasmolytic medication that is considered to be effective in reducing smooth muscle spasm. 3,5-Dichloroaniline (DCL) is a specified PHG pharmacopoeial impurity which needs to be monitored to avoid its toxic effects. Different smart approaches are presented to provide simple, reliable, and economic spectrophotometric methods able to resolve the severe overlap in the spectra of PHG and TMG in their pure and pharmaceutical forms, in addition to their estimation in the presence of DCL as a toxic impurity of PHG without any need for initial separation. The presented work includes univariate methods, derivative ratio (DR), ratio difference (RD), mean centering (MCR) and deconvulated Fourier method (DF), which were able to determine PHG and TMG simultaneously in their binary mixture. Firstly, DCL was estimated in the zero order, where the two drugs have zero absorption at 247.0 nm, and then its contribution was eliminated by applying ratio subtraction method. Multivariate chemometric partial least squares (PLS) and principal component regression (PCR) models were also applied to determine PHG and TMG simultaneously in the presence of the impurity, DCL. Univariate methods were applied in the range 5.0-30.0, 2.5-25.0, and 1.0-12.0 µg/mL for PHG, TMG, and DCL, respectively. The proposed chemometric models were used in the range 6.0-14.0, 5.0-25.0 and 2.0-10.0 µg/mL for PHG, TMG, and DCL, respectively. These analytical approaches succeeded in estimating the cited drugs in their pharmaceutical formulation and assessing content uniformity of dosage units. The methods were statistically compared with a reported HPLC method, and the results revealed no significance statistical difference. This work provides for the first time successful univariate and multivariate PLS and PCR methods to assess PHG and TMG in the presence of DCL as a toxic impurity along with content uniformity testing of dosage units. Comparative univariate and multivariate spectrophotometric analytical approaches are presented, for the first time, for estimation of spasmolytic formulation of PHG and TMG in the presence of DCL as a PHG toxic impurity. Successful application to content uniformity testing of Stopspasm® dosage form is demonstrated. A statistical study, including t-tests and one-way analysis of variance (ANOVA), was conducted.

Assessing Grapevine Biophysical Parameters From Unmanned Aerial Vehicles Hyperspectral Imagery.

Over the last 50 years, many approaches for extracting plant key parameters from remotely sensed data have been developed, especially in the last decade with the spread of unmanned aerial vehicles (UAVs) in agriculture. Multispectral sensors are very useful for the elaboration of common vegetation indices (VIs), however, the spectral accuracy and range may not be enough. In this scenario, hyperspectral (HS) technologies are gaining particular attention thanks to the highest spectral resolution, which allows deep characterization of vegetative/soil response. Literature presents few papers encompassing UAV-based HS applications in vineyard, a challenging conditions respect to other crops due to high presence of bare soil, grass cover, shadows and high heterogeneity canopy structure with different leaf inclination. The purpose of this paper is to present the first contribution combining traditional and multivariate HS data elaboration techniques, supported by strong ground truthing of vine ecophysiological, vegetative and productive variables. Firstly the research describes the UAV image acquisition and processing workflow to generate a 50 bands HS orthomosaic of a study vineyard. Subsequently, the spectral data extracted from 60 sample vines were elaborated both investigating the relationship between traditional narrowband VIs and grapevine traits. Then, multivariate calibration models were built using a double approach based on Partial Least Square (PLS) regression and interval-PLS (iPLS), to evaluate the correlation performance between the biophysical parameters and HS imagery using the whole spectral range and a selection of more relevant bands applying a variable selection algorithm, respectively. All techniques (VIs, PLS and iPLS) provided satisfactory correlation performances for the ecophysiological (R2 = 0.65), productive (R2 = 0.48), and qualitative (R2 = 0.63) grape parameters. The novelty of this work is represented by the first assessment of a UAV HS dataset with the expression of the entire vine ecosystem, from the physiological and vegetative state to grapes production and quality, using narrowband VIs and multivariate PLS regressions. A correct non-destructive estimation of key parameters in vineyard, above all physiological parameters which must be measured in a short time as they are extremely influenced by the variability of environmental conditions during the day, represents a powerful tool to support the winegrower in vineyard management.

