ANOVA-simultaneous Component Analysis Research Articles

Influence of regional and yearly weather patterns on multi-mycotoxin occurrence in Austrian wheat: a liquid chromatographic-tandem mass spectrometric and multivariate statistics approach.

Mycotoxin surveys play an essential role in our food safety system. The obtained occurrence data form the basis for the assessment of the exposure of humans and animals to these toxic fungal secondary metabolites. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become the gold standard for mycotoxin determination because it enables selective and sensitive multi-toxin analysis. Simultaneous determination of several hundreds of secondary fungal metabolites is feasible using this technique. In this study, we combined a targeted dilute-and-shoot LC-MS/MS-based multi-analyte approach with multivariate statistics for the analysis of Austrian wheat from two different years and different geographical origins. We quantified 47 secondary fungal metabolites, including regulated emerging and masked mycotoxins. The resulting multi-mycotoxin occurrence data were further analyzed using both multivariate and univariate statistics. Principal component analysis (PCA) and analysis of variance (ANOVA) simultaneous component analysis (ASCA) were employed to identify regional and yearly trends within the dataset and to quantify the variance in metabolite occurrence attributed to the different effects. In addition, secondary fungal metabolites significantly impacted by these factors were selected via ANOVA. Of the 47 secondary metabolites identified, 39 were affected by the year, region or a combined effect. Moreover, our findings show that 43 of the secondary fungal metabolites were significantly influenced by the weather conditions. The results presented in this study underline the added value of combining targeted LC-MS/MS with multivariate statistics for monitoring a broad spectrum of secondary fungal metabolites in food crops. Through multivariate statistics, trends associated with the year or region can be readily studied. The approach presented could pave the way for a better understanding of the impact of climate change on plant pathogenic fungi and its implications for food safety. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Flow-modulation comprehensive two-dimensional enantio-gas chromatography: A valid and flexible alternative to heart-cutting multidimensional enantio-gas chromatography

The present research is focused on the proposal of use of flow-modulation comprehensive two-dimensional enantio-gas chromatography (FM eGC × GC) as a valid, flexible, and possibly superior alternative to heart-cutting multidimensional enantio-GC (eMDGC). The latter, a technique of demonstrated utility, is used specifically for the targeted separation of chiral compounds, whereas FM eGC × GC can produce both targeted and high-resolution untargeted information in a single run. It is clearly possible to use eMDGC for untargeted analysis, often with a flame ionization detector (stand-by analysis), to monitor a first-dimension (1D) separation, of much lower peak capacity compared to FM eGC × GC. If eMDGC is used with mass spectrometry (MS), it is normally exploited to monitor the second-dimension (2D) separation.The analytical instrument consisted of automated solid-phase microextraction (SPME), and a low duty-cycle FM eGC × GC system (with time-of-flight MS), equipped with an enantioselective 1D column (2,3-di-O-methyl-6-t-butyl silyl β-cyclodextrin derivative) and a 2D polyethylene glycol one. Ten Marsala wines were subjected to analysis, for the determination of chiral lactones (many at the low ppb level, due to the high concentration capacity of SPME) and for general analyte profiling. In many instances, highly complex chromatograms were attained, with statistical analysis (ANOVA-simultaneous component analysis and partial least squares discriminant analysis) used for sample differentiation.

First comprehensive untargeted metabolomics study of suramin-treated Trypanosoma brucei: an integrated data analysis workflow from multifactor data modelling to functional analysis.

