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.     

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.     

Paper spray mass spectrometry and PLS-DA improved by variable selection for the forensic discrimination of beers

Paper spray mass spectrometry (PS-MS) combined with partial least squares discriminant analysis (PLS-DA) was applied for the first time in a forensic context to a fast and effective differentiation of beers. Eight different brands of American standard lager beers produced by four different breweries (141 samples from 55 batches) were studied with the aim at performing a differentiation according to their market prices. The three leader brands in the Brazilian beer market, which have been subject to fraud, were modeled as the higher-price class, while the five brands most used for counterfeiting were modeled as the lower-price class. Parameters affecting the paper spray ionization were examined and optimized. The best MS signal stability and intensity was obtained while using the positive ion mode, with PS(+) mass spectra characterized by intense pairs of signals corresponding to sodium and potassium adducts of malto-oligosaccharides. Discrimination was not apparent neither by using visual inspection nor principal component analysis (PCA). However, supervised classification models provided high rates of sensitivity and specificity. A PLS-DA model using full scan mass spectra were improved by variable selection with ordered predictors selection (OPS), providing 100% of reliability rate and reducing the number of variables from 1701 to 60. This model was interpreted by detecting fifteen variables as the most significant VIP (variable importance in projection) scores, which were therefore considered diagnostic ions for this type of beer counterfeit.     

Metabolic profiling using principal component analysis, discriminant partial least squares, and genetic algorithms

The aim of this study was to evaluate evolutionary variable selection methods in improving the classification of ¹H nuclear magnetic resonance (NMR) metabonomic profiles, and to identify the metabolites that are responsible for the classification. Human plasma, urine, and saliva from a group of 150 healthy male and female subjects were subjected to ¹H NMR-based metabonomic analysis. The ¹H NMR spectra were analyzed using two pattern recognition methods, principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA), to identify metabolites responsible for gender differences. The use of genetic algorithms (GA) for variable selection methods was found to enhance the classification performance of the PLS-DA models. The loading plots obtained by PCA and PLS-DA were compared and various metabolites were identified that are responsible for the observed separations. These results demonstrated that our approach is capable of identifying the metabolites that are important for the discrimination of classes of individuals of similar physiological conditions.     

Simultaneous analysis of plasma and CSF by NMR and hierarchical models fusion

Because cerebrospinal fluid (CSF) is the biofluid which interacts most closely with the central nervous system, it holds promise as a reporter of neurological disease, for example multiple sclerosis (MScl). To characterize the metabolomics profile of neuroinflammatory aspects of this disease we studied an animal model of MScl-experimental autoimmune/allergic encephalomyelitis (EAE). Because CSF also exchanges metabolites with blood via the blood-brain barrier, malfunctions occurring in the CNS may be reflected in the biochemical composition of blood plasma. The combination of blood plasma and CSF provides more complete information about the disease. Both biofluids can be studied by use of NMR spectroscopy. It is then necessary to perform combined analysis of the two different datasets. Mid-level data fusion was therefore applied to blood plasma and CSF datasets. First, relevant information was extracted from each biofluid dataset by use of linear support vector machine recursive feature elimination. The selected variables from each dataset were concatenated for joint analysis by partial least squares discriminant analysis (PLS-DA). The combined metabolomics information from plasma and CSF enables more efficient and reliable discrimination of the onset of EAE. Second, we introduced hierarchical models fusion, in which previously developed PLS-DA models are hierarchically combined. We show that this approach enables neuroinflamed rats (even on the day of onset) to be distinguished from either healthy or peripherally inflamed rats. Moreover, progression of EAE can be investigated because the model separates the onset and peak of the disease.     

Discrimination of biological and chemical threat simulants in residue mixtures on multiple substrates

The potential of laser-induced breakdown spectroscopy (LIBS) to discriminate biological and chemical threat simulant residues prepared on multiple substrates and in the presence of interferents has been explored. The simulant samples tested include Bacillus atrophaeus spores, Escherichia coli, MS-2 bacteriophage, α-hemolysin from Staphylococcus aureus, 2-chloroethyl ethyl sulfide, and dimethyl methylphosphonate. The residue samples were prepared on polycarbonate, stainless steel and aluminum foil substrates by Battelle Eastern Science and Technology Center. LIBS spectra were collected by Battelle on a portable LIBS instrument developed by A3 Technologies. This paper presents the chemometric analysis of the LIBS spectra using partial least-squares discriminant analysis (PLS-DA). The performance of PLS-DA models developed based on the full LIBS spectra, and selected emission intensities and ratios have been compared. The full-spectra models generally provided better classification results based on the inclusion of substrate emission features; however, the intensity/ratio models were able to correctly identify more types of simulant residues in the presence of interferents. The fusion of the two types of PLS-DA models resulted in a significant improvement in classification performance for models built using multiple substrates. In addition to identifying the major components of residue mixtures, minor components such as growth media and solvents can be identified with an appropriately designed PLS-DA model.     

