Application of infrared spectroscopy to the assessment of authenticity of tea

Diffusion reflectance fourier transform infrared spectroscopy in the mid-IR region was used to assess the authenticity of tea varieties. The differences between the spectra of 12 different tea varieties (seven black, two green, three semifermented grades) were sufficient to allow their discrimination by the soft independent modelling of class analogy classification method or linear discriminant analysis, despite a significant heterogeneity of the samples as revealed by variance analysis.     

Multivariate analysis of reflectance spectra from propolis: Geographical variation in Romanian samples

The present study described reflectance spectroscopy as a suitable analytical tool to discriminate the floral origin of 39 Romanian propolis samples. Relevant differences between the UV-vis reflectance spectra of the investigated propolis samples within the 220-850 nm spectral range were found. The results obtained applying cluster analysis, principal component analysis and linear discriminant analysis to the digitized data of zero order, zero order normalized and first order derivative spectra support the reliability of this technique. In addition, the application of the linear discriminant analysis to the score matrices corresponding to the first principal components appeared to be an illuminating solution. Generally, the samples have been assigned to two large groups in a good agreement with their vegetal sampling location, samples originating from predominant forest area and samples originating from meadows. Within the first group, two subgroups were identified according to the dominant type of the forest, deciduous or resinous, while within the last group three subgroups were found according to the extend and variety of the meadow.     

Accurate discrimination of Gastrodia elata from different geographical origins using high‐performance liquid chromatography fingerprint combined with boosting partial least‐squares discriminant analysis

Gastrodia elata from different geographical origins varies in quality and pharmacological activity. This study focused on the classification and identification of Gastrodia elata from six producing areas using high‐performance liquid chromatography fingerprint combined with boosting partial least‐squares discriminant analysis. Before recognition analysis, a principal component analysis was applied to ascertain the discrimination possibility with high‐performance liquid chromatography fingerprints. And then, boosting partial least‐squares discriminant analysis and conventional partial least‐squares discriminant analysis were applied in this study. Experimental results indicated that the adaptive iteratively reweighted penalized least‐squares algorithm could eliminate the baseline drift of high‐performance liquid chromatography chromatograms effectively. And compared with partial least‐squares discriminant analysis, the total recognition rates using high‐performance liquid chromatography fingerprint combined with boosting partial least‐squares discriminant analysis for the calibration sets and prediction sets were improved from 94 to 100% and 86 to 97%, respectively. In conclusion, high‐performance liquid chromatography combined with boosting partial least‐squares discriminant analysis, which has such advantages as effective, specific, accurate, non‐polluting, has an edge for discrimination of traditional Chinese medicine from different geographical origins. And the proposed methodology is a useful tool to classify and identify Gastrodia elata from different geographical origins.     

Discrimination of coatings on wooden materials using the gas sensor system

The applicability of sensor system for the discrimination of sources of indoor pollution was investigated. As examples of indoor pollution sources, paint and lacquer coatings were considered. Commercially available preparations: Akrylux, Doamlux, Bejca and White Scandinavian were selected for headspace measurements using TGS sensor array. Following issues were investigated: (1) discrimination between water- and solvent-based coatings, (2) discrimination between one component coatings, and (3) discrimination between one component and two component coatings. Following data analysis methods were used: principal component analysis (PCA), linear discriminant analysis (LDA) and probabilistic neural network (PNN). Results showed that coatings could be discriminated successfully, provided the surface covered was solid wood (0-1.8% error). The interference of fibreboard volatiles in sensor measurements of coatings was most likely encountered. It could have significantly impaired discrimination of coatings on fibreboard (2.8-5.6% error) as compared to wood. Worst results were obtained for the discrimination of coatings on unknown material(12.5-28.7% error).     

Influence of variable selection on partial least squares discriminant analysis models for explosive residue classification

Using a series of thirteen organic materials that includes novel high-nitrogen energetic materials, conventional organic military explosives, and benign organic materials, we have demonstrated the importance of variable selection for maximizing residue discrimination with partial least squares discriminant analysis (PLS-DA). We built several PLS-DA models using different variable sets based on laser induced breakdown spectroscopy (LIBS) spectra of the organic residues on an aluminum substrate under an argon atmosphere. The model classification results for each sample are presented and the influence of the variables on these results is discussed. We found that using the whole spectra as the data input for the PLS-DA model gave the best results. However, variables due to the surrounding atmosphere and the substrate contribute to discrimination when the whole spectra are used, indicating this may not be the most robust model. Further iterative testing with additional validation data sets is necessary to determine the most robust model.     

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.     

