[Background] The analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each “feature” (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field., [Results] Here, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications., [Conclusions] The methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data., The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007–2013) / ERC Grant Agreement n. 320737. The first author acknowledges the Spanish Government (Ministerio de Educación, Cultura y Deporte) for a predoctoral FPU scholarship. The authors acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI). Grant support from Generalitat de Catalunya 2017-SGR-753 and Spanish Ministry of Economy, Industry and Competitiveness (project CTQ2015–66254-C2–1-P) is also acknowledged., Peer reviewed

Multivariate Curve Resolution (MCR)methods have been widely used to resolve the spectra (instrumental responses)and concentration profiles of the unknown constituents of chemical mixtures especially when no prior information is available about the nature and composition of these mixtures. Based on the fulfillment of a bilinear model, like the multivariate extension of Beer's law, MCR solutions are affected by rotation ambiguity, which means that a range of feasible solutions can explain the observed data equally well fulfilling the same constraints. The MCR-BANDS method has been proposed to provide a measure of the extension of rotation ambiguity associated to a particular MCR feasible solution. In this work, the two extreme (maximum and minimum)feasible solutions obtained by the MCR-BANDS method are investigated and projected on to the area of feasible solution (AFS)obtained by other methods like the FACPACK method, and compared under the application of different constraints. In contrast to other methods that estimate the whole set of feasible solutions (i.e. the AFS), MCR-BANDS provides a simpler and flexible way to give an estimation of the extension of rotation ambiguity associated to a particular MCR solution (for instance using the MCR-ALS method)of systems with any number of components and under any type of constraints, in the concentration and spectral domains. © 2019 Elsevier B.V., Xin Zhang and Zhuoyong Zhang acknowledge the support received by Natural Science Foundation of China (21705112), and Youth Innovative Research Team of Capital Normal University (009175301300) (China). Roma Tauler acknowledges financial support from the Ministerio de Economia y Competividad (Spain) CTQ2015-66254-C2-1-P project and from Generalitat de Catalunya 2017 SGR 753 support to research groups grant., Peer reviewed

Most of the Mediterranean rivers are suffering the effects of industrial, urban and mining discharges, as well as a reduction in water quantity and quality. Additionally, due to the Mediterranean climate, the natural water resource availability is periodically lower than the water demand in the area. Operation of drinking water plants in these geographical areas needs advanced process control systems where real-time and in-line water quality monitoring tools are key components. Data sets with parameters generated by monitoring sensors and from laboratory analysis are used to reveal the main factors that characterize water quality. Chemometric tools like Principal Component Analysis (PCA) can be used to explore and analyze correlations among different physicochemical and microbiological parameters with the aim to assess the river water quality at the water intake of drinking water treatment plants (DWTPs). Strong seasonal trends in the organic and inorganic matter contents and unusual events in the raw river water quality at the DWTP water intake are revealed. Organic and inorganic patterns are then associated with climatological, meteorological and industrial pollution circumstances typical for the geographical region under study. In addition, microbiological events can be detected at the water intake of DWTP which may occur simultaneously with increasing water contents of organic matter, especially at the beginning of rainfall episodes. The application of PCA on sensors data in the water intake at DWTPs offers new possibilities for improved quality assurance and control procedures for DWTP management and its strategy. © 2019, SP and RT acknowledge the Ministerio de Economía y Competividad, Spain for the grant CTQ2015-66254-C2-1-P.

This work has been financially supported by R+i Alliance, an internal funding programme of Suez Group., Peer reviewed