Fast partial quantile regression

Partial least squares (PLS) is a dimensionality reduction technique used as an alternative to ordinary least squares (OLS) in situations where the data is colinear or high dimensional. Both PLS and OLS provide mean based estimates, which are extremely sensitive to the presence of outliers or heavy tailed distributions. In contrast, quantile regression is an alternative to OLS that computes robust quantile based estimates. In this work, the multivariate PLS is extended to the quantile regression framework, obtaining a theoretical formulation of the problem and a robust dimensionality reduction technique that we call fast partial quantile regression (fPQR), that provides quantile based estimates. An efficient implementation of fPQR is also derived, and its performance is studied through simulation experiments and the chemometrics well known biscuit dough dataset, a real high dimensional example.

Personalized Connectome-Based Modeling in Patients with Semi-Acute Phase TBI: Relationship to Acute Neuroimaging and 6 Month Follow-Up.

Following traumatic brain injury (TBI), cognitive impairments manifest through interactions between microscopic and macroscopic changes. On the microscale, a neurometabolic cascade alters neurotransmission, while on the macroscale diffuse axonal injury impacts the integrity of long-range connections. Large-scale brain network modeling allows us to make predictions across these spatial scales by integrating neuroimaging data with biophysically based models to investigate how microscale changes invisible to conventional neuroimaging influence large-scale brain dynamics. To this end, we analyzed structural and functional neuroimaging data from a well characterized sample of 44 adult TBI patients recruited from a regional trauma center, scanned at 1–2 weeks postinjury, and with follow-up behavioral outcome assessed 6 months later. Thirty-six age-matched healthy adults served as comparison participants. Using The Virtual Brain, we fit simulations of whole-brain resting-state functional MRI to the empirical static and dynamic functional connectivity of each participant. Multivariate partial least squares (PLS) analysis showed that patients with acute traumatic intracranial lesions had lower cortical regional inhibitory connection strengths than comparison participants, while patients without acute lesions did not differ from the comparison group. Further multivariate PLS analyses found correlations between lower semiacute regional inhibitory connection strengths and more symptoms and lower cognitive performance at a 6 month follow-up. Critically, patients without acute lesions drove this relationship, suggesting clinical relevance of regional inhibitory connection strengths even when traumatic intracranial lesions were not present. Our results suggest that large-scale connectome-based models may be sensitive to pathophysiological changes in semi-acute phase TBI patients and predictive of their chronic outcomes.

Residence time distribution as a traceability method for lot changes in a pharmaceutical continuous manufacturing system

Residence time distribution (RTD) models were developed to track raw material lots and investigate batch transitions in a continuous manufacturing system. Two raw materials with similar physical properties (granular metformin and lactose) were identified via Principal Component Analysis (PCA) from a library of bulk material properties and used to simulate the switching of lots during production. In-line near-infrared (NIR) spectra were collected with the powder flowing through a chute in a continuous manufacturing system to monitor metformin and lactose concentration in step-change experiments with Partial Least Squares (PLS) models. RTD provided an understanding of raw material propagation through the continuous manufacturing system. Transition times between raw material changes were identified using the results of two multivariate approaches PLS and PCA. The methodology was implemented to discriminate the transition zone in a raw material change, contributing to design control strategies for acceptance and diverting mechanisms.

Interracial contact differentially shapes brain networks involved in social and non-social judgments from faces: a combination of univariate and multivariate approaches.

The present work explores the relationship between interracial contact and the neural substrates of explicit social and non-social judgments about both racial ingroup and outgroup targets. Convergent evidence from univariate and multivariate partial least squares (PLS) analyses reveals that contact shapes the recruitment of brain regions involved in social cognition similarly for both ingroup and outgroup targets. Results support the hypothesis that increased contact is associated with generalized changes in social cognition toward both ingroup and outgroup faces. Specifically, regardless of target race, low- and average-contact perceivers showed the typically observed increased recruitment of temporoparietal junction and dorsomedial prefrontal cortex during social compared to perceptual judgments. However, high-contact perceivers did not show selective recruitment of these brain regions for social judgments. Complimenting univariate results, multivariate PLS analyses reveal that greater perceiver contact leads to reduced co-activation in networks of brain regions associated with face processing (e.g. fusiform gyrus) and salience detection (e.g. anterior cingulate cortex and insula). Across univariate and multivariate analyses, we found no evidence that contact differentially impacted cross-race face perception. Instead, when performing either a social or a novel perceptual task, interracial contact appears to broadly shape how perceivers engage with all faces.

