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In this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensional gas or liquid chromatography, the main novelty of this work is the demonstration of their applicability to LC×LC-MS datasets. Besides, steps such as peak alignment and modelling, commonly applied in this type of data analysis, are not required with the approaches proposed here. In a first step, regions of interest (ROI) strategy is used for the spectral compression of the LC×LC-MS datasets. Then the first strategy consists of building a calibration curve from the areas obtained in this ROI compression step. Alternatively, the ROI intensity matrices can be used as input for a second analysis step employing the multivariate curve resolution alternating least squares (MCR-ALS) method. The main benefit of MCR-ALS is the resolution of elution and spectral profiles for each of the analytes in the mixture, even in the case of strong coelutions and high signal overlapping. Classical MCR-ALS based calibration curve from the peak areas resolved only applying non-negativity constraints (second strategy) is compared to the results obtained when an area correlation constraint is imposed during the ALS optimization (third strategy). All in all, similar quantification results were achieved by the three approaches but, especially in prediction studies, the more accurate quantification is obtained when the calibration curve is built from the peak areas obtained with MCR-ALS when the area correlation constraint is imposed.

In this work, we evaluate the performance of three multivariate ANOVA-based methods (ANOVA simultaneous component analysis—ASCA, regularized MANOVA–rMANOVA, and Group-wise ANOVA-simultaneous component analysis—GASCA) in the framework of betabolomics studies., Citation: Pérez-Cova, M.; Platikanov, S.; Stoll, D.R.; Tauler, R.; Jaumot, J. Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies. Molecules 2022, 27, 3304. https://doi.org/10.3390/ molecules27103304 Academic Editor: Irini Doytchinova Received: 26 March 2022 Accepted: 19 May 2022 Published: 20 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil-iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). molecules Article Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies Miriam Pérez-Cova 1,2 , Stefan Platikanov 1, Dwight R. Stoll 3, Romà Tauler 1 and Joaquim Jaumot 1,* 1 Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, E08034 Barcelona, Spain; mpcqam@idaea.csic.es (M.P.-C.); stefan.platikanov@idaea.csic.es (S.P.); rtaqam@idaea.csic.es (R.T.) 2 Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Diagonal 647, E08028 Barcelona, Spain 3 Department of Chemistry, Gustavus Adolphus College, 800 West College Avenue, Saint Peter, MN 56082, USA; dstoll@gustavus.edu * Correspondence: joaquim.jaumot@idaea.csic.es Abstract: The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, clas-sical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with metabolomics data. For this reason, in recent years, different options have appeared that overcome these limitations. In this work, we evaluate the performance of three of t

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

The stability of an industrial Salinity Gradient Solar Pond (SGSP) has been studied by applying classical Principal Component Analysis (PCA) on three datasets with a different number of variables and time evolution. Temperature, density and heat extraction have been measured in the upper convective (UCZ), non-convective (NCZ) and low convective (LCZ) zones in a 500 m2 solar pond, located near Granada, South of Spain. Local environmental weather conditions have been also considered in the data analysis. Two operational seasons of the solar pond were considered in order to establish: (1) PCA exploratory models for 2014–2015 operational period and (2) to validate the obtained results during the 2015–2016 operational period. PCA results showed that three factors explained the variance in the monitored stability gradients. The first factor was related to the major effect of the seasonal temperature variation on the entire stability gradient. The second factor, related to the diurnal temperature variation, solar irradiance and wind variables, showed a strong impact on the solar pond temperature and salinity gradients and could affect strongly the UCZ and its border with the NCZ. The third factor affected the stability of salinity and temperature gradients at the LCZ and its border with the NCZ. The latter was related to the increase in temperature and salinity at the bottom of the solar pond, which suggests special attention during the initial formation and settlement of the salinity gradient and the subsequent heat extraction activities. This paper shows PCA modelling as a powerful tool for solar pond operation process surveillance and control., Peer Reviewed, Postprint (author's final draft)

The stability of an industrial Salinity Gradient Solar Pond (SGSP) has been studied by applying classical Principal Component Analysis (PCA) on three datasets with a different number of variables and time evolution. Temperature, density and heat extraction have been measured in the upper convective (UCZ), non-convective (NCZ) and low convective (LCZ) zones in a 500 m2 solar pond, located near Granada, South of Spain. Local environmental weather conditions have been also considered in the data analysis. Two operational seasons of the solar pond were considered in order to establish: (1) PCA exploratory models for 2014–2015 operational period and (2) to validate the obtained results during the 2015–2016 operational period. PCA results showed that three factors explained the variance in the monitored stability gradients. The first factor was related to the major effect of the seasonal temperature variation on the entire stability gradient. The second factor, related to the diurnal temperature variation, solar irradiance and wind variables, showed a strong impact on the solar pond temperature and salinity gradients and could affect strongly the UCZ and its border with the NCZ. The third factor affected the stability of salinity and temperature gradients at the LCZ and its border with the NCZ. The latter was related to the increase in temperature and salinity at the bottom of the solar pond, which suggests special attention during the initial formation and settlement of the salinity gradient and the subsequent heat extraction activities. This paper shows PCA modelling as a powerful tool for solar pond operation process surveillance and control.