Human African trypanosomiasis, commonly known as sleeping sickness, is a vector-borne parasitic disease prevalent in sub-Saharan Africa and transmitted by the tsetse fly. Suramin, a medication with a long history of clinical use, has demonstrated varied modes of action against Trypanosoma brucei. This study employs a comprehensive workflow to investigate the metabolic effects of suramin on T. brucei, utilizing a multimodal metabolomics approach. The primary aim of this study is to comprehensively analyze the metabolic impact of suramin on T. brucei using a combined liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance spectroscopy (NMR) approach. Statistical analyses, encompassing multivariate analysis and pathway enrichment analysis, are applied to elucidate significant variations and metabolic changes resulting from suramin treatment. A detailed methodology involving the integration of high-resolution data from LC-MS and NMR techniques is presented. The study conducts a thorough analysis of metabolite profiles in both suramin-treated and control T. brucei brucei samples. Statistical techniques, including ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA), ANOVA 2 analysis, and bootstrap tests, are employed to discern the effects of suramin treatment on the metabolomics outcomes. Our investigation reveals substantial differences in metabolic profiles between the control and suramin-treated groups. ASCA and PCA analysis confirm distinct separation between these groups in both MS-negative and NMR analyses. Furthermore, ANOVA 2 analysis and bootstrap tests confirmed the significance of treatment, time, and interaction effects on the metabolomics outcomes. Functional analysis of the data from LC-MS highlighted the impact of treatment on amino-acid, and amino-sugar and nucleotide-sugar metabolism, while time effects were observed on carbon intermediary metabolism (notably glycolysis and di- and tricarboxylic acids of the succinate production pathway and tricarboxylic acid (TCA) cycle). Through the integration of LC-MS and NMR techniques coupled with advanced statistical analyses, this study identifies distinctive metabolic signatures and pathways associated with suramin treatment in T. brucei. These findings contribute to a deeper understanding of the pharmacological impact of suramin and have the potential to inform the development of more efficacious therapeutic strategies against African trypanosomiasis.

Uv–Vis molecular spectral resolution of multi-level mixture design-fingerprints of yerba mate leaves by chemometric methods

This study proposes the use of multivariate curve resolution with alternating least squares (MCR-ALS) to comprehensively analyze mixture design-fingerprints in yerba mate leaves, investigating the influence of multi-level factors such as sexual dimorphism (male and female plants) and cultivation systems (monoculture and agroforestry). Plant extracts were obtained according to a mixture design with ethanol, dichloromethane, and hexane and their binary and ternary combinations. Through simultaneous resolution of the diverse profiles obtained using these different solvents, seven components were successfully identified, collectively explaining 98.57% of the data variance. Each component was associated with distinct absorption bands indicative of their chemical composition with compounds such as chlorogenic acids, xanthophylls, catechin, caffeine, kaempferol, lycopene, carotene, and chlorophyll. The MCR-ALS resolved profiles revealed significant variations in component concentrations across experimental conditions, facilitating the determination of the effects of sexual dimorphism and cultivation systems on yerba mate metabolism. The ANOVA-simultaneous component analysis (ASCA) highlighted the chemical differences of yerba mate leaves due to the cultivation system. Binary and ternary mixture extractants resulted to be more effective than pure solvents in determining the significance of effects on the chemical composition of yerba mate. Partial least squares discriminant analysis (PLS-DA) demonstrated the most distinct metabolic profiles of yerba mate leaves under monoculture and agroforestry conditions, with specific components playing crucial roles in their discrimination. This study shows the robustness of the MCR-ALS method in resolving complex chemical data, offering valuable insights into the diverse chemical composition of yerba mate under different experimental conditions. The congruence of results across various analyses reinforced the reliability and validity of the methodology, emphasizing its potential use for exploring the composition and properties of natural products.

Release kinetic modeling of Satureja Khuzestanica Jamzad essential oil from fish gelatin/succinic anhydride starch nanocomposite films: The effects of temperature and nanocellulose concentration

Fish gelatin (FG) and octenyl succinic anhydride starch (OSAS) composite films loaded with 1, 2, 3 and 4 wt% bacterial nanocellulose (BNC) and Satureja Khuzestanica Jamzad essential oil (SKEO) were achieved successfully and their physicochemical and release properties were investigated. The results revealed that incorporation of BNC improved the tensile strength which was associated with FE-SEM, FTIR and XRD. Moreover, this study focused on the release modeling of SKEO in 4, 25 and 37 °C from nanocomposite films using different release kinetic and Arrhenius models. Also, analysis of variance-simultaneous component analysis (ASCA) and exploratory data visualization by principal component analysis (PCA) were carried out to investigate the effects of two controlled factors. Consequently, the Peleg model showed the best fitting of experimental data. The activation energies decreased by increasing the BNC concentration. This research demonstrated the nanocomposite film containing SKEO would be a suitable candidate for active food packaging.

