Multivariate calibration standardization across instruments for the determination of glucose by Fourier transform near-infrared spectrometry

The transfer of multivariate calibration models is investigated between a primary (A) and two secondary Fourier transform near-infrared (near-IR) spectrometers (B, C). The application studied in this work is the use of bands in the near-IR combination region of 5000-4000 cm(-)(1) to determine physiological levels of glucose in a buffered aqueous matrix containing varying levels of alanine, ascorbate, lactate, triacetin, and urea. The three spectrometers are used to measure 80 samples produced through a randomized experimental design that minimizes correlations between the component concentrations and between the concentrations of glucose and water. Direct standardization (DS), piecewise direct standardization (PDS), and guided model reoptimization (GMR) are evaluated for use in transferring partial least-squares calibration models developed with the spectra of 64 samples from the primary instrument to the prediction of glucose concentrations in 16 prediction samples measured with each secondary spectrometer. The three algorithms are evaluated as a function of the number of standardization samples used in transferring the calibration models. Performance criteria for judging the success of the calibration transfer are established as the standard error of prediction (SEP) for internal calibration models built with the spectra of the 64 calibration samples collected with each secondary spectrometer. These SEP values are 1.51 and 1.14 mM for spectrometers B and C, respectively. When calibration standardization is applied, the GMR algorithm is observed to outperform DS and PDS. With spectrometer C, the calibration transfer is highly successful, producing an SEP value of 1.07 mM. However, an SEP of 2.96 mM indicates unsuccessful calibration standardization with spectrometer B. This failure is attributed to differences in the variance structure of the spectra collected with spectrometers A and B. Diagnostic procedures are presented for use with the GMR algorithm that forecasts the successful calibration transfer with spectrometer C and the unsatisfactory results with spectrometer B.     

In vivo noninvasive measurement of blood glucose by near-infrared diffuse-reflectance spectroscopy

This paper reports in situ noninvasive blood glucose monitoring by use of near-infrared (NIR) diffuse-reflectance spectroscopy. The NIR spectra of the human forearm were measured in vivo by using a pair of source and detector optical fibers separated by a distance of 0.65 mm on the skin surface. This optical geometry enables the selective measurement of dermis tissue spectra due to the skin's optical properties and reduces the interference noise arising from the stratum corneum. Oral glucose intake experiments were performed with six subjects (including a single subject with type I diabetes) whose NIR skin spectra were measured at the forearm. Partial least-squares regression (PLSR) analysis was carried out and calibration equations were obtained with each subject individually. Without exception among the six subjects, the regression coefficient vectors of their calibration models were similar to each other and had a positive peak at around 1600 nm, corresponding to the characteristic absorption peak of glucose. This result indicates that there is every possibility of glucose detection in skin tissue using our measurement system. We also found that there was a good correlation between the optically predicted values and the directly measured values of blood samples with individual subjects. The potential of noninvasive blood glucose monitoring using our methodology was demonstrated by the present study.     

New methodology to obtain a calibration model for noninvasive near-infrared blood glucose monitoring

This paper reports new methodology to obtain a calibration model for noninvasive blood glucose monitoring using diffuse reflectance near-infrared (NIR) spectroscopy. Conventional studies of noninvasive blood glucose monitoring with NIR spectroscopy use a calibration model developed by in vivo experimental data sets. In order to create a calibration model, we have used a numerical simulation of light propagation in skin tissue to obtain simulated NIR diffuse reflectance spectra. The numerical simulation method enables us to design parameters affecting the prediction of blood glucose levels and their variation ranges for a data set to create a calibration model using multivariate analysis without any in vivo experiments in advance. By designing the parameters and their variation ranges appropriately, we can prevent a calibration model from chance temporal correlations that are often observed in conventional studies using NIR spectroscopy. The calibration model (regression coefficient vector) obtained by the numerical simulation has a characteristic positive peak at the wavelength around 1600 nm. This characteristic feature of the regression coefficient vector is very similar to those obtained by our previous in vitro and in vivo experimental studies. This positive peak at around 1600 nm also corresponds to the characteristic absorption band of glucose. The present study has reinforced that the characteristic absorbance of glucose at around 1600 nm is useful to predict the blood glucose level by diffuse reflectance NIR spectroscopy. We have validated this new calibration methodology using in vivo experiments. As a result, we obtained a coefficient of determination, r2, of 0.87 and a standard error of prediction (SEP) of 12.3 mg/dL between the predicted blood glucose levels and the reference blood glucose levels for all the experiments we have conducted. These results of in vivo experiments indicate that if the parameters and their vibration ranges are appropriately taken into account in a numerical simulation, the new calibration methodology provides us with a very good calibration model that can predict blood glucose levels with small errors without conducting any experiments in advance to create a calibration model for each individual patient. This new calibration methodology using numerical simulation has promising potential for NIR spectroscopy, especially for noninvasive blood glucose monitoring.     