Discrimination of Osteonecrosis and Normal Tissues by Near-Infrared Spectroscopy and Successive Projections Algorithm-Linear Discriminant Analysis

Osteonecrosis of femoral head (ONFH) is a disease characterized by an impaired blood flow in the bone. The pathogenesis is still unknown, which makes an exact diagnosis troublesome and heavily dependent on experience. Exploring the information of molecular level by modern spectroscopy may help to discover the underlying pathogenesis and find its diagnostic application in clinical medicine. The study focuses on the combination of near-infrared (NIR) spectroscopy and classification models for discriminating ONFH and normal tissues. A total of 128 surgical specimens was prepared and NIR spectra were recorded by an integrating sphere. The experiment data set was divided into three subsets, i.e., the training set, validation set, and test set. Successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to compress variables and build the diagnostic model. Partial least square-discriminant analysis (PLS-DA) was used as the reference. Principal component analysis (PCA) was used for exploratory analysis. The results showed that compared to PLS-DA, SPA-LDA provided a more parsimonious model using only seven variables and achieved better performance, i.e., sensitivity of 90.5 and 85%, and specificity of 100 and 95.5% for the validation and test sets, respectively. It indicated that NIR spectroscopy combined with SPA-LDA algorithm was a feasible aid tool for discriminating ONFH from normal tissue.     

Recognition of active ingredients in tablets by chemometric processing of X-ray diffractometric data

The paper presents an approach to use Partial Least Squares Discriminant Analysis (PLS-DA) on X-ray powder diffractometry (XRPD) dataset to build a model which recognizes a presence (or absence) of particular drug substance (acetaminophen) in unknown mixture (OTC tablet). The dataset consisted of 33 XRPD signals, measured for 12 pure substances and 21 tablets containing them in different quantitative and qualitative ratios, along with unknown excipients. The model was built with an external validation dataset chosen by Kennard-Stone algorithm. The RMSECV value was equal to 0.3461 (87.8% of explained variance) and external predictive error (RMSEP) was equal to 0.3123 (86.2% of explained variance). The result suggests that small but properly prepared training datasets give ability to construct well-working discriminant models on XRPD signals.     

A comparison of multivariate analysis techniques and variable selection strategies in a laser-induced breakdown spectroscopy bacterial classification

Laser-induced breakdown spectroscopy has been used to obtain spectral fingerprints from live bacterial specimens from thirteen distinct taxonomic bacterial classes representative of five bacterial genera. By taking sums, ratios, and complex ratios of measured atomic emission line intensities three unique sets of independent variables (models) were constructed to determine which choice of independent variables provided optimal genus-level classification of unknown specimens utilizing a discriminant function analysis. A model composed of 80 independent variables constructed from simple and complex ratios of the measured emission line intensities was found to provide the greatest sensitivity and specificity. This model was then used in a partial least squares discriminant analysis to compare the performance of this multivariate technique with a discriminant function analysis. The partial least squares discriminant analysis possessed a higher true positive rate, possessed a higher false positive rate, and was more effective at distinguishing between highly similar spectra from closely related bacterial genera. This suggests it may be the preferred multivariate technique in future species-level or strain-level classifications.     

Structural classification of acyl-substituted Quillaja saponins by electrospray ionisation ion trap multiple-stage mass spectrometry in combination with multivariate analysis

Thirty-eight saponins in two chromatographic fractions (QH-B and QH-C) from Quillaja saponaria Molina have been separated by a two-step high-performance liquid chromatography (HPLC) procedure and investigated by electrospray ionisation ion trap multiple-stage mass spectrometry (ESI-ITMS(n)) in positive ion mode. MS(2) and MS(3) spectra of the compounds were investigated by principal component analysis (PCA) and could be classified by partial least squares - discriminant analysis (PLS-DA) according to the structures of the oligosaccharides at C-3 and C-28 of the saponins. Four minor components with novel structures were found in a previously non-investigated fraction of QH-C. The structures of two of these components, J1 and J1a, were predicted by PLS-DA whereas the structures of the two others, J2 and J3, were only partly predicted. The structures of J1 and J1a were composed of structural elements found in the 34 known saponins whereas a new acyl substituent, not included in the training set used for calibration of the PLS-DA models, was found in J2 and J3, making these two components outliers. The complete structures of the four components were confirmed by monosaccharide analysis, MS(n) data and (1)H NMR spectroscopy.     