On the complementary nature of ANOVA simultaneous component analysis (ASCA+) and Tucker3 tensor decompositions on designed multi‐way datasets

AbstractThe complementary nature of analysis of variance (ANOVA) Simultaneous Component Analysis (ASCA+) and Tucker3 tensor decompositions is demonstrated on designed datasets. We show how ASCA+ can be used to (a) identify statistically sufficient Tucker3 models; (b) identify statistically important triads making their interpretation easier; and (c) eliminate non‐significant triads making visualization and interpretation simpler. For multivariate datasets with an experimental design of at least two factors, the data matrix can be folded into a multi‐way tensor. ASCA+ can be used on the unfolded matrix, and Tucker3 modeling can be used on the folded matrix (tensor). Two novel strategies are reported to determine the statistical significance of Tucker3 models using a previously published dataset. A statistically sufficient model was created by adding factors to the Tucker3 model in a stepwise manner until no ASCA+ detectable structure was observed in the residuals. Bootstrap analysis of the Tucker3 model residuals was used to determine confidence intervals for the loadings and the individual elements of the core matrix and showed that 21 out of 63 core values of the 3 × 7 × 3 model were not significant at the 95% confidence level. Exploiting the mutual orthogonality of the 63 triads of the Tucker3 model, these 21 factors (triads) were removed from the model. An ASCA+ backward elimination strategy is reported to further simplify the Tucker3 3 × 7 × 3 model to 36 core values and associated triads. ASCA+ was also used to identify individual factors (triads) with selective responses on experimental factors A, B, or interactions, A × B, for improved model visualization and interpretation.

Untargeted cannabinomics reveals the chemical differentiation of industrial hemp based on the cultivar and the geographical field location

Cannabis sativa has long been harvested for industrial applications related to its fibers. Industrial hemp cultivars, a botanical class of Cannabis sativa with a low expression of intoxicating Δ9-tetrahydrocannabinol (Δ9-THC) have been selected for these purposes and scarcely investigated in terms of their content in bioactive compounds. Following the global relaxation in the market of industrial hemp-derived products, research in industrial hemp for pharmaceutical and nutraceutical purposes has surged. In this context, metabolomics-based approaches have proven to fulfill the aim of obtaining comprehensive information on the phytocompound profile of cannabis samples, going beyond the targeted evaluation of the major phytocannabinoids. In the present paper, an HRMS-based metabolomics study was addressed to seven distinct industrial hemp cultivars grown in four experimental fields in Northern, Southern, and Insular Italy. Since the role of minor phytocannabinoids as well as other phytocompounds was found to be critical in discriminating cannabis chemovars and in determining its biological activities, a comprehensive characterization of phytocannabinoids, flavonoids, and phenolic acids was carried out by LC-HRMS and a dedicated data processing workflow following the guidelines of the metabolomics Quality Assurance and Quality Control Consortium. A total of 54 phytocannabinoids, 134 flavonoids, and 77 phenolic acids were annotated, and their role in distinguishing hemp samples based on the geographical field location and cultivar was evaluated by ANOVA-simultaneous component analysis. Finally, a low-level fused model demonstrated the key role of untargeted cannabinomics extended to lesser-studied phytocompound classes for the discrimination of hemp samples.