Determination of tetraethyllead and inorganic lead in water by solid phase microextraction/gas chromatography

A new method for the determination of tetraethyllead (TEL) and ionic lead in water by SPME has been developed. TEL is extracted from the headspace over the sample. Inorganic lead is first derivatized with sodium tetraethylborate to form TEL, which is extracted in the same way as pure TEL samples. The analytical procedure was optimized with respect to pH, amount of derivatizing reagent added, stirring conditions, and extraction time. The detection limit obtained for TEL was found to be 100 ppt when using FID and 5 ppt when using ion trap MS (ITMS). The detection limit for Pb(2+), limited by the nonzero blank, was found to be 200 ppt. Linear calibration curves were obtained for both analytes when FID was used for detection. For lead they spanned over 4 orders of magnitude. ITMS offered excellent sensitivity and selectivity, but the calibration curves were nonlinear when the m/z = 295 ion was used for quantitation. The method has been verified on spiked tap water samples. An excellent agreement was found between the results obtained for standard solutions prepared using NANOpure water and spiked tap water samples.     

Effect of diet composition on the determination of ash and moisture content in solid cattle manure using visible and near-infrared spectroscopy

Visible and near-infrared (Vis-NIR, 350-2500 nm) diffuse reflection spectroscopy (DRS) models built from "as-collected" samples of solid cattle manure accurately predict concentrations of moisture and crude ash. Because different organic molecules emit different spectral signatures, variations in livestock diet composition may affect the predictive accuracy of these models. This study investigates how differences in livestock diet composition affect Vis-NIR DRS prediction of moisture and crude ash. Spectral signatures of solid manure samples (n = 216) from eighteen groups of cattle on six different diets were used to calibrate and validate partial least squares (PLS) regression models. Seven groups of PLS models were created and validated. In the first group, two-thirds of all samples were randomly selected as the calibration set and the remaining one-third were used for the validation set. In the remaining six groups, samples were grouped by livestock diet (ration). Each ration in turn was held out of calibrations and then used as a validation set. When predicting crude ash, the fully random calibration model produced a root mean square deviation (RMSD) of 2.5% on a dry basis (db), ratio of standard error of prediction to the root mean squared deviation (RPD) of 3.1, bias of 0.14% (db), and correlation coefficient r(2) of 0.90., When predicting moisture, an RMSD of 1.5% on a wet basis (wb), RPD of 4.3, bias of -0.09% (wb), and r(2) of 0.95 was achieved. Model accuracy and precision were not impaired by exclusion of any single ration from model calibration.     

In situ determination of growing stages and harvest time of tomato (Lycopersicon esculentum ) fruits using fiber-optic visible-near-infrared (Vis-NIR) spectroscopy

