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Ripening is a crucial step to guarantee the high commercial value of cheddar cheese, one of the dairy products the European Union exports the most. Although several methods have lately been proposed to assess its ageing process from a chemical point of view, the majority of them is not particularly time-efficient and implies destructive analytical tests, thus, exhibiting limitations for, e.g., industrial applications. Here, a fast approach based on combining Raman and Mid-InfraRed (MIR) spectroscopy with ANOVA-Simultaneous Component Analysis (ASCA) is proposed in a low-level data fusion framework. This approach allowed to evaluate how storage temperature and time (as well as their interaction) influence cheddar ripening in a relatively cheap, rapid and green fashion.

Data tables from experimental designs with multiple response variables and a large number of responses with respect to the number of observations are becoming more common, particularly in the omics and chemometrics fields. Traditional multivariate modelling approaches are not suitable for this type of data and more complex approaches like ANOVA Simultaneous Component Analysis (ASCA) and ANOVA Principal Component Analysis (APCA) have been developed to analyse them. They combine a matrix decomposition based on an ANOVA model with a multivariate step, Principal Component Analysis (PCA), which is applied to each decomposed matrix. This article compares ASCA and APCA to three other and less known techniques combining ANOVA and a multivariate step: PArallel Factor - Simultaneous Component Analysis (PARAFASCA), ANOVA Common Dimensions (AComDim), and ANOVA Multi-block Orthogonal Partial Least Squares (AMOPLS). The main advantages of these three methods are their ability to explore in a single and global procedure all (or a part) of the model effects, extract automatically the most important ones, interpret them visually and quantify their respective importance and significance. Theses approaches are presented in a common framework to make their comparison easier, some enhancements are introduced and they are also extended to unbalanced experimental designs thanks to a GLM version of the matrix decomposition. The three methods are applied to a 1H NMR data set with a three-factor unbalanced experimental design. Quantitative and visual results are compared for the three techniques and show that they are all suitable for the analysis of a complex model, but differ in the type of outputs and the ease of interpretation of the results. This opens perspectives for the analysis of even more complex data, including models with random or quantitative effects.

This study explores the effect of different settings on beer fermentation process applying an interval-based version of ASCA on FT-IR data. Three main factors (yeast type, temperature, fermentation time) are included in the experimental design, being high sources of variation in brewing and strictly interdependent; thus, difficult to be studied through a univariate approach. The three-factor full factorial design leads to a spectral multi-set data, with a total of 12 independent fermentations, which is explored combining ASCA and an interval adaptation of ASCA (interval-ASCA or i-ASCA). The ASCA models calculated on two separate regions (2900–2250 cm −1 and 1500–980 cm −1 ) shows differences for average time levels and the interaction between yeast types and time linked to carbon dioxide, maltose consumption and ethanol production, respectively. To better investigate the punctual influence of the studied factors on the so-called IR fingerprint region, permutation testing of ASCA in variable intervals is investigated. The analysis highlights the significant effect not only of the fermentation in all intervals considered, but also the role of other factors, such as time × yeast, yeast and temperature, in smaller variable regions. The proposed approach demonstrates how interval-ASCA on FT-IR data, isolating the variation in the data according to the experimental design used, allows a rapid and accurate test for parameter control in beer manufacturing.

Introduction Integrative analysis of multiple data sets can provide complementary information about the studied biological system. However, data fusion of multiple biological data sets can be complicated as data sets might contain different sources of variation due to underlying experimental factors. Therefore, taking the experimental design of data sets into account could be of importance in data fusion concept. Objectives In the present work, we aim to incorporate the experimental design information in the integrative analysis of multiple designed data sets. Methods Here we describe penalized exponential ANOVA simultaneous component analysis (PE-ASCA), a new method for integrative analysis of data sets from multiple compartments or analytical platforms with the same underlying experimental design. Results Using two simulated cases, the result of simultaneous component analysis (SCA), penalized exponential simultaneous component analysis (P-ESCA) and ANOVA-simultaneous component analysis (ASCA) are compared with the proposed method. Furthermore, real metabolomics data obtained from NMR analysis of two different brains tissues (hypothalamus and midbrain) from the same piglets with an underlying experimental design is investigated by PE-ASCA. Conclusions This method provides an improved understanding of the common and distinct variation in response to different experimental factors.

