Application of principal component analysis to a full profile correlative analysis of FTIR spectra

We have demonstrated an informatics methodology for finding correlations between the full profile Fourier transform infrared spectra of polycrystalline 3C‐silicon carbide (poly‐SiC) films and their growth conditions, thereby developing high‐throughput structure‐process relationships. Because SiC films are a structural element in photonic sensors, this paper focuses on the interpretation of their optical response, the multivariate tracking of critical processing pathways, and the identification of controlling processing mechanisms. Using principal component analysis, we have developed a data analysis tool to aid in the assessment of the relative contributions of experimental parameters in low‐pressure chemical vapor deposition processes to optical responses on the basis of the size of eigenvalues of the spectral data set. The applied methodology for identifying spectral relationships of stoichiometry, dopant chemistry, and microstructure of poly‐SiC provides more effective guidelines to manipulate optical responses by controlling multiple experimental parameters. Copyright © 2011 John Wiley & Sons, Ltd.     

Temporal characterisation of river waters in urban and semi-urban areas using physico-chemical parameters and chemometric methods

Three sampling campaigns were carried out in rivers located at two hydrographic basins affected by urban and semi-urban areas around the Metropolitan area of A Coruña (ca. 500,000 inhabitants, NW-Spain) to study local and temporal variations of 21 physicochemical parameters (pH, conductivity, Cl⁻, SO₄²⁻, SiO₂, Ca²⁺, Mg²⁺, Na⁺, K⁺, hardness, NO₃⁻, NO₂⁻, NH₄⁺, COD, PO₄³⁻, Zn²⁺, Cu²⁺, Mn²⁺, Pb²⁺, alkalinity and acidity) in 23 sampling points. The temporal evolution of the water quality was assessed by matrix augmentation principal components analysis (MA-PCA) and parallel factor analysis (PARAFAC). Moreover, classical principal components analysis (PCA) (one per sampling campaign) was applied with exploratory and comparison purposes. The first factor of the different studies comprised variables associated to the mineral content and it differentiated the samples according to their hydrographic basins. The second factor was mainly associated to organic matter, from domestic wastes and decomposition of natural debris. The temporal evolution of the water quality was mostly related to seasonal increments of the physicochemical parameters defining the decomposition of the organic matter.The three models applied (PCA, MA-PCA and PARAFAC) led to similar conclusions, nonetheless, MA-PCA excelled, since the refolding of scores provided more straightforward and convenient overview of sample time and geographical variations than individual PCA and it is more flexible and adaptable to environmental studies than PARAFAC.     

Determination of rank by median absolute deviation (DRMAD): a simple method for determining the number of principal factors responsible for a data matrix

Median absolute deviation (MAD) is a well‐established statistical method for determining outliers. This simple statistic can be used to determine the number of principal factors responsible for a data matrix by direct application to the residual standard deviation (RSD) obtained from principal component analysis (PCA). Unlike many other popular methods the proposed method, called determination of rank by MAD (DRMAD), does not involve the use of pseudo degrees of freedom, pseudo F‐tests, extensive calibration tables, time‐consuming iterations, nor empirical procedures. The method does not require strict adherence to normal distributions of experimental uncertainties. The computations are direct, simple to use and extremely fast, ideally suitable for online data processing. The results obtained using various sets of chemical data previously reported in the chemical literature agree with the early work. Limitations of the method, determined from model data, are discussed. An algorithm, written in MATLAB format, is presented in the Appendix. Copyright © 2008 John Wiley & Sons, Ltd.     

Self-modeling curve resolution method applied for the evaluation of dissolution testing data: A case study of meloxicam-mannitol binary systems

This paper introduces some chemometric methods, i.e., self-modeling curve resolution (SMCR), multivariate curve resolution-alternating least squares (MCR-ALS) and parallel factor analysis (PARAFAC and PARAFAC2), which are used to evaluate in vitro dissolution testing data detected by a UV-vis spectrophotometer on meloxicam-mannitol binary systems. These systems were chosen because of their relative simplicity to apply as part of the validation process illustrating the effectiveness of the developed and applied chemometric method. The paper illustrates the failure of PARAFAC methods used before for pharmaceutical data evaluations as well, and we suggest application of the feasible band form given by SMCR as a more general procedure.Steps to improve the dissolution behavior of drugs have become among the most interesting aspects of pharmaceutical technology, and our results show that a larger particle size of meloxicam is advantageous for dissolution. Instead of the use of only one characteristic wavelength, appropriate chemometric methods can furnish more information from dissolution testing data, i.e., the individual dissolution rate profiles and the individual spectra for all the components can be obtained without resorting to any separation techniques such as HPLC.     