Investigation of Lactation Period and Technological Treatments on Mineral Composition and IR-Profiles of Donkey Milk by Chemometrics

Donkey milk represents an efficient substitute for human milk in infants’ diets being unlikely to cause allergic reactions. In this study, different donkey milks were collected at two lactation times (T0 and T1), subjected to freezing–thawing and freeze-drying, and analyzed by Inductively Coupled Plasma–Optical Emission Spectroscopy (ICP-OES) and ATR-FT-IR. The data collected on freeze–thaw (FT-) and reconstituted (R-)milks were investigated by ANOVA–Simultaneous Component Analysis (ASCA) and Principal Component Analysis (PCA). The following concentrations (µg/mL) for FT and R-milks, respectively, at T0, were found: Ca: 712 ± 71, 600 ± 72; Fe: 0.7 ± 0.3, 0.1 ± 0.1; K: 595 ± 49, 551 ± 59; Mg: 75 ± 5, 67 ± 4; Na: 117 ± 16, 114 ± 16; P: 403 ± 30, 404 ± 38; Zn: 1.6 ± 0.2, 1.6 ± 0.3. At T1, the concentrations (µg/mL for FT and R-milks, respectively) were: Ca: 692 ± 60, 583 ± 43; Fe: 0.13 ± 0.02, 0.13 ± 0.03; K: 641 ± 71, 574 ± 61; Mg: 72 ± 4, 63 ± 1; Na: 116 ± 9, 109 ± 8; P: 412 ± 30, 405 ± 24; Zn: 1.6 ± 0.3, 1.6 ± 0.3. ASCA demonstrated the treatment has a substantial effect, and PCA revealed that the largest quantities of metals, specifically Fe, Mg, and Ca for T0 and K, P, and Na for T1, are present in the FT-milk samples. The IR spectra of FT- and R-milks revealed no macroscopic changes among them or between lactation periods, indicating this technique may not suitably capture variability in lactation or conservation processes in donkey milk. Despite the relatively small sample size, this study offers insight on the mineral composition changes in donkey milk and emphasizes the significance of milk preprocessing and the lactation period on it.

Effect of wheat roasting conditions and wheat type on short-wave infrared (SWIR) spectral data of whole and milled wheat by ANOVA-simultaneous component analysis

ANOVA-simultaneous component analysis (ASCA) was used to investigate the effect of roasting and wheat type on shortwave-infrared (SWIR) spectra of whole wheat and flour through assessment of statistical significance and characterisation of the contributing spectral features. The full factorial experimental design included two wheat types, three roasting temperatures and three roasting frequencies. SWIR spectral images were collected from the two roasted wheat types and their two milled samples. Three ASCA models, one for each wheat conformation (kernel, whole wheat flour, white flour) were investigated. It was evidenced that all factors and interaction in the whole wheat flour model had a significant (p ≤ 0.05) effect on spectral data. Only the factor roasting frequency was not significant in white flour model and only the interaction between roasting frequency and wheat type was not significant for the kernel model. The main variations in the loading line plots were identified and characterised by chemical structural differences that occur within the sample. The effect of roasting frequency had a more adverse effect on protein stability, moisture evaporation, water soluble carbohydrates and aromatic amino acids, compared to roasting temperature. A Rapid Visco-Analyser (RVA) was used to further investigate difference in wheat type as almost all spectral data sets differed significantly. The most prominent difference between the two wheat types was observed as differences in amylase activity and presence of lipids. ASCA applied to SWIR whole wheat and flour spectral data effectively characterised the significant effect of roasting on wheat starch and protein structures.

Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA.

The intricate dependency structure of biological "omics" data, particularly those originating from longitudinal intervention studies with frequently sampled repeated measurements renders the analysis of such data challenging. The high-dimensionality, inter-relatedness of multiple outcomes, and heterogeneity in the studied systems all add to the difficulty in deriving meaningful information. In addition, the subtle differences in dynamics often deemed meaningful in nutritional intervention studies can be particularly challenging to quantify. In this work we demonstrate the use of quantitative longitudinal models within the repeated-measures ANOVA simultaneous component analysis+ (RM-ASCA+) framework to capture the dynamics in frequently sampled longitudinal data with multivariate outcomes. We illustrate the use of linear mixed models with polynomial and spline basis expansion of the time variable within RM-ASCA+ in order to quantify non-linear dynamics in a simulation study as well as in a metabolomics data set. We show that the proposed approach presents a convenient and interpretable way to systematically quantify and summarize multivariate outcomes in longitudinal studies while accounting for proper within subject dependency structures.