Potential applications of functional data analysis in chemometrics

In spectroscopy the measured spectra are typically plotted as a function of the wavelength (or wavenumber), but analysed with multivariate data analysis techniques (multiple linear regression (MLR), principal components regression (PCR), partial least squares (PLS)) which consider the spectrum as a set of m different variables. From a physical point of view it could be more informative to describe the spectrum as a function rather than as a set of points, hereby taking into account the physical background of the spectrum, being a sum of absorption peaks for the different chemical components, where the absorbance at two wavelengths close to each other is highly correlated. In a first part of this contribution, a motivating example for this functional approach is given. In a second part, the potential of functional data analysis is discussed in the field of chemometrics and compared to the ubiquitous PLS regression technique using two practical data sets. It is shown that for spectral data, the use of B-splines proves to be an appealing basis to accurately describe the data. By applying both functional data analysis and PLS on the data sets the predictive ability of functional data analysis is found to be comparable to that of PLS. Moreover, many chemometric datasets have some specific structure (e.g. replicate measurements, on the same object or objects that are grouped), but the structure is often removed before analysis (e.g. by averaging the replicates). In the second part of this contribution, we suggest a method to adapt traditional analysis of variance (ANOVA) methods to datasets with spectroscopic data. In particular, the possibilities to explore and interpret sources of variation, such as variations in sample and ambient temperature, are examined. Copyright © 2008 John Wiley & Sons, Ltd.     

Sparse optimal scoring for multiclass cancer diagnosis and biomarker detection using microarray data

Gene expression data sets hold the promise to provide cancer diagnosis on the molecular level. However, using all the gene profiles for diagnosis may be suboptimal. Detection of the molecular signatures not only reduces the number of genes needed for discrimination purposes, but may elucidate the roles they play in the biological processes. Therefore, a central part of diagnosis is to detect a small set of tumor biomarkers which can be used for accurate multiclass cancer classification. This task calls for effective multiclass classifiers with built-in biomarker selection mechanism. We propose the sparse optimal scoring (SOS) method for multiclass cancer characterization. SOS is a simple prototype classifier based on linear discriminant analysis, in which predictive biomarkers can be automatically determined together with accurate classification. Thus, SOS differentiates itself from many other commonly used classifiers, where gene preselection must be applied before classification. We obtain satisfactory performance while applying SOS to several public data sets.     

In vivo SPECTROPHOTOMETRIC EVALUATION OF NEOPLASTIC AND NON-NEOPLASTIC SKIN PIGMENTED LESIONS. II: DISCRIMINANT ANALYSIS BETWEEN NEVUS AND MELANOMA

Reflectance spectrophotometry from 420 to 780 nm on 31 primary melanoma and 31 benign nevi has been performed by using an external integrating sphere coupled to a spectrophotometer. Measurements show that reflectance spectra of melanoma and nevi manifest dissimilar patterns. From these spectra four variables, whose physical and/or physiological meanings remain to be investigated, have been derived. All of them are significantly different when compared between melanoma and nevi. A discriminant function between the two groups of lesions has been determined by using a stepwise discriminant analysis, resulting in a test with a sensitivity of 90.3% and a specificity of 77.4%. This method of discrimination between melanoma and nevi seems to have a discriminating power almost equal to that of a clinical judgement from a specialized medical doctor, thus suggesting a new method for screening skin pigmented lesions.     

Near-infrared spectroscopy and chemometric modelling for rapid diagnosis of kidney disease

Rapid diagnosis is important for efficient treatment in clinical medicine. This study aimed at development of a method for rapid and reliable diagnosis using near-infrared (NIR) spectra of human serum samples with the help of chemometric modelling. The NIR spectra of sera from 48 healthy individuals and 16 patients with suspected kidney disease were analyzed. Discrete wavelet transform (DWT) and variable selection were adopted to extract the useful information from the spectra. Principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLSDA) were used for discrimination of the samples. Classification of the two-class sera was obtained using LDA and PLSDA with the help of DWT and variable selection. DWT-LDA produced 93.8% and 83.3% of the recognition rates for the validation samples of the two classes, and 100% recognition rates were obtained using DWT-PLSDA. The results demonstrated that the tiny differences between the spectra of the sera were effectively explored using DWT and variable selection, and the differences can be used for discrimination of the sera from healthy and possible patients. NIR spectroscopy and chemometrics may be a potential technique for fast diagnosis of kidney disease.     

