Data Size Reduction Strategy for the Classification of Breath and Air Samples Using Multicapillary Column-Ion Mobility Spectrometry

A novel hybrid filter/wrapper method for feature selection in archaeological ceramics classification by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) has been appreciated as a valuable analytical tool in the cultural heritage field owing to its unique technological superiority, particularly in combination with chemometric methods. Feature selection (FS) as an indispensable pre-processing step in data optimization, for eliminating the redundant or irrelevant features from high-dimensional data to enhance the predictive capacity and result comprehensibility of multivariate classification based on LIBS technology. In this paper, a novel hybrid filter/wrapper method based on the MI-DBS algorithm was proposed to enhance the qualitative analysis performance of the LIBS technique. The proposed method combines the advantages of the mutual information (MI) algorithm based filter method and bi-directional selection (DBS) algorithm based wrapper method. The MI algorithm is the first to remove the redundant or uncorrelated features so that a simplified input subset can be established. Then, the DBS algorithm is used to further select the retained features and hence to seek an optimal feature subset with good predictive performance. To benefit the above feature selection process, the wavelet transform denoising (WTD) method was used to reduce the noise from LIBS spectra. LIBS experiments were performed using 35 archaeological ceramic samples. Besides, the proposed hybrid filter/wrapper method was implemented through a random forest (RF) based nonlinear multivariate classification method. Through a comparison between several other feature selection methods and the proposed method, it has been seen that the proposed method is the best regarding the predictive performance and number of the selected features. Finally, the MI-DBS algorithm is used to seek the optimal features from the full spectrum (220-720 nm); the corresponding sensitivity, specificity and accuracy acquired through the RF classifier for the test set were 0.9722, 0.9956 and 0.9850. It is shown from the general results that the MI-DBS algorithm is more effective in terms of improving the model performance and decreasing the redundant or uncorrelated features and computational time and serves as a good alternative for FS in multivariate classification.

Dietary Supplementation of Vine Tea Ameliorates Glucose and Lipid Metabolic Disorder via Akt Signaling Pathway in Diabetic Rats

A traditional Chinese tea with many pharmacological effects, vine tea (VT) is considered a potential dietary supplement to improve type 2 diabetes (T2D). To investigate the effect and mechanism of VT on glucose and lipid metabolic disorders in T2D rats, Wistar rats fed a normal diet served as the normal control, while rats fed a high-fat diet combined with low-dose streptozotocin (STZ)-induced T2D were divided into three groups: The model group (MOD); the positive control group (MET, metformin at 200 mg/kg/d); and the VT-treated group (VT500, allowed to freely drink 500 mg/L VT). After four weeks of intervention, biochemical metrics indicated that VT significantly ameliorated hyperglycemia, hyperlipidemia and hyperinsulinemia in T2D rats. Metabolomics research indicated that VT regulated the levels of metabolites closely related to glucose and lipid metabolism and promoted glycogen synthesis. Furthermore, VT had a significant influence on the expression of key genes involved in the Akt signaling pathway, inhibited gluconeogenesis through the Akt/Foxo1/Pck2 signaling pathway, and reduced fatty acid synthesis via the SREBP1c/Fasn signaling pathways. In conclusion, VT has great potential as a dietary supplement to ameliorate glucose and lipid metabolic disorders via the Akt signaling pathway in T2D rats.

An ensemble variable selection method for vibrational spectroscopic data analysis

Wavelength selection is a critical factor for pattern recognition of vibrational spectroscopic data. Not only does it alleviate the effect of dimensionality on an algorithm's generalization performance, but it also enhances the understanding and interpretability of multivariate classification models. In this study, a novel partial least squares discriminant analysis (PLSDA)-based wavelength selection algorithm, termed ensemble of bootstrapping space shrinkage (EBSS), has been devised for vibrational spectroscopic data analysis. In the algorithm, a set of subsets are generated from a data set using random sampling. For an individual subset, a feature space is determined by maximizing the expected 10-fold cross-validation accuracy with a weighted bootstrap sampling strategy. Then an ensemble strategy and a sequential forward selection method are applied to the feature spaces to select characteristic variables. Experimental results obtained from analysis of real vibrational spectroscopic data sets demonstrate that the ensemble wavelength selection algorithm can reserve stable and informative variables for the final modeling and improve predictive ability for multivariate classification models.

