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One-class classifiers are widely used to solve the classification problems where control or class modeling of a target class is necessary, e.g., untargeted analysis of food adulterations and frauds, tracing the origins of a food with Protected Denomination of Origin, fault diagnosis, etc. Recently, one-class partial least squares (OCPLS) has been developed and demonstrated to be a useful technique for class modeling. For analysis of nonlinear and outlier-contaminated data, nonlinear and robust OCPLS algorithms are required. This paper describes a free MATLAB toolbox for class modeling using OCPLS classifiers. The toolbox includes ordinary, nonlinear and robust OCPLS methods. The nonlinear algorithm is based on the Gaussian radial basis function (GRBF), and the robust algorithm is based on the partial robust M-regression (PRM). The usage of the toolbox is demonstrated by analysis of a real data set. [ABSTRACT FROM AUTHOR]

Abstract: One-class partial least squares (OCPLS) classifier is investigated as a tool for multivariate statistical quality control (MSQC). According to the OCPLS score distance (SD) and absolute centered residual (ACR) of predicted response, an object can be classified into one of the four groups: regular points (with a small SD and a small ACR), class outliers (with a small SD and a large ACR), good leverage points (with a large SD and a small ACR) and bad leverage points (with a large SD and a large ACR). The correlation between OCPLS distance measures and some existing methods, including D-statistic, Q-statistic and correlation coefficient (Pearson's r), is briefly discussed. OCPLS is applied to non-targeted detection of adulterations in whole milk powder using near-infrared (NIR) spectroscopy. The results demonstrate OCPLS can provide an effective tool for MSQC by including both SD and ACR of predicted response. [Copyright &y& Elsevier]

Abstract: Class modeling techniques are required to tackle various one-class problems. Because the training of class models is based on the target class and the origins of future test objects usually cannot be exactly predefined, the criteria for feature selection of class models are not very straightforward. Although feature reduction can be expected to improve class models performance, more features retained can provide a sufficient description of the sought-for class. This paper suggests a strategy to balance class description and model specificity by ensemble learning of sub-models based on separate local wavelength intervals. The acceptance or rejection of a future object can be explicitly determined by examining its acceptance frequency by sub-models. Considering the lack of information about sub-model independence, we propose to use a data-driven method to control the sensitivity of the ensemble model by cross validation. In this way, all the wavelength intervals are used for class description and the local wavelength intervals are highlighted to enhance the ability to detect out-of-class objects. The proposed strategy was performed on one-class partial least squares (OCPLS) and soft independent modeling of class analogy (SIMCA). By analysis of two infrared spectral data sets, one for geographical origin identification of white tea and the other for discrimination of adulterations in pure sesame oil, the proposed ensemble class modeling method was demonstrated to have similar sensitivity and better specificity compared with total-spectrum SIMCA and OCPLS models. The results indicate local spectral information can be extracted to enhance class model specificity. [Copyright &y& Elsevier]

Abstract: The paper focuses on solving a common and important problem of NIR quantitative analysis in multi-component systems: how to significantly reduce the size of the calibration set while not impairing the predictive precision. To cope with the problem orthogonal discrete wavelet packet transform (WPT), the least correlation design and correlation coefficient test (r-test) have been combined together. As three examples, a two-component carbon tetrachloride system with 21 calibration samples, a two-component aqueous system with 21 calibration samples, and a two-component aqueous system with 41 calibration samples have been treated with the proposed strategy, respectively. In comparison with some previous methods based on much more calibration samples, the results out of the strategy showed that the predictive ability was not obviously decreased for the first system while being clearly strengthened for the second one, and the predictive precision out of the third one was even satisfactory enough for most cases of quantitative analysis. In addition, all important factors and parameters related to our strategy are discussed in detail. [Copyright &y& Elsevier]

Abstract: In the traditional framework of multivariate spectroscopic calibration, the most popular method, partial least squares (PLS), shrinks the regression coefficients based on the information of training sample concentrations. Motivated by the concept of parallel calibration, the second direction for shrinkage of regression coefficients, the direction towards unknown sample spectra is investigated in this paper. A different multivariate calibration method, parallel calibration model based on partial least squares, PCPLS is proposed. With both theoretical support and analysis of some real data sets, it is demonstrated that the second shrinkage direction is at least as natural and necessary as the traditional one. An interesting difference of the proposed method from traditional methods is the involvement of unknown sample spectra and consideration of their error in the training process. Some new related problems and potential applications of this method are also briefly discussed. [Copyright &y& Elsevier]