Nondestructive in situ measurement of tomato fruits is essential to determine growing stages and to assist in automatic picking of fruits. This study evaluates the applicability of visible and near-infrared (Vis-NIR) spectroscopy for in situ determination of growing stages and harvest time of three cultivars of tomato fruits. A mobile fiber-type AgroSpec Vis-NIR spectrophotometer (Tec5 Co., Germany) with a spectral range of 350-2200 nm was used to measure tomato spectra in reflection mode. A new growing stage (GS) index defined as the ratio of the current growing age in days to the on-vine duration before harvest in days was proposed. After dividing spectra into a calibration set (70%) and an independent prediction set (30%), spectra in the calibration set were subjected to a partial least squares regression (PLSR) with leave-one-out cross-validation to establish calibration models relating GS to the spectra of tomato fruits. Separate models were developed for each tomato cultivar and compared with a general model that used combined spectra of all three cultivars. The results show that PLSR based on the new GS is successful and robust in predicting the growing stages and harvest time of tomato fruits. Validation of calibration models on the independent prediction set indicates that successful prediction of GS can be achieved using the three models developed separately for each cultivar with a coefficient of determination (R(2)) of 0.91-0.92, root mean square error of prediction (RMSEP) of 0.081-0.097, and residual prediction deviation (RPD) of 3.29-3.70. General calibration using the combined spectra produces good prediction performance, although less accurate than that for the three individual cultivar models. The analysis of regression coefficient plots resulting from PLSR analysis indicates consistent assignment of important wavelengths for individual cultivar spectra and combined spectra. It is concluded that the Vis-NIR PLSR based on GS index can be adopted successfully for in situ determination of growing stages and harvest time of on-vine tomato fruits, which allows for automatic picking of fruits by a horticultural robot.     

A liquid chromatography--tandem mass spectrometry multiresidue method for quantification of specific metabolites of organophosphorus pesticides, synthetic pyrethroids, selected herbicides, and deet in human urine

The ability to estimate low-dose human exposure to commonly used pesticides often is requested in epidemiologic studies. Therefore, fast and robust methods are necessary that can measure many analytes in the same sample. We have developed a method for high-throughput analysis of 19 markers of commonly used pesticides in human urine. The analytes were seven specific metabolites of organophosphorus pesticides, five metabolites of synthetic pyrethroids, six herbicides or their metabolites, and one insect repellant. Human urine (2 mL) was spiked with stable isotopically labeled analogues of the analytes, enzymatically hydrolyzed, extracted using solid-phase extraction, concentrated, and analyzed using high-performance liquid chromatography-tandem mass spectrometry. The sample was divided into two portions and analyzed on two different mass spectrometers, one using atmospheric pressure chemical ionization (APCI) and the other using turbo ion spray atmospheric pressure ionization (TIS). All analytes except the pyrethroid metabolites were analyzed using APCI. The detection limits for all analytes ranged from 0.1 to 1.5 ng/mL of urine, with the majority (17) below 0.5 ng/mL. The analytical precision for the different analytes, estimated as both the within-day and between-day variation, was 3-14 and 4-19%, respectively. The extraction recoveries of the analytes ranged from 68 to 114%. The throughput, including calibration standards and quality control samples, is approximately 50 samples a day. However, the analysis time with the TIS application is much shorter, and if only pyrethroid metabolite data are of interest, the throughput can be increased to 100-150 samples/day.     

Spectral variable selection for partial least squares calibration applied to authentication and quantification of extra virgin olive oils using Fourier transform Raman spectroscopy

The limits of quantitative multivariate assays for the analysis of extra virgin olive oil samples from various Greek sites adulterated by sunflower oil have been evaluated based on their Fourier transform (FT) Raman spectra. Different strategies for wavelength selection were tested for calculating optimal partial least squares (PLS) models. Compared to the full spectrum methods previously applied, the optimum standard error of prediction (SEP) for the sunflower oil concentrations in spiked olive oil samples could be significantly reduced. One efficient approach (PMMS, pair-wise minima and maxima selection) used a special variable selection strategy based on a pair-wise consideration of significant respective minima and maxima of PLS regression vectors, calculated for broad spectral intervals and a low number of PLS factors. PMMS provided robust calibration models with a small number of variables. On the other hand, the Tabu search strategy recently published (search process guided by restrictions leading to Tabu list) achieved lower SEP values but at the cost of extensive computing time when searching for a global minimum and less robust calibration models. Robustness was tested by using packages of ten and twenty randomly selected samples within cross-validation for calculating independent prediction values. The best SEP values for a one year's harvest with a total number of 66 Cretian samples were obtained by such spectral variable optimized PLS calibration models using leave-20-out cross-validation (values between 0.5 and 0.7% by weight). For the more complex population of olive oil samples from all over Greece (total number of 92 samples), results were between 0.7 and 0.9% by weight with a cross-validation sample package size of 20. Notably, the calibration method with Tabu variable selection has been shown to be a valid chemometric approach by which a single model can be applied with a low SEP of 1.4% for olive oil samples across three different harvest years.     

