Detection of Bacillus globigii spores using a Fourier transform infrared-attenuated total reflection method

The use of an alumina-coated ZnSe internal reflection element (IRE) to detect spores by attenuated total reflection infrared spectroscopy (FTIR-ATR) was investigated. Two methods for coating the IRE with alumina are described. It is shown that the adsorption proceeds through an interaction of the carboxylate groups on Bacillus globigii (BG) and positively charged sites on the alumina. The amount adsorbed is highly dependent on solution pH and passes through a maximum value near pH 5, which is dictated by the charge density on the spores and the charge density on the alumina surface. Furthermore, it is shown that lateral-lateral repulsion between the spores limits the maximum adsorbed amount, giving rise to a detection limit of 10(7) spores per cm(2) of the IRE.     

Complex process monitoring using modified partial least squares method of independent component regression

In this paper, first, some disadvantages of original partial least squares method of independent component analysis (ICA-PLS) are analyzed. Then ICA-PLS is modified for regression purpose.Disadvantages of the original ICA-PLS algorithm are as follows: 1) the regression coefficient matrix and residual matrix cannot been given so that the computation time may increase with the number of samples; and 2) ICA-PLS lacks the ability to give better monitoring performance when the correlation structure of measured variables is nonlinear, which is often the case for industrial processes.To solve the above problems, we modified the original algorithm in following aspects: 1) the regression coefficient matrix and residual matrix in ICA-PLS are given so that the computation time is decreased; and 2) to solve the nonlinear problem, ICA-PLS and kernel trick is first combined for nonlinear regression purpose, which is called iterative ICA-KPLS in this paper. The iterative calculation of ICA-KPLS will be time consuming when the sample number becomes larger. Hence, the regression coefficient matrix and residual matrix in ICA-KPLS are given to avoid the expensive computation time when the number of samples is huge.The proposed methods are applied to the quality prediction in fermentation process and Tennessee Eastman process. Applications indicate that the proposed approach effectively captures the relations in the process variables and use of ICA-KPLS instead of ICA-PLS improves the predictive ability. The expensive computation time is avoided by using the coefficient matrix and residual matrix.     

Application of latent root regression for calibration in near-infrared spectroscopy. Comparison with principal component regression and partial least squares

Several analytical applications of spectroscopy are based on the assessment of a linear model, linking laboratory values to spectral data. Among various procedures, the following three methods have been used, i.e. principal component regression (PCR), partial least squares (PLS) and latent root regression (LRR). These methods can be applied in order to tackle the high collinearity commonly observed with spectral data. A collection of 99 near-infrared spectra, each including 351 data points, was used for the comparison of the 3 methods. The dependent variable was the specific production of pelleting. The spectral collection was divided into 49 and 50 observations for calibration and validation, respectively. The main elements of comparison were the minimum error observed on the verification set, the number of regressors introduced in the models and the stability of the errors around the minimum values. The minimum errors were 3.29, 3.13 and 3.07 for PCR, PLS and LRR, respectively. LRR required a large number of regressors in order to obtain the minimum error. Nevertheless, it gave very stable results, and the errors were not markedly increased when an arbitrary large number of regressors was introduced into the LRR model.     

Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis

Principal component analysis (PCA) is the most commonly used dimensionality reduction technique for detecting and diagnosing faults in chemical processes. Although PCA contains certain optimality properties in terms of fault detection, and has been widely applied for fault diagnosis, it is not best suited for fault diagnosis. Discriminant partial least squares (DPLS) has been shown to improve fault diagnosis for small-scale classification problems as compared with PCA. Fisher's discriminant analysis (FDA) has advantages from a theoretical point of view. In this paper, we develop an information criterion that automatically determines the order of the dimensionality reduction for FDA and DPLS, and show that FDA and DPLS are more proficient than PCA for diagnosing faults, both theoretically and by applying these techniques to simulated data collected from the Tennessee Eastman chemical plant simulator.     

