Search

In this paper, we propose a least-squares estimator based on Logistic transformation of order statistics (LLSE) and grouped LLSE for the generalized Logistic distribution. Some asymptotic results are provided. Two simulations are undertaken to assess the performance of the proposed method and to compare them with other methods suggested in this paper. The simulation results indicate that LLSE performs better than some other methods and grouped LLSE performs fairly well in small sample size. Finally, LLSE is applied to a real dataset. [ABSTRACT FROM AUTHOR]

A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0–3 kilometers, 7.52–88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0–0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable. [ABSTRACT FROM AUTHOR]

The partial least squares (PLS) approach to structural equation modeling (SEM) appears across a wide array of business research publications, including those in operations management (OM). However, the authors' summary of PLS use in the OM literature suggests some concerns and issues. First, the debate on the use of PLS-SEM is intensifying instead of being mediated despite the increasing use of PLS-SEM. Second, a lack of clarity exists among OM researchers about the use of reflective and formative measurements for constructs. Third, the validation of formative measurement is not routinely conducted in studies, which supports the need to summarize and illustrate the validation procedure of formative measurement. Without addressing these questions, the rigor involved in selecting reflective versus formative measures, especially in the OM field, is compromised. This research summarizes the procedures for choosing and validating formative measurement. The authors provide an illustrative OM example to demonstrate how the specific steps are applied. Through proactive selection and judicious operationalization of the measurement model and appropriate comparisons of the overall research model effectiveness based on criteria such as the R2 of the dependent variable OM, researchers provide a tool to help them extend existing theoretical frameworks and explore new theories. [ABSTRACT FROM AUTHOR]

Dampening during processing or storage can largely influence the quality of white lotus seeds (WLS). This paper investigated the feasibility of using near-infrared (NIR) spectroscopy and chemometrics for rapid and nondestructive discrimination of the dampened WLS. Regular (n=167) and dampened (n=118) WLS objects were collected from five main producing areas and NIR reflectance spectra (4000–12000 cm−1) were measured for bare kernels. The influence of spectral preprocessing methods, including smoothing, taking second-order derivatives (D2), and standard normal variate (SNV), on partial least squares discrimination analysis (PLSDA) was compared to select the optimal data preprocessing method. A moving-window strategy was combined with PLSDA (MWPLSDA) to select the most informative wavelength intervals for classification. Based on the selected spectral ranges, the sensitivity, specificity, and accuracy were 0.927, 0.950, and 0.937 for SNV-MWPLSDA, respectively. [ABSTRACT FROM AUTHOR]

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]

• 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]

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]