Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization.

BACKGROUND: Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance. RESULTS: We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model. CONCLUSIONS: We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

Polygonati Rhizoma varieties and origins traceability based on multivariate data fusion combined with an artificial intelligence classification algorithm.

This study collected multidimensional feature data such as spectra, texture, and component contents of Polygonati Rhizoma from different origins and varieties (Polygonatum kingianum Coll. et Hemsl from Yunnan and Guizhou; Polygonatum cyrtonema Hua from Anhui and Jiangxi; Polygonatum sibiricum Red from Hunan). Multivariate statistical analysis was used to select 39 characteristic factors for distinguishing PR origins and 14 characteristic factors for discriminating PR varieties (VIP > 1 and P < 0.05). In addition, by combining multivariate statistical analysis with a deep belief network (DBN) classification algorithm, a novel artificial intelligence algorithm was developed and optimized. Compared to traditional discriminant analysis methods, the accuracy of this new approach was significantly improved, achieving a 100% discrimination rate for PR varieties and a 100% accuracy rate for tracing the origin of PR. This research provides a reference and data support for constructing intelligent algorithms based on multidimensional data fusion, to achieve food variety discrimination and origin tracing.

Evaluating long-term impacts of land use/land cover changes on pollution loads at a catchment scale.

Evaluating how pollutant loads react to changes in land use/land cover (LULC) is a challenging task due to the intricate relationships among the many elements within a watershed. However, the difficulty in connecting LULC change and nonpoint source (NPS) pollution loads to streams may be lessened by combining hydrological modeling with geospatial tools and multivariate statistics. The objective of this study was to investigate the long-term effects of LULC change on NPS pollution loads in a highly human-dominated catchment, in central Ethiopia. In the study, hydrologic modeling was used to estimate the NPS parameters from multispectral Landsat images, and multivariate statistical techniques were then used to extract major LULC types that explain the variances of NPS loads between 1981 and 2020. The results demonstrated that there were human-induced LULC changes in the area, as the built-up and agricultural landscapes are rising (186.4% and 5.8%, respectively), and shrub and forest lands are decreasing (67.1% and 41%, respectively). As a result of these changes, the concentrations of nitrate (NO3), total P, total N, organic N, and organic P loads were increased by 69.41, 19.83, 18.45, 18.88, and 24.05%, respectively. Reductions in natural vegetation, as well as agriculture intensification, are the major contributors to the NPS pollutant losses to surface water sources. The result also revealed that pollution nutrients are strongly related to deforestation and agricultural land expansion. Proper adaptation strategies should be implemented to minimize the negative impact of LULC changes in the area.

Morphological and Anatomical Differentiation of Potamogeton gramineus in Relation to the Presence of Invasive Species Elodea nuttallii: A Case Study from Vlasina Lake, Serbia.

Elodea nuttallii represents non-native and highly invasive species in Europe that significantly influence freshwater plant communities by decreasing the diversity of native species. This study aimed to determine whether the morphological and anatomical features of Potamogeton gramineus, a native species in Vlasina Lake, differ between sites where it coexists with E. nuttallii and those where E. nuttallii is not present. Environmental variables such as water depth, temperature, pH, conductivity, saturation, and O2 concentration were included in the analysis. Analyses were conducted on 32 morphological and anatomical features of P. gramineus collected from six sites within Vlasina Lake, comprising three sites where E. nuttallii was present and three sites where it was absent. The datasets containing morphometric and environmental variables underwent analysis using standard univariate techniques (Descriptive, ANOVA), Tukey's Honest Significant Difference (HSD) test, Student's t-test, and the Mann-Whitney U test, as well as multivariate statistical methods such as Canonical Discriminant Analysis (CDA). The results show the presence of morphological differentiation among P. gramineus individuals across the analyzed sites. These findings suggest that morphological and anatomical features, such as epidermis, mesophyll, palisade, and aerenchyma tissue thickness in floating leaves, number, length, width, and the surface area of stomata, as well as the width of submersed leaves and stem aerenchyma tissue thickness, effectively differentiate individuals that coexist with E. nuttallii and individuals that growth without its presence. Moreover, they indicate that P. gramineus exhibits a notable ability to modify its morphological traits in response to invasion.

Characterization of the key flavor compounds in cream cheese by GC-MS, GC-IMS, sensory analysis and multivariable statistics.