Simultaneous monitoring of organic acids and sugars in fresh and processed apple juice by Fourier transform infrared-attenuated total reflection spectroscopy

A combination of Fourier transform infrared spectroscopy (FT-IR) and chemometrics was used as a screening tool for the determination of sugars and organic acids such as sucrose, glucose, fructose, sorbitol, citric acid, and malic acid in processed commercial and extracted fresh apple juices. Prepared samples of synthetic apple juice in different constituent concentration ranges were scanned by attenuated total reflectance (ATR) accessory and the spectral region in the range between 950 and 1500 cm(-1) was selected for calibration model development using partial least squares (PLS) regression and principal component regression (PCR). The calibration models were successfully validated by high-performance liquid chromatography (HPLC) measurements against several commercial juice varieties as well as juice extracted from different apple varieties to provide an overall R2 correlation of 0.998. The present study demonstrates that Fourier transform infrared spectroscopy could be used for rapid and nondestructive determination of multiple constituents in commercial and fresh apple juices. Results indicate this approach to be a rapid and cost-effective tool for routine monitoring of multiple constituents in a fruit juice production facility.     

Macromolecular concentrations in bovine nasal cartilage by Fourier transform infrared imaging and principal component regression

Fourier transform infrared imaging (FT-IRI) and principal component regression (PCR) were used to quantitatively determine collagen and proteoglycan concentrations in bovine nasal cartilage (BNC). An infrared spectral library was first established by obtaining eleven infrared spectra from a series of collagen and chondroitin 6-sulfate mixed in different ratios. FT-IR images were obtained from 6-μm-thick sections of BNC specimens at 6.25-μm pixel size. The spectra from the FT-IR images were imported into a PCR program to obtain the relative concentrations of collagen and proteoglycan in BNC, based on the spectral library of pure chemicals. These PCR-determined concentrations agreed with the molecular concentrations determined biochemically using an enzyme digestion assay. Use of the imaging approach revealed that proteoglycan loss in the specimens occurs first at the surface of the tissue block when compared with the middle portion of the tissue block. The quantitative correlation of collagen and proteoglycan revealed that their infrared absorption peak areas at 1338 and 1072-855 cm(-1) can only be used as qualitative indicators of the molecular contents. The use of PCR with FT-IRI offers an accurate tool to spatially determine the distributions of macromolecular concentration in cartilage.     

Determination of the percentage of homopolymer component in Ziegler/Natta catalyst linear low-density polyethylene resins using high-temperature cell Fourier transform infrared and partial least squares quantitative analysis technique

A new method for the determination of the percentage of homopolymer component, using high-temperature cell Fourier transform infrared (FT-IR) by partial least squares (PLS) quantitative analysis technique, was developed and applied to Ziegler Natta linear low-density polyethylene (LLDPE). The method is based on the IR spectrum changes between the 730 cm(-1) band and 720 cm(-1) band at the temperature of 110 degrees C, which is near the melting point of the polyethylene. The HD % (the percentage of high-density component, i.e., the percentage of homopolymer component) results obtained by CTREF (CRYSTAF in TREF mode) technique are used as the input data together with the respective FT-IR spectra for PLS analyses to establish a calibration curve. The PLS quality is characterized by a correlation coefficient of 0.997 (cross-validation) using four factors and a root mean square error of calibration (RMSEC) of 0.772. The HD% of the unknown can then be predicted by the PLS software from the unknown FT-IR spectrum. A control resin was tested seven times by CTREF and FT-IR. The HD% of the control resin was 28.59+/-0.88% by CTREF and 29.05+/-2.37% by FT-IR. It was found that the method was applicable for the same comonomer type of LLDPE within a melt index range and density.     

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.     

Determination of organic and inorganic carbon in forest soil samples by mid-infrared spectroscopy and partial least squares regression

