Multivariate Data Analysis Research Articles

A semi-supervised learning algorithm for multi-label classification and multi-assignment clustering problems based on a Multivariate Data Analysis

Classically, in classification and clustering problems, an individual is exclusively assigned to a class (labeled data) or a cluster (unlabeled data). However, the assignment to a single class or cluster may be too strict in several real-world domains. In many cases, an individual may belong to multiple classes or clusters at the same time. On the other hand, we are starting to see machine learning algorithms that solve the problem of multi-label classification and multiple cluster assignment, but there are no algorithms that solve both problems simultaneously. Classic semi-supervised algorithms can work with labeled and unlabeled data simultaneously, but these types of algorithms assign individuals to a single class or a single cluster. Today, artificial intelligence faces the challenge of developing semi-supervised learning algorithms to work with semi-labeled data that can be assigned to different classes or clusters at the same time. In particular, this paper proposes a semi-supervised learning algorithm to fill this gap, which can solve the problems of multi-label classification and multiple cluster assignment simultaneously, on a semi-labeled dataset. This proposal is based on the LAMDA algorithm (Learning Algorithm for Multivariate Data Analysis), which calculates the degree of membership of a data to a group/class. In this work, a membership threshold is defined, which allows individuals to be assigned to classes or clusters that have a membership greater than this membership threshold. Thus, the main contribution of this work is the development of a semi-supervised algorithm that can process semi-labeled datasets to assign them to multiple classes and/or clusters. Furthermore, the work defines a metric to evaluate its efficiency in a semi-supervised context, called Multi Label-Cluster Index (MULCI). This proposal is tested on several datasets from the domains of multi-label classification or multi-assignment clustering, or a combination of both, showing very encouraging results. Very good quality metrics results are achieved in multiclass and multicluster tasks.

Neck and shoulder pain and inflammatory biomarkers in plasma among forklift truck operators - A case-control study.

The aim of this study was to investigate a panel of inflammatory biomarkers in plasma from forklift truck operators (FLTOs) and healthy controls, and their relation to neck pain characteristics. From employees in a warehouse, 26 FLTOs were recruited and 24 healthy age- and sex-matched controls (CONs) were recruited via advertisement. The inclusion criterion for FLTOs was that they should operate reach decker and/or counterbalanced tilting mast forklift trucks. All participants were asked to answer a questionnaire covering demographic data, pain intensity numeric rating scale (NRS), anatomical spread, psychological distress, and health aspects. Pain sensitivity was measured using a pressure algometer. Blood samples were collected and analyzed for inflammatory proteins in plasma using a panel of 71 cytokines and chemokines. Multivariate data analysis including orthogonal partial least square-discriminant analysis (OPLS-DA) was performed to identify significant biomarkers. Thirty percent of FLTOs reported NRS > 3 in the neck. Shoulder pain was common in 26% of the FLTOs. Pain and discomfort that most often prevented completion of activities were in the neck (20%), lower back (32%), and hips (27%). The FLTOs reported significantly (p = 0.04) higher levels of anxiety than the CON group and they had significantly lower pressure pain thresholds in the trapezius muscle on both right (p < 0.001) and left sides (p = 0.003). A significant OPLS-DA model could discriminate FLTOs from CON based on nine inflammatory proteins where the expression levels of four proteins were upregulated and five proteins were downregulated in FLTOs compared to CONs. Twenty-nine proteins correlated multivariately with pain intensity. The profile of self-reported health, pain intensity, sensitivity, and plasma biomarkers can discriminate FLTOs with pain from healthy subjects. A combination of both self-reported and objective biomarker measurements can be useful for better understanding the pathophysiological mechanisms underlying work-related neck and shoulder pain.