Near-infrared spectroscopy (NIRS) can be a faster and more economical alternative to traditional methods for screening varietal mixtures of nursery plants during the propagation process to ensure varietal purity and to avoid errors in the dispatch batches. The global objective of this work was to develop and optimize a NIR spectral collection method for construction of robust multivariate discrimination models. Three different varieties of Prunus dulcis (Avijor, Guara, and Pentacebas) of agricultural interest were used for this study. Sources of variation were investigated, including the position of the leaves on the trees, differences among trees of the same variety, and differences at the varietal level. Three types of processed samples were investigated. Fresh leaves, dried leaves, and dried leaves in powder form were included in each analysis. A study of spectral pre-treatment methods was also performed, and multivariate methods were applied to analyze the influence of different factors on classification. These included principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and ANOVA simultaneous component analysis (ASCA). The results indicated that variety was the most important factor for classification. The spectral pre-treatment that provided the best results was a combination of standard normal variate (SNV), Savitzky-Golay first derivative, and mean-centering methods. With regard to the type of processed sample, the highest percentages of correct classifications were obtained with fresh and dried powdered leaves at both the training set and test set validation levels. This study represents the first step towards the consolidation of NIRS as a method to identify Prunus dulcis varieties.

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 &lt, 0.05) in explaining differences between the basil aroma profiles, with more relevant effects of variety and year.

NIR spectroscopy combined with chemometric analysis has proven to be a rapid and cost-effective screening tool for the detection of syrup-adulterated honey. Processing and storage conditions which alter the chemical and physical state of honey may affect the spectra. The effects of age, storage temperature, syrup adulteration (10 and 20% w/w) and irradiation treatment on the NIR spectra of honey were investigated as a function of time with ANOVA-simultaneous component analysis (ASCA), an experimental design-focused exploratory data analysis method. The factors 'time', 'temperature' and 'adulteration' were found to have significant effects (p 0.05), but no significant effect was observed for irradiation treatment. A significant interaction effect was found between factors 'time' and 'adulteration', with the greatest disparity between authentic and adulterated class signals found immediately after adulteration and decreasing within three months thereafter.

The application of 1H NMR spectroscopy and chemometrics for the analysis of extracts of Lantana camara is described. This approach allowed to predict the leishmanicidal activity of samples obtained at different harvest times from their 1H NMR spectra. The anti-leishmanial activity of dichloromethane extracts obtained from the aerial parts of L. camara was measured using an in vitro assay. As the extracts displayed differences in their activity according to a one-way ANOVA analysis, their 1H NMR spectra were subjected to multivariate analysis using exploratory (Principal Component Analysis (PCA) and Anova Simultaneous Component Analysis (ASCA)) and regression, (Partial Least Squares Regression to Latent Structures (PLS)) chemometrics methods. These analyses allowed to establish and characterize a predictive model capable of determining the anti-leishmanial activity of Lantana camara dichloromethane extracts from their 1H NMR spectra. Figures of merit of the developed method are given as well. The identified chemical signals responsible for the iPLS calibration model corresponded to the presence of eicosane, caryophyllene oxide, β-ionone, tiglic acid, lantanilic acid, camaric acid, and lantadene B; the chemical markers. This study proposed a fast and simple method that avoids the need of using complex biological assays to predict the leishmanicidal activity of L. camara dichloromethane extracts.

