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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.

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The development of portable NIR instruments facilitates widespread use among non-specialists. However, untrained operators may follow non-optimal measurement procedures. This work investigates how different factors in the measurement procedure influence the spectra of pig feed samples produced by SCiO, a handheld NIR. Measurement conditions were studied by means of Design of Experiments and evaluated with analysis of variance - simultaneous component analysis (ANOVA-SCA or ASCA). We quantified and visualized how measurement distance, angle, background lighting, the use of plastic lids and different devices interactively affect the resulting spectra. The samples could be distinguished with 100% accuracy with Partial Least Squares-Discriminant Analysis (PLS-DA) a scanning distance of 0.5 cm. Replication of the experiment with special attention to reproducing the conditions still lead to some differences, which highlights both the challenges in controlling conditions and the importance of considering them. Based on the results, generalizable guidelines for acceptance of spectra were proposed for this case study. Of main importance are performing measurements at distances of 0.5 cm or at least in an environment without background lighting. Overall, the provided guidelines for measurement conditions and a methodology to investigate this for other devices are a key enabler to spreading handheld spectrometry to a non-expert audience.

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The estimation of the postmortem interval (PMI) from skeletal remains represents a challenging task in forensic science. PMI is often influenced by extrinsic factors (humidity, dryness, scavengers, etc.) and intrinsic factors (age, sex, pathology, way of life, medical treatments, etc.). Raman spectroscopy combined with multivariate data analysis represents a promising tool for forensic anthropologists. Despite all the advantages of the technique, Raman spectra of skeletal remains are influenced by these extrinsic and intrinsic factors, which impairs precision and reproducibility. Both parameters have to reach a high level of confidence when such spectroscopy is used as a way to predict PMI. As a consequence, advanced multivariate data analysis is necessary to quantify the effect of all factors to improve the estimation of the PMI. The objective of this work is to evaluate the effect of intrinsic and extrinsic factors on the Raman spectra of skeletal remains. We designed a protocol close to a real-world scenario. We used ANOVA-simultaneous component analysis (ASCA) to unmix and quantify the effect of 1 intrinsic (source body) and 1 extrinsic (burial time) factors on the Raman spectra. In our model, the burial time was found to generate the highest variability after the source body. ASCA showed that the variability due to the burial time has 2 mixed contributions. Seasonal variations are the first contribution. The second contribution is attributed to diagenesis. A decrease in the mineral bands and an increase in the organic bands are observed. The source body was also found to contribute to the variability in Raman spectra. ASCA showed that the source body induces variability related to the composition of bones. This quantification cannot be assessed by basic chemometrics methods such as PCA. The results of this study highlighted the need to use an advanced chemometric data analysis tool (like ASCA) combined with Raman spectroscopy to estimate the postmortem interval.

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Mid-infrared spectroscopy has been developed as a reliable and rapid tool for routine analysis of fat, protein, lactose and other components in liquid milk. However, variations within and between FTIR instruments, even within the same milk testing laboratory, present a challenge to the accuracy of measurement of particularly minor components in the milk, such as individual fatty acids or proteins. In this study we have used Analysis of variance-Simultaneous Component Analysis (ASCA), to monitor the spectral variation between and within each of four different FOSS FTIR spectrometers over each week in an independent milk testing laboratory over 4 years, between August 2017 and March 2021 (223 weeks). On everyday of each week, spectra of the same pilot milk sample were recorded approximately every hour on each of the four instruments. Overall, variations between instruments had the largest effect on spectral variation over each week, making a significant contribution every week. Within each instrument, day-to-day variations over the week were also significant for all but two of the weeks measured, however it contributed less to the variance overall. At certain times other factors not explained by weekday variation or inter-instrument variation dominated the variance in the spectra. Examination of the scores and loadings of the weekly ASCA analysis allowed identification of changes in the spectral regions affected by drifts in each instrument over time. This was found to particularly affect some of the fatty acid predictions.

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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.

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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.