Abstract: In ordinary multivariate calibration methods, when the calibration set is determined to build the model describing the relationship between the dependent variables and the predictor variables, each sample in the calibration set makes the same contribution to the model, where the difference of representativeness between the samples is ignored. In this paper, by introducing the concept of weighted sampling into partial least squares (PLS), a new multivariate regression method, optimized sample-weighted PLS (OSWPLS) is proposed. OSWPLS differs from PLS in that it builds a new calibration set, where each sample in the original calibration set is weighted differently to account for its representativeness to improve the prediction ability of the algorithm. A recently suggested global optimization algorithm, particle swarm optimization (PSO) algorithm is used to search for the best sample weights to optimize the calibration of the original training set and the prediction of an independent validation set. The proposed method is applied to two real data sets and compared with the results of PLS, the most significant improvement is obtained for the meat data, where the root mean squared error of prediction (RMSEP) is reduced from 3.03 to 2.35. For the fuel data, OSWPLS can also perform slightly better or no worse than PLS for the prediction of the four analytes. The stability and efficiency of OSWPLS is also studied, the results demonstrate that the proposed method can obtain desirable results within moderate PSO cycles. [Copyright &y& Elsevier]

• Untargeted analysis of frauds in kiwifruit juice was developed. • Traditional and double-quantum-dots enhanced fluorescent sensor was established. • One-class partial least squares was used to develop class models of pure juice. • The best model sensitivity was 0.929. • It could detect 2.0% or more of sucrose syrup and artificial fruit powder. The feasibility of simultaneous detection of multiple cheap materials in kiwifruit juice (KFJ) was studied by two fluorescent methods and chemometrics. Because the actual frauds usually involve many known and unknown adulterants, the traditional methods to detect one or more known components are insufficient to identify adulterated KFJ. Therefore, class models of pure KFJ were established using one-class partial least squares (OCPLS) to perform untargeted analysis. To enhance the detection specificity, traditional and double-quantum-dots enhanced fluorescent spectroscopic techniques were combined to characterize pure and adulterated KFJ. By data fusion and using standard normal variables (SNV) transformation, OCPLS model with sensitivity of 0.929 was obtained. The proposed method could detect adulterations of 2% (w/w) or more syrup and artificial fruit powder. The fusion of traditional and quantum dots enhanced fluorescence was demonstrated to provide a rapid and highly sensitive method for untargeted analysis of adulterated KFJ. [ABSTRACT FROM AUTHOR]

To enhance the power of untargeted detection, a "turn-off" fluorescent probe with double quantum dots (QDs) was developed and coupled with chemometrics for rapid detection of multiple adulterants in an herbal (Rhus chinensis Mill., RCM) honey. The double water-soluble ZnCdSe-CdTe QDs have two separate and strong fluorescent peaks, which can be quenched by honey and extraneous adulterants with varying degrees. Class models of pure RCM honey samples collected from 6 different producing areas (n = 122) were developed using one-class partial least squares (OCPLS). Four extraneous adulterants, including glucose syrup, sucrose syrup, fructose syrup, and glucose-fructose syrup were added to pure honey samples at the levels of 0.5% to 10% (w/w). As a result, the OCPLS model using the second-order derivative (D2) spectra could detect 1.0% (w/w) of different syrups in RCM honey, with a sensitivity of 0.949. The double water-soluble QDs, which can be adjusted for analysis of other water-soluble food samples, has largely extended the capability of traditional fluorescence and will provide a potentially more sensitive and specific analysis method for food frauds. Unlabelled Image • A fluorescent probe of double quantum dots was used for analysis of honey frauds. • Untargeted detection of multiple sugar syrups was performed using chemometrics. • The method is highly sensitive and the lowest detected doping level was 1.0%. • The double quantum dots are water-soluble and useful for many other food systems. [ABSTRACT FROM AUTHOR]