Multiway partial least-squares coupled to residual trilinearization: a genuine multidimensional tool for the study of third-order data. Simultaneous analysis of procaine and its metabolite p-aminobenzoic acid in equine serum

A new third-order multivariate calibration approach, based on the combination of multiway-partial least-squares with a separate procedure called residual trilinearization (N-PLS/RTL), is presented and applied to multicomponent analysis using third-order data. The proposed chemometric algorithm is able to predict analyte concentrations in the presence of unexpected sample components, which require strict adherence to the second-order advantage. Results for the determination of procaine and its metabolite p-aminobenzoic acid in equine serum are discussed, based on kinetic fluorescence excitation-emission four-way measurements and application of the newly developed multiway methodology. Since the analytes are also the reagent and product of the hydrolysis reaction followed by fast-scanning fluorescence spectroscopy, the classical approach based on parallel factor analysis is challenged by strong linear dependencies and multilinearity losses. In comparison, N-PLS/RTL appears an appealing genuine multiway alternative that avoids the latter complications, yielding analytical results that are statistically comparable to those rendered by related unfolded algorithms, which are also able to process four-way data. Prediction was made on validation samples with a qualitative composition similar to the calibration set and also on test samples containing unexpected equine serum components.     

Quantitative measurements of loss on ignition in iron ore using laser-induced breakdown spectroscopy and partial least squares regression analysis

Laser-induced breakdown spectroscopy (LIBS) and partial least squares regression (PLSR) have been applied to perform quantitative measurements of a multiple-species parameter known as loss on ignition (LOI), in a combined set of run-of-mine (ROM) iron ore samples originating from five different iron ore deposits. Global calibration models based on 65 samples and their duplicates from all the deposits with LOI ranging from 0.5 to 10 wt% are shown to be successful for prediction of LOI content in pressed pellets as well as bulk ore samples. A global independent dataset comprising a further 60 samples was used to validate the model resulting in the best validation R(2) of 0.87 and root mean square error of prediction (RMSEP) of 1.1 wt% for bulk samples. A validation R(2) of 0.90 and an RMSEP of 1.0 wt% were demonstrated for pressed pellets. Data preprocessing is shown to improve the quality of the analysis. Spectra normalization options, automatic outlier removal and automatic continuum background correction, which were used to improve the performance of the PLSR method, are discussed in detail.     

On-line fermentation monitoring by mid-infrared spectroscopy

A new method for on-line monitoring of fermentations using mid-infrared (MIR) spectroscopy has been developed. The method has been used to predict the concentrations of glucose and ethanol during a baker's yeast fermentations. A completely automated flow system was employed as an interface between the bioprocess under study and the Fourier transform infrared (FT-IR) spectrometer, which was equipped with a flow cell housing a diamond attenuated total reflection (ATR) element. By using the automated flow system, experimental problems related to adherence of CO(2) bubbles to the ATR surface, as well as formation of biofilms on the ATR surface, could be efficiently eliminated. Gas bubbles were removed during sampling, and by using rinsing steps any biofilm could be removed from the ATR surface. In this way, constant measuring conditions could be guaranteed throughout prolonged fermentation times (approximately 8 h). As a reference method, high-performance liquid chromatography (HPLC) with refractive index detection was used. The recorded data from different fermentations were modeled by partial least-squares (PLS) regression comparing two different strategies for the calibration. On the one hand, calibration sets were constructed from spectra recorded from either synthetic standards or from samples drawn during fermentation. On the other hand, spectra from fermentation samples and synthetic standards were combined to form a calibration set. Differences in the kinetics of the studied fermentation processes used for calibration and prediction, as well as the precision of the HPLC reference method, were identified as the main chemometric sources of error. The optimal PLS regression method was obtained using the mixed calibration set of samples from fermentations and synthetic standards. The root mean square errors of prediction in this case were 0.267 and 0.336 g/L for glucose and ethanol concentration, respectively.     