Estimating the covariance matrix of the coefficient estimator in multivariate partial least squares regression with chemical applications

The partial least squares (PLS) regression is a statistical learning technique that solves collinearity and/or high-dimensionality in the space of covariates. In this paper, we propose a new estimator for the covariance matrix of the estimator of the regression coefficients in the multivariate PLS model. This new estimator is simple to be calculated and with a low computational cost. We conduct a Monte Carlo simulation study to assess the performance of the proposed estimator. Then, we apply our proposal to analyze a multivariate real chemical data set. These numerical results show the excellent performance of our proposal.

Partial least squares and silver nanoparticles in spectrophotometric prediction of total hardness of water

AbstractAmong parameters relating to quality of water, total hardness has been investigated owed to its environmental, domestic, and industrial impact. In the present work, silver nanoparticles were greenly synthesized and used to determine total hardness of water. The results showed that in the presence of different water samples, UV–Vis absorption spectrum of AgNPs varies in intensity and location. It was observed that these changes can be related to total hardness of water samples. For this purpose, multivariate calibration method of partial least squares (PLS) was employed. A linear multivariate PLS model for total hardness in the range of 56–400 mg L−1 was constructed with root‐mean‐square error of cross validation (RMSECV) of 96.7 with three latent variables. The model was successfully used to predict total hardness of 16 bottled or natural water samples.

Multi-trait genomic selection indexes applied to identification of superior genotypes

Most studies on genomic selection in plant breeding compare different statistical methods of univariate approach. However, multi-trait methodologies should be considered since they allow the simultaneous selection of superior genotypes in several economic traits. Here, the aims were to compare the selection accuracy and efficiency of the multivariate partial least square (MPLS) method compared with random regression best linear unbiased predictor (rrBLUP), Bayesian Lasso (Blasso) and univariate partial least square (UPLS) and to develop genomic selection indexes efficient for superior genotypes identification in plant breeding. Ten F2 populations with 800 individuals were simulated, considering four traits with different heritabilities. Genomic selection analyses using rrBLUP, Blasso, UPLS, and MPLS were conducted. Four genomic selection indexes were elaborated by the sum of the marker effects obtained for each trait, weighted by the respective residual variance. Multi-trait indexes were developed based on the assumptions of each methodology mentioned (rrBLUP, Blasso, UPLS, and MPLS), and were denominated I-rrBLUP, I-Blasso, I-UPLS, and I-MPLS. Processing time, selective accuracy, selection gains, and selection coincidence were used to compare the methods and the selection indexes proposed. The MPLS method had similar results compared to UPLS method for the low heritability traits and was less efficient than the rrBLUP and Blasso. The genome selection indexes provided the highest total genetic gains. The I-rrBLUP and I-MPLS indexes stood out for high efficiency in selecting superior genotypes in the shortest processing time. Results suggest that the genomic selection indexes proposed in this study may be promising for plant breeding programs.

Effect of Genetic Algorithm-Based Wavelength Selection as a Preprocessing Tool on Multivariate Simultaneous Determination of Paracetamol, Orphenadrine Citrate, and Caffeine in the Presence of p-Aminophenol Impurity.

The utilization of selection methods such as genetic algorithm (GA) aims to construct better partial least squares (PLS) and principal component regression (PCR) models than those established from the full-spectrum range. Determination of paracetamol (PAR), orphenadrine citrate (Or.cit), and caffeine (CAF) in the presence of PAR nephrotoxic impurity [p-aminophenol (PAP)]. GA was applied to select the optimum wavelengths used. A calibration set was prepared in which the three drugs, together with PAP, were modeled by multilevel multifactor design. This calibration set was used to build the PLS and PCR models, either with or without preprocessing the data using GA. Results were compared with and without preprocessing, and this revealed that GA can find an optimized combination of spectral wavelengths, yielding a lower root mean square error of prediction as well as a lower number of latent variables used. The results of the two models show that simultaneous determination of the aforementioned drugs can be performed in the concentration ranges of 20-60, 3-11, and 1-9 μg/mL for PAR, Or.cit, and CAF, respectively. The proposed models were applied for the determination of the three drugs in their pharmaceutical formulations, and the results were verified by the standard addition technique. GA can be useful as a wavelength selection tool before applying multivariate PLS and PCR methods. GA gives an improvement in the predictive ability of the models with lower RMSEP and less number of latent variables (LVs). The proposed PLS, PCR, GA-PLS, and GA-PCR spectrophotometric methods were able to determine paracetamol, orphenadrine citrate, and caffeine in the presence of p-aminophenol and severe spectral overlapping.