Analyses of organic and inorganic carbon are of great interest in the field of soil analyses. Soil samples from a national monitoring project were provided for this study, including more than 130 forest sites from Austria. We investigated the humus layers (if present undecomposed litter (L), of mixed samples of F- (intermediate decomposed organic matter) and H-(highly decomposed organic matter) (FH)) and upper mineral soil layers (0-5 and 5-10 cm) of the samples. Mid-infrared spectra were recorded and evaluated by their band areas; subsequently we calculated models with the partial least squares approach. This was done by correlating calculated data of the mid-infrared spectra with gas-volumetrically determined carbonate values and measurements of organic carbon from an elemental analyzer. For carbonate determination, this approach gave satisfying results. For measurements of organic carbon, it was necessary to discriminate into humus layers and mineral soils or even more groups to obtain satisfactory correlations between spectroscopically determined and conventionally measured values. These additional factors were the presence of carbonate, the forest type, and the dominant tree species. In mineral soils, fewer subdivisions were necessary to obtain useful results. In humus layers, groupings of sites with more similar characteristics had to be formed in order to obtain satisfying results. The conclusion is that the chemical background of soil organic matter leading to different proportions of functional groups, especially in the less humified organic matter of the humus layers, plays a key role in analyses with mid-infrared spectroscopy. Keeping this in mind, the present approach has a significant potential for the prediction of properties of forest soil layers, such as, e.g., carbonate and organic carbon contents.     

Analysis of fiber blends using horizontal attenuated total reflection Fourier transform infrared and discriminant analysis

We have investigated the utility of a horizontal attenuated total reflection Fourier transform infrared spectrometer (HATR/FT-IR) for the analysis of fiber and textile blends. The identification of a blended textile can be accomplished by subtracting a reference spectrum of the textile's most abundant component, leading to a difference spectrum that infers the identity of the second constituent of the blended textile. Mathematical post-processing of the spectra employing discriminant analysis provided a useful statistical tool to confirm the fiber blend 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.     

Solvent interactions in methanol/N, N-dimethylamide binary systems studied by Fourier transform infrared-attenuated total reflection (FT-IR/ATR) and two-dimensional correlation spectroscopy (2D-COS)

The interaction of N,N-dimethyl formamide (DMF) and N,N-dimethyl acetamide (DMA) with methanol in solution mixtures was studied using Fourier transform infrared-attenuated total reflection (FT-IR/ATR) spectroscopy. The concentration-dependent FT-IR/ATR spectra of DMF/methanol and DMA/methanol mixtures were recorded in the wavenumber range 4000-650 cm(-1) to investigate wavenumber shifts as a consequence of hydrogen bonding interactions. In combination with two-dimensional correlation spectroscopy (2D-COS), the positional fluctuations observed in the ν(C=O) and ν(O-H) regions of DMF/DMA and methanol, respectively, have been discussed in terms of changing populations of differently hydrogen-bonded and interacting species of the same and different component molecules.     

Impulse-induced compression rheo-optics study of polymers using attenuated total reflection based step-scan Fourier transform infrared time-resolved spectroscopy

An impulse-induced attenuated total reflection (ATR) based dynamic compression step-scan time-resolved Fourier transform rheo-optical system has been developed. This system was used to observe different viscoelastic properties of poly(ethylene terephthalate) (PET), poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHx), and carbon-black-filled polyester-polyamide blend. In the case of PET, almost no viscoelastic response extending beyond 15 ms was observed in the dynamic absorbance difference time domain spectrum. In contrast, PHBHx showed apparently different viscoelastic responses in the dynamic absorbance difference spectrum, especially in the C=O stretching band region. A long relaxation tail of the 1723 cm(-1) band lasting about 2.7 milliseconds was clearly seen. The tail corresponds to the structural or morphological reorganization of a less ordered crystalline form (Type II) under compressive perturbation. The carbon-black-filled polyester-polyamide blend film also shows different viscoelastic response tails. In this case, the amide C=O stretching vibration band does not show distinct viscoelastic responses, suggesting that the polyamide component does not contribute much to the viscoelastic properties. The present method shows promise for characterizing a wide variety of viscoelastic materials, including polymer alloys, blends, composites, copolymers, and semicrystalline polymers.     

Spectrophotometric simultaneous determination of nitrophenol isomers by orthogonal signal correction and partial least squares

A simple, novel and sensitive spectrophotometric method was described for simultaneous determination of nitrophenol isomers mixtures. All factors affecting on the sensitivity were optimized and the linear dynamic range for determination of nitrophenol isomers were found. The simultaneous determination of nitrophenol mixtures by using spectrophotometric methods is a difficult problem, due to the spectral interferences. The partial least squares modeling was used for the multivariate calibration of the spectrophotometric data. The orthogonal signal correction was used for preprocessing of data matrices and the prediction results of model, with and without using orthogonal signal correction, were statistically compared. The experimental calibration matrix was designed by measuring the absorbance over the range 300-520 nm for 21 samples of 1-20, 1-20 and 1-10 microg ml(-1) of m-nitrophenol, o-nitrophenol and p-nitrophenol, respectively. The RMSEP for m-nitrophenol, o-nitrophenol and p-nitrophenol with and without OSC were 0.3682, 0.5965, 0.3408 and 0.7351, 0.9962, 1.0055, respectively. The proposed method was successfully applied for the determination of m-nitrophenol, o-nitrophenol and p-nitrophenol in synthetic and real matrix samples such as water.     