The aroma types of cream cheese affect its commercial value and consumer acceptability. However, the types of volatile substances and sensory characteristics of cream cheese at different fermentation stages are still unclear. Therefore, in this study, headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) were used to analyze the volatile substances in cream cheese fermentation. Orthogonal partial least squares discriminant analysis (OPLS-DA), odor activity value (OAV), relative odor activity value (ROAV) and variable projection importance (VIP) were used to identify the characteristic flavor substances in cream cheese fermentation. Finally, the relationship between key flavor substances and sensory characteristics was determined by partial least squares (PLS) analysis. A total of 34 and 36 volatile organic compounds were identified by HS-SPME-GC-MS and HS-GC-MS, respectively, and 14 characteristic flavor substances were found, based on VIP, ROAV and OAV models. Combined with sensory analysis and flavor substance changes, it was found that the cream cheese fermented for 15 d had the best flavor and taste. This study reveals the characteristics and contribution of volatile substances in cream cheese at different fermentation stages, which provides new insights into improving flavor and quality control.

Exploring Types of Parent Attachment via the Clustering Modules of a New Free Statistical Software, ROP-R.

The aim of the paper is threefold: (1) to demonstrate the rich repertoire of clustering capabilities of a ROPstat and R-based new and free software, called ROP-R, by illustrating several analyses with real psychological data; (2) to show how well ROP-R works in tandem with ROPstat software in complex classification analyses; and (3) to explore some nontrivial types of parent attachment using the clustering modules of ROP-R. Four modules of ROP-R are available for performing cluster analyses (CAs), with several methods (e.g., divisive hierarchical CA, k-medoids CA, k-medians CA, model-based CA) not found in other user-friendly menu-driven software. In the paper, mother and father attachment data are used from a study with adolescents (Mirnics et al., 2021) to illustrate how the ROP-R software can be used to perform various CAs and evaluate the results using attractive graphs and useful tables. Comparing different clustering methods, it was found that both standard AHCA and k-means CA could discover a 7-type structure, which was also verified by the nonstandard k-medians CA. However, the nonstandard k-medoids CA and MBCA methods were not very effective in identifying a structure with an acceptable overall homogeneity. Nevertheless, they were able to identify some types through extremely homogeneous clusters.

A hyaena on stilts: comparison of the limb morphology of Ictitherium ebu (Mammalia: Hyaenidae) from the Late Miocene of Lothagam, Turkana Basin, Kenya with extant Canidae and Hyaenidae.

The long, gracile morphology of the limb bones of the Late Miocene hyaenid Ictitherium ebu has led to the hypothesis that this animal was cursorial. The forelimb and femur of the holotype were compared with specimens of extant Hyaenidae and Canidae. Two morphometric methods were used. The first used measurements to calculate indices of different morphological characters. The second method involved capturing photographs of the anterior distal humerus of each specimen, mapping six landmarks on them, and calculating truss distances. These distances represent a schematic reproduction of the elbow. Multivariate statistical analysis primarily separated the data based on taxonomy, yet locomotor and habitat categories were also considered. Ictitherium ebu has an overall morphology similar to that of the maned wolf and a distal humerus reminiscent of that of the aardwolf. The long, gracile limb bones of I. ebu are suggested to be adaptations for pouncing on prey, for locomotor efficiency, and for looking over the tall grass of the open environments the animal lived in, much like the present-day maned wolf.

Hydrodynamics and water quality of a highly anthropized wetland: the case study of the Massaciuccoli basin (Tuscany, Italy).