Improved analysis of multivariate data by variable stability scaling: application to NMR-based metabolic profiling

Variable scaling alters the covariance structure of data, affecting the outcome of multivariate analysis and calibration. Here we present a new method, variable stability (VAST) scaling, which weights each variable according to a metric of its stability. The beneficial effect of VAST scaling is demonstrated for a data set of ¹H NMR spectra of urine acquired as part of a metabonomic study into the effects of unilateral nephrectomy in an animal model. The application of VAST scaling improved the class distinction and predictive power of partial least squares discriminant analysis (PLS-DA) models. The effects of other data scaling and pre-processing methods, such as orthogonal signal correction (OSC), were also tested. VAST scaling produced the most robust models in terms of class prediction, outperforming OSC in this aspect. As a result the subtle, but consistent, metabolic perturbation caused by unilateral nephrectomy could be accurately characterised despite the presence of much greater biological differences caused by normal physiological variation. VAST scaling presents itself as an interpretable, robust and easily implemented data treatment for the enhancement of multivariate data analysis.     

基于稳定同位素的5种蔬菜产地真实性判别

为保护蔬菜产地真实性,以上海市场5种常见蔬菜(生菜、茼蒿、辣椒、番茄和黄瓜)为研究对象,应用单因素方差分析上海和其他产地蔬菜的 δ13 C、δ15 N、δ2 H和 δ18 O值差异,并通过主成分分析(PCA)和偏最小二乘判别分析(PLS-DA)建立上海地产蔬菜真实性判别模型.结果表明,上海地产5种蔬菜总的δ15 N、δ...     

正交信号校正在正常成人血清1H NMR谱的代谢组分析中的滤噪作用评价

观察、比较正交信号校正(OSC)滤噪前后, 用不同的模式识别方法对正常成人血清代谢组¹H NMR谱进行分析的效果, 以探讨NMR代谢组学技术应用于临床研究和疾病早期诊断的可行性. 78例正常成人在采血前按常规要求禁食8 h, 记录血清一维600 MHz氢谱后, 分别采用主成分分析(PCA)、偏最小二乘法-判别分析(PLS-DA)以及簇类的独立软模式法(SIMCA)对氢谱进行模式识别分析. 结果表明: 虽然采血前并无其它诸如饮食、生活方式、生理周期等方面的严格限制, 采用OSC 滤噪后, PLS-DA能够完全区分不同性别的血清氢谱, 其判别能力优于PCA和SIMCA. 而且采用OSC滤噪与文献报道的未经OSC处理的PLS-DA法获得的与性别分类有关的主要NMR积分区段基本相同. 从OSC去除不同数目的隐变量后所致的PLS-DA模型的性能改变可见: OSC去除两个隐变量时, 前两个隐变量的特征值明显比后面的大; 剩余残差为20.82%, 即去除了79.18%的X变量中与反应变量Y不相关的系统变异. 此时PLS-DA计算所得的隐变量个数为1; 而不使用OSC或用OSC去除一个隐变量时, PLS-DA所得的隐变量个数分别为3和2. 作为PLS-DA模型质量的评价指标, R2X表示PLS-DA模型计算所获得的隐变量反映自变量X的变异的百分比, R2Y则表示隐变量反映因变量Y的变异的百分比, Q2 (cum)为交叉验证后PLS-DA模型所获隐变量能够预测X和Y变异的累计百分比. R2X在OSC去除两个隐变量时达到最低值, 表明此时PLS-DA计算模型包含的系统变异最少; R2Y与Q2 (cum)都达到80%以上并趋于稳定, 说明OSC去除两个隐变量时PLS-DA模型的质量优良. 显然, OSC可去除饮食、环境等因素的影响, 降低临床样本的不均一性, 这对于NMR代谢组学技术应用于临床研究至关重要. OSC滤噪去除的隐变量个数应根据剩余残差、去除隐变量的特征值大小、PLS-DA模型计算所得的隐变量个数和反映模型质量的相关指标加以判断.     

复方乌头汤配伍贝母化学成分变化研究

将超高效液相色谱-质谱联用(UPLC-MS) 及UPLC-MSⁿ技术与偏最小二乘法判别分析(PLS-DA)结合, 对乌头汤分别与川贝、 浙贝按照制川乌与贝母生药质量比1: 1配伍前后化学成分的变化情况进行了研究. 在PLS-DA模型中, 乌头汤与川贝、 浙贝配伍前后的煎煮液均可明显区分. 乌头汤与川贝共煎后获得8个化学标志物, 可鉴定出7个化合物, 其中尼奥灵、 附子灵及苯甲酰基新乌头原碱在共煎后含量上升, 塔拉定、 10-OH苯甲酰基乌头原碱、 10-OH苯甲酰基新乌头原碱和苯甲酰基去氧乌头原碱在配伍后含量下降. 乌头汤与浙贝共煎后获得7个化学标志物, 分别为Chuanfumine、 Songorine、 塔拉定、 尼奥灵、 附子灵、 苯甲酰基新乌头原碱和10-OH苯甲酰基新乌头原碱, 所有化学标志物的含量在共煎后均降低.     