Assessing the influence of spontaneous fermentation on consumer emotional responses to roasted arabica coffee in a biometric approach

Beyond sensory quality, food-evoked emotions play a crucial role in consumers acceptance and willingness to try, which are essential for product development. The link between fermented coffee sensory characteristics and elicited emotional responses from consumers is underexplored. This study aimed to evaluate consumers’ acceptability of spontaneously fermented and unfermented roasted coffee through self-reported sensory evaluation and biometrics assessment. Self-reported liking in 15-cm non-structured scale, multiple choice of negative, neutral, and positive emojis, and subconscious emotional responses from 85 regular coffee consumers were analysed. Their relationship with the pattern of volatile aromatic compounds were also investigated. Fermented (F) and unfermented (UF) coffee beans with light- (L), dark- (D), and commercial dark (C) roasting levels were brewed and evaluated along with gas chromatography-mass spectrometry measurement. Multivariate data analysis was conducted to explore the inner relationships among volatile compounds, self-reported liking, and biometrics. Unfermented-dark roasted coffee (UFD) had highest overall consumer liking response ± standard error (8.68 ± 0.40), followed by the fermented-dark roasted (FD) at 7.73 ± 0.43 with no significant differences (p > 0.05). Fermented light-roasted coffee was associated with lower liking scores and negative emotional responses. In contrast, dark roasted coffee, which was linked to positive emojis and emotional responses, exhibited less detected peak area of volatile compounds contributing fruity and vegetative aromas, such as benzaldehyde, furfuryl acetate, 2-acetyl-1-methyl pyrrole, and isovaleric acid, potentially as negative drivers of consumer liking. Findings from this study could guide coffee manufacturers in developing specialty coffee if spontaneous fermentation is offered.

Comparative metabolome variation in Brassica juncea different organs from two varieties as analyzed using SPME and GCMS techniques coupled to chemometrics

Indian mustard (Brassica juncea; Brassicaceae) is an edible, oilseeds-yielding crop widely consumed as a food spice owing to its richness in nutrients with several health benefits. The current study aims to dissect the B. juncea metabolome heterogeneity among its different organs including leaf, stem, flower, and seed. Moreover, assessing the metabolome differences between two different varieties RH-725 and RH-761 grown at the same conditions. Gas chromatography-mass spectrometry (GC–MS) post-silylation was used to dissect the composition of nutrient metabolites coupled to multivariate data analysis. Variation in sulphur aglycones was measured using headspace-solid phase-microextraction HS-SPME coupled to GC–MS. A total of 101 nutrient metabolites were identified with the abundance of sugars represented by monosaccharides in all organs, except for seeds which were enriched in disaccharides (sucrose). α-Linolenic acid was detected as a marker fatty acid in leaf from RH-725 at 12.5 µg/mg. Malic acid was detected as a significant variant metabolite between the two varieties as detected in the leaf from the RH-725 variety at ca. 128.2 µg/mg compared to traces in RH-761. 7 Volatile sulphur compounds were detected at comparable levels in RH-725 and RH-761, with 3-butenyl isothiocyanate was the most abundant at 0.8–2 ng/mg.

Effectiveness of LOF, iForest and OCSVM in detecting anomalies in stream sediment geochemical data

This paper compares three unsupervised machine-learning algorithms – local outlier factor (LOF), Isolation Forest (iForest) and one-class support vector machine (OCSVM) – for anomaly detection in a multivariate geochemical dataset in northeastern Iran. This area contains several Au, Cu and Pb–Zn mineral occurrences. The methodology incorporates single-element geochemistry, multivariate data analysis and application of the three unsupervised machine-learning algorithms. Principal component analysis unveiled diverse elemental associations for the first seven principal components (PCs): PC1 shows a Co–Cr–Ni–V–Sn association indicating a lithological influence; PC2 shows a Au–Bi–Cu–W association suggesting epithermal Au mineralization; PC3 shows variability in Zn–V–Co–Sb–Cu–Cr; PC4 shows a Au–Cu–Ba–Sr–Ag association indicating Au and polymetallic mineralization; PC5 reflects Zn–Ag–Ni–Pb related to hydrothermal mineralization; and PC6 and PC7 show element associations suggesting epithermal and intrusive-related polymetallic mineralization. It was found that OCSVM performed slightly better than LOF and iForest in detecting anomalies associated with known Cu occurrences, and it successfully delineated dispersion from all known Au occurrences. LOF outperformed iForest and OCSVM in identifying all four Pb–Zn occurrences, and the three methods substantially limited the areas of the anomaly class. The analysis showed that LOF produced a less cluttered anomaly map compared to the isolated patterns in the iForest map. LOF was accurate in identifying anomalies associated with Au–Pb mineralization, while iForest detected anomalies associated with Pb–Zn–Cu occurrences and neighbouring Pb–Zn occurrence. OCSVM performed similarly in the northern and western areas but displayed unique discrepancies in the SE and west by detecting anomalies associated with two Cu occurences and a Pb–Cu occurrence. This study examined the influence of contamination fraction on detection of geochemical anomalies, revealing a noteworthy rise in the count of mineral occurrences delineated by anomalies when the contamination fraction increases from 5 to 10%. However, even with a 35% contamination fraction, some Cu occurrences remained outside the anomaly category, indicating potentially overlooked geochemical signals from mineral occurrences due to sampling schemes.

