Main inorganic component measurement of seawater using near-infrared spectroscopy

The potential of near-infrared (NIR) spectroscopy to measure the main inorganic components of seawater as salt-manufacturing materials was investigated. A total of 72 seawater samples collected from six locations was used, and spectra (1100-1800 nm) were acquired by a NIR spectrophotometer with a 1-mm path length. Principal component analysis (PCA), canonical correlation analysis (CCA), and partial least-squares (PLS) regression were performed based on the reference inorganic components. As a result, the principal component analysis and canonical correlation analysis showed that the near-infrared spectra could be related to the inorganic components of seawater. The partial least-squares regression analysis showed that the inorganic components (ion concentration of Cl, Na+, K+, SO4(2-), and Ca2+) could be predicted with good accuracy using NIR spectra and their second derivatives. For Cl ion and K+ ion concentrations, the accuracy was high.     

Sugar interaction with metals in aqueous solution: indirect determination from infrared and direct determination from nuclear magnetic resonance spectroscopy

In this article, mid-infrared Fourier transform (Mid-FT-IR) and carbon thirteen nuclear magnetic resonance (13C NMR) spectroscopy have been used to determine possible interactions between sucrose and various alkali or alkaline earth metals in aqueous solution. In the presence of these metals, significant shifts in the absorption bands of sucrose were noted by mid-FT-IR coupled with principal component analysis (PCA). These shifts were explained on the basis of weakening of the H-bond network between sucrose and water and possible interactions between sucrose and the metal ion. Factorial maps were established and the spectral patterns obtained show that these interactions vary according to the nature of the metal ion. 13C NMR analysis showed that the carbon atoms of sucrose undergo shielding or deshielding in the presence of metal ions in aqueous solutions. Two factors were invoked to account for the variation of chemical shifts: the rupture of hydrogen bonds due to hydration of the metal ion and the possible coordination of the metal ion to the oxygen atoms of sucrose.     

Evaluation techniques for two-way data from in situ fourier transform mid-infrared reaction monitoring in aqueous solution

A model chemical reaction was monitored with in situ Fourier transform mid-infrared spectroscopy using an attenuated total reflectance probe. The evaluation of the IR spectra is complicated by the fact that the reaction runs in nonisothermal aqueous solution with large variations in pH. Despite this, it was possible to extract large amounts of useful information on the reaction after suitable pretreatment of the spectra. Alternating least-squares (ALS) multivariate curve resolution is shown to be a useful technique for obtaining pure component spectra and concentrations if suitable spectral regions are analyzed. Rank mapping methods are used as the basis for this sectioning into smaller regions. Techniques for finding and analyzing selective spectral regions are also shown to be applicable to this type of data. Partial least-squares (PLS) regression models based on spectral data were used to verify the results where possible. The correlation between the concentrations predicted from PLS and ALS is excellent.     

TOF-SIMS analysis of a 576 micropatterned copolymer array to reveal surface moieties that control wettability

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) was used in a high-throughput fashion to obtain mass spectra from the surfaces of 576 novel acrylate-based polymers, synthesized using a combinatorial approach and in a micropatterned format. To identify variations in surface chemistry within the library, principal component analysis (PCA) was used. PCA clearly identified surface chemical commonality and differences within the library. The TOF-SIMS spectra were also used to determine the relationship between water contact angle (WCA) and the surface chemistry of the polymer library using partial least-squares regression (PLS). A good correlation between the TOF-SIMS data from the novel polymers and water contact angle was obtained. Examination of the PLS regression vector allowed surface moieties that correlate with high and low WCA to be identified. This in turn provided an insight into molecular structures that significantly influence wettability. This study demonstrates that multivariate analysis can be successfully applied to TOF-SIMS data from a large library of samples and highlights the potential of these techniques for building complex surface property/chemistry models.     