Owing to increasing anthropogenic impacts, wetlands have suffered a serious environmental decline in recent decades. The sustainable management of these natural resources is fundamental to maintain both the ecosystems and the economic activities. The Lake Massaciuccoli and nearby areas represent one of the largest residual coastal marshy areas in Tuscany (Italy). This wetland is characterized by large-scale and intensive agricultural use and affected by reclamation activities, with consequent problems of erosion, subsidence and lake eutrophication and siltation. In this context, an integrated study combining hydrochemical data (water levels, electrical conductivity, pH, turbidity, major ions, trace metals) and stable isotopes (H, O, S) has been performed in the southernmost part of the basin, to better disentangle processes and interactions between groundwater and surface water and to understand the origin of solutes and their evolution. Our results indicated that both groundwater and surface water have a meteoric origin and that geochemical composition of groundwater is mainly affected by local geological and biological processes. Moreover, surface water is affected by sea water mixing and evapotranspiration/precipitation processes. The impact of agricultural activity and the use of fertilizers on the water quality appears to be limited as regards nitrates, indicating that less intense agricultural practices implemented in recent years have been successful. As regards sulfates, Fe, and Mn, we cannot fully elucidate the mechanisms underlying human influence, but the oscillation of water level and degradation of peat enhanced by reclamation and agriculture activities likely played an important role in controlling the fate of these elements. Overall, these results underline the importance of integrated approaches to disentangle geochemical processes and will be useful in supporting policy implementation and environmental protection in this valuable area of Tuscany. Findings from this work suggest the need for policy-making authorities to take actions as soon as possible to mitigate risks. Closer co-operation is essential between authorities and farmers to reduce inputs of fertilizers and chemicals into the lake and the surrounding area. Also, additional policy measures should be enforced to reduce the mechanical soil tillage and limit erosion and runoff, such as the NBSs implemented within the Phusicos Project.

Non-target analysis of Danish wastewater treatment plant effluent: Statistical analysis of chemical fingerprinting as a step toward a future monitoring tool.

In an attempt to discover and characterize the plethora of xenobiotic substances, this study investigates chemical compounds released into the environment with wastewater effluents. A novel non-targeted screening methodology based on ultra-high resolution Orbitrap mass spectrometry and nanoflow ultra-high performance liquid chromatography together with a newly optimized data-processing pipeline were applied to effluent samples from two state-of-the-art and one small wastewater treatment facility. In total, 785 molecular structures were obtained, of which 38 were identified as single compounds, while 480 structures were identified at a putative level. Most of these substances were therapeutics and drugs, present as parent compounds and metabolites. Using R packages Phyloseq and MetacodeR, originally developed for bioinformatics, significant differences in xenobiotic presence in the wastewater effluents between the three sites were demonstrated.

Residual loads from tilapia farming on the sediment of a Brazilian reservoir.

This study investigated the sediment geochemistry of a fish farming area in net cage tanks in the Rosário reservoir, Brazil. Three areas were investigated: reference (RA), fish farming (FFA), and dispersion (DA). The results were analyzed through correlation, similarity, principal component analysis, comparison with legislation, sediment quality guidelines, and sediment pollution indices. The mean concentrations for RA, FFA, and DA areas were respectively: Cu (mg.kg-1) 37.74, 62.23, and 71.83; Mn (mg.kg-1) 22.55, 66.48, and 55.90; Zn (mg.kg-1) 9.13, 114.83, and 94.27; Fe (%) 0.28, 0.40, and 0.43; OM (%) 15.84, 21.95, and 18.45; TOC (%) 1.86, 3.69, and 6.05; TN (mg.kg-1) 2365.00, 5015.00, and 3447.51; TP (mg.kg-1) 780.00, 6896.00, and 2585.50; ORP (mV) -95.50, -135.20, and -127.10; pH 6.60, 6.58, and 6.05; <63 µm 90.59, 78.68, and 87.30. Statistically, the influence of fish farming on sediment, organic matter, and pollutant sedimentation was demonstrated. Cu and Zn concentrations were below sediment quality guidelines. Regarding legal limits (resolution 454/2012/CONAMA), nutrients in the FFA area exceeded by 60% (TN) and 100% (TP), while in DA and RA areas they were 100% lower. TOC was 100% lower in all areas. Organic matter exceeded the limit by 100% in all areas. Pollution indices resulted in: low contamination factor 78%; unpolluted for 87% of pollution load and 83% of combined pollution; moderately polluted for 75% of the Nemerow index. The greatest impacts and influence of farming on pollutant sedimentation were more concentrated in the fish farming area. In terms of legal aspects and pollution indices, fish farming produced low levels of trace metal pollution and nutrient concentrations exceeded legal limits.

Population exposure to emerging perfluoroalkyl acids (PFAAs) via drinking water resources: Application of multivariate statistics and risk assessment models.