Why nothing beats NIRS technology: The green analytical choice for the future sustainable food production

In this perspective paper we argue for the fact that near infrared (NIR) technology, due to its unique properties, will become an indispensable green sensor technology in the future digitalized and sustainable food production.The future of near infrared spectroscopy (NIRS) in green analytics is bright. Ongoing advancements in NIR technology, coupled with increased accessibility and integration with advanced multivariate data analysis such as machine learning and artificial intelligence will further amplify the impact of NIRS across food, agricultural, environmental, and renewable energy domains. The miniaturization, increased portability, and enhanced affordability of NIR instruments, coupled with its integration into emerging technologies, will empower a diverse range of industries and researchers to address pressing global challenges with unprecedented precision and efficiency. The implementation of NIR technology in process analytical technology will enable the transition to future digitalized and sustainable food production. In a future circular economy, where waste streams, co-products and water are reclaimed and valorized, continuous measurements are necessary and in many cases, there are no sensor alternatives to NIR technology.

Assessing the impact of temperature, pH, light and chemical oxidation on fucoxanthin colour changes, antioxidant activity and the resulting metabolites.

The present study evaluated the effects of temperature, pH, light and chemical oxidation on fucoxanthin changes in terms of colour, antioxidant activity and metabolomic profile. Additionally, the correlation between antioxidant activity and identified metabolites was analysed. It was found that colour change was significantly reduced at elevated heat (100 °C, *∆E = 0.81 ± 0.05), reduced pH (pH 3, *∆E = 0.59 ± 0.04) and length of light exposure (*∆E = 3.16 ± 0.04). Antioxidant activity decreased under all treatments. Among the temperatures tested, fucoxanthin exhibited the highest activity at 60 °C, ranging from 0.92 to 3.04 mg Trolox equivalents (TE) g-1. Significant activity reductions (P < 0.05) were observed as a result of pH changes in the 2,2-diphenyl-1-picrylhydrazyl and β-carotene bleaching assays. Exposure to light 2: warm white lamp for 120 h significantly reduced antioxidant activity (0.01 to 1.70 mg TE g-1). Chemical oxidation also led to reduced activity, ranging from 0.18 to 0.29 mg TE g-1. Multivariate data analysis revealed distinct profiles for temperature, pH, light and chemical oxidation treatments. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics analysis identified 10 metabolites, and significant correlations (P < 0.05) indicate that these metabolites contributed to the samples' antioxidant activities. In conclusion, fucoxanthin tolerates well at 60 °C, within pH range 3-9, and within 8 h of light exposure, as indicated by its consistent antioxidant activity and minimal colour change. Each treatment resulted in distinct metabolite concentrations, as shown by LC-MS/MS-based metabolomics analysis. Further research into these metabolites could advance the understanding of their roles and aid in optimising processing conditions to favour beneficial metabolites. © 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.