Redundancy analysis includes analysis of variance-simultaneous component analysis (ASCA) and outperforms its extensions

Chemometrics and statistical ecology share interest in the analysis of multivariate data. In ecology, unconstrained and constrained ordination are popular methods to analyze and visualize multivariate data, with principal component analysis (PCA) and redundancy analysis (RDA) as prototype methods. Constraints give more insight and power by focusing on the response of the variables to particular external predictors or experimental factors, after optional adjustment for covariates. In chemometrics, analysis of variance - simultaneous component analysis (ASCA) was proposed decades later, with particular emphasis on the multivariate main and interaction effects in factorial experiments. This paper shows the similarities and differences between ASCA, its extensions, and (partial) RDA, alias reduced-rank regression. ASCA and RDA (understood as a sequence of partial RDAs, just as ASCA uses a sequence of PCAs) are shown to be mathematically identical for equireplicated designed experiments. Differences appear with unequal replication. As a corollary we show that, with equal replication, a particularly attractive form of ASCA, which displays a main effect together with an interaction, is a special case of principal response curve analysis. RDA is a least-squares method and uses the optimal weights in the dimension reduction of the treatment effects, whereas ASCA extensions for unbalanced data use alternative, sub-optimal weights.

Limpca: An R package for the linear modeling of high‐dimensional designed data based on ASCA/APCA family of methods

SummaryMany modern analytical methods are used to analyze samples issued from an experimental design, for example, in medical, biological, chemical, or agronomic fields. Those methods generate most of the time, highly multivariate data like spectra or images, where the number of variables (descriptor responses) tends to be much larger than the number of experimental units. Therefore, multivariate statistical tools are necessary to identify variables that are consistently affected by experimental factors. In this context, two recent methods combining ANOVA and PCA, namely, ASCA (ANOVA‐Simultaneous Component Analysis) and APCA (ANOVA‐Principal Component Analysis), were developed. They provide powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design. Their main limitation is that they produce biased estimators of the factor effects when the design of experiment is unbalanced. This article presents the R package limpca (for linear models with principal component effects analysis) that implements ASCA+ and APCA+, an enhanced version of ASCA and APCA methods based on several principles from the theory of general linear models (GLM). In this paper, the methodology is reviewed, the package structure and functions are presented, and a metabolomics data set is used to clearly demonstrate the potential of ASCA+ and APCA+ methods to highlight true biomarkers corresponding to effects of interest in unbalanced designs.

Direct NIR spectral determination of genetic improvement, light availability, and their interaction effects on chemically selected yerba-mate leaves

Monitoring plant chemical modifications provides useful information about plant genetic variability owing to environmental stresses. This allows determining if the pattern of chemical compounds of interest remains constant. Classically, chemical compounds are monitored through classical analytical procedures that are costly and environmentally harmful. Decaffeinated and high caffeine yerba-mate trees were selected based on their chemical profiles. Their behavior was evaluated at five different shading levels (0%, 40%, 51%, 76%, and 82%), imitating the environmental characteristics of yerba-mate in agroforestry systems. Near-infrared spectroscopy (NIR) fingerprints were obtained for yerba-mate leaves, and ANOVA-simultaneous component analysis (ASCA) models were developed for chemical monitoring. The preprocessing of NIR spectra was assessed with a three-level full factorial design. The best preprocessing set of conditions found was standard normal variate followed by second derivatives, computed according to the Savitzky–Golay method (11-points window and second-degree interpolating polynomial). The ASCA models satisfactorily showed significant differences in chemical behavior for genetic improvement and its interaction with light availibility effects. The genetic improvement effect had a variance of 44.30% over NIR fingerprints, indicating that the chemical differences are maintained at any light level. The interaction effect with 1.84% of the total variance illustrated that the behaviors of decaffeinated and high-caffeinated yerba mate were different when subjected to the same light level. The main absorptions indicated by the ASCA model were the O-H stretching and bending combination band at 1654 nm, and the –CH stretching aromatic 1st overtone at 1675 nm. The ASCA method offered a powerful tool for monitoring chemically selected plants combined with NIR spectroscopy. The method is cheap, fast, environmentally friendly, and can be used for field research.