The presence of pharmaceuticals and personal care products (PPCPs) in natural water resources due to incomplete removal in Wastewater Treatment Plants (WWTPs) is a serious environmental concern at present. In this work, the effects of three pharmaceuticals (propranolol, triclosan, and nimesulide) on Gammarus pulex metabolic profiles at different doses and times of exposure have been investigated by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The complex data sets generated in the different exposure experiments were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest (ROI) data and on their analysis by the Multivariate Curve-Resolution Alternating Least Squares (MCR-ALS) chemometrics method. This approach, allowed the resolution and identification of the metabolites present in the analyzed samples, as well as the estimation of their concentration changes due to the exposure experiments. ANOVA Simultaneous Component Analysis (ASCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were then conducted to assess the changes in the concentration of the metabolites for the three pharmaceuticals at the different conditions of exposure. The three tested pharmaceuticals changed the concentrations of metabolites, which were related to different KEGG functional classes. These changes summarize the biochemical response of Gammarus pulex to the exposure by the three investigated pharmaceuticals. Possible pathway alterations related to protein synthesis and oxidative stress were observed in the concentration of identified metabolites., The research leading to these results has received funding from Spanish Ministerio de Ciencia e Innovación, Spain (PID2019-105732GB-C21) and Generalitat de Catalunya, Spain (project 2017-SGR-753). This work was partly supported by funding from the Biotechnology and Biological Sciences Research Council (project reference: BB/P005187/1). Funding bodies played no role in the design of the study or decision to publish. Dr. Thomas H. Miller is acknowledged for help in the sampling of Gammarus pulex from the River Cray, UK.

The dairy industry is continuously developing new strategies to obtain healthier dairy products preserving expected properties. However, when modifying a food process, the reassessment of each parameters and their interaction should be considered as highly influencing the final quality. Among others, rennet process features are fundamental for both sensory properties and typical characteristics of a cheese. In this contest, the research addresses the development of a FT-NIR spectroscopic method, coupled with chemometrics, for the study of the effect of process variables on milk renneting. The effects of temperature (30 °C, 35 °C, 40 °C), milk fat concentration (0.1, 2.55, 5 g/100 mL), and pH (6.3, 6.5, 6.7) were investigated by means of a Box-Behnken experimental design. FT-NIR data collected along the 17 trials were explored by interval-PCA (i-PCA) and ANOVA-simultaneous component analysis (ASCA). i-PCA revealed differences in the occurrence and trends of coagulation phases, related to the three considered factors. ASCA allowed the characterization of renneting evolution and the assessment of the factor role, demonstrating that main and interaction effects are significant for the process progress. The proposed approach demonstrated that i-PCA and ASCA on FT-NIR data, highlighting the effects of the operating factors, allow a rapid and accurate analysis of process modifications in cheese manufacturing.

The effect of mango solar drying methods (traditional and tunnel dryers) on the quality of dried mango slices was investigated by visible (VIS) and near-infrared (NIR) spectroscopy combined with the chemometric analysis methods of principal component analysis (PCA) and ANOVA-simultaneous component analysis (ASCA). Five batches of mango were investigated, each subjected to two/three different positions of three different types of dryers: electrical (reference), traditional, and tunnel dryers. The results of the visible and near-infrared spectra showed that the quality of the mango samples obtained from the electrical dryer (reference method) was the most uniform and conserved the highest content of carotenoids and chlorophyll pigments. The samples from the traditional dryer had the highest variation and content of residual water. ASCA analysis of visible and shortwave near-infrared (VIS-NIR) (including color information) showed that the batch effect was most dominant with a significance effect of 47.5%, whereas longwave near-infrared (NIR) spectra (chemistry) showed that the dryer effect was the most dominant with a significance effect of 38.3%. The factor of the fruit positions in the dryers proved not to be significant. The VIS-NIR spectrum is thus well adapted to measure the fruit maturity and the NIR spectrum has great potential to investigate and control the dryer performance. The latter clearly demonstrated that the tunnel dryer provides much more gentle and consistent drying process compared to the traditional dryer. In contrast, the samples from the traditional dryer showed chemical changes/decomposition.

Lipidome profile of fluids and tissues is a growing field as the role of lipids as signaling molecules is increasingly understood, relying on an effective and representative extraction of the lipids present. A number of solvent systems suitable for lipid extraction are commonly in use, though no comprehensive investigation of their effectiveness across multiple lipid classes has been carried out. To address this, human LDL from normolipidemic volunteers was used to evaluate five different solvent extraction protocols [Folch, Bligh and Dyer, acidified Bligh and Dyer, methanol (MeOH)-tert-butyl methyl ether (TBME), and hexane-isopropanol] and the extracted lipids were analyzed by LC-MS in a high-resolution instrument equipped with polarity switching. Overall, more than 350 different lipid species from 19 lipid subclasses were identified. Solvent composition had a small effect on the extraction of predominant lipid classes (triacylglycerides, cholesterol esters, and phosphatidylcholines). In contrast, extraction of less abundant lipids (phosphatidylinositols, lyso-lipids, ceramides, and cholesterol sulfates) was greatly influenced by the solvent system used. Overall, the Folch method was most effective for the extraction of a broad range of lipid classes in LDL, although the hexane-isopropanol method was best for apolar lipids and the MeOH-TBME method was suitable for lactosyl ceramides.