Improving cardiovascular risk stratification through multivariate time-series analysis of cardiopulmonary exercise test data

Nowadays cardiorespiratory fitness (CRF) is assessed using summary indexes of cardiopulmonary exercise tests (CPETs). Yet, raw time-series CPET recordings may hold additional information with clinical relevance. Therefore, we investigated whether analysis of raw CPET data using dynamic time warping combined with k-medoids could identify distinct CRF phenogroups and improve cardiovascular (CV) risk stratification. CPET recordings from 1,399 participants (mean age, 56.4 years; 37.7% women) were separated into 5 groups with distinct patterns. Cluster 5 was associated with the worst CV profile with higher use of antihypertensive medication and a history of CV disease, while cluster 1 represented the most favorable CV profile. Clusters 4 (hazard ratio: 1.30; p= 0.033) and 5 (hazard ratio: 1.36; p= 0.0088) had a significantly higher risk of incident adverse events compared to clusters 1 and 2. The model evaluation in the external validation cohort revealed similar patterns. Therefore, an integrative CRF profiling might facilitate CV risk stratification and management.

Multivariate Data Analysis of Maximum Stress Concentration Factors in FRP-Retrofitted Two-Planar KT-Joints under Axial Loads for Offshore Renewables

With growing concerns about the danger of global climate change and worldwide demand for energy, the interest in the investigation and construction of renewable energy technologies has increased. Fixed platforms are a type of support structure for wind turbines composed of different types of tubular joints. These structures are under different kinds of cyclic loadings in ocean environmental conditions, which must be designed and reinforced against fatigue. In the present paper, the relationships between the parameters in DKT-joints reinforced with FRP under axial loads are investigated using several models, under 16 axial loading cases, with different nondimensional parameters and different FRP materials, and orientations were generated in ANSYS (total 5184) and analyzed. The four loading conditions that cause the maximum stress concentration factors were selected. After analyzing the 1296 reinforced models, relevant data were extracted, and possible samples were created. The extracted data were used in a multivariate data analysis of maximum stress concentration factors. The Pearson correlation coefficient is utilized to study the relationship between parameters and subsequently to make predictions. To reduce the number of variables and to group the data points into clusters based on certain similarities, hierarchical and non-hierarchical classifications are used, respectively.

Optimization of diagnostic processes using decision trees in ECG data analysis - lets web system analysis engine

The article presents the capabilities of the LETS Web system, which uses Internet of Things (IoT) technology to analyze medical data to optimize diagnostic processes. The article focuses on implementing decision tree algorithms that analyze electrocardiogram (ECG) data to identify cardiac conditions. The study used three variants of decision trees that differed in structure and ECG parameters. Each variant was tested for its ability to accurately classify cardiac health conditions ranging from simple arrhythmias to complex arrhythmic changes. The study showed that modifications to the structure of the decision trees significantly affected their effectiveness. The most advanced variant of the tree, using multivariate data analysis, showed the highest efficiency in diagnosing complex conditions. The effectiveness of the different variants of decision trees varied, confirming the importance of selecting the suitable diagnostic model for the specifics of the data and clinical goals.

Normalization of gene counts affects principal components-based exploratory analysis of RNA-sequencing data

Normalization of gene expression count data is an essential step of in the analysis of RNA-sequencing data. Its statistical analysis has been mostly addressed in the context of differential expression analysis, that is in the univariate setting. However, relationships among genes and samples are better explored and quantified using multivariate exploratory data analysis tools like Principal Component Analysis (PCA). In this study we investigate how normalization impacts the PCA model and its interpretation, considering twelve different widely used normalization methods that were applied on simulated and experimental data. Correlation patterns in the normalized data were explored using both summary statistics and Covariance Simultaneous Component Analysis. The impact of normalization on the PCA solution was assessed by exploring the model complexity, the quality of sample clustering in the low-dimensional PCA space and gene ranking in the model fit to normalized data. PCA models upon normalization were interpreted in the context gene enrichment pathway analysis. We found that although PCA score plots are often similar independently form the normalization used, biological interpretation of the models can depend heavily on the normalization method applied.