Using combinations of principal component scores from different spectral ranges in near-infrared region to improve discrimination for samples of complex composition

Principal component analysis (PCA) is widely used as an exploratory data analysis tool in the field of vibrational spectroscopy, particularly near-infrared (NIR) spectroscopy. PCA represents original spectral data containing large variables into a few feature-containing variables, or scores. Although multiple spectral ranges can be simultaneously used for PCA, only one series of scores generated by merging the selected spectral ranges is generally used for qualitative analysis. Alternatively, the combined use of an independent series of scores generated from separate spectral ranges has not been exploited.The aim of this study is to evaluate the use of PCA to discriminate between two geographical origins of sesame samples, when scores independently generated from separate spectral ranges are optimally combined. An accurate and rapid analytical method to determine the origin is essentially required for the correct value estimation and proper production distribution. Sesame is chosen in this study because it is difficult to visually discriminate the geographical origins and its composition is highly complex. For this purpose, we collected diffuse reflectance near-infrared (NIR) spectroscopic data from geographically diverse sesame samples over a period of eight years. The discrimination error obtained by applying linear discriminant analysis (LDA) was improved when separate scores from two spectral ranges were optimally combined, compared to the discrimination errors obtained when scores from singly merged two spectral ranges were used.     

Multivariate classification of pigments and inks using combined Raman spectroscopy and LIBS

The authenticity of objects and artifacts is often the focus of forensic analytic chemistry. In document fraud cases, the most important objective is to determine the origin of a particular ink. Here, we introduce a new approach which utilizes the combination of two analytical methods, namely Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS). The methods provide complementary information on both molecular and elemental composition of samples. The potential of this hyphenation of spectroscopic methods is demonstrated for ten blue and black ink samples on white paper. LIBS and Raman spectra from different inks were fused into a single data matrix, and the number of different groups of inks was determined through multivariate analysis, i.e., principal component analysis, soft independent modelling of class analogy, partial least-squares discriminant analysis, and support vector machine. In all cases, the results obtained with the combined LIBS and Raman spectra were found to be superior to those obtained with the individual Raman or LIBS data sets.     

Noninvasive Surface Detection of Papillary Thyroid Carcinoma by Fourier Transform Infrared Spectroscopy

Previously Fourier transform infrared(FTIR) spectroscopy has been applied to detecting thyroid cancer during operations and to discriminating cervical metastatic ones from non-metastatic lymph nodes. This study explored the possibility of establishing a sensitive, accurate and noninvasive screen or diagnosis by preoperative FTIR spectroscopy. 111 patients undergone a thyroid operation and 50 healthy volunteers were enrolled in the study. The FTIR spectra were obtained by two mid-infrared optical fibers with an attenuated total reflectance(ATR) probe closely contacting the subjects' skin on the thyroid nodules. The FTIR spectra obtained from normal thyroid, nodular goiter(NG) and papillary thyroid carcinoma(PTC) patients were compared. A Fisher's discriminant analysis was created based on these data. There were 41 PTC patients and 70 NG patients according to their histopathological examinations. A total of 23(of 39) parameters were statistically different among the three groups(P<0.05). The F₁₃₀₀ and F₁₀₈₀ parameters were significantly different between the three groups. In total, 9 out of 39 FTIR parameters were selected as independent factors by the Wilks' lambda stepwise discriminant analysis. The discrimination accuracy of papillary thyroid carcinoma in the three groups was 88.8%. Surface detection of PTC by FTIR spectroscopy is feasible. FTIR spectroscopy can be used for rapid and noninvasive PTC screen and auxiliary diagnosis.     

Combining visible and near-infrared spectroscopy with chemometrics to trace muscles from an autochthonous breed of pig produced in Uruguay: a feasibility study

Visible (Vis) and near-infrared reflectance (NIR) spectroscopy combined with chemometrics was explored as a tool to trace muscles from autochthonous and crossbreed pigs from Uruguay. Muscles were sourced from two breeds, namely, the Pampa-Rocha (PR) and the Pampa-Rocha x Duroc (PRxD) crossbreed. Minced muscles were scanned in the Vis and NIR regions (400–2,500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), discriminant partial least square regression (DPLS), linear discriminant analysis (LDA) based on PCA scores and soft independent modelling of class analogy (SIMCA) were used to identify the origin of the muscles based on Vis and NIR data. Full cross validation was used as validation method when classification models were developed. DPLS correctly classified 87% of PR and 78% of PRxD muscle samples. LDA calibration models correctly classified 87 and 67% of muscles as PR and PRxD, respectively. SIMCA correctly classified 100% of PR muscles. The results demonstrated the usefulness of Vis and NIR spectra combined with chemometrics as rapid method for authentication and identification of muscles according to the breed of pig.     