Strategies for solving matrix effects in the analysis of sulfathiazole in honey samples using three-way photochemically induced fluorescence data

A widely employed compound for honey treatment, sulfathiazole (ST), was determined in commercial honey samples, employing a combination of photochemically induced fluorescence excitation-emission matrices (EEMs) and chemometric processing of the recorded second-order data. Parallel Factor Analysis (PARAFAC) and Self-Weighted Alternating Trilinear Decomposition (SWATLD) methods were used for calibration. An appropriately designed calibration with a set of standards composed of 18 samples, coupled to the use of the second-order advantage offered by the applied chemometric techniques, allowed quantitation of sulfathiazole in spiked commercial honey samples. No previous separation or sample pretreatment steps were required. The results were compared with other calibration methods such as N-PLS and PLS-1 that produced good results on synthetic samples but not on the investigated commercial honey samples.     

Application of near infrared spectroscopy (NIR), X-ray fluorescence (XRF) and chemometrics to the differentiation of marmora samples from the Mediterranean basin

Abstract

Near-infrared (NIR) and X-ray fluorescence spectra were recorded for 15 different samples of marmora, from the Mediterranean Basin and of different colours. After appropriate pretreatment (SNV transform + second derivative), the results were subjected to principal component analysis (PCA) treatment with a view to differentiating them. The observed differences among the samples were chemically interpreted by highlighting the NIR wavelengths and minerals, respectively, contributing the most to the PCA models. Moreover, a mid-level data fusion protocol allowed integrating the information from the different techniques and, in particular, to correctly identify (based on the distance in the score space) three test samples of known type. Moreover, it should be stressed that positive results on the differentiation and identification of marmora were obtained using two completely non-invasive, non-destructive and relatively inexpensive techniques, which can also be used in situ.     

Independent component analysis as a pretreatment method for parallel factor analysis to eliminate artefacts from multiway data

Parallel factor analysis (PARAFAC) has successfully been used in many applications for the analysis of excitation-emission fluorescence data. However, some measurement “artefacts”, such as Rayleigh or Raman scattering, can pose a problem for the extraction of the PARAFAC components and their interpretation. Replacing the spectral zones corresponding to these signals by missing values in the data is not necessarily a method of choice in the cases where informative signals lie in the same wavelength regions. In this article, independent component analysis (ICA) is used on the unfolded cubic array, and the independent components related to the Rayleigh and Raman scattering are identified and removed prior to the reconstruction of the excitation-emission fluorescence data cube. PARAFAC is then applied on these data reconstructed after selective artefact removal, and satisfactory models can be obtained. This procedure, although particularly useful for 3D fluorescence data, may be applied to other types of data as well.     

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.     

Bridging the gap between metabolic profile determination and visualization in neurometabolic disorders: a multivariate analysis of proton magnetic resonance in vivo spectra

The purpose of this work was to check the degree of overlap between rare inborn errors of metabolism and other neurological disorders using principal component analysis of proton magnetic resonance spectroscopy (¹H MRS) in vivo data. We examined 60 patients (median age of 22 months). Fourteen of them were diagnosed with neurometabolic disorders (three cases of metachromatic leukodystrophy, two cases of Canavan disease, two cases of megalencephalic leukoencephalopathy with subcortical cysts, three cases of mitochondrial cytopathy, one case of nonketotic hyperglycinemia, one case of globoid leukodystrophy, one case of congenital disorders of glycosylation, and one case of ethylmalonic encephalopathy). The remaining 46 patients were diagnosed with epilepsy, cerebral palsy, and developmental delay. Results obtained from principal component analysis of complete unresolved ¹H MRS in vivo spectra were interpreted parallelly with LCModel‐derived metabolite levels. The main attention was paid to the following metabolites: N‐acetylaspartate, glutamate + glutamine, creatine, choline, myo‐inositol signal with an uncertain contribution of glycine, and glucose. ¹H MRS in vivo coupled with multivariate analysis is an efficient tool in visualization of metabolic abnormalities in several inborn errors of metabolism (metachromatic leukodystrophy, globoid leukodystrophy, megalencephalic leukoencephalopathy with subcortical cysts, and Canavan disease). Copyright © 2013 John Wiley & Sons, Ltd.     