Dissecting reactive astrocyte responses: lineage tracing and morphology-based clustering

Brain damage triggers diverse cellular and molecular events, with astrocytes playing a crucial role in activating local neuroprotective and reparative signaling within damaged neuronal circuits. Here, we investigated reactive astrocytes using a multidimensional approach to categorize their responses into different subtypes based on morphology. This approach utilized the StarTrack lineage tracer, single-cell imaging reconstruction and multivariate data analysis. Our findings identified three profiles of reactive astrocyte responses, categorized by their effects on cell size- and shape- related morphological parameters: “moderate”, “strong,” and “very strong”. We also examined the heterogeneity of astrocyte reactivity, focusing on spatial and clonal distribution. Our research revealed a notable enrichment of protoplasmic and fibrous astrocytes within the “strong” and “very strong” response subtypes. Overall, our study contributes to a better understanding of astrocyte heterogeneity in response to an injury. By characterizing the diverse reactive responses among astrocyte subpopulations, we provide insights that could guide future research aimed at identifying novel therapeutic targets to mitigate brain damage and promote neural repair.Graphical

Vacuum freeze drying combined with catalytic infrared drying to improve the aroma quality of chives: Potential mechanisms of their formation

This study aimed to investigate the effect of vacuum freeze drying combined with catalytic infrared drying (FD-CIRD) process on aromas, free amino acids, reducing sugars and free fatty acids in chive leaves and stems. Gas chromatography-mass spectrometry combined with multivariate data analysis revealed that dipropyl disulfide was the key aroma that distinguished the differences between chive leaves and stems. The key aromas benzeneacetaldehyde, decanal and 1-octen-3-ol enhanced FD-CIRD chive leaves and stems aromas. The free amino acid content was highest at FD-CIRD stage in all samples except for the control (FD), while the reducing sugar content was lowest. The content of unsaturated fatty acids gradually decreased at FD stage and increased at FD-CIRD stage. Additionally, correlation analysis revealed that phenylalanine was a potential precursor of benzacetenealdehyde, oleic and linolenic acids were potential precursors of decanal and 1-octen-3-ol. Therefore, FD-CIRD technique helps to improve the sensory profile of dried chives.

Acoustic Levitation-Controlled Droplets for Contactless Enrichment and Sensitive Detection of Volatile Organic Substances Integrated with GC-MS.

Analyzing trace-level volatile organic compounds (VOCs) remains challenging due to initial sampling and preconcentration limitations. Inspired by the highly reproducible and constantly renewable electrode surface of dropping mercury electrode (DME), a contactless enrichment process was first reported by using an acoustic levitation device to trap and concentrate VOCs from gas samples onto suspended droplets, which were then directly transferred into gas chromatography-mass spectrometry (GC-MS) for real-time analysis. Compared with traditional methods injection methods, this method achieves a 46-fold increase in nicotine peak area. The detection sensitivity was enhanced significantly, attributed to the high specific surface area of the droplets and the accelerating extraction vibration. Notably, the number of identified VOCs from burning cigarettes significantly increased from 17 to 212, including 22 aromatic compounds with distinct aromas. The remarkable versatility of this method was demonstrated by effectively monitoring the dynamic changes of 16 VOCs in environmental tobacco smoke (ETS) following cigarette burning, revealing the persistence of these compounds, even after 40 min. Moreover, directly analyzing human-exhaled aerosol found that nicotine rapidly decreased while its metabolite cotinine increased, showcasing the potential for tracking human metabolism and behavior in vivo. Furthermore, multivariate data analysis of VOC profiles from six cigarette brands allowed for their visual differentiation. With versatility, sensitivity, and the ability to distinguish trace-level VOCs in realtime, this method offers promising avenues for environmental monitoring, metabolic studies, and various analytical applications.

Integrative taxonomy reveals inflated biodiversity in the European Temnothorax unifasciatus complex (Hymenoptera: Formicidae)

AbstractTemnothorax unifasciatus (Latreille, 1798) is a widely distributed pan‐European species from the Iberian Peninsula to the Caucasus. This taxon's relatively high morphological variability prompts the taxonomists of earlier times and today to mention the morphologically different elements at specific or subspecific ranks. This paper aims to understand the population structure and genetic diversity within this lineage via integrative taxonomy, incorporating molecular phylogenetics, species delimitation analyses and multivariate analyses of continuous morphometric data from across the geographic range of the T. unifasciatus complex. Phylogenetic analyses yielded incongruent trees. The genealogical diversity index (gdi) and the confirmatory analyses on morphological data found only weak, ambiguous delimitations within the unifasciatus complex. The most highly supported scenario splits T. brackoi from the remaining unifasciatus complex with ambiguous support (gdi = 0.56). This scenario is supported by multivariate morphometry with 100% accuracy in classification success. Instead, our results suggest complex morphological and genetic population structuring within the broad range of T. unifasciatus. Therefore, we confirm the validity of two species, T. brackoi Salata &amp; Borowiec, 2019 and T. unifasciatus (Latreille, 1798), and propose five new junior synonymies, T. cordieri (Bondroit, 1918) syn. nov., T. tauricus (Ruzsky, 1902) syn. nov., T. berlandi (Bondroit, 1918) syn. nov., T. unifasciatus staegeri (Bondroit, 1918) syn. nov., T. tuberum ciscaucasicus (Arnol'di, 1977) syn. nov. with the latter. To achieve maximal taxonomic stability, we designated a lectotype for Temnothorax unifasciatus (Latreille, 1798).