This study assessed the occurrence, origins, and potential risks of emerging perfluoroalkyl acids (PFAAs) for the first time in drinking water resources of Khyber Pakhtunkhwa, Pakistan. In total, 13 perfluoroalkyl carboxylic acids (PFCAs) with carbon (C) chains C4-C18 and 4 perfluoroalkyl sulfonates (PFSAs) with C chains C4-C10 were tested in both surface and ground drinking water samples using a high-performance liquid chromatography system (HPLC) equipped with an Agilent 6460 Triple Quadrupole liquid chromatography-mass spectrometry (LC-MS) system. The concentrations of ∑PFCAs, ∑PFSAs, and ∑PFAAs in drinking water ranged from 1.46 to 72.85, 0.30-8.03, and 1.76-80.88 ng/L, respectively. Perfluorobutanoic acid (PFBA), perfluorohexanoic acid (PFHxA), and perfluoropentanoic acid (PFPeA) were the dominant analytes in surface water followed by ground water, while the concentration of perfluorobutane sulfonate (PFBS), perfluorooctanoic acid (PFOA), perfluoroheptanoic acid (PFHpA), perfluorooctane sulfonate (PFOS), perfluorohexane sulfonate (PFHxS), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUnDA), and perfluorododecanoic acid (PFDoDA) were greater than long-chain PFOA and PFOS. The correlation statistics, which showed a strong correlation (p < 0.05) between the PFAA analytes, potentially indicated the fate of PFAAs in the area's drinking water sources, whereas the hierarchical cluster analysis (HCA) and principal component analysis (PCA) statistics identified industrial, domestic, agricultural, and commercial applications as potential point and non-point sources of PFAA contamination in the area. From risk perspectives, the overall PFAA toxicity in water resources was within the ecological health risk thresholds, where for the human population the hazard quotient (HQ) values of individual PFAAs were < 1, indicating no risk from the drinking water sources; however, the hazard index (HI) from the ∑PFAAs should not be underestimated, as it may significantly result in potential chronic toxicity to exposed adults, followed by children.

In-depth analysis of volatolomic and odorous profiles of novel craft beer by permutation test features selection and multivariate correlation analysis.

This research explored the impact of binary cereal blends [barley with durum wheat (DW) and soft wheat (CW)], four autochthonous yeast strains (9502, 9518, 14061 and 17290) and two refermentation sugar concentrations (6-9 g/L), on volatolomics (VOCs) and odour profiles of craft beers using unsupervised statistics. For the first time, we applied permutation test to select volatiles with higher significance in explaining variance among samples. The unsupervised approach on the 19 selected VOCs revealed cereal-yeast interaction to be the main source of variability and DW-9502-6/9, DW-17290-6, CW-17290-6 and CW-9518-6 being the best technological strategies. In particular, in samples DW-9502-6/9, concentrations of some of the selected volatiles were observed to be approximately three to more than seven times higher than the average. PLS-correlation between VOCs and odour profiles proved to be very useful in assessing the weight of each of the selected VOCs on the perception of odour notes.

Exploring the bioaccessibility of polyphenols and glucosinolates from Brassicaceae microgreens by combining metabolomics profiling and computational chemometrics.

Microgreens constitute natural-based foods with health-promoting properties mediated by the accumulation of glucosinolates (GLs) and phenolic compounds (PCs), although their bioaccessibility may limit their nutritional potential. This work subjected eight Brassicaceae microgreens to in vitro gastrointestinal digestion and large intestine fermentation before the metabolomics profiling of PCs and GLs. The application of multivariate statistics effectively discriminated among species and their interaction with in vitro digestion phases. The flavonoids associated with arugula and the aliphatic GLs related to red cabbage and cauliflower were identified as discriminant markers among microgreen species. The multi-omics integration along in vitro digestion and fermentation predicted bioaccessible markers, featuring potential candidates that may eventually be responsible for these functional foods' nutritional properties. This combined analytical and computational framework provided a promising platform to predict the nutritional metabolome-wide outcome of functional food consumption, as in the case of microgreens.

Graphical, statistical and index-based techniques integrated for identifying the hydrochemical fingerprints and groundwater quality of In Salah, Algerian Sahara.