Chemometric analysis for optimizing derivatization in gas chromatography‐based procedures

This paper focuses on the application of principal component analysis (PCA) to facilitate the optimization of the derivatization of oestrogenic steroids—estrone, 17β‐estradiol, estriol, 17α‐ethinylestradiol and diethylstilbestrol—in order to achieve (1) the complete derivatization of all the hydroxyl groups contained in the structure of the compounds and (2) the greatest effectiveness of this reaction. Six different derivatization reagents were used in this study, whereas 2‐methyl‐anthracene was applied as the internal standard to evaluate the effectiveness of the reactions. The experimental data were subjected to PCA. With PCA, the dimensionality of the original multivariable data set could be reduced and the selection of optimum conditions for derivatization facilitated. The mixture of 99% N,O‐bis(trimethylsilyl)trifluoroacetamide + 1% trimethylchlorosilane and pyridine (1:1, v/v) at 60 °C for 30 min has been established as the most convenient and efficient means of derivatizing the aforementioned oestrogenic steroids and diethylstilbestrol; the N‐methyl‐N‐(trimethylsilyl)trifluoroacetamide + pyridine (1:1, v/v) mixture seems to be a promising alternative. The application of PCA for optimizing the derivatization procedure, proposed for the first time in this study, is particularly useful in the development of multicomponent methods across several chemical classes of compounds. Copyright © 2011 John Wiley & Sons, Ltd.     

Development of diagnostic models for canine osteoarthritis based on serum and joint fluid mid‐infrared spectral data using five different discrimination and classification methods

Osteoarthritis (OA) is an insidious joint disease that gradually leads to cartilage loss and the morphological impairment of other joint tissues. Therefore, early diagnosis and timely therapeutic intervention are of importance. Although there are a few diagnostic techniques used in clinics, these methods have various drawbacks. Infrared spectroscopy has emerged as an important analytical technique with wide applications in a variety of areas including clinical diagnosis. Research has shown that the presence of OA is associated with biochemical changes that are presumed to be reflected in serum or joint fluid. Hence, OA may be detected provided that serum or joint fluid is measured by infrared spectroscopy and appropriate data analysis methods are used to extract the diagnostic information from the infrared spectra. In this work, 5 discrimination and classification methods ([1] principal component analysis coupled with linear discriminant analysis, [2] principal component analysis coupled with multiple logistic regression, [3] partial least squares discriminant analysis, [4] regularized linear discriminant analysis, and [5] support vector machine) were used to build OA diagnostic models based on mid‐infrared spectra of serum and joint fluid. Useful diagnostic models were developed, indicating that infrared spectroscopy coupled with multivariate data analysis methods is very promising as a simple and accurate approach for OA diagnosis. The results also showed that models built from the 5 methods were different, as were the models' predictive performances. Therefore, choice of appropriate data analysis methods in model development should be taken into account.     