Investigation of electrolyte measurement in diluted whole blood using spectroscopic and chemometric methods

The feasibility of using near-infrared (NIR) spectroscopy in combination with partial least-squares (PLS) regression was explored to measure electrolyte concentration in whole blood samples. Spectra were collected from diluted blood samples containing randomized, clinically relevant concentrations of Na+, K+, and Ca2+. Sodium was also studied in lysed blood. Reference measurements were made from the same samples using a standard clinical chemistry instrument. Partial least squares (PLS) was used to develop calibration models for each ion with acceptable results (Na+, R2 = 0.86, CVSEP = 9.5 mmol/L; K+, R2 = 0.54, CVSEP = 1.4 mmol/L; Ca2+, R2 = 0.56, CVSEP = 0.18 mmol/L). Slightly improved results were obtained using a narrower wavelength region (470-925 nm) where hemoglobin, but not water, absorbed indicating that ionic interaction with hemoglobin is as effective as water in causing measurable spectral variation. Good models were also achieved for sodium in lysed blood, illustrating that cell swelling, which is correlated with sodium concentration, is not required for calibration model development.     

Development of the photoacoustic rapid-scan FT-IR-based method for measurement of ink concentration on printed paper

Photoacoustic rapid-scan FT-IR spectroscopy was used to collect spectra of paper samples printed with mineral- and vegetable-oil-based inks at different concentrations. Partial least-squares (PLS) analysis and principal component analysis (PCA) were combined to form a model that, with data collected in the 3600-3200, 3000-2800, and 1800-1000 cm-1 spectral regions, enables one to predict the concentration of ink in printed samples. Prediction is statistically robust provided the selected optical path difference (OPD) velocities are within the range 0.05-1.00 cm/s.     

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.     

The influence of correlated calibration samples on the prediction performance of multivariate models based on mid-infrared spectra of animal cell cultures

The effect of the presence of metabolism-induced concentration correlations in the calibration samples on the prediction performance of partial least-squares regression (PLSR) models and mid-infrared spectra from Chinese hamster ovary cell cultures was investigated. Samples collected from batch cultures contained highly correlated metabolite concentrations as a result of metabolic relations. Calibrations based on such samples could only be used to predict concentrations in new samples if a similar correlation structure was present and failed when the new samples were randomly spiked with the analytes. On the other hand, such models were able to predict glucose correctly even if they were based on a spectral range in which glucose does not absorb, provided that the correlations in the calibration and in the new samples were similar. If however, samples from a calibration culture were randomly spiked with the main analytes, much more robust PLSR models resulted. It was possible to predict analyte concentrations in new samples irrespective of whether the correlation structure was maintained or not. Validity of all established models for any given use could be predicted a priori by computing the space inclusion and observer conditions. Predictions from these computations agreed in all cases with the experimental test of model validity.     

Quality and statistical classification of Brazilian vegetable oils using mid-infrared and Raman spectroscopy

Palm oil, soy oil, sunflower oil, corn oil, castor oil, and rapeseed oil were analyzed with Fourier transform infrared (FT-IR) and FT-Raman spectroscopy. The quality of different oils was evaluated and statistically classified by principal component analysis (PCA) and a partial least squares (PLS) regression model. First, a calibration set of spectra was selected from one sampling batch. The qualitative variations in spectra are discussed with a prediction of oil composition (saturated, mono- and polyunsaturated fatty acids) from mid-infrared analysis and iodine value from FT-Raman analysis, based on ratioing the intensity of bands at given wavenumbers. A more robust and convincing oil classification is obtained from two-parameter statistical models. The statistical analysis of FT-Raman spectra favorably distinguishes according to the iodine value, while the mid-infrared spectra are most sensitive to hydroxyl moieties. Second, the models are validated with a set of spectra from another sampling batch, including the same oil types as-received and after different aging times together with a hydrogenated castor oil and high-oleic sunflower oil. There is very good agreement between the model predictions and the Raman measurements, but the statistical significance is lower for mid-infrared spectra. In the future, this calibration model will be used to check vegetable oil qualities before using them in polymerization processes.     

Compound identification using partial and semipartial correlations for gas chromatography-mass spectrometry data

Compound identification is a key component of data analysis in the applications of gas chromatography-mass spectrometry (GC-MS). Currently, the most widely used compound identification is mass spectrum matching, in which the dot product and its composite version are employed as spectral similarity measures. Several forms of transformations for fragment ion intensities have also been proposed to increase the accuracy of compound identification. In this study, we introduced partial and semipartial correlations as mass spectral similarity measures and applied them to identify compounds along with different transformations of peak intensity. The mixture versions of the proposed method were also developed to further improve the accuracy of compound identification. To demonstrate the performance of the proposed spectral similarity measures, the National Institute of Standards and Technology (NIST) mass spectral library and replicate spectral library were used as the reference library and the query spectra, respectively. Identification results showed that the mixture partial and semipartial correlations always outperform both the dot product and its composite measure. The mixture similarity with semipartial correlation has the highest accuracy of 84.6% in compound identification with a transformation of (0.53,1.3) for fragment ion intensity and m/z value, respectively.     