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Quantitative analysis under various perturbations is a difficult problem because the analytical signal changes with different factors. In this work, three-level simultaneous component analysis (3-MSCA) was used for analyzing the near-infrared (NIR) spectra of aqueous solutions under different perturbations. The spectral data of aqueous proline solutions at different pH, concentration and temperature were measured, and a three-level model was built to describe the effects of the three perturbations on the spectra, respectively. The first level model describes the change of the spectra with pH, from which significant aggregation of proline was observed around the isoelectric point. The second and third level model show the spectral change with concentration and temperature, respectively, and the spectral feature has a very good linear relationship with the corresponding influencing factors. Therefore, the pH and concentration scores can be used as the calibration curve for quantitative analysis of the pH and the content of proline, and the temperature scores can be used to predict the temperature of the solutions. In addition, the structural change of water molecules under different conditions is obtained from the loadings. A decline of the bulk water was found with the increase of concentration, implying an ascending trend of the bonded water due to the interaction of proline and water. The dissociation of water clusters with the increase of temperature is also displayed.

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The aim of the present study was to establish a standard methodology for the extraction of epoxy resin precursors from several types of food packages (cans, multi-layered composite material, and cups) with selected simulation media (distilled water, 5% ethanol, 3% dimethyl sulfoxide, 5% acetic acid, artificial saliva) at different extraction times and temperatures (factors). Biological analyses were conducted to determine the acute toxicity levels of the extracts (with Vibrio fischeri bacteria) and their endocrine potential (with Saccharomyces cerevisiae yeasts). In parallel, liquid chromatography-tandem mass spectrometry was performed to determine levels of bisphenol A diglycidyl ether (BADGE), bisphenol F diglycidyl ether (mixture of isomers, BFDGE), ring novolac glycidyl ether (3-ring NOGE), and their derivatives. The variation induced by the different experimental factors was statistically evaluated with analysis of variance simultaneous component analysis (ASCA). Our findings demonstrate the value of using a holistic approach to best partition the effects contributing to the end points of these assessments, and offer further guidance for adopting such a methodology, thus being a broadly useful reference for understanding the phenomena related to the impacts of food packaging materials on quality for long- and short-term storage, while offering a general method for analysis.

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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.

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Chronic atrophic gastritis (CAG) is one of the most important pre-cancerous states with a high prevalence. Deciphering its mechanical network is of significant importance for its diagnosis and treatment. The time-series factor associated with CAG progression specially needs to be considered together with its biological condition. In the present work, 1H NMR-based dynamic metabonomics was firstly performed to analyze the urinary metabolic features of CAG coupled with ANOVA-simultaneous component analysis (ASCA). As results, 4 (alanine, lipids, creatine, and dimethylglycine), 2 (α-ketoglutarate and alanine) and 5 (succinate, α-ketoglutarate, alanine, hippurate, and allantoin) urine metabolites were finally selected as the candidate biomarkers related to phenotype, time, and their interaction, respectively. Mechanistically, the network pharmacology analysis further revealed these metabolites were involved into mitochondrial function, oxidation reduction, cofactor binding, generation of precursor metabolites and energy, nucleotide binging, coenzyme metabolic process, cofactor metabolic process, cellular respiration, and tricarboxylic acid cycle. Especially, mitochondria were the most important targeted organelle referred 30 targeted proteins. The present work provided a novel network pharmacology approach for elucidating the mechanisms underlying the pathogenesis of CAG based on urinary time dependent metabonomics.

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The effects of genotype, agro-climatic conditions (ACC), and cooking method as well as their interactions on the content of individual carotenoids and hydroxycinnamic acids in different potato tubers were evaluated. While zeaxanthin content was highly influenced by the ACC (up to 631-fold change), chlorogenic acid was similarly influenced by the cooking method (up to 3.1-fold increase after cooking), by the interactions ACC × cooking method (up to 2.1-fold increase) and genotype × cooking method (up to 1.7-fold increase). Stability/extractability of compounds after cooking was found to be genotype and ACC dependent, which suggest that genotype and ACC induces differential expression of genes for the biosynthesis pathways of carotenoids and hydroxycinnamic acids is different among, as well as components of the cellular matrix. These results are promising to apply in potato breeding programs with the perspective to develop new potato cultivars selected by their nutritional attributes.