Prediction of Drug Content and Hardness of Intact Tablets Using Artificial Neural Network and Near-Infrared Spectroscopy

The purpose of this study was to predict drug content and hardness of intact tablets using artificial neural networks (ANN) and near-infrared spectroscopy (NIRS). Tablets for the drug content study were compressed from mixtures of Avicel® PH-101, 0.5% magnesium stearate, and varying concentrations (0%, 1%, 2%, 5%, 10%, 20%, and 40% w/w) of theophylline. Tablets for the hardness study were compressed from mixtures of Avicel PH-101 and 0.5% magnesium stearate at varying compression forces ranging from 0.4 to 1 ton. An Intact Analyzer™ was used to obtain near infrared spectra from the tablets with varying drug contents, whereas a Rapid Content Analyzer™ (RCA) was used to obtain spectral data from the tablets with varying hardness. Two sets of tablets from each batch (i.e., tablets with varying drug content and hardness) were randomly selected. One set of tablets was used to generate appropriate calibration models, while the other set was used as the unknown (test) set. A total of 10 ANN calibration models (5 each with 10 and 160 inputs at appropriate wavelengths) and five separate 4-factor partial least squares (PLS) calibration models were generated to predict drug contents of the test tablets from the spectral data. For the prediction of tablet hardness, two ANN calibration models (one each with 10 and 160 inputs) and two 4-factor PLS calibration models were generated and used to predict the hardness of test tablets. The PLS calibration models were generated using Vision® software. Prediction of drug contents of test tablets using the ANN calibration models generated with 10 inputs was significantly better than the prediction obtained with the ANN calibration models with 160 inputs. For tablets with low drug concentrations (less than or equal to 2%w/w), prediction of drug content was better with either of the two ANN calibration models than with the PLS calibration models. However, prediction of drug contents of tablets with greater than or equal to 5% w/w drug was better with the PLS calibration models than with the ANN calibration models. Prediction of tablet hardness was better with the ANN calibration models generated with either 10 or 160 inputs than with the PLS calibration models. This work demonstrated that a well-trained ANN model is a powerful alternative technique for analysis of NIRS data. Moreover, the technique could be used in instances when the conventional modeling of data does not work adequately.     

Determination of glucose in human aqueous humor using Raman spectroscopy and designed-solution calibration

Glucose concentrations of in vitro human aqueous humor (HAH) samples from cataract patients were determined using 785 nm Raman spectra and partial least squares (PLS) calibration. PLS models were created from spectra of prepared calibration solutions rather than aqueous humor samples. Spectra were obtained with an excitation energy (100 mW for 150 s), which was higher than can be applied in vivo, to decrease the models' contribution to prediction uncertainty. The solutions contained experimentally designed levels of glucose, bicarbonate, lactate, urea, and ascorbate. Multiplicative signal correction of spectra helped compensate for the +/-20% drift in laser power observed at the sample over six noncontiguous days of data collection. Seventeen HAH samples containing 38-775 mg/dL of glucose exhibited a root-mean-square error (RMSEP) of 22 mg/dL, coefficient of determination (r(2)) of 0.989, and bias of 6 mg/dL when predicted from lower energy (30 s) spectra collected contemporaneously with fifty calibration spectra. Similar results were obtained even when spectral data were gathered separately for human aqueous humor samples and calibration samples: 10 HAH samples, calibrated on 25 solutions measured 3.6 weeks earlier, exhibited an RMSEP of 23 mg/dL, r(2) of 0.992, and bias of 9 mg/dL. The results demonstrate progress toward the determination of glucose levels in patient-derived aqueous humor using laboratory-derived "artificial aqueous humor" calibration solutions.     

Coupled vectors resolution method for chemometric calibration with three-way data

A new second-order calibration procedure, the coupled vectors resolution (COVER) method, has been developed. The objective of the method is to seek a couple of vectors that minimize a least-squares criterion. With the knowledge indispensable for quantitation, the method yields direct solutions to various cases of second-order calibration. Moreover, it allows a statistically plausible way to make use of multisample information. In the case of multiple calibration samples, the method uses the calibration samples to resolve the profiles of the analytes in each order, and then calculates the concentrations of the analytes. This offers the advantage that unknown mixtures newly collected can be predicted in a direct manner. In the case of one calibration sample, the method provides an effective way to utilize the information of spectral profiles of the analytes. Results of simulated experiments and a real analytical example show that the proposed method produces acceptable performance in profile resolution and concentration estimation.     