Nanoliter serum sample analysis by mid-infrared spectroscopy for minimally invasive blood-glucose monitoring

The aim of this study was to demonstrate that mid-infrared spectroscopy is able to quantify glucose in a serum matrix with sample volumes well below 1 muL. For this, we applied mid-infrared attenuated total reflectance (ATR) or transmission-based spectroscopic methods to glucose quantification in microsamples of dry-film sera, either undiluted or diluted 10 times in distilled water. The sample series spanned physiological glucose concentrations between 50 and 600 mg/dL and volumes of 80, 8, and 1 nL. Calibration was carried out using multivariate partial least-squares (PLS) modeling with spectral data between 1180 and 940 cm(-1). Best performance was achieved in the ATR experiments. For raw ATR spectra, the optimum standard error of prediction (SEP) of 13.3 mg/dL was obtained for the 8 nL sample series with subsequent 10-fold dilution. With respect to the coefficient of variation of the glucose assay, CV(pred), we obtained a value of 3% for the 80 nL volume samples with spectral preprocessing using matrix protein absorption bands as an internal standard, 4% for the 8 nL samples, and 6% for the 1 nL samples with raw data. Spectral standardization resulted in significant improvement, especially for the 80 nL volume sample series. By contrast, the accuracy of the glucose assay for the 1 nL sample volume series could not be improved either by internal standardization or by considering the dry film areas for normalization, which we attribute to varying topographies of the dry films.     

Infrared transmission spectrometry for the determination of urea in microliter sample volumes of blood plasma dialysates

Application of mid-infrared spectroscopy for the determination of urea in blood plasma dialysates of microliter sample volumes using a transmission microcell was investigated. Infrared spectra of the dialysates of plasma samples collected from 75 different patients using CMA 60 microdialysis catheters were evaluated with multivariate partial least squares regression. Using the absorbance spectral data from 1520-1420 cm(-1) and 1220-1120 cm(-1), a minimum standard error of prediction (SEP) of 0.88 mg/dL (0.14 mM) was achieved with spectral variable selection. Our findings suggest the feasibility of developing a mid-infrared sensor in combination with micro-fluidics for on-line monitoring of urea in patients undergoing dialysis treatment.     

Identification of sporulated and vegetative bacteria using statistical analysis of fourier transform mid-infrared transmission data

A combined mid-infrared spectroscopic/statistical modeling approach for the discrimination and identification, at the strain level, of both sporulated and vegetative bacterial samples is presented. Transmission mode spectra of bacteria dried on ZnSe windows were collected using a Fourier transform mid-infrared (FT-IR) spectrometer. Five Bacillus bacterial strains (B. atrophaeus 49337, B. globigii Dugway, B. thuringiensis spp. kurstaki 35866, B. subtilis 49760, and B. subtilis 6051) were used to construct a reference spectral library and to parameterize a four-step statistical model for the systematic identification of bacteria. The statistical methods used in this initial feasibility study included principal component analysis (PCA), classification and regression trees (CART), and Mahalanobis distance calculations. Internal cross-validation studies successfully classified 100% of the samples into their correct physiological state (sporulated or vegetative) and identified 67% of the samples correctly as to their bacterial strain. Analysis of thirteen blind samples, which included reference and other bacteria, nonbiological materials, and mixtures of both nonbiological and bacterial samples, yielded comparable accuracy. The primary advantage of this approach is the accurate identification of unknown bacteria, including spores, in a matter of minutes.     