Groundwater, a predominant reservoir of freshwater, plays a critical role in providing a sustainable potable water and water for agricultural and industry uses in the In Salah desert region of Algeria. This research collected 82 underground water samples from Albian aquifers to assess water quality and identify hydrogeochemical processes influencing mineralization. To achieve this objective, various methods were employed to evaluate water quality based on its intended uses. The drinking water quality index utilized revealed the water potability status, while the indicators of irrigation potability were employed to evaluate its quality for agricultural purposes. Additionally, an assessment of groundwater susceptibility to corrosion and scaling in an industrial context was conducted using several indices, e.g., Langelier index, Larson-Skold index, Ryznar index, chloride-sulfate mass ratio, Puckorius index, aggressiveness index, and the Revelle index. The findings of this study revealed that the groundwater quality for consumption fell into four categories: good (2.44%), fair (29.27%), poor (65.85%), and non-potable (2.44%). Concerning agricultural irrigation, the indexical results indicated that 15.85% of the waters exhibited adequate quality, while 84.15% were questionable for irrigation. Calculations based on various corrosion and scaling evaluation indices showed that most wells were prone to corrosion, with a tendency for calcium bicarbonate deposit formation. Furthermore, the hydrochemical study identified three water types: Na-Cl (53.66%), Ca-Mg-Cl (37.80%), and Ca-Cl (8.54%) waters. Analyses of correlation matrices, R-type clustering, factor loadings, Gibbs diagrams, scatterplots, and chloro-alkaline indices highlighted that the chemistry of the Albian groundwater is fundamentally impacted by a number of processes such as silicate weathering, evaporite dissolution, ionic exchange, and anthropogenic inputs, that played impactful role in the aquifer's water chemistry.

An analytical strategy for challenging members of the microplastic family: Particles from anti-corrosion coatings.

Potentially hazardous particles from paints and functional coatings are an overlooked fraction of microplastic (MP) pollution since their accurate identification and quantification in environmental samples remains difficult. We have applied the most relevant techniques from the field of microplastic analysis for their suitability to chemically characterize anti-corrosion coatings containing a variety of polymer binders (LDIR, Raman and FTIR spectroscopy, Py-GC/MS) and inorganic additives (ICP-MS/MS). We present the basis of a possible toolbox to study the release and fate of coating particles in the (marine) environment. Our results indicate that, due to material properties, spectroscopic methods alone appear to be unsuitable for quantification of coating/paint particles and underestimate their environmental abundance. ICP-MS/MS and an optimized Py-GC/MS approach in combination with multivariate statistics enables a straightforward comparison of the multi-elemental and organic additive fingerprints of paint particles. The approach can improve the identification of unknown particles in environmental samples by an assignment to different typically used coating types. In future, this approach may facilitate allocation of emission sources of different environmental paint/coating particles. Indeed, future work will be required to tackle various remaining analytical challenges, such as optimized particle extraction/separation of environmental coating particles.

The Shelf Life of Milk-A Novel Concept for the Identification of Marker Peptides Using Multivariate Analysis.

The quality of food is influenced by several factors during production and storage. When using marker compounds, different steps in the production chain, as well as during storage, can be monitored. This might enable an optimum prediction of food's shelf life and avoid food waste. Especially, proteoforms and peptides thereof can serve as indicators for exogenous influences. The development of a proteomics-based workflow for detecting and identifying differences in the proteome is complex and time-consuming. The aim of the study was to develop a fast and universal workflow with ultra-high temperature (UHT) milk as a proteinaceous model food with expectable changes in protein/peptide composition. To find an optimum shelf life without sticking to a theoretically fixed best-before date, new evaluation and analytical methods are needed. Consequently, a modeling approach was used to monitor the shelf life of the milk after it was treated thermally and stored. The different peptide profiles determined with high-resolution mass spectrometry (HRMS) showed a significant difference depending on the preparation method of the samples. Potential marker peptides were determined using orthogonal projections to latent structures discriminant analysis (OPLSDA) and principal component analysis (PCA) following a typical proteomics protocol with tryptic hydrolysis. An additional Python-based algorithm enabled the identification of eight potential tryptic marker peptides (with mass spectrometric structural indications m/z 885.4843, m/z 639.3500, m/z 635.8622, m/z 634.3570, m/z 412.7191, m/z 623.2967, m/z 880.4767, and m/z 692.4041), indicating the effect of the heat treatment. The developed workflow is flexible and can be easily adapted to different research questions in the field of peptide analysis. In particular, the process of feature identification can be carried out with significantly less effort than with conventional methods.

Natural Radioactivity in Raw Building Materials for Underground Parking Lots and Assessment of Radiological Health Risk for the Population.

