Modelling phenolic and technological maturities of grapes by means of the multivariate relation between organoleptic and physicochemical properties

The ripeness of grapes at the harvest time is one of the most important parameters for obtaining high quality red wines. Traditionally the decision of harvesting is to be taken only after analysing sugar concentration, titratable acidity and pH of the grape juice (technological maturity). However, these parameters only provide information about the pulp ripeness and overlook the real degree of skins and seeds maturities (phenolic maturity). Both maturities, technological and phenolic, are not simultaneously reached, on the contrary they tend to separate depending on several factors: grape variety, cultivar, adverse weather conditions, soil, water availability and cultural practices. Besides, this divergence is increasing as a consequence of the climate change (larger quantities of CO2, less rain, and higher temperatures). 247 samples collected in vineyards representative of the qualified designation of origin Rioja from 2007 to 2011 have been analysed. Samples contain the four grape varieties usual in the elaboration of Rioja wines (‘tempranillo’, ‘garnacha’, ‘mazuelo’ and ‘graciano’). The present study is the first systematic investigation on the maturity of grapes that includes the organoleptic evaluation of the degree of grapes maturity (sugars/acidity maturity, aromatic maturity of the pulp, aromatic maturity of the skins and tannins maturity) together with the values of the physicochemical parameters (probable alcohol degree, total acidity, pH, malic acid, K, total index polyphenolics, anthocyans, absorbances at 420, 520 and 620 nm, colour index and tartaric acid) determined over the same samples. A varimax rotation of the latent variables of a PLS model between the physicochemical variables and the mean of four sensory variables allows identifying both maturities. Besides, the position of the samples in the first plane defines the effect that the different factors exert on both phenolic and technological maturitiesMinisterio de Economía y Competitividad (CTQ2011-26022) and Junta de Castilla y León (BU108A11-2

Causal networks for climate model evaluation and constrained projections

Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanalyses. We demonstrate how the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation. Models with fingerprints closer to observations better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe and North America. We further identify expected model interdependencies due to shared development backgrounds. Finally, our network metrics provide stronger relationships for constraining precipitation projections under climate change as compared to traditional evaluation metrics for storm tracks or precipitation itself. Such emergent relationships highlight the potential of causal networks to constrain longstanding uncertainties in climate change projections. Algorithms to assess causal relationships in data sets have seen increasing applications in climate science in recent years. Here, the authors show that these techniques can help to systematically evaluate the performance of climate models and, as a result, to constrain uncertainties in future climate change projections

Underlying dimensions of knowledge assessment : factor analysis of the knowledge assessment methodology data

The Knowledge Assessment Methodology (KAM) database measures variables that may be used to assess the readiness of countries for the knowledge economy and has many policy uses. Formal analysis using KAM data is faced with the problem of which variables to choose and why. Rather than make these decisions in an ad hoc manner, the authors recommend factor-analytic methods to distill the information contained in the many KAM variables into a smaller set of"factors."Their main objective is to quantify the factors for each country, and to do so in a way that allows comparisons of the factor scores over time. The authors investigate both principal components as well as true factor analytic methods, and emphasize simple structures that help provide a clear political-economic meaning of the factors, but also allow comparisons over time.Statistical&Mathematical Sciences,Econometrics,Economic Theory&Research,Education for Development (superceded),Innovation

Statistical Workflow for Feature Selection in Human Metabolomics Data.

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations

Assessment of drinking water quality using principal component analysis and partial least square discriminant analysis: a case study at water treatment plants, Selangor

This study characterizes the drinking water quality on 28 water treatmentt plants in Selangor from 2009 to 2012 using multivariate techniques. The objectives of this study are to analyze the quality of collected drinking water and to detect the source of pollution for the most revealing  parameters.  The  Partial  Least  Square  Discriminant  Analysis (PLS-DA)  model showed a high correlation matrix of analysis for physicochemical quality of two types of water with  99.43% significant  value.  The classification  matrix  accuracy of the principal component  analysis  (PCA) highlighted  13  significant  physico-chemical water quality parameters and 14 significant heavy metal parameters. PCA was carried out to identify the origin and source of pollution of each water quality parameters. Therefore, this study proves that chemometric method is the principle way to characterize the drinking water quality.Keywords: partial least square, discriminant analysis; principal component analysis; drinking water qualit

Robust factor analysis in the presence of normality violations, missing data, and outliers: Empirical questions and possible solutions

Although a mainstay of psychometric methods, several reviews suggest factor analysis is often applied without testing whether data support it, and that decision-making process or guiding principles providing evidential support for FA techniques are seldom reported. Researchers often defer such decision-making to the default settings on widely-used software packages, and unaware of their limitations, might unwittingly misuse FA. This paper discusses robust analytical alternatives for answering nine important questions in exploratory factor analysis (EFA), and provides R commands for running complex analysis in the hope of encouraging and empowering substantive researchers on a journey of discovery towards more knowledgeable and judicious use of robust alternatives in FA. It aims to take solutions to problems like skewness, missing values, determining the number of factors to extract, and calculation of standard errors of loadings, and make them accessible to the general substantive researcher