Fisher判别在气相色谱-质谱分析助燃剂燃烧残留物中的应用

为寻找一种用于火场助燃剂燃烧残留物鉴定的更为准确、有效的模式识别方法,对7种常见助燃剂在不同载体上的燃烧残留物样品及未知送检样品进行气相色谱-质谱（GC-MS）分析测试,通过特征组分分析鉴定出未知样品中含有汽油成分。同时运用Fisher判别及PCA（主成分分析）/Fisher判别联用两种判别方法对样本数据进行了分析处理,PCA/Fisher判别联用的结果表明送检样本中含有硝基油漆稀料成分,而仅使用Fisher判别的结果表明送检样本中含有93^#汽油。通过将两种分析方法所得结果与GC-MS特征组分分析的结果进行比对发现,Fisher判别能够对7种助燃剂燃烧残留物的样本实现更有效的分类,对未知样本的判别更为有效。该研究结果为火场助燃剂鉴定提供了新的数据分析手段。     

Comparison of oil spillages using mid-IR indexes and 3-way procrustes rotation, matrix-augmented principal components analysis and parallel factor analysis

Three different approaches for 3-way analyses, namely, Procrustes rotation, parallel factor analysis (PARAFAC) and matrix-augmented principal component analysis (MA-PCA), have been used to compare six different oil spillages made under controlled conditions (one of them corresponding to the heavy oil released after the sunk of the Prestige tanker off the Galician coast-NW Spain on November 2002). Each spillage was monitored during three and a half months by attenuated total reflectance (ATR) mid-IR spectroscopy. Ten characteristic band ratios were defined. Results showed that the three 3-way chemometric techniques lead to essentially the same conclusions, where from it was concluded that the most relevant pattern defining the oil weathering was related to ‘total aromaticity’, i.e., the total number of CC bonds in the molecules which form the products. In addition, weathering of the samples got clearly characterized by a steady evolution on the scores (sample weights), with a clear increase after 11-14 days. Differentiation of the products (slices of the data cube) was also possible due to their intrinsic characteristics as, in general, heavy products oppose to the lightest ones.     

Application of mid-infared spectroscopy with multivariate analysis for the discrimination of toxic plant,Gelsemium elegans

Gelsemium elegans is a commonly used herb to treat different kinds of diseases. However, the indole alkaloid present in the plant might cause serious side effects. In this research, the infrared spectroscopic identification approach including Fourier transform infrared spectroscopy (FT-IR), Second derivative infrared spectra (SD-IR) and two-dimensional correlation infrared spectra (2D-IR) was used to develop a simple and rapid method to discriminate the stem, leaf and root of the Gelsemium elegans plant. This is because the stem, leaf and root contained different amount of indole alkaloid that contributed to the toxicity. Through this study, all the three parts were successfully identified and discriminated through the infrared spectroscopic identification method. The identification approach was also validated by comparing the samples of the mixture of both stem and root (SR) to the stem and root, respectively and also by comparing different plants with Gelsemium elegans plant. Besides that, all the samples of different parts of the Gelsemium elegans were analyzed with the Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) pattern recognition technique to test and verify the experimental results. The SIMCA model was validated by comparing 70 standard herbs to the model. From the results, macroscopic IR fingerprint method and the classification analysis successfully discriminate not only between Gelsemium elegans samples and standard herbs but also successfully distinguished the three different parts of Gelsemium elegans plant.     

Evaluation of mass spectrometric data using principal component analysis for determination of the effects of organic lakes on protein binder identification

Matrix‐assisted laser desorption/ionisation–time of flight (MALDI‐TOF) mass spectrometry is commonly used for the identification of proteinaceous binders and their mixtures in artworks. The determination of protein binders is based on a comparison between the m/z values of tryptic peptides in the unknown sample and a reference one (egg, casein, animal glues etc.), but this method has greater potential to study changes due to ageing and the influence of organic/inorganic components on protein identification. However, it is necessary to then carry out statistical evaluation on the obtained data. Before now, it has been complicated to routinely convert the mass spectrometric data into a statistical programme, to extract and match the appropriate peaks. Only several ‘homemade’ computer programmes without user‐friendly interfaces are available for these purposes. In this paper, we would like to present our completely new, publically available, non‐commercial software, ms‐alone and multiMS‐toolbox, for principal component analyses of MALDI‐TOF MS data for R software, and their application to the study of the influence of heterogeneous matrices (organic lakes) for protein identification. Using this new software, we determined the main factors that influence the protein analyses of artificially aged model mixtures of organic lakes and fish glue, prepared according to historical recipes that were used for book illumination, using MALDI‐TOF peptide mass mapping. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimation of source spectra profiles and simultaneous determination of polycomponent in mixtures from ultraviolet spectra data using kernel independent component analysis and support vector regression