Primary Treatment Effects for High-Grade Serous Ovarian Carcinoma Evaluated by Changes in Serum Metabolites and Lipoproteins

High-grade serous ovarian carcinoma (HGSOC) is the most common and deadliest ovarian cancer subtype. Despite advances in treatment, the overall prognosis remains poor. Regardless of efforts to develop biomarkers to predict surgical outcome and recurrence risk and resistance, reproducible indicators are scarce. Exploring the complex tumor heterogeneity, serum profiling of metabolites and lipoprotein subfractions that reflect both systemic and local biological processes were utilized. Furthermore, the overall impact on the patient from the tumor and the treatment was investigated. The aim was to characterize the systemic metabolic effects of primary treatment in patients with advanced HGSOC. In total 28 metabolites and 112 lipoproteins were analyzed by nuclear magnetic resonance (NMR) spectroscopy in longitudinal serum samples (n = 112) from patients with advanced HGSOC (n = 24) from the IMPACT trial with linear mixed effect models and repeated measures ANOVA simultaneous component analysis. The serum profiling revealed treatment-induced changes in both lipoprotein subfractions and circulating metabolites. The development of a more atherogenic lipid profile throughout the treatment, which was more evident in patients with short time to recurrence, indicates an enhanced systemic inflammation and increased risk of cardiovascular disease after treatment. The findings suggest that treatment-induced changes in the metabolome reflect mechanisms behind the diversity in disease-related outcomes.

Miniaturized NIR Spectrometers in a Nutshell: Shining Light over Sources of Variance

The increasing portability and accessibility of miniaturized NIR spectrometers are promoting the spread of in-field and online applications. Alongside the successful outcomes, there are also several problems related to the acquisition strategies for each instrument and to experimental factors that can influence the collected signals. An insightful investigation of such factors is necessary and could lead to advancements in experimental set-up and data modelling. This work aimed to identify variation sources when using miniaturized NIR sensors and to propose a methodology to investigate such sources based on a multivariate method (ANOVA—Simultaneous Component Analysis) that considers the effects and interactions between them. Five different spectrometers were chosen for their different spectroscopic range and technical characteristics, and samples of worldwide interest were chosen as the case study. Comparing various portable sensors is interesting since results could significantly vary in the same application, justifying the idea that this kind of spectrometer is not to be treated as a general class of instruments.

Effect of Drying Technique on the Volatile Content of Ecuadorian Bulk and Fine-Flavor Cocoa.

Cocoa (Theobroma cacao L.) is one of the most widely consumed edible seeds in the world affected by on-farm processing. This study investigated the effect of different drying techniques, namely oven drying (OD), sun drying (SD), and a modification of sun drying using black plastic sheeting (SBPD), on the volatile profile of fine-flavor and bulk cocoa varieties analyzed by HS-SPME-GC-MS. A total of sixty-four volatile compounds were identified in fresh and dried cocoa. As expected, the volatile profile was clearly modified after the drying step, showing strong differences among cocoa varieties, this factor and its interaction with the drying technique having greater influence according to the ANOVA simultaneous component analysis. A principal component analysis revealed a close relationship between the volatile content of bulk cocoa samples dried by the OD and SD techniques, whereas slight volatile modifications were perceived among fine-flavor samples dried using the three different techniques under study. Overall, the results provide the basis for the potential application of the simplest inexpensive SBPD technique in order to accelerate the drying process of sun drying and produce cocoa with similar (fine-flavor cocoa) or improved (bulk cocoa) aromatic quality to that formed using the traditional SD or the small-scale OD.

Assessment of Variability Sources in Grape Ripening Parameters by Using FTIR and Multivariate Modelling.