A new ensemble method for wavelength selection.

Plasma biomarker discovery for early chronic kidney disease diagnosis based on chemometric approaches using LC-QTOF targeted metabolomics data

Chronic kidney disease (CKD) is a progressive pathological condition in which renal function deteriorates in time. The first diagnosis of CKD is often carried out in general care attention by general practitioners by means of serum creatinine (CNN) levels. However, it lacks sensitivity and thus, there is a need for new robust biomarkers to allow the detection of kidney damage particularly in early stages. Multivariate data analysis of plasma concentrations obtained from LC-QTOF targeted metabolomics method may reveal metabolites suspicious of being either up-regulated or down-regulated from urea cycle, arginine methylation and arginine-creatine metabolic pathways in CKD pediatrics and controls. The results show that citrulline (CIT), symmetric dimethylarginine (SDMA) and S-adenosylmethionine (SAM) are interesting biomarkers to support diagnosis by CNN: early CKD samples and controls were classified with an increase in classification accuracy of 18% when using these 4 metabolites compared to CNN alone. These metabolites together allow classification of the samples into a definite stage of the disease with an accuracy of 74%, being the 90% of the misclassifications one level above or below the CKD stage set by the nephrologists. Finally, sex-related, age-related and treatment-related effects were studied, to evaluate whether changes in metabolite concentration could be attributable to these factors, and to correct them in case a new equation is developed with these potential biomarkers for the diagnosis and monitoring of pediatric CKD.

Transferring results from NIR-hyperspectral to NIR-multispectral imaging systems: A filter-based simulation applied to the classification of Arabica and Robusta green coffee

Due to the differences in terms of both price and quality, the availability of effective instrumentation to discriminate between Arabica and Robusta coffee is extremely important. To this aim, the use of multispectral imaging systems could provide reliable and accurate real-time monitoring at relatively low costs. However, in practice the implementation of multispectral imaging systems is not straightforward: the present work investigates this issue, starting from the outcome of variable selection performed using a hyperspectral system. Multispectral data were simulated considering four commercially available filters matching the selected spectral regions, and used to calculate multivariate classification models with Partial Least Squares-Discriminant Analysis (PLS-DA) and sparse PLS-DA. Proper strategies for the definition of the training set and the selection of the most effective combinations of spectral channels led to satisfactory classification performances (100% classification efficiency in prediction of the test set).

Ion Mobility in Clinical Analysis: Current Progress and Future Perspectives

BACKGROUND

Ion mobility spectrometry (IMS) is a rapid separation tool that can be coupled with several sampling/ionization methods, other separation techniques (e.g., chromatography), and various detectors (e.g., mass spectrometry). This technique has become increasingly used in the last 2 decades for applications ranging from illicit drug and chemical warfare agent detection to structural characterization of biological macromolecules such as proteins. Because of its rapid speed of analysis, IMS has recently been investigated for its potential use in clinical laboratories.

CONTENT

This review article first provides a brief introduction to ion mobility operating principles and instrumentation. Several current applications will then be detailed, including investigation of rapid ambient sampling from exhaled breath and other volatile compounds and mass spectrometric imaging for localization of target compounds. Additionally, current ion mobility research in relevant fields (i.e., metabolomics) will be discussed as it pertains to potential future application in clinical settings.

SUMMARY

This review article provides the authors' perspective on the future of ion mobility implementation in the clinical setting, with a focus on ambient sampling methods that allow IMS to be used as a “bedside” standalone technique for rapid disease screening and methods for improving the analysis of complex biological samples such as blood plasma and urine.

Chemometrics for ion mobility spectrometry data: recent advances and future prospects

This is the first comprehensive review on chemometric techniques used in ion mobility spectrometry data analysis.