Multivariate analysis of trace metals in textile effluents in relation to soil and groundwater

This paper deals with the multivariate analysis of metal data in effluents, soil and groundwater to find the distribution and source identification of the selected metals in the three media. Samples were collected from three textile industries located in Hattar Industrial Estate, Pakistan. Metals were estimated by flame atomic absorption spectrophotometry. The results showed elevated levels of Cr, Pb, Ni, Co, Fe, Ca, Na, K and Zn in these media, following the order: soil>effluent>water. Principle component analysis (PCA) of the data showed that the textile effluents are contaminating the soil wherein Cr and Pb were dominant toxic metals having concentrations of 5.96 mg/kg and 4.46 mg/kg, respectively. Other toxic metals such as Co, Cd, Zn, Ni, Mn and Fe, were found to have common origin in the textile effluents. The correlation study along with linear regression and PCA, supported the fact that various elevated metal concentrations emerged from the textile industrial effluents ultimately leading to contamination of the soil and groundwater in their proximity. The estimated metal levels in the water/soil system are compared with the safe limits laid down by WHO.     

Simultaneous determination of the micro-, meso-, and macropore size fractions of porous polymers by a combined use of Fourier transform near-infrared diffuse reflection spectroscopy and multivariate techniques

Fourier transform near-infrared (FT-NIR) diffuse reflection spectroscopy was used in combination with principal component analysis and partial least-squares regression to simultaneously determine the physical and the chemical parameters of a porous poly(p-methylstyrene-co-1,2-bis(p-vinylphenyl)ethane) (MS/BVPE) monolithic polymer. Chemical variations during the synthesis of the polymer material can alter the pore volume and pore area distributions within the polymer scaffold. Furthermore, mid-infrared and near-infrared (NIR) spectroscopic chemical imaging was implemented as a tool to assess the uniformity of the samples. The presented study summarizes the comparative results derived from the spectral FT-NIR data combined with chemometric techniques. The relevance of the interrelation of physical and chemical parameters is highlighted whereas the amount of MS/BVPE (%, v/v) and the quantity (%) of micropores (diameter, d < 6 nm), mesopores (6 nm < d < 50 nm), and macropores (50 nm < d < 200 nm) could be determined with one measurement. For comparison of the quantitative data, the standard error of prediction (SEP) was used. The SEP for determining the MS/BVPE amount in the samples showed 0.35%, for pore volume quantiles 1.42-8.44%, and for pore area quantiles 0.38-1.45%, respectively. The implication of these results is that FT-NIR spectroscopy is a suitable technique for the screening of samples with varying physicochemical properties and to quantitatively determine the parameters simultaneously within a few seconds.     

Detection of minute chemical species by principal-component analysis

A novel analytical technique based on the detection of minute bands in a mixture spectrum with the use of principal-component analysis (PCA) is presented. This new aspect of PCA indicates that overlapped spectra of some components can be separated with no a priori knowledge of the components when the absorbances of the components vary greatly. This technique can be used for the detection of minute chemical species. The concept was confirmed by computer simulations. In the simulations, abstract spectra (loading vectors) were successfully obtained, and the changes of the component absorbances were also successfully followed semiquantitatively by calculating their scores. The method developed with PCA was applied to the analysis of infrared reflection-absorption (RA) spectra to study molecular interaction mechanism between alkyl-deuterated dipalmitoylphosphatidylcholine (DPPC-d(62)) monolayer and sucrose. The samples were Langmuir-Blodgett (LB) films of the DPPC-d(62) monolayer that was prepared on a sucrose solution. The LB films consisted of the following phases: air/DPPC-d(62) + sucrose/sucrose/substrate (gold). The abstract spectra corresponding to "DPPC-d(62) + sucrose" and "sucrose" phases were successfully separated by PCA, and the absorbance change of sucrose in each phase was semiquantitatively calculated from the score. The absorbance change was experimentally confirmed with quartz-crystal microbalance (QCM) experiments. In addition, minute water molecules that remained in the LB films after drying were readily detected from an abstract spectrum, and their binding site was found to be the phospholipid moiety in the head group of DPPC-d(62).     