A method that use kernel independent component analysis (KICA) and support vector regression (SVR) was proposed for estimation of source ultraviolet (UV) spectra profiles and simultaneous determination of polycomponents in mixtures. In KICA-SVR procedure, the UV source spectra profiles were estimated using KICA, then the mixing matrix of the components were calculated using the estimated sources, and the calibration model was build using SVR based on the calculated mixing matrix. A simulated UV dataset of three-component mixtures was used to test the ability of KICA for estimating source spectra profiles from spectra data of mixtures. It was found that KICA has the potential power to estimate pure UV spectra profiles, and correlation coefficient of estimated sources correspond to the real adopted ones are better compared with that by FastICA and Infomax ICA. An UV dataset of polycomponent vitamin B was processed using the proposed KICA-SVR method. The results show that the estimated source spectra profiles are correlative with the real UV spectra of the components and chemically interpretable, and accurate results were obtained.     

高效液相色谱指纹图谱法分析南方红豆杉药材的氯仿提取物

建立了南方红豆杉药材氯仿提取物的高效液相色谱(HPLC)指纹图谱分析方法。采用Eurospher 100 C18色谱柱(250 mm×4 mm, 5 μm),以甲醇和水为流动相进行梯度洗脱,流速为1 mL/min,检测波长为232 nm,柱温为30 ℃。以10-脱乙酰巴卡亭III(10-DABIII)为参照物,在相同的色谱条件下测定了10批不同产地的南方红豆杉药材氯仿提取物的指纹图谱,获得了11个共有指纹峰,并利用主成分分析法(PCA)对指纹图谱进行统计分析。结果表明南方红豆杉药材的质量与种植区域有关。该方法稳定、可靠,可用于南方红豆杉药材的质量控制。     

Preprocessing and exploratory analysis of chromatographic profiles of plant extracts

The characterization of herbal extracts to compare samples from different origin is important for robust production and quality control strategies. This characterization is now mainly performed by analysis of selected marker compounds. Metabolic fingerprinting of full metabolite profiles of plant extracts aims at a more rapid and thorough screening or classification of plant material. We will show that HPLC is an appropriate technique for metabolic fingerprinting of secondary metabolites, given that adequate preprocessing of raw profiles is performed. Additional variation, which results from sample preparation and changing measurement conditions, usually obscures the information of interest in these raw profiles. This paper illustrates the importance of preprocessing of chromatographic fingerprinting data. Different alignment methods are discussed as well as the influence of normalization. Weighted principal component analysis is introduced as a valuable alternative to autoscaling of data. LC-UV data on Willow (Salix sp.) extracts is used to evaluate these preprocessing methods and their influence on exploratory data analysis.     

An improvement of window factor analysis for resolution of noisy HPLC-DAD data

Window factor analysis (WFA) is a powerful tool in analyzing evolutionary process. However, it was found that window factor analysis is much sensitive to the noise involved in original data matrix. An error analysis was done with the fact that the concentration profiles resolved by the conventional window factor analysis are easily distorted by the noise reserved by the abstract factor analysis (AFA), and a modified algorithm for window factor analysis was proposed. Both simulated and experimental HPLC-DAD data were investigated by the conventional and the improved methods. Results show that the improved method can yield less noise-distorted concentration profiles than the conventional method, and the ability for resolution of noisy data sets can be greatly enhanced.     

Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel-kerosene mixtures: a comparative study

Quantitative determination of kerosene fraction present in diesel has been carried out based on excitation emission matrix fluorescence (EEMF) along with parallel factor analysis (PARAFAC) and N-way partial least squares regression (N-PLS). EEMF is a simple, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. Calibration models consisting of varying compositions of diesel and kerosene were constructed and their validation was carried out using leave-one-out cross validation method. The accuracy of the model was evaluated through the root mean square error of prediction (RMSEP) for the PARAFAC, N-PLS and unfold PLS methods. N-PLS was found to be a better method compared to PARAFAC and unfold PLS method because of its low RMSEP values.