Fluorescence-suppressed Raman technique for quantitative analysis of protein solution using a micro-Raman probe, the shifted excitation method, and partial least squares regression analysis

A practical Raman analyzing technique with suppression of the strong fluorescent background in order to obtain quantitative information is proposed in the present study. The technique is based on the shifted excitation method and partial least squares regression (PLSR) analysis. The Raman system consists of a single Raman spectrometer, a background-free electrically tunable Ti:Sapphire laser (BF-ETL), and a micro-Raman probe (MRP). The system allows one to obtain reliable shifted excitation Raman spectra with a simple operation. The PLSR analysis successfully provides quantitative information from the obtained spectra with the suppression of random noise including photon shot noise. The present study demonstrates that the technique is effective for extracting quantitative information concealed behind a fluorescent background that is more than 200 times stronger than the Raman signal.     

Fourier transform infrared (FT-IR) spectroscopy and improved principal component regression (PCR) for quantification of solid analytes in microalgae and bacteria

In bioanalytical chemistry, a detailed chemical understanding of biomaterials is often difficult to obtain due to the sheer number of analytes contained in a sample along with the samples' generally low reproducibility. This study presents a Fourier transform infrared (FT-IR) spectroscopic technique in conjunction with innovations in sample preparation and chemometric data preprocessing to overcome these limitations. These methodologies were applied to quantitative analyses of 31 representative compounds commonly found in biomaterial, which have been incorporated into a spectroscopic calibration database, that is, albumin (protein); D-alanine, glycine, histidine, valine, arginine, cysteine, phenylalanine, tyrosine, methionine, L-glutamine, and glutamic acid, (amino acids); glucose, fructose, galactose, mannose, sucrose, lactose, glycogen, agarose, and starch (carbohydrates); DNA (salmon sperm), sulphonoquinovosyl diglyceride ( sulpho-lipid ), and 1,2-diacyl-sn-glycero-3-phospho-L-serine ( phospho-lipid ); succinic acid and malic acid ( carboxylic acids ); glycolic acid (a -hydroxy acid), sodium pyruvate, b -carotene, frustules (microalgae silica-shells), and ammonium formate. Two proof-of-principle applications were based on calibration models incorporating these solids, i.e., characterization of E. coli and microalgae. The former aims for detection of bacterial contamination and the latter to enable investigations of changes in chemical composition of microalgae cells in response to shifting environmental conditions. Chemometric preprocessing steps have been developed for handling sample-to-sample fluctuations of absorption path lengths and baselines; the former incorporated mass normalization while the latter utilized a novel baseline correction method that requires no a priori information. Data preprocessing, chemometric calibration, and evaluation algorithms have been combined, together with an extensive spectral database of the aforementioned compounds (～1500 samples), for quantitative calibration purposes through the remotely accessible Virtual Chemometrics Lab , which can be utilized for a multitude of applications through a graphical user interface.     

Waveband selection for determination of nitrate in soil using mid-infrared attenuated total reflectance spectroscopy

This paper investigates the possibility of determining nitrate concentration in soil pastes using spectral absorbance at several fixed wavebands. A three-step procedure for determining the most appropriate wavebands, as well as their width, is described. This procedure is applied to a dataset that includes eleven soils with various nitrate concentrations ranging from 0 to approximately 150 mg [N]/kg [dry soil]. The results show that nitrate concentration can be determined quite acceptably using only four 12 cm(-1) wide wavebands, centered at 1280, 1330, 1379, and 1430 cm(-1). The prediction errors range from approximately 1.5 to 14.0 mg [N]/kg [dry soil], depending on soil composition and moisture content, with the lighter and more vulnerable (pollution-wise) soils having errors inferior to 10 mg [N]/kg [dry soil]. These results are similar to results obtained by applying partial least square to the 'continuous' spectrum, and indicate that the development of a soil nitrate attenuated total reflectance (ATR) sensor based on a few fixed mid-infrared (MIR) wavebands could be considered.     