The application of the multivariate curve resolution method to the analysis of temporal and spatial data variability of hourly measured O3 and NO2 concentrations at nineteen air quality monitoring stations across Catalonia, Spain, during 2015 is shown. Data analyzed included ground-based experimental measurements and predicted concentrations by the CALIOPE air quality modelling system at three horizontal resolutions (Europe at 12 × 12 km2, Iberian Peninsula at 4 × 4 km2 and Catalonia at 1 × 1 km2). Results obtained in the analysis of these different data sets allowed a better understanding of O3 and NO2 concentration changes as a sum of a small number of different contributions related to daily sunlight radiation, seasonal dynamics, traffic emission patterns, and local station environments (urban, suburban and rural). The evaluation of O3 and NO2 concentrations predicted by the CALIOPE system revealed some differences among data sets at different spatial resolutions. NO2 predictions, showed in general a better performance than O3 predictions for the three model resolutions, specially at urban stations. Our results confirmed that the application of the trilinearity constraint during the multivariate curve resolution factor analysis decomposition of the analyzed data sets is a useful tool to facilitate the understanding of the resolved variability sources.

The application of the multivariate curve resolution method to the analysis of temporal and spatial data variability of hourly measured O3 and NO2 concentrations at nineteen air quality monitoring stations across Catalonia, Spain, during 2015 is shown. Data analyzed included ground-based experimental measurements and predicted concentrations by the CALIOPE air quality modelling system at three horizontal resolutions (Europe at 12 × 12 km2, Iberian Peninsula at 4 × 4 km2 and Catalonia at 1 × 1 km2). Results obtained in the analysis of these different data sets allowed a better understanding of O3 and NO2 concentration changes as a sum of a small number of different contributions related to daily sunlight radiation, seasonal dynamics, traffic emission patterns, and local station environments (urban, suburban and rural). The evaluation of O3 and NO2 concentrations predicted by the CALIOPE system revealed some differences among data sets at different spatial resolutions. NO2 predictions, showed in general a better performance than O3 predictions for the three model resolutions, specially at urban stations. Our results confirmed that the application of the trilinearity constraint during the multivariate curve resolution factor analysis decomposition of the analyzed data sets is a useful tool to facilitate the understanding of the resolved variability sources., SP and RT would like to acknowledge the Spanish Ministerio de Ciencia e Innovación for the project PID2019-105732GB-C21. SP and RT work at IDAEA-CSIC which is a Center of Excellence Severo Ochoa (Spanish Ministry of Science and Innovation, Project CEX2018-000794-S). MTP would like to acknowledge the Spanish Ministry of Economy and Competitiveness and FEDER funds under the PAISA (CGL2016-75725-R) project., Peer Reviewed, Postprint (published version)

This study compares the application of two variable selection methods in partial least squares regression (PLSR), the variable importance in projection (VIP) method and the selectivity ratio (SR) method. For this purpose, three different data sets were analysed: (a) physiochemical water quality parameters related to sensorial data, (b) gas chromatography-mass spectrometry (GC-MS) chemical (organic compound) profiles from fossil sea sediment samples related to sea surface temperature (SST) changes, and (c) exposed genes of Daphnia magna female samples related to their total offspring production. Correlation coefficients (r), levels of significance (p-value) and interpretation of the underlying experimental phenomena allowed the discussion about the best approach for variable selection in each case. The comparison of the two variable selection methods in the first water quality data set showed that the SR method is more accurate for sensorial prediction. For the climate data set, when raw total ion current (TIC) GC-MS chromatograms were considered, variables selected using the VIP method were easier to interpret compared with those selected by the SR method. However, when only some chromatographic peak areas (concentrations) were considered, the SR method was more efficient for prediction, and the VIP method selected the most relevant variables for the interpretation of SST changes. Finally, for the transcriptomic data set, the SR method was found again to be more reliable for prediction purposes.