Is surgeon annual case volume related with intra and postoperative complications after ventral hernia repair? Uni- and multivariate analysis of prospective registry-based data

BackgroundLiterature on a potential relationship between surgeon case volume and intra- or postoperative complications after ventral hernia repair remains scarce.MethodsPatients who underwent ventral hernia repair between 2011 and 2023 were selected from the prospectively maintained French Hernia-Club Registry. Outcome variables were: intraoperative events, postoperative general complications, surgical site occurrences, surgical site infections, length of intensive care unit (ICU), and patient-reported scar bulging during follow-up. Surgeons’ annual case volume was categorized as 1–5, 6–50, 51–100, 101–125, and > 125 cases, and its association with outcome volume was evaluated using uni- and multivariable analyses.ResultsOver the study period, 199 titular or temporary members registered 15,332 ventral hernia repairs, including 7869 primary, 6173 incisional, and 212 parastomal hernia repairs. In univariate analysis, surgeons’ annual case volume was significantly related with all the postoperative studied outcomes. After multivariate regression analysis, annual case volume remained significantly associated with intra-operative complications, postoperative general complications and length of ICU stay. A primary repair was independently associated with fewer intra- and post-operative complications.ConclusionIn the present multivariable analysis of a large registry on ventral hernia repairs, higher surgeon annual case volume was significantly related with fewer postoperative general complications and a shorter length of stay, but not with fewer surgical site occurrences, nor with less patient-reported scar bulging. Factors in the surgeons’ case mix such as the type of hernia have significant impact on complication rates.

Selective Attention and Decision-Making Have Separable Neural Bases in Space and Time.

Attention and decision-making processes are fundamental to cognition. However, they are usually experimentally confounded, making it difficult to link neural observations to specific processes. Here we separated the effects of selective attention from the effects of decision-making on brain activity obtained from human participants (both sexes), using a two-stage task where the attended stimulus and decision were orthogonal and separated in time. Multivariate pattern analyses of multimodal neuroimaging data revealed the dynamics of perceptual and decision-related information coding through time with magnetoencephalography (MEG), through space with functional magnetic resonance imaging (fMRI), and their combination (MEG-fMRI fusion). Our MEG results showed an effect of attention before decision-making could begin, and fMRI results showed an attention effect in early visual and frontoparietal regions. Model-based MEG-fMRI fusion suggested that attention boosted stimulus information in the frontoparietal and early visual regions before decision-making was possible. Together, our results suggest that attention affects neural stimulus representations in the frontoparietal regions independent of decision-making.

Determination of the blending parameters of ternary blends containing a terpolymer as a component copolymer by multivariate analysis of NMR data

Heat-resistant ABS resin, an industrial polymer material, is generally prepared by mixing AN/MS bipolymer and/or AN/ST/MS terpolymer with the matrix of ABS resin, where AN, ST and MS denote acrylonitrile, styrene and α-methylstyrene, respectively. In order to predict the thermal properties of ABS resins, it is important to know which copolymers are blended and in what ratio. In this study, statistical structural analysis was performed on polymer blends consisting of AN/ST bipolymer, AN/MS bipolymer and AN/ST/MS terpolymer to predict blending parameters for heat-resistant ABS resin. Nuclear magnetic resonance (NMR) spectral data of the polymer blends were used as explanatory variables to determine the chemical composition and mole fraction of the component copolymers. Partial least-squares (PLS) regression and least absolute shrinkage and selection operator (LASSO) regression were used as the exploratory techniques. The use of 1H NMR spectral data showed poor prediction, probably because of low resolution due to the narrow spectral width. Accordingly, 13C NMR spectra, which have wider spectral widths, were used, resulting in the successful prediction of the blending parameters of the ternary blends containing a terpolymer as a component. It should be noted that it was not necessary to obtain the NMR spectra of all the polymer blends as the explanatory variables, i.e., linear combinations of the NMR spectra of the component copolymers were sufficient.