Large-class-number classification (LCNC) would bring new challenges to pattern recognition due to increased data complexity and class overlapping. In this study, a novel ensemble strategy (ES) was proposed to tackle LCNC problems. By combining the One-Versus-Rest (OVR) and One-Versus-One (OVO) strategies to design a set of classifiers with reduced class numbers, ES assigns a new object to the class receiving the most votes. When two or more classes obtain the most votes, an additional OVR model is developed to discriminate them. ES, OVR, OVO and the softmax function were investigated to discriminate the geographical origins of 25 green tea samples using near-infrared (NIR) spectroscopy and Partial Least Squares Discriminant Analysis (PLSDA). Using the Standard Normal Variate (SNV) as a spectral scatter correction technique, the total accuracy was 0.6468 for OVR-PLSDA, 0.8494 for OVO-PLSDA, 0.9299 for PLSDA-softmax, and 0.9377 for ES-PLSDA, respectively. Using other preprocessing methods and multiple random splitting of the data sets obtained the similar results. The poor performance of OVR can be attributed to the increased possibility of class overlapping and high sub-model complexity. OVO was less influenced by LCNC because it is based on a set of relatively simpler two-class classifiers. PLSDA-softmax could overcome the class overlapping by nonlinear transformations. ES was demonstrated to be capable of extracting more useful information from sub-models and achieved improved performance in LCNC. [ABSTRACT FROM AUTHOR]

Abstract: Interpretation and mining of complex metabonomic data depend heavily on proper use of chemometric methods. Due to the “small n” paradigm and the absence of sufficient information concerning distribution of data, the classical parametric methods based on known theoretical distributions are sometimes unsuitable or unreliable to treat such data. Therefore, nonparametric methods requiring no or very limited assumptions provide useful alternative tools in many practical applications. In this paper, a new discriminant partial least squares combined with bootstrap and uninformative variable elimination (DPLS–BS–UVE) method is proposed for biomarker discovery in metabonomics. The method was tested on two real chromatographic data sets containing plasma metabolic profilings for S180 and H22 tumor-bearing mice. A robust version of c j was used as the cutoff criterion. The results of biomarker discovery were compared with those obtained using variable importance in the projection (VIP) as well as BS. It is demonstrated that similar results are obtained using the three methods and DPLS–BS–UVE could provide easy interpretation of raw data. When the resampling unit increases to 500, the results were not significantly affected. In conclusion, DPLS–BS–UVE is a reliable alternative method for biomarker discovery, especially when the sample size is small. [Copyright &y& Elsevier]

Abstract: This paper demonstrates the application of near-infrared (NIR) process analysis to study gas–solid adsorption process non-invasively: its experimental setup, data treatment, and potentials as a convenient tool to investigate the gas–solid adsorption process. The experimental setup includes a differential adsorption bed (DAB) monitored by a NIR spectrometer via an optical fiber probe, which makes it convenient and reliable to construct adsorption mass-transfer models. A chemometrics strategy based on back propagation-artificial neural network (BP-ANN) and partial least squares (PLS) has been developed to treat NIR spectra collected during the adsorption process because of the obvious nonlinearity in concentration prediction. This nonlinear problem results from the great concentration variation of the adsorbate adsorbed by the adsorbent during the whole adsorption process, the extraordinarily low concentration of the adsorbed adsorbate at the beginning of the process, and probably NIR distinction between the adsorbate on the first adsorption layer at the beginning of the process and that on the other layers afterward. With the strategy, NIR spectra are pretreated with PLS for data compression and noise reduction, and then a BP-ANN is built as the nonlinear calibration model. As compared with linear calibration algorithm, our strategy has the higher predication ability for the whole adsorption process, even with less calibration samples. Finally, as an example the kinetics of aniline–silica gel adsorption process has been studied through the experimental setup and chemometrics strategy. [Copyright &y& Elsevier]

Abstract: Multivariate spectral analysis has been widely applied in chemistry and other fields. Spectral data consisting of measurements at hundreds and even thousands of analytical channels can now be obtained in a few seconds. It is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In this paper, the concept of traditional wavelength variable selection has been extended and the idea of variable weighting is incorporated into least-squares support vector machine (LS-SVM). A recently proposed global optimization method, particle swarm optimization (PSO) algorithm is used to search for the weights of variables and the hyper-parameters involved in LS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. All the computation process of this method is automatic. Two real data sets are investigated and the results are compared those of PLS, uninformative variable elimination-PLS (UVE-PLS) and LS-SVM models to demonstrate the advantages of the proposed method. [Copyright &y& Elsevier]