The variability in grape ripening is associated with the fact that each grape berry undergoes its own biochemical processes. Traditional viticulture manages this by averaging the physicochemical values of hundreds of grapes to make decisions. However, to obtain accurate results it is necessary to evaluate the different sources of variability, so exhaustive sampling is essential. In this article, the factors "grape maturity over time" and "position of the grape" (both in the grapevine and in the bunch/cluster) were considered and studied by analyzing the grapes with a portable ATR-FTIR instrument and evaluating the spectra obtained with ANOVA-simultaneous component analysis (ASCA). Ripeness over time was the main factor affecting the characteristics of the grapes. Position in the vine and in the bunch (in that order) were also significantly important, and their effect on the grapes evolves over time. In addition, it was also possible to predict basic oenological parameters (TSS and pH with errors of 0.3 °Brix and 0.7, respectively). Finally, a quality control chart was built based on the spectra obtained in the optimal state of ripening, which could be used to decide which grapes are suitable for harvest.

Spatially Offset Raman Spectroscopic (SORS) Analysis of Wine Alcoholic Fermentation: A Preliminary Study

Spatially offset Raman spectroscopy (SORS) is a non-invasive analytical technique that allows the analysis of samples through a container. This makes it an effective tool for studying food and beverage products, as it can measure the sample without being affected by the packaging or the container. In this study, a portable SORS equipment was used for the first time to analyse the alcoholic fermentation process of white wine. Different sample measurement arrangements were tested in order to determine the most effective method for monitoring the fermentation process and predicting key oenological parameters. The best results were obtained when the sample was directly measured through the glass container in which the fermentation was occurring. This allowed for the accurate monitoring of the process and the prediction of density and pH with a root mean square error of cross-validation (RMSECV) of 0.0029 g·L−1 and 0.04, respectively, and R2 values of 0.993 and 0.961 for density and pH, respectively. Additionally, the sources of variability depending on the measurement arrangements were studied using ANOVA-Simultaneous Component Analysis (ASCA).

Methodologies based on ASCA to elucidate the influence of a subprocess: Vinification as a case of study

AbstractIn food manufacturing and processing, food matrix complexity usually makes it difficult to detect unwanted subprocesses, which can impact the quality of the final product. In the case of wine alcoholic fermentation, the main process is the conversion of sugars into ethanol and carbon dioxide, but the presence of some unwanted microorganisms could lead to wine contamination by production of undesired minor compounds. In the study we present, an intentional contamination of the vinification process by the addition of acetic acid bacteria was studied using a portable Fourier transform infrared (FT‐IR) spectrometer. ANOVA simultaneous component analysis (ASCA) was used to unravel these minor variability sources. However, as the subprocess is two orders of magnitude lower in concentration than the main process, different methodologies were used to enhance the ASCA results, such as to select a specific spectral region related to acetic acid bacteria metabolism, to divide the process in time intervals related to the different phases, or to unfold the data matrix in different ways. In addition, spectral preprocessing was optimized to scale up small peaks related to the subprocess. Our results show that several methodologies to build ASCA models can be applied to emphasize and better characterize bacteria contamination subprocesses.

Multivariate data analysis strategy to monitor Trentingrana cheese real-scale production through volatile organic compounds profiling

Volatile organic compounds (VOCs) in cheese, as result of the chemical, physical and microbiological properties of the raw milk, are related to its sensory properties and consumer's acceptability. Measurement of VOCs can be related to the quality of the production process, highlighting changes in the raw materials or the process conditions. In the present study, we tested the suitability of ANOVA-Simultaneous Component Analysis (ASCA) to extract useful information from volatile organic compound data measured over two years of production of Trentingrana cheese in a real production context where several confounding factors are present.A total of 317 cheese wheels were collected from the 15 cooperative dairy factories every two months. The ASCA analysis indicates that the milk collection affects the VOC profiles. To deeper investigate this factor, an Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model was developed to estimate the associations between VOCs and process characteristics of the dairy factory. Results showed that the milk collection procedure affects the content of organic acids, esters, and ketones of the cheeses.