Objective chemical fingerprinting of oil spills by partial least-squares discriminant analysis

An objective method based on partial least-squares discriminant analysis (PLS-DA) was used to assign an oil lump collected on the coastline to a suspected source. The approach is an add-on to current US and European oil fingerprinting standard procedures that are based on lengthy and rather subjective visual comparison of chromatograms. The procedure required an initial variable selection step using the selectivity ratio index (SRI) followed by a PLS-DA model. From the model, a "matching decision diagram" was established that yielded the four possible decisions that may arise from standard procedures (i.e., match, non-match, probable match, and inconclusive). The decision diagram included two limits, one derived from the Q-residuals of the samples of the target class and the other derived from the predicted y of the PLS model. The method was used classify 45 oil lumps collected on the Galician coast after the Prestige wreckage. The results compared satisfactorily with those from the standard methods.     

A Comparative Study of Elution Gradients in UPLC-TOF-MS-Based Metabonomics Research

Serum and urine samples from patients with type 2 diabetes mellitus and control samples were analyzed by UPLC-TOF-MS; fast and slow separation gradients were compared using both positive and negative ionization modes. The resulting data were analyzed using partial least squares discriminant analysis (PLS-DA), and models were developed to differentiate between patient and control samples. The models were evaluated using external test sets to classify their predictive ability. Under both fast and slow gradient conditions, the PLS-DA models generated using serum samples were more robust than those generated using urine samples, and the positive ionization mode produced better differentiation and higher classification rates than negative ionization mode. In addition, fast gradient conditions were found to have a comparable ability for differentiation to slow gradient conditions.     

Feasibility study on the use of infrared spectroscopy for the direct authentication of Iberian pig fattening diet

The feasibility of using both middle- and near-infrared spectroscopy for discrimination between subcutaneous fat of Iberian pigs reared on different fattening diets has been evaluated. The sample set was formed by subcutaneous fat of pigs fattened outdoors (extensively) with natural resources (montanera) and pigs fattened on commercial feeds, either with standard feed or with especial formulations with higher content in oleic acid (HO-formulated feed). Linear discriminant analysis was used to classify the samples according to the fattening diet using the scores obtained from principal component analysis of near- and middle-infrared spectra as variables to construct the discriminant functions. The most influential variables were identified using a stepwise procedure. The discriminant potential of each spectral region was investigated. Best results were obtained with the combination of both regions with 91.7% of the standard feed and 100% of montanera and HO-formulated feed samples correctly classified. Chemical explanations are provided based on the correlation of these variables with fatty acid content in the samples.     

Synergistic effect of the simultaneous chemometric analysis of H NMR spectroscopic and stable isotope (SNIF-NMR,O, C) data: Application to wine analysis

It is known that ¹H NMR spectroscopy represents a good tool for predicting the grape variety, the geographical origin, and the year of vintage of wine. In the present study we have shown that classification models can be improved when ¹H NMR profiles are fused with stable isotope (SNIF-NMR, ¹⁸O, ¹³C) data. Variable selection based on clustering of latent variables was performed on ¹H NMR data. Afterwards, the combined data of 718 wine samples from Germany were analyzed using linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), factorial discriminant analysis (FDA) and independent components analysis (ICA). Moreover, several specialized multiblock methods (common components and specific weights analysis (ComDim), consensus PCA and consensus PLS-DA) were applied to the data.     

Local classification: Locally weighted–partial least squares-discriminant analysis (LW–PLS-DA)

The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW–PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones.     

Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis

Near infrared (NIR) spectroscopy based on effective wavelengths (EWs) and chemometrics was proposed to discriminate the varieties of fruit vinegars including aloe, apple, lemon and peach vinegars. One hundred eighty samples (45 for each variety) were selected randomly for the calibration set, and 60 samples (15 for each variety) for the validation set, whereas 24 samples (6 for each variety) for the independent set. Partial least squares discriminant analysis (PLS-DA) and least squares-support vector machine (LS-SVM) were implemented for calibration models. Different input data matrices of LS-SVM were determined by latent variables (LVs) selected by explained variance, and EWs selected by x-loading weights, regression coefficients, modeling power and independent component analysis (ICA). Then the LS-SVM models were developed with a grid search technique and RBF kernel function. All LS-SVM models outperformed PLS-DA model, and the optimal LS-SVM model was achieved with EWs (4021, 4058, 4264, 4400, 4853, 5070 and 5273 cm⁻¹) selected by regression coefficients. The determination coefficient (R²), RMSEP and total recognition ratio with cutoff value ±0.1 in validation set were 1.000, 0.025 and 100%, respectively. The overall results indicted that the regression coefficients was an effective way for the selection of effective wavelengths. NIR spectroscopy combined with LS-SVM models had the capability to discriminate the varieties of fruit vinegars with high accuracy.