This article reports the results of an investigation into the activity concentration of natural radionuclides in raw building materials for underground parking lots, together with the assessment of the radiation hazard for the public related to exposure to ionizing radiations. To this purpose, high-purity germanium (HPGe) γ-ray spectrometry was employed in order to quantify the average specific activity of 226Ra, 232Th, and 40K natural radioisotopes. With the aim to assess any possible radiological health risk for the population, the absorbed γ-dose rate (D), the annual effective dose equivalent outdoor (AEDEout) and indoor (AEDEin), the activity concentration index (I), and the alpha index (Iα) were also estimated, resulting in values that were lower than the maximum recommended ones for humans. Finally, the extent of the correlations existing between the observed radioactivity and radiological parameters and of these parameters with the analyzed samples was quantified through statistical analyses, including Pearson's correlation, a principal component analysis (PCA), and a hierarchical cluster analysis (HCA). As a result, three clusters of the investigated samples were recognized based on their chemical composition and mineralogical nature. Noteworthily, this paper covers a certain gap in science since its topic does not appear in literature in this form. Thus, the authors underline the importance of this work to global knowledge in the environmental research and public health fields.

Trace the origin of yak meat in Xizang based on stable isotope combined with multivariate statistics.

In this study, the feasibility of tracing the origin of yak meat in Xizang Autonomous Region based on stable isotope combined with multivariable statistics was researched. The δ13C, δ15N, δ2H and δ18O in yak meat were determined by stable isotope ratio mass spectrometry, and the data were analyzed by analysis of variance, fisher discriminant analysis (FDA), back propagation (BP) neural network and orthogonal partial least squares discrimination analysis (OPLS-DA). The results showed that the δ13C, δ15N, δ2H and δ18O had significant differences among different origins (P < 0.05). The overall original correct discrimination rate of fisher discriminant analysis was 89.7 %, and the correct discrimination rate of cross validation was 88.2 %. The correct classification rate of BP neural network based on training set was 93.38 %, and the correct classification rate of BP neural network based on test set was 89.83 %. The OPLS-DA model interpretation rate parameter R2Y was 0.67, the model prediction rate parameter Q2 was 0.409, which could distinguish yak meat from seven different producing areas in Xizang Autonomous Region. The results showed that the origin of yak meat in Xizang Autonomous Region can be traced based on stable isotope combined with multivariate statistics.

Untargeted metabolomics and machine learning unveil quality and authenticity interactions in grated Parmigiano Reggiano PDO cheese.

The chemical composition of Parmigiano Reggiano (PR) hard cheese can be significantly affected by different factors across the dairy supply chain, including ripening, altimetric zone, and rind inclusion levels in grated hard cheeses. The present study proposes an untargeted metabolomics approach combined with machine learning chemometrics to evaluate the combined effect of these three critical parameters. Specifically, ripening was found to exert a pivotal role in defining the signature of PR cheeses, with amino acids and lipid derivatives that exhibited their role as key discriminant compounds. In parallel, a random forest classifier was used to predict the rind inclusion levels (> 18%) in grated cheeses and to authenticate the specific effect of altimetry dairy production, achieving a high prediction ability in both model performances (i.e., ∼60% and > 90%, respectively). Overall, these results open a novel perspective to identifying quality and authenticity markers metabolites in cheese.

Tabular Two-Dimensional Correlation Analysis for Multifaceted Characterization Data.

We propose tabular two-dimensional correlation spectroscopy analysis for extracting features from multifaceted characterization data, essential for understanding material properties. This method visualizes similarities and phase lags in structural parameter changes through heatmaps, combining hierarchical clustering and asynchronous correlations. We applied the proposed method to data sets of carbon nanotube (CNT) films annealed at various temperatures and revealed the complexity of their hierarchical structures, which include elements such as voids, bundles, and amorphous carbon. Our analysis addresses the challenge of attempting to understand the sequence of structural changes, especially in multifaceted characterization data where 11 structural parameters derived from eight characterization methods interact with complex behavior. The results show how phase lags (asynchronous changes from stimuli), and parameter similarities can illuminate the sequence of structural changes in materials, providing insights into phenomena such as the removal of amorphous carbon and graphitization in annealed CNTs. This approach is beneficial even with limited data and holds promise for a wide range of material analyses, demonstrating its potential in elucidating complex material behaviors and properties.