Abstract: In the present study a new version of nonlinear partial least-square method based on artificial neural network transformation (ANN-NLPLS) has been proposed. This algorithm firstly transforms the training descriptors into the hidden layer outputs using the universal nonlinear mapping carried by an artificial neural network, and then utilizes PLS to relate the outputs of the hidden layer to the bioactivities. The weights between the input and hidden layers are optimized by a particle swarm optimization (PSO) method using the criterion of minimized model error via PLS modeling. An F-statistic is introduced to determine automatically the number of PLS components during the weight optimization. The performance is assessed using a simulated data set and two quantitative structure–activity relation (QSAR) data sets. Results of these three data sets demonstrate that ANN-NLPLS offers enhanced capacity in modeling nonlinearity while circumventing the overfitting frequently involved in nonlinear modeling. [Copyright &y& Elsevier]

Biomarker selection has played an increasingly important part in modern medicine with advances of omics techniques. Kohonen self-organizing map is a well-established variable reduction algorithm in identifying significant biomarkers based on variable clustering. However, high dimensionality but small sample size of omics data makes self-organizing map-based model problematic in terms of selection stability and reproducibility. A novel feature screening system is presented in this study by coupling bootstrap with synergy self-organizing map-based orthogonal partial least squares discriminant analysis for stable and biologically meaningful metabolic biomarker selection. In the proposed feature screening system, particle swarm optimization algorithm is utilized to configure synergy self-organizing map-based orthogonal partial least squares discriminant analysis to perform the combination of clusters in a heuristic learning manner, enabling flexible selection of more informative features cost-effectively. Based on the paradigm of ensemble feature selection, bootstrap is adopted to explore significant variables consistently identified across multiple feature selectors rather than a single one. The feasibility of the novel feature screening system is evaluated by two most common inherited metabolic diseases, methylmalonic academia and propionic academia, using urinary metabolomics data. With the desirable classification performance, the proposed feature screening system outperforms simpler techniques in the identification of more features closely correlated with the metabolic mechanisms and the stability of selected candidate biomarkers against sample variations. Besides, the novel feature screening system greatly degrades the sensitivity of identified candidate biomarkers to the network size of self-organizing map, benefiting the identification of a suitable and stable final candidate biomarker list. Image 1 • A novel feature screening system was developed for stable metabolic biomarker selection. • Strategies of cluster combination and ensemble feature selection were involved. • Urinary metabolomics data of methylmalonic academia and propionic academia samples were modeled. • Desirable results were obtained in stable metabolic biomarker selection for inherited metabolic diseases. [ABSTRACT FROM AUTHOR]

Urinary metabolomics coupled with GC-MS has become a leading technology in newborn screening. Because of non-specificity, complexity and high-variety in clinical characteristics and metabolomic profiling, the simultaneous detection of multiple inherited metabolic diseases (IMDs) is often challenging. As a substantial health problem, a competent chemometrics multi-class classification system for the early detection and diagnosis of IMDs would be advantageous. Beyond the commonly used binarization techniques of one-against-all (OAA) and one-against-one (OAO), an exhaustive and parallel half-against-half (EPHAH) decomposition is described in this study to deal with multi-class classification. For a K -class problem, EPHAH employs uniform class binary partition strategy to induce the binary classifier evaluating a half of K classes against the other half. With K -class problem exhaustively decomposed into all uniform binary partitions of K classes, EPHAH parallelly arranges the corresponding binary classifiers and aggregates their outputs to obtain the multi-class prediction using max-wins voting strategy. Based on orthogonal partial least squares discriminant analysis (OPLS-DA) with feature selection using particle swarm optimization (PSO) algorithm, EPHAH is investigated by GC-MS urinary metabolomics data among healthy controls and 9 most common IMDs. The results show that EPHAH enables a complete learning of the complex multi-class decision boundaries of 10 classes, exhibiting significant superiority in classification accuracies over OAA, OAO and traditional HAH. Meanwhile, compared with OAO using the same max-wins voting strategy, EPHAH gives an effective break of the tie problem in classification and enhanced resolution in votes. • An exhaustive and parallel half-against-half (EPHAH) decomposition was proposed for multi-class classification. • EPHAH involved uniform class binary partition and parallel arrangement with exhaustive decomposition of K -class problem. • EPHAH coupled with OPLS-DA profiled GC-MS urinary metabolomics data of multi-class IMDs with desired results. • A competent system for multi-class IMDs simultaneous detection was developed, aiding their early detection and diagnosis. [ABSTRACT FROM AUTHOR]