Online Monitoring of Solutions Within Microfluidic Chips: Simultaneous Raman and UV-Vis Absorption Spectroscopies.

Microfluidics is an appealing analytical tool in the global effort to close the nuclear fuel cycle. Using a microfluidic chip permits the analysis of greatly reduced sample volumes compared to what is necessary for traditional analytical methods. There is a commensurate reduction in disposal volume and cost. The development of novel sensors is necessary to take full advantage of the microchip configuration, where optical-spectroscopy-based approaches offer a powerful route to characterize chemical composition. This study uses simultaneously applied UV-vis and micro-Raman spectroscopies adapted to function on the microscale to analyze in situ both the Nd3+ (UV-vis-active) and HNO3 (Raman-active) concentrations in the same sample. An adjustable translation platform was designed to hold the micro-Raman probe above and perpendicular to the chip face and the UV-vis probe in the plane of the chip. These complimentary spectral techniques when processed through multivariate partial least-squares (PLS) models gave an accurate picture of the widely varying solution concentrations as a function of time for each solution component. Solution matrix effects can drastically alter analyte signatures as measured by both UV-vis absorbance and Raman spectroscopy. PLS methods successfully modeled these spectral changes and accurately measured concentrations of components of interest within the microfluidic chip.

Novel spectrophotometric and factor-based multivariate calibration-prediction techniques for determination of two inhibitors of hepatitis C-virus and hepatocellular carcinoma in pure, human urine, and human plasma.

Novel univariate and multivariate factor-based calibration-prediction techniques were validated for simultaneous ultraviolet spectrophotometric determination of ribavirin (RIV), daclatasvir (DAV), sofosbuvir (SOV), and sorafenib (SON) which are co-administered for treatment of hepatocellular carcinoma (HCC) that results from Hepatitis C-virus (HCV) infection in their commercial products and in biological fluids. Determination of these compounds is essential owing to their pharmacotherapeutic benefits. Due to spectral overlapping of RIV, DAV, SOV, and SON, univariate extended derivative ratio (EDR) method and multivariate partial least-squares (PLS) and principal component regression (PCR) methods were used for constructing the calibration curves. The extended derivative ratio (EDR) absorption maxima at 215 nm and minima at 310.5 nm was used for determination of RIV and DAV, respectively and absorption maxima at 240.3 nm and minima at 284.5 nm for determination of SOV and SON, respectively. The linearity was established over the range of 6-42 μg mL-1, 4-16 μg mL-1, 10-70 μg mL-1, and 3-9 μg mL-1 for RIV, DAV, SOV and SON with correlation coefficient (r2) of 0.9997, 0.9997, 0.9999 and 0.9997, respectively. This method was effectively applied to pure, pharmaceutical preparations and to spiked human urine and plasma. PLS and PCR models were established for the determination of the studied drugs in the range of 6-42, 4-16, 10-70 and 3-9 μg mL-1 for RIV, DAV, SOV, and SON, respectively. Furthermore, updating the PLS model (PLS model update) were allowed for the determination of these drugs in spiked human urine, plasma and drug-dissolution test of their tablets. The obtained results were compared to official and reported method showing that there were no significant differences. The results of applying PLS and PCR models for evaluation of RIV, DAV, SOV, and SON in human urine samples as real samples were also encouraging. It is expected that the suitable features of the proposed method make it helpful for biological and clinical applications.