Integrated metabolomics analysis of chill-stored rose shrimp (Parapenaeus longirostris) treated with different pressure levels of high hydrostatic pressure by 1H-NMR spectroscopy.

The antimicrobial effects of high hydrostatic pressure (HHP) treatments on chill-stored seafood are well-documented, while their impact on the metabolic profile of seafood, especially the metabolome of fish flesh, and remains underexplored. Addressing this gap, this study investigates the effects of HHP on the metabolome of chill-stored rose shrimp by conducting multivariate data analysis based on untargeted proton nuclear magnetic resonance observations. Vacuum-packed rose shrimp samples were subjected to HHP at 0, 400, 500, and 600MPa for 10 min and then stored at 2-4°C. The microorganism analysis and metabolic analysis were carried out on days 1 and 14. HHP treatment effectively deactivated Lactobacillus spp., Escherichia coli, Pseudomonas spp., total Coliforms, and sulfite-reducing anaerobic bacteria. Consequently, HHP treatment significantly reduced the formation rate of decay-related metabolites, such as hypoxanthine, trimethylamine, and biogenic amines, which exhibited significant accumulation in untreated samples. Multivariate unsupervised analyses provided insights into the overall changes in the metabolite profile induced by HHP. Metabolic pathway analysis revealed several pathways underlying spoilage, including pyruvate metabolism, valine, leucine, and isoleucine biosynthesis, purine metabolism, methane metabolism, glycine, serine, and threonine metabolism, citrate cycle (TCA cycle), glycolysis/gluconeogenesis, alanine, aspartate, and glutamate metabolism, sulfur metabolism, pantothenate and CoA biosynthesis, glutathione metabolism, and glyoxylate and dicarboxylate metabolism. Importantly, these pathways underwent alterations due to the application of HHP, particularly at high-pressure levels. In summary, the results unveil the potential mechanisms of HHP effects on chill-stored rose shrimps.

Lipidome and metabolome profiling of longissimus lumborum beef with different ultimate pH and postmortem aging

The objective of this exploratory study was to assess the changes on lipidome and metabolome profiling of Longissimus lumborum bull muscle with different ultimate pH (pHu) and aging periods. The bull muscles classified as normal, intermediate, or high pHu were collected from a Brazilian commercial slaughterhouse, cut into steaks, individually vacuum-packaged, and aged for 3 days (3-d) or 21 days (21-d) at 2 °C. Muscle extracts were analyzed for the profiles of both lipids, by mass spectrometry (via direct flow-injection), and metabolites, by nuclear magnetic resonance, with downstream multivariate data analysis. As major results, pairwise comparisons identified C12:0 and C14:0 acylcarnitines as potential biomarkers of the intermediate pHu-muscle, which are related to lipid catabolism for alternative energy metabolism and indicate less protein breakage postmortem. Interestingly, the concentration of arginine at early postmortem aging (3-d) may influence the previously reported improved tenderness in normal and high pHu-muscles. Moreover, upregulation of fumarate, formate, and acetate with increased pHu muscle at 21-d aging indicate more intense tricarboxylic acid cycle, amino acid degradation, and pyruvate oxidation by reactive oxygen species, respectively. These three compounds (fumarate, formate, and acetate) discriminated statistically the muscle with high pHu at 21-d aging. The normal pHu-muscle showed higher concentrations of glycogenolysis and glycolysis metabolites, including glucose, mannose, and pyruvate. Hence, our results enhance knowledge of postmortem biochemical changes of beef within different pHu groups aged up to 21 days, which is essential to understand the mechanisms underpinning bull meat quality changes.