Characterization of Basil Volatile Fraction and Study of Its Agronomic Variation by ASCA

Basil is a plant known worldwide for its culinary and health attributes. It counts more than a hundred and fifty species and many more chemo-types due to its easy cross-breeds. Each species and each chemo-type have a typical aroma pattern and selecting the proper one is crucial for the food industry. Twelve basil varieties have been studied over three years (2018–2020), as have four different cuts. To characterize the aroma profile, nine typical basil flavour molecules have been selected using a gas chromatography–mass spectrometry coupled with an olfactometer (GC–MS/O). The concentrations of the nine selected molecules were measured by an ultra-fast CG e-nose and Principal Component Analysis (PCA) was applied to detect possible differences among the samples. The PCA results highlighted differences between harvesting years, mainly for 2018, whereas no observable clusters were found concerning varieties and cuts, probably due to the combined effects of the investigated factors. For this reason, the ANOVA Simultaneous Component Analysis (ASCA) methodology was applied on a balanced a posteriori designed dataset. All the considered factors and interactions were statistically significant (p < 0.05) in explaining differences between the basil aroma profiles, with more relevant effects of variety and year.     

Chemometric evaluation of different experimental conditions on wheat (Triticum aestivum L.) development using liquid chromatography mass spectrometry (LC–MS) profiles of benzoxazinone derivatives

Different chemometric techniques have been used to evaluate the effect of distinct experimental conditions and factors on Triticum aestivum L. plant development. The study was conducted using three wheat varieties, Astron, Ritmo and Stakado. These varieties were grown under organic and conventional cultivation systems. Samples were collected at five growth stages. Shoots and roots of each plant at these stages were analysed. Three replicates of each analysed sample were performed to improve representativeness and to allow for the evaluation of natural variability and interaction effects. All samples were analysed using Liquid Chromatography Mass–Spectrometry (LC–MS), and the Total Ion Current (TIC) profiles of benzoxazinone derivatives obtained for each sample were investigated. Qualitative and quantitative assessments of these TIC profiles and of their changes in the analysed samples were carried out using different chemometric techniques. Estimation of main effects, and of their possible interaction, was performed by means of Analysis of Variance combined to Principal Component Analysis (ANOVA–PCA) and of Analysis of Variance combined to Simultaneous Component Analysis (ASCA).     

Improving the detection of significant factors using ANOVA-PCA by selective reduction of residual variability

Selective elimination of residual error can be used when applying Harrington's ANOVA-PCA in order to improve the capabilities of the method. ANOVA-PCA is sometimes unable to discriminate between levels of a factor when sources of high residual variability are present. In some cases this variability is not random, possesses some structure and is large enough to be responsible for the first principal components calculated by the PCA step in the ANOVA-PCA. This fact sometimes makes it impossible for the interesting variance to be in the first two PCA components. By using the proposed selective residuals elimination procedure, one may improve the ability of the method to detect significant factors as well as have an understanding of the different kinds of residual variance present in the data.Two datasets are used to show how the method is used in order to iteratively detect variance associated with the factors even when it is not initially visible. A permutation method is used to confirm that the observed significance of the factors was not accidental.     

WE-ASCA: The Weighted-Effect ASCA for Analyzing Unbalanced Multifactorial Designs—A Raman Spectra-Based Example

Analyses of multifactorial experimental designs are used as an explorative technique describing hypothesized multifactorial effects based on their variation. The procedure of analyzing multifactorial designs is well established for univariate data, and it is known as analysis of variance (ANOVA) tests, whereas only a few methods have been developed for multivariate data. In this work, we present the weighted-effect ASCA, named WE-ASCA, as an enhanced version of ANOVA-simultaneous component analysis (ASCA) to deal with multivariate data in unbalanced multifactorial designs. The core of our work is to use general linear models (GLMs) in decomposing the response matrix into a design matrix and a parameter matrix, while the main improvement in WE-ASCA is to implement the weighted-effect (WE) coding in the design matrix. This WE-coding introduces a unique solution to solve GLMs and satisfies a constrain in which the sum of all level effects of a categorical variable equal to zero. To assess the WE-ASCA performance, two applications were demonstrated using a biomedical Raman spectral data set consisting of mice colorectal tissue. The results revealed that WE-ASCA is ideally suitable for analyzing unbalanced designs. Furthermore, if WE-ASCA is applied as a preprocessing tool, the classification performance and its reproducibility can significantly improve.     