AUTHENTICATION OF TURMERIC USING PROTON-NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY AND MULTIVARIATE ANALYSIS

Objective: The objective of this study was to apply 1H-NMR spectroscopy-based metabolite fingerprinting in combination with multivariate analysis for authentication of turmeric (Curcuma longa) from C. heyneana and C. manga.Methods: Partial least square-discriminant analysis (PLS-DA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were used for differentiation of authentic and adulterated C. longa with C. manga and C. heyneana. The variables used were peaks with certain chemical shifts at optimized 1H-NMR spectra of authentic and adulterated C. longa.Results: All of the authentic C. longa samples were clearly separated from the adulterated ones. The multivariate calibration of partial least square (PLS) was successfully applied to predict of adulterants in C. longa. The lower RMSEC (root mean square error of calibration) values, 0.94% for adulterated C. longa with C. heyneana and 1.37% for adulterated C. longa with C. manga, and the lower RMSEP (root mean square error of prediction) values, 0.83% for adulterated C. longa with C. heyneana and 1.34% for adulterated C. longa with C. manga indicated the good of accuracy and precision of the calibration models.Conclusion: The combination of 1H-NMR spectroscopy and chemometrics of multivariate analysis PLS-DA, OPLS-DA, and PLS proves an adequate technique for authentication of turmeric.

Comparison of Portable and Bench-Top Spectrometers for Mid-Infrared Diffuse Reflectance Measurements of Soils.

Mid-infrared (MIR) spectroscopy has received widespread interest as a method to complement traditional soil analysis. Recently available portable MIR spectrometers additionally offer potential for on-site applications, given sufficient spectral data quality. We therefore tested the performance of the Agilent 4300 Handheld FTIR (DRIFT spectra) in comparison to a Bruker Tensor 27 bench-top instrument in terms of (i) spectral quality and measurement noise quantified by wavelet analysis; (ii) accuracy of partial least squares (PLS) calibrations for soil organic carbon (SOC), total nitrogen (N), pH, clay and sand content with a repeated cross-validation analysis; and (iii) key spectral regions for these soil properties identified with a Monte Carlo spectral variable selection approach. Measurements and multivariate calibrations with the handheld device were as good as or slightly better than Bruker equipped with a DRIFT accessory, but not as accurate as with directional hemispherical reflectance (DHR) data collected with an integrating sphere. Variations in noise did not markedly affect the accuracy of multivariate PLS calibrations. Identified key spectral regions for PLS calibrations provided a good match between Agilent and Bruker DHR data, especially for SOC and N. Our findings suggest that portable FTIR instruments are a viable alternative for MIR measurements in the laboratory and offer great potential for on-site applications.

Rapid Determination of Phenolics, Flavonoids, and Antioxidant Properties of Physalis ixocarpa Brot. ex Hornem. and Physalis angulata L. by Infrared Spectroscopy and Partial Least Squares

ABSTRACTPhysalis ixocarpa Brot. ex Hornem. and Physalis angulata L. are two edible species of the family Solanaceae, which have an important variety of antioxidant compounds present in their roots, stems, leaves, calyces, and fruits. This work reports the development of multivariate models based on the use of partial least square (PLS) analysis and Fourier transform infrared (FTIR) spectroscopy for the quantitative determination of total phenolics, total flavonoids, free radical scavenging activity, total antioxidant capacity, and reducing power in the extracts of roots, stems, and leaves of both P. ixocarpa and P. angulata. Standard chromatographic and colorimetric techniques were used to determine the quantitative actual values (references) in the extracts, which served as input data to develop the multivariate PLS models. Optimized FTIR-PLS models were realized by cross-validation procedures, obtaining the determination coefficients for prediction between 0.792 and 0.905 for P. ixocarpa, and between 0.756 and 0.893 for P. angulata. In this form, FTIR spectroscopy with multivariate analysis could represent a versatile tool to evaluate quantitatively concentrations of bioactive compounds and antioxidant properties in the extracts of both species, requiring a very short time at low cost.

Predicting the width and average fracture frequency of damage zones using a partial least squares statistical analysis: Implications for fault zone development