Metabolomics analysis of oil palm leaves is a promising strategy to prospect new added-value compounds of this underutilized oil industry by-product. Although previous studies had reported some metabolites identified in this matrix, they had been focused on few compounds using conventional analytical techniques. This study aimed to develop a new high throughput method based on metabolomics able to detect a wide range of metabolites classes in Elaeis guineensis leaves. Furthermore, we investigate the effects caused by harvesting/sample preparation steps for the metabolites identification. Metabolites analyses were performed by ultra-high liquid chromatography—mass spectrometry (UHPLC–MS) using both ionization modes, ESI(+)–MS and ESI(−)–MS. ANOVA simultaneous component analysis (ASCA) of the resulting complex multivariate dataset was applied to evaluate metabolic alterations. Identification of major metabolites was performed by high resolution mass spectrometry and MS/MS experiments. A high throughput method based on UHPLC–MS was successfully developed to E. guineensis leaves, detecting from polar to non-polar acid and basic metabolites. According to ASCA, oil palm leaves metabolic fingerprintings have shown influence of transportation/storage and extraction solvent used chosen. In fact, the most significant effect is due to the solvent composition. A total of thirteen metabolites were assigned based on HRMS and MS/MS experiments. However, only seven metabolites identified were previously reported, which represents a potential field to discover new metabolites. Sample preparation steps should be carefully performed in metabolomics studies, because metabolites will be extracted and identified based on transport and solvent of extraction conditions. In this study, we established a reliable analytical protocol, from sample preparation to data analyses, to prospect new metabolites in oil palm leaves. This protocol could be further applied to similar oil-bearing crops.

Residential wood combustion emissions are one of the major global sources of particulate and gaseous organic pollutants. However, the detailed chemical compositions of these emissions are poorly characterized due to their highly complex molecular compositions, nonideal combustion conditions, and sample preparation steps. In this study, the particulate organic emissions from a masonry heater using three types of wood logs, namely, beech, birch, and spruce, were chemically characterized using thermal desorption in situ derivatization coupled to a GCxGC-ToF/MS system. Untargeted data analyses were performed using the comprehensive measurements. Univariate and multivariate chemometric tools, such as analysis of variance (ANOVA), principal component analysis (PCA), and ANOVA simultaneous component analysis (ASCA), were used to reduce the data to highly significant and wood type-specific features. This study reveals substances not previously considered in the literature as meaningful markers for differentiation among wood types.

The analysis of pollen chemical composition is important to many fields, including agriculture, plant physiology, ecology, allergology, and climate studies. Here, the potential of a combination of different spectroscopic and spectrometric methods regarding the characterization of small biochemical differences between pollen samples was evaluated using multivariate statistical approaches. Pollen samples, collected from three populations of the grass Poa alpina, were analyzed using Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, surface enhanced Raman scattering (SERS), and matrix assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS). The variation in the sample set can be described in a hierarchical framework comprising three populations of the same grass species and four different growth conditions of the parent plants for each of the populations. Therefore, the data set can work here as a model system to evaluate the classification and characterization ability of the different spectroscopic and spectrometric methods. ANOVA Simultaneous Component Analysis (ASCA) was applied to achieve a separation of different sources of variance in the complex sample set. Since the chosen methods and sample preparations probe different parts and/or molecular constituents of the pollen grains, complementary information about the chemical composition of the pollen can be obtained. By using consensus principal component analysis (CPCA), data from the different methods are linked together. This enables an investigation of the underlying global information, since complementary chemical data are combined. The molecular information from four spectroscopies was combined with phenotypical information gathered from the parent plants, thereby helping to potentially link pollen chemistry to other biotic and abiotic parameters.