Chemometric detection of acetaminophen in pharmaceuticals by infrared spectroscopy combined with pattern recognition techniques: comparison of attenuated total reflectance-FTIR and Raman spectroscopy

This paper presents and discusses the building of discriminant models from attenuated total reflectance (ATR)-FTIR and Raman spectra that were constructed to detect the presence of acetaminophen in over-the-counter pharmaceutical formulations. The datasets, containing 11 spectra of pure substances and 21 spectra of various formulations, were processed by partial least squares (PLS) discriminant analysis. The models found in the present study coped greatly with the discrimination, and their quality parameters were acceptable. A root mean square error of cross-validation was in the 0.14-0.35 range, while a root mean square error of prediction was in the 0.20-0.56 range. It was found that standard normal variate preprocessing had a negligible influence on the quality of ATR-FTIR; in the Raman case, it lowered the prediction error by 2. The influence of variable selection with the uninformative variable elimination by PLS method was studied, and no further model improvement was found.     

New discrimination method combining hit quality index based spectral matching and voting

A new discrimination method, called hit quality index (HQI)-voting, that uses the HQI for discriminant analysis has been developed. HQI indicates the degree of spectral matching between two spectra as known. In this method, a library sample yielding the highest HQI value for an unknown sample was initially searched and a group containing this sample was chosen as the group for the unknown sample. When overall spectral features of two groups are quite close to each other, many library samples with similar HQI values could be available for an unknown sample. In this situation, the simultaneous consideration of multiple votes (several library samples with close HQI values) for final decision would be more robust. In order to evaluate the discrimination performance of HQI-voting, three different near-infrared (NIR) spectroscopic datasets composed of two sample groups were used: (1) domestic and imported sesame samples, (2) domestic and imported Angelica gigas samples, and (3) diesel and light gas oil (LGO) samples. For the purpose of comparison, principal component analysis–linear discriminant analysis (PCA–LDA), partial least squares–discriminant analysis (PLS–DA) as well as k-nearest neighbor (k-NN) were also performed using the same datasets and the resulting accuracies were compared. The discrimination performances improved with the use of HQI-voting in comparison with those resulted from PCA–LDA and PLS–DA. The overall results support that HQI-voting is a comparable discrimination method to that of existing factor-based multivariate methods.     

Shrunken centroids regularized discriminant analysis as a promising strategy for metabolomics data exploration

Metabolomics datasets generated by modern analytical instruments tend to be increasingly complex. In this study, a recent method named shrunken centroids regularized discriminant analysis (SCRDA) has been introduced and applied in the exploration of metabolomics dataset. It is a supervised method for variable selection, discriminant analysis and biomarker screening. By regularizing the estimate of the within‐class covariance matrix, SCRDA can deal with the singularity issue of linear discriminant analysis. Then a shrinkage estimator is applied to perform variable selection. The method presented is illustrated through the simulated datasets and three complex metabolomics datasets. Commonly used orthogonal partial least squares discriminant analysis and two other similar statistical methods, penalized linear discriminant analysis and nearest shrunken centroids, are used for comparisons. The results illustrate that SCRDA has some desirable abilities in variable selection, classification and prediction. Moreover, the biomarkers identified by SCRDA are further demonstrated to be in accordance with the biochemical research. It has been proved that SCRDA can be applied as a promising strategy in metabolomics. Copyright © 2014 John Wiley & Sons, Ltd.     

Discrimination of Ephedra plants with diffuse reflectance FT-NIRS and multivariate analysis

The secondary metabolites of different Ephedra plants are various. Therefore, the discrimination of different Ephedra plants is significant. An objective, easy-to-use, rapid and pollution-free approach is proposed for discriminating Ephedra plants of different species, habitats and picking times on the basis of diffuse reflectance Fourier transform near infrared spectroscopy (FT-NIRS) measurements and multivariate analysis. The Fourier transform near infrared diffuse reflectance spectra (NIRDRS) were acquired from 37 pulverized samples of Ephedra plants put in glass vials in the near infrared (NIR) region between 10 000 and 4000 cm⁻¹, averaging 64 scans per spectrum at a resolution of 4 cm⁻¹. After spectra processing and data pre-processing, spectral data were analyzed respectively with three multivariate analysis techniques: discriminant analysis (DA), self-organizing map (SOM) and back-propagation artificial neural network (BP-ANN). The proposed method could distinguish not only the Ephedra plants of three species and two habitats but also the plants picked at different times of day without special sample treatment and the use of chemical reagents. The performance indexes of the DA model were 84.2-91.9% and the prediction accuracies of both the SOM and the BP-ANN models reached 93.3-100.0%.