We introduce the partial least squares (PLS) statistical analysis that quantifies and predicts the observed relationships among normal fault slip, fracturing associated with the fault, and lithology. We describe the systematic process for constructing a multivariate PLS model that predicts the average fracture frequency and the width of fracture-dominated fault damage zones from fault, lithologic and fracture data. Conversely, the model can also predict normal fault net slip for a defined lithology given the average fracture frequency and width of a fracture-dominated fault damage zone, hereafter defined as a fracture intensification domain (FID). Fracture, fault and lithologic data were collected in the Mohawk Valley of New York State from outcrops in the Upper Ordovician Utica Group and Lorraine Group. Data collection was focused on faults with observable slip, associated FIDs, and no observable lateral restriction. Our statistical analysis used three variables to describe the geometry of the FID: FID width (FIDw), average fracture frequency within the FID (FIDƒ), and the power law regression exponent (FIDR) of the least squares trend line. We incorporated additional data from literature and tested multiple PLS models in order to refine the analysis using quality indicators provided by the PLS summary statistics output. Variables included in the final predictive model included FIDw, FIDƒ, fault slip, grain size and clay percent. Fault slip and grain size were found to have a positive covariance with FIDw while clay percent had a negative covariance. Fault slip, grain size and clay percent all showed a negative covariance with FIDƒ. Results from this research indicate that increasing fault slip leads to wider FIDs and lower average fracture frequency within the FID. The lower average fracture frequency in wider FIDs is primarily attributed to an increase in the length of the low-frequency FID tail away from the associated fault. A possible secondary influence reducing fracture frequency is due to the progressive development of a fault core at the expense of the adjacent damage zone and the consumption of the highest-frequency fractures adjacent to the fault surface.

Comparative study on ATR-FTIR calibration models for monitoring solution concentration in cooling crystallization

In this paper calibration model building based on using an ATR-FTIR spectroscopy is investigated for in-situ measurement of the solution concentration during a cooling crystallization process. The cooling crystallization of L-glutamic Acid (LGA) as a case is studied here. It was found that using the metastable zone (MSZ) data for model calibration can guarantee the prediction accuracy for monitoring the operating window of cooling crystallization, compared to the usage of undersaturated zone (USZ) spectra for model building as traditionally practiced. Calibration experiments were made for LGA solution under different concentrations. Four candidate calibration models were established using different zone data for comparison, by using a multivariate partial least-squares (PLS) regression algorithm for the collected spectra together with the corresponding temperature values. Experiments under different process conditions including the changes of solution concentration and operating temperature were conducted. The results indicate that using the MSZ spectra for model calibration can give more accurate prediction of the solution concentration during the crystallization process, while maintaining accuracy in changing the operating temperature. The primary reason of prediction error was clarified as spectral nonlinearity for in-situ measurement between USZ and MSZ. In addition, an LGA cooling crystallization experiment was performed to verify the sensitivity of these calibration models for monitoring the crystal growth process.

Sympathoadrenal Activation is Associated with Acute Traumatic Coagulopathy and Endotheliopathy in Isolated Brain Injury.

ABSTRACTBackground:Acute coagulopathy after traumatic brain injury (TBI) involves a complex multifactorial hemostatic response that is poorly characterized. Objectives: To examine early posttraumatic alterations in coagulofibrinolytic, endothelial, and inflammatory blood biomarkers in relation to sympathetic nervous system (SNS) activation and 6-month patient outcomes, using multivariate partial least-squares (PLS) analysis.Patients and Methods:A multicenter observational study of 159 adult isolated TBI patients admitted to the emergency department at an urban level I trauma center, was performed. Plasma concentrations of 6 coagulofibrinolytic, 10 vascular endothelial, 19 inflammatory, and 2 catecholamine biomarkers were measured by immunoassay on admission and 24 h postinjury. Neurological outcome at 6 months was assessed using the Extended Glasgow Outcome Scale. PLS-discriminant analysis was used to identify salient biomarker contributions to unfavorable outcome, whereas PLS regression analysis was used to evaluate the covariance between SNS correlates (catecholamines) and biomarkers of coagulopathy, endotheliopathy, and inflammation.Results:Biomarker profiles in patients with an unfavorable outcome displayed procoagulation, hyperfibrinolysis, glycocalyx and endothelial damage, vasculature activation, and inflammation. A strong covariant relationship was evident between catecholamines and biomarkers of coagulopathy, endotheliopathy, and inflammation at both admission and 24 h postinjury.Conclusions:Biomarkers of coagulopathy and endotheliopathy are associated with poor outcome after TBI. Catecholamine levels were highly correlated with endotheliopathy and coagulopathy markers within the first 24 h after injury. Further research is warranted to characterize the pathogenic role of SNS-mediated hemostatic alterations in isolated TBI.