Multi-analyte calibration curve for high-performance liquid chromatography with an inductively coupled plasma carbon emission detector

A liquid chromatography system with an inductively coupled plasma detector is used to prepare a single calibration curve that is useful for multiple analytes. The detector monitors the atomic emission from carbon at 193.09 nm. Hence, the analytes need not exhibit appreciable molar absorptivity or native fluorescence. Since the carbon signal is independent of molecular structure, the sensitivities for different compounds are similar as long as nebulization efficiencies are comparable. In fact, with a suitable internal standard, no calibration curve is necessary. The capability of the system is demonstrated with a test mixture of nine amino acids separated with a C30 reversed-phase column and a 20 mM phosphate buffered mobile phase. The system provides a detection limit of 30 ng carbon. A multi-analyte calibration curve is prepared with 135 distinct measurements: each of nine analytes, at five different concentrations, repeated in triplicate. The average relative standard deviation for 27 measurements of different amino acids at a given concentration is 2.5%. Clearly, a single analyte will suffice for the calibration of all nine test compounds. Similarly, the internal standard method provides an average percent error of 2.0% for the determination of 45 different amino acid concentrations using only a single replicate for each sample.     

On-line near-infrared spectrometer to monitor urea removal in real time during hemodialysis

The ex vivo removal of urea during hemodialysis treatments is monitored in real time with a noninvasive near-infrared spectrometer. The spectrometer uses a temperature-controlled acousto optical tunable filter (AOFT) in conjunction with a thermoelectrically cooled extended wavelength InGaAs detector to provide spectra with a 20 cm(-1) resolution over the combination region (4000-5000 cm(-1)) of the near-infrared spectrum. Spectra are signal averaged over 15 seconds to provide root mean square noise levels of 24 micro-absorbance units for 100% lines generated over the 4600-4500 cm(-1) spectral range. Combination spectra of the spent dialysate stream are collected in real-time as a portion of this stream passes through a sample holder constructed from a 1.1 mm inner diameter tube of Teflon. Real-time spectra are collected during 17 individual dialysis sessions over a period of 10 days. Reference samples were extracted periodically during each session to generate 87 unique samples with corresponding reference concentrations for urea, glucose, lactate, and creatinine. A series of calibration models are generated for urea by using the partial least squares (PLS) algorithm and each model is optimized in terms of number of factors and spectral range. The best calibration model gives a standard error of prediction (SEP) of 0.30 mM based on a random splitting of spectra generated from all 87 reference samples collected across the 17 dialysis sessions. PLS models were also developed by using spectra collected in early sessions to predict urea concentrations from spectra collected in subsequent sessions. SEP values for these prospective models range from 0.37 mM to 0.52 mM. Although higher than when spectra are pooled from all 17 sessions, these prospective SEP values are acceptable for monitoring the hemodialysis process. Selectivity for urea is demonstrated and the selectivity properties of the PLS calibration models are characterized with a pure component selectivity analysis.     

基于可见/近红外光谱分析技术的水性油墨黏度预测模型

针对水性油墨黏度测量方法存在操作复杂、主观性强等问题,利用可见/近红外光谱分析技术结合化学计量学方法,建立水性油墨黏度预测模型,实现水性油墨黏度的快速无损检测。首先,利用微型光纤光谱仪采集水性油墨样本的反射光谱;再通过比较不同预处理方法对原始光谱数据的预处理效果,分别基于原始全光谱及预处理后的光谱数据构建水性油墨黏度的偏最小二乘回归(PLSR)和主成分回归(PCR)预测模型;最后,将预处理后的光谱数据采用连续投影算法(SPA)和竞争性自适应重加权算法(CARS)提取特征波长,并基于特征波长的光谱数据建立水性油墨黏度的PLS预测回归模型。结果表明,采用SPA算法从全光谱中只提取了4个特征波长,不仅显著简化了模型,提升了模型的运算效率,建立的SNV-SPA-PLS模型还具有最佳的预测性能(R_p²=0.9992,RMSEP=0.0732)。该研究结果表明应用光谱分析技术实现对水性油墨黏度检测是有效可行的,为进一步通过光谱分析技术进行水性油墨在线黏度检测提供了新方法,为提高印刷品质量稳定性提供了技术基础。     