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The use of hyperspectral imaging techniques in biological studies has increased in the recent years. Hyperspectral images (HSI) provide chemical information and preserve the morphology and original structure of heterogeneous biological samples, which can be potentially useful in environmental -omics studies when effects due to several factors, e.g., contaminant exposure, phenotype,…, at a specific tissue level need to be investigated. Yet, no available strategies exist to exploit adequately this kind of information. This work offers a novel chemometric strategy to pass from the raw image information to useful knowledge in terms of statistical assessment of the multifactor effects of interest in -omic studies. To do so, unmixing of the hyperspectral image measurement is carried out to provide tissue-specific information. Afterwards, several specific ANOVA-Simultaneous Component Analysis (ASCA) models are generated to properly assess and interpret the diverse effect of the factors of interest on the spectral fingerprints of the different tissues characterized. The unmixing step is performed by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) on multisets of biological images related to each studied condition and provides reliable HSI spectral signatures and related image maps for each specific tissue in the regions imaged. The variability associated with these signatures within a population is obtained through an MCR-based resampling step on representative pixel subsets of the images analyzed. All spectral fingerprints obtained for a particular tissue in the different conditions studied are used to obtain the related ASCA model that will help to assess the significance of the factors studied on the tissue and, if relevant, to describe the associated fingerprint modifications. The potential of the approach is assessed in a real case of study linked to the investigation of the effect of exposure time to chlorpyrifos-oxon (CPO) on ocular tissues of different phenotypes of zebrafish larvae from Raman HSI of eye cryosections. The study allowed the characterization of melanin, crystalline and internal eye tissue and the phenotype, exposure time and the interaction of the two factors were found to be significant in the changes found in all kind of tissues. Factor-related changes in the spectral fingerprint were described and interpreted per each kind of tissue characterized.

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Standardization of near infrared (NIR) spectra is indispensable in practical applications because the spectra measured on different instruments are commonly used and the difference between the instruments must be corrected. A two-level standardization method is proposed in this study based on multi-level simultaneous component analysis (MSCA) algorithm for correcting the spectral difference between instruments. A two-level MSCA model is used to model the difference between instruments (the first level) and samples (the second level). With the two models, the spectral difference due to instruments and measurement operation can be corrected, respectively. Three NIR spectral datasets of pharmaceutical tablet, corn and plant leaf are used to evaluate the efficiency of the proposed method. The results show that the score of the first level model describes the overall spectral difference between instruments, and the score of the second level model depictures the spectral difference of the same sample between the measurements. The latter difference may include the spectral variations caused by instrument, operation and the measurement conditions. Therefore, both the spectral difference due to the instrument and measurement can be corrected by adjusting the coefficients in the scores of the two level models, respectively. The proposed method provides a good way for standardizing the spectra measured on different instruments when the measurement is not reproducible.

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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.

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This article introduces a package developed for R (R Core Team, 2017) for performing an integrated analysis of multiple data blocks (i.e., linked data) coming from different sources. The methods in this package combine simultaneous component analysis (SCA) with structured selection of variables. The key feature of this package is that it allows to (1) identify joint variation that is shared across all the data sources and specific variation that is associated with one or a few of the data sources and (2) flexibly estimate component matrices with predefined structures. Linked data occur in many disciplines (e.g., biomedical research, bioinformatics, chemometrics, finance, genomics, psychology, and sociology) and especially in multidisciplinary research. Hence, we expect our package to be useful in various fields.

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Depression has been correlated with metabolic disorders, and the gut microbiota and its metabolites have been reported to be key factors affecting metabolic disorders. Several metabolites generated by the gut microbiota have been reported to exert antidepressant-like effects, including the short chain fatty acid (SCFA) butyrate. However, recent work has suggested that the abundance of butyrate is not significantly changed in neither human nor experimental animals with depression, and butyrate has been reported to decrease upon the administration of prebiotics with antidepressant-like effects. Supplementation of endogenous metabolites that are unchanged in depression may induce additional metabolic disorders and may lead to poorer clinical outcomes. However, the endogenous metabolites that are imbalanced in depression may include several antidepressant candidates that could circumvent these problems. In this study, we used GC-MS spectrometry to study the fecal metabolome of rats under Chronic Unpredictable Mild Stress (CUMS). We carried out static and dynamic metabolomics analyses to identify the differential metabolites between the CUMS rats and control rats. We identified propionic acid, rather than butyric acid, as a differential metabolite of the CUMS rats. Consistent with this, a 1-week intrarectal administration of sodium propionate (NaP, the salt form of propionic acid) induced antidepressant-like effects and partially rebalanced the plasma metabolome. The antidepressant-like effects of NaP were correlated with differential rescue of neurotransmitters in the prefrontal cortex, which may be achieved through the reduction of catabolism of noradrenaline, tryptophan and dopamine, rather than serotonin. These findings support NaP as a potential candidate in fighting depression by administering an endogenous metabolite.