Comparison of spectral fluorescent signatures-based models to characterize DOM in treated water samples

Statistical procedures enable a multivariate analysis of the measurements to identify specific characteristics of the dissolved organic matter (DOM) fractions in raw natural water, including the concentrations. In this work, three already established models were used to predict the concentrations of fractions of DOM from spectral fluorescent signatures (SFSs): a general linear regression (GLR), loadings and scores of a principal components analysis (PCA), and a partial least squares regression (PLS). Details about the method undertaken to prepare the fractions were given. Water samples from surface water treatment plants in New Jersey were used for the testing. In all cases, PLS have shown much better biases and accuracies than GLR and PCA models. Hydrophilic neutral, however, showed poor performances (bias 33%) due to the isolation technique used. Recommendations were provided in order to improve the DOM characterization through SFS, which linked to PLS make a powerful and cost-effective surrogate parameter to characterize DOM.     

Statistical analysis of trace metals in the plasma of cancer patients versus controls

The plasma of cancer patients (n=112) and controls (n=118) were analysed for selected trace metals (Al, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Sb, Sr and Zn) by flame atomic absorption spectroscopy. In the plasma of cancer patients, mean concentrations of macronutrients/essential metals, Na, K, Ca, Mg, Fe and Zn were 3971, 178, 44.1, 7.59, 4.38 and 3.90 ppm, respectively, while the mean metal levels in the plasma of controls were 3844, 151, 74.2, 18.0, 6.60 and 2.50 ppm, respectively. Average concentrations of Cd, Cr, Cu, Mn, Mo, Ni, Pb, Sb, Sr and Zn were noted to be significantly higher in the plasma of cancer patients compared with controls. Very strong mutual correlations (r>0.70) in the plasma of cancer patients were observed between Fe-Mn, Ca-Mn, Ca-Ni, Ca-Co, Cd-Pb, Co-Ni, Mn-Ni, Mn-Zn, Cr-Li, Ca-Zn and Fe-Ni, whereas, Ca-Mn, Ca-Mg, Fe-Zn, Ca-Zn, Mg-Mn, Mg-Zn, Cd-Sb, Cd-Co, Cd-Zn, Co-Sb and Sb-Zn exhibited strong relationships (r>0.50) in the plasma of controls, all were significant at p<0.01. Principal component analysis (PCA) of the data extracted five PCs, both for cancer patients and controls, but with considerably different loadings. The average metals levels in male and female donors of the two groups were also evaluated and in addition, the general role of trace metals in the carcinogenesis was discussed. The study indicated appreciably different pattern of metal distribution and mutual relationships in the plasma of cancer patients in comparison with controls.     

Determination of glucose in whole blood samples by mid-infrared spectroscopy

We have determined the glucose concentration of whole blood from mid-infrared spectra without sample preparation or use of chemical reagents. We selected 1119-1022 cm(-1) as the optimal wavelength range for our measurement by making a first-loading vector analysis based on partial least-squares regression. We examined the influence of hemoglobin on samples by using different calibration and prediction sets. The accuracy of glucose prediction depended on the hemoglobin level in the calibration model; the sample set should represent the entire range of hemoglobin concentration. We obtained an accuracy of 5.9% in glucose prediction, and this value is well within a clinically acceptable range.     

Limits of recognition for simple vapor mixtures determined with a microsensor array

The "limit of recognition" (LOR) has been defined as the minimum concentration at which reliable individual vapor recognition can be achieved with a multisensor array, and methodology for determining the LORs of individual vapors probabilistically on the basis of sensor array response patterns has been reported. This article explores the problems of defining and evaluating LORs for vapor mixtures in terms of the absolute and relative component vapor concentrations, where the mixture must be discriminated from those component vapors and from the subset of possible lower-order component mixtures. Monte Carlo simulations and principal components regression analyses of an extant database of calibrated responses to a set of 16 vapors from an array of 6 diverse polymer-coated surface acoustic wave sensors are used to illustrate the approach and to examine trends in LOR values among the 120 possible binary mixtures and 560 possible ternary mixtures in the data set. At concentrations exceeding the LOD, 89% of the binary mixtures could be reliably recognized (<5% error) over some composite concentration range, while only 3% of the ternary mixtures could be recognized. Most binary mixtures could be recognized only if the constituent vapor relative concentration ratio, defined in terms of multiples of the LOD for each vapor, was < or =20. Correlations with the Euclidean distance(s) separating the normalized constituent vapor response vectors allow reasonably accurate predictions of the limiting recognizable mixture composition ranges for binary and ternary cases. Results are considered in the context of using microsensor arrays for vapor detection and recognition in microanalytical systems.