ANOVA–principal component analysis and ANOVA–simultaneous component analysis: a comparison

ANOVA–simultaneous component analysis (ASCA) is a recently developed tool to analyze multivariate data. In this paper, we enhance the explorative capability of ASCA by introducing a projection of the observations on the principal component subspace to visualize the variation among the measurements. We compare the significance of experimental effects for ASCA and ANOVA–principal component analysis (PCA), a similar tool to explore multivariate data, by using permutation tests. Furthermore, we quantify the quality of the loadings estimate obtained with ASCA and compare this with the loadings estimate obtained with ANOVA–PCA. Copyright © 2011 John Wiley & Sons, Ltd.     

Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies

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, classical 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 these multivariate ANOVA-based methods (ANOVA simultaneous component analysis—ASCA, regularized MANOVA–rMANOVA, and Group-wise ANOVA-simultaneous component analysis—GASCA) in the framework of metabolomics studies. Our main goals are to compare these various ANOVA-based approaches and evaluate their performance on experimentally designed metabolomic studies to find the significant factors and identify the most relevant variables (potential markers) from the obtained results. Two experimental data sets were generated employing liquid chromatography coupled to mass spectrometry (LC-MS) with different complexity in the design to evaluate the performance of the statistical approaches. Results show that the three considered ANOVA-based methods have a similar performance in detecting statistically significant factors. However, relevant variables pointed by GASCA seem to be more reliable as there is a strong similarity with those variables detected by the widely used partial least squares discriminant analysis (PLS-DA) method.     

PARAFASCA: ASCA combined with PARAFAC for the analysis of metabolic fingerprinting data

Novel post‐genomics experiments such as metabolomics provide datasets that are highly multivariate and often reflect an underlying experimental design, developed with a specific experimental question in mind. ANOVA‐simultaneous component analysis (ASCA) can be used for the analysis of multivariate data obtained from an experimental design instead of the widely used principal component analysis (PCA). This increases the interpretability of the model in terms of the experimental question. Aside from the levels of individual factors, variation that can be described by the experimental design may also depend on levels of multiple (crossed) factors simultaneously, e.g. the interactions. ASCA describes each contribution with a PCA model, but a contribution depending on crossed factors may be described more parsimoniously by multiway models like parallel factor analysis (PARAFAC). The combination of PARAFAC and ASCA, named PARAFASCA, provides a view on the data that is both parsimonious and focused on the experimental question. The novel method is used to analyze a dataset in which the effect of two doses of hydrazine on the urinary chemical composition of rats is investigated by time‐resolved metabolic fingerprinting with nuclear magnetic resonance (NMR) spectroscopy. This experiment has been conducted to monitor the dose‐specific urine composition changes in time upon hydrazine administration. Comparison of the PCA, the ASCA and the PARAFASCA models shows that ASCA and PARAFASCA describe the data more dedicated to the experimental question than PCA, but that PARAFASCA is more parsimonious than ASCA, and separates the variation underlying different effects better. Copyright © 2008 John Wiley & Sons, Ltd.     

Interpretation of analysis of variance models using principal component analysis to assess the effect of a maternal anticancer treatment on the mineralization of rat bones

The goal of the present study is to assess the effects of anticancer treatment with cyclophosphamide and cytarabine during pregnancy on the mineralization of mandible bones in 7-, 14- and 28-day-old rats. Each bone sample was described by its X-ray fluorescence spectrum characterizing the mineral composition. The data collected are multivariate in nature and their structure is difficult to visualize and interpret directly. Therefore, methods like analysis of variance–principal component analysis (ANOVA–PCA) and ANOVA–simultaneous component analysis (ASCA), which are suitable for the analysis of highly correlated spectral data and are able to incorporate information about the underlined experimental design, are greatly valued. In this study, the ASCA methodology adapted for unbalanced data was used to investigate the impact of the anticancer drug treatment during pregnancy on the mineralization of the mandible bones of newborn rats and to examine any changes in the mineralization of the bones over time.The results showed that treatment with cyclophosphamide and cytarabine during pregnancy induces a decrease in the K and Zn levels in the mandible bones of newborns. This suppresses the development of mandible bones in rats in the early stages (up to 14 days) of formation. An interesting observation was that the levels of essential minerals like K, Mg, Na and Ca vary considerably in the different regions of the mandible bones.