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To compare static and dynamic metabolomics data analysis of CUMS (chronic unpredictable mild stress)-induced depression, GC-MS spectrometry was conducted on the plasma metabolome. S-Plot and ANOVA (analysis of variance)-simultaneous component analysis (ASCA) were respectively applied to static and dynamic analysis of metabolomics data. Static metabolomics data analysis revealed three typical plasma metabolites including propionic acid, D-allose, and 9,12,15-octadecatrienoic acid, while dynamic me-tabolomics data analysis found seven typical metabolites including propionic acid, D-allose, My-inositol, me-thylamine, etc. The abundances of typical metabolites observed by dynamic metabolomics data analysis were consistent with the variation trends of body weight and sugar water preference rate of CUMS rats. In conclusion, dynamic metabolomics analysis revealed more typical plasma metabolites, which have the potential to explain variations of body weight and behavior parameter of CUMS-induced depression rats. Combination of static and dynamic metabolomics data analysis may provide a strong support to the pathological study of complex diseases.

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An ultimate goal of investigations of rooibos plant material subjected to different stages of fermentation is to identify the chemical changes taking place in the phenolic composition, using an untargeted approach and chromatographic fingerprints. Realization of this goal requires, among others, identification of the main components of the plant material involved in chemical reactions during the fermentation process. Quantitative chromatographic data for the compounds for extracts of green, semi-fermented and fermented rooibos form the basis of preliminary study following a targeted approach. The aim is to estimate whether treatment has a significant effect based on all quantified compounds and to identify the compounds, which contribute significantly to it. Analysis of variance is performed using modern multivariate methods such as ANOVA-Simultaneous Component Analysis, ANOVA - Target Projection and regularized MANOVA. This study is the first one in which all three approaches are compared and evaluated. For the data studied, all tree methods reveal the same significance of the fermentation effect on the extract compositions, but they lead to its different interpretation.

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Near infrared (NIR) spectra are sensitive to the variation on water structure caused by perturbations, such as temperature and additives. In this work, water was applied as a probe to detect glucose in aqueous glucose solutions and human serum samples. Spectral changes of water were captured from the temperature dependent NIR spectra using multilevel simultaneous component analysis (MSCA). The first and second level model were established to describe the quantitative spectra-temperature relationship (QSTR) and the quantitative spectra-concentration relationship (QSCR), i.e., the calibration curve, respectively. The score of the first level model shows that the content of free OH in water molecules increases with temperature elevation. The correlation coefficients (R2) of the QSTR model between the score and temperature are higher than 0.99, and that of the calibration model (QSCR) between the spectral features of water clusters and the concentration of glucose are 0.99 and 0.84 for glucose solutions and serum samples, respectively. External validation of the calibration model was further performed with human serum samples. The standard error of the prediction is 0.45. In addition, the linearity of the QSCR models may reveal that glucose interacts with small water clusters and enhances the formation of the hydration shell. Therefore, using water as a probe may provide a new way for quantitative determination of the analytes in aqueous solutions by NIR spectroscopy.

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When comparing the component structures of a multitude of variables across different groups, the conclusion often is that the component structures are very similar in general and differ in a few variables only. Detecting such "outlying variables" is substantively interesting. Conversely, it can help to determine what is common across the groups. This article proposes and evaluates two formal detection heuristics to determine which variables are outlying, in a systematic and objective way. The heuristics are based on clusterwise simultaneous component analysis, which was recently presented as a useful tool for capturing the similarities and differences in component structures across groups. The heuristics are evaluated in a simulation study and illustrated using cross-cultural data on values.

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