Identification of natural dyes on laboratory-dyed wool and ancient wool, silk, and cotton fibers using attenuated total reflection (ATR) Fourier transform infrared (FT-IR) spectroscopy and Fourier transform Raman spectroscopy

Attenuated total reflection (ATR) infrared and Fourier transform (FT) Raman spectra were obtained from wool threads dyed in the laboratory with natural dyes used in antiquity, following a procedure similar to ancient methods for dyeing wool. The ATR spectra were primarily dominated by the signals of the wool, making it difficult to identify the dye on the fibers only by visual inspection of the infrared spectrum. However, the Raman spectra showed more significant characteristics attributable to the dyes as previously studied in the literature on modern synthetic dyes. A library-search method was thus applied to the second derivatives of both the ATR and Raman spectra to verify the possibility of identifying the dye. Two libraries were constructed, one consisting of the ATR spectra of undyed wool (raw, washed, and mordanted) and the transmission spectra of pure dyes and the other consisting of the Raman spectra of undyed wool and of pure dyes. Correlation and first-derivative correlation search algorithms were used. The results presented here suggest that the two types of spectroscopy are complementary in this kind of work, allowing the almost complete identification of historic dyes on wool. In fact, through the combined use of the two searches, most dyes were identified with a good index of similarity and within the first five hits. Only for annatto was identification totally impossible using either technique. Subsequently the same method was applied to wool, silk, and cotton threads taken from ancient Caucasian and Chinese textiles.     

Using multiple calibration sets to improve the quantitative accuracy of partial least squares (PLS) regression on open-path Fourier transform infrared (OP/FT-IR) spectra of ammonia over wide concentration ranges

The use of multiple calibration sets in partial least squares (PLS) regression was proposed to improve the quantitative determination of NH(3) over wide concentration ranges from open-path Fourier transform infrared (OP/FT-IR) spectra. The spectra were measured near animal farms, where the path-integrated concentration of NH(3) can fluctuate from nearly zero to as high as approximately 1000 ppm-m. PLS regression with a single calibration set did not cover such a large concentration range effectively, and the quantitative accuracy was degraded due to the nonlinear relationship between concentration and absorbance for spectra measured at low resolution (1 cm(-1) and poorer.) In PLS regression with multiple calibration sets, each calibration set covers a part of the entire concentration range, which significantly decreases the serious nonlinearity problem in PLS regression occurring when only a single calibration set is used. The relative error was reduced from approximately 6% to below 2%, and the best results were obtained with four calibration sets, each covering one quarter of the entire concentration range. It was also found that it was possible to build the multiple calibration sets easily and efficiently without extra measurements.     

Determination of humic acids content in composts by means of near- and mid-infrared spectroscopy and partial least squares regression models

Humic acids are part of the stable organic matter fraction in soils and composts. Due to their favorable properties for soils and plants, and their role in carbon sequestration, they are considered a quality criterion of composts. Time-consuming chemical extraction of humic acids and the inherent source of errors require alternative approaches for humic acids quantification. Different measurement techniques in the mid-infrared (MIR: KBr pellet technique) and near-infrared (NIR: fiber probe as well as an integrating sphere with a sample rotator) regions were applied. Partial least squares regression (PLSR) models based on infrared spectra were developed to determine humic acids contents in composts. As the wavenumber regions used (NIR: 6105-5380 cm(-1) and 4360-4220 cm(-1), MIR: 1745-1685 cm(-1) and 1610-1567 cm(-1)) represent different molecular vibrations, the importance of the methylene-group-derived vibrations for the NIR models is discussed. The correlation coefficients obtained for the KBr pellet technique, the NIR fiber probe technique, and the NIR integrating sphere (r = 0.94, 0.93, and 0.94) and the root mean square errors of cross-validation (RMSECV = 2.2% organic dry matter (ODM), 2.5% ODM, and 2.2% ODM) make the models appropriate for application in composting practice.