Analysis of pharmaceuticals in fish using liquid chromatography-tandem mass spectrometry

A liquid chromatography-tandem mass spectrometry (LC-MS/MS) screening method has been developed targeting 23 pharmaceuticals and 2 metabolites with differing physicochemical properties in fish tissue. Reversed-phase separation of target compounds was achieved using a C18 column and a nonlinear gradient consisting of 0.1% (v/v) formic acid and methanol. Eluted analytes were introduced into the mass analyzer using positive or negative electrospray ionization, as appropriate. A variety of extraction solvents, differing in polarity, pH, or both, were investigated in order to assess recovery of target compounds from 1-g tissue homogenates. Among 10 solvents tested, a 1:1 mixture of 0.1 M aqueous acetic acid (pH 4) and methanol was identified as optimal, resulting in extraction recoveries for 24 of 25 compounds exceeding 60%. Tissue extracts were found to influence the LC-MS/MS response for several analytes. Consequently, matrix-matched calibration standards were employed to determine analyte concentrations in environmental samples. Statistically derived method detection limits were <6 ng/g for most analytes. The method was subsequently used to screen for target analytes in fish from an effluent-dominated stream. Diphenhydramine, diltiazem, carbamazepine, and norfluoxetine were detected in 11 of 11 environmental samples at concentrations ranging from 0.11 to 5.14 ng/g.     

Pure component selectivity analysis of multivariate calibration models from near-infrared spectra

A novel procedure is proposed as a method to characterize the chemical basis of selectivity for multivariate calibration models. This procedure involves submitting pure component spectra of both the target analyte and suspected interferences to the calibration model in question. The resulting model output is analyzed and interpreted in terms of the relative contribution of each component to the predicted analyte concentration. The utility of this method is illustrated by an analysis of calibration models for glucose, sucrose, and maltose. Near-infrared spectra are collected over the 5000-4000-cm(-)(1) spectral range for a set of ternary mixtures of these sugars. Partial least-squares (PLS) calibration models are generated for each component, and these models provide selective responses for the targeted analytes with standard errors of prediction ranging from 0.2 to 0.7 mM over the concentration range of 0.5-50 mM. The concept of the proposed pure component selectivity analysis is illustrated with these models. Results indicate that the net analyte signal is solely responsible for the selectivity of each individual model. Despite strong spectral overlap for these simple carbohydrates, calibration models based on the PLS algorithm provide sufficient selectivity to distinguish these commonly used sugars. The proposed procedure demonstrates conclusively that no component of the sucrose or maltose spectrum contributes to the selective measurement of glucose. Analogous conclusions are possible for the sucrose and maltose calibration models.     

Multiblock analysis of environmental measurements: A case study of using Proton Induced X-ray Emission and meteorology dataset obtained from Islamabad Pakistan

This paper reports the analysis of a multiblock environmental dataset consisting of 176 samples collected in Islamabad Pakistan between February 2006 and August 2007. The concentrations of 32 elements in each sample were measured using Proton Induced X-ray Emission plus black carbon for both coarse and fine particulate matter. Six meteorological parameters were also recorded, namely maximum and minimum daily temperatures, humidity, rainfall, windspeed and pressure. The data were explored using Principal Components Analysis (PCA), Partial Least Squares (PLS), Consensus PCA, Multiblock PLS, Mantel test, Procrustes analysis and the R_V coefficient. Seasonal trends can be identified and interpreted. Using the elemental composition of the particulates it is possible to predict meteorological parameters. Based on the models from PLS, it is possible to use elemental composition in the airborne particulates matter (APM) to predict meteorological parameters. The results from block similarity measures show that fine APM resembles meteorological parameters better than coarse APM. Multiblock PLS models however are not better than classical PLSR. This paper also demonstrates the potential of multiblock approach in environmental monitoring.