The Effect of Processing Conditions on the Microstructure of Homopolymer High-Density Polyethylene Blends: A Multivariate Approach.

In this work, a multivariate approach was utilized for gaining some insights into the processing-structure-properties relationships in polyethylene-based blends. In particular, two high-density polyethylenes (HDPEs) with different molecular weights were melt-compounded using a twin-screw extruder, and the effects of the screw speed, processing temperature and composition on the microstructure of the blends were evaluated based on a Design of Experiment-multilinear regression (DoE-MLR) approach. The results of the thermal characterization, interpreted trough the MLR (multilinear regression) response surfaces, demonstrated that the composition of the blends and the screw rotation speed are the two most important parameters in determining the crystallinity of the materials. Furthermore, the rheological data were examined using a Principal Component Analysis (PCA) multivariate approach, highlighting also in this case the most prominent effect of the weight ratio of the two base polymers and the screw rotation speed.

Integration of rice apocarotenoid profile and expression pattern of Carotenoid Cleavage Dioxygenases reveals a positive effect of ß-ionone on mycorrhization.

Carotenoids are susceptible to degrading processes initiated by oxidative cleavage reactions mediated by Carotenoid Cleavage Dioxygenases that break their backbone, leading to products called apocarotenoids. These carotenoid-derived metabolites include the phytohormones abscisic acid and strigolactones, and different signaling molecules and growth regulators, which are utilized by plants to coordinate many aspects of their life. Several apocarotenoids have been recruited for the communication between plants and arbuscular mycorrhizal (AM) fungi and as regulators of the establishment of AM symbiosis. However, our knowledge on their biosynthetic pathways and the regulation of their pattern during AM symbiosis is still limited. In this study, we generated a qualitative and quantitative profile of apocarotenoids in roots and shoots of rice plants exposed to high/low phosphate concentrations, and upon AM symbiosis in a time course experiment covering different stages of growth and AM development. To get deeper insights in the biology of apocarotenoids during this plant-fungal symbiosis, we complemented the metabolic profiles by determining the expression pattern of CCD genes, taking advantage of chemometric tools. This analysis revealed the specific profiles of CCD genes and apocarotenoids across different stages of AM symbiosis and phosphate supply conditions, identifying novel reliable markers at both local and systemic levels and indicating a promoting role of ß-ionone in AM symbiosis establishment.

Comparison of the biological effects of gadodiamide (Omniscan) and gadoteridol (ProHance) by means of multi-organ and plasma metabolomics.

Gadolinium-based contrast agents (GBCAs) are massively employed in radiology to increase the diagnostic power of MRI. However, investigations aiming at detecting possible metabolic perturbations or adverse health effects due to gadolinium deposition are still lacking. In this work, aqueous organs extract and plasma samples were analyzed by GC-MS and 1H-NMR, respectively, to investigate the effects of multiple administrations of one linear (Omniscan) and one macrocyclic (ProHance) GBCA, on the main metabolic pathways in healthy mice. Multivariate analysis revealed that plasma metabolome was not differently perturbed by the two GBCAs, while, the multiorgan analysis displayed a clear separation of the Omniscan-treated from the control and the ProHance-treated groups. Interestingly, the most affected organs were the brain, cerebellum and liver. Thus, this work paves the way to both the safest use of the commercially available GBCAs and the development of new GBCAs characterized by lower general toxicity.

Endosperm structure and Glycemic Index of Japonica Italian rice varieties.

Introduction: Given that rice serves as a crucial staple food for a significant portion of the global population and with the increasing number of individuals being diagnosed with diabetes, a primary objective in genetic improvement is to identify and cultivate low Glycemic Index (GI) varieties. This must be done while ensuring the preservation of grain quality. Methods: 25 Italian rice genotypes were characterized calculating their GI "in vivo" and, together with other 29 Italian and non-Italian genotypes they were studied to evaluate the grain inner structure through Field Emission Scanning Electron Microscopy (FESEM) technique. Using an ad-hoc developed algorithm, morphological features were extracted from the FESEM images, to be then inspected by means of multivariate data analysis methods. Results and Discussion: Large variability was observed in GI values (49 to 92 with respect to glucose), as well as in endosperm morphological features. According to the percentage of porosity is possible to distinguish approximately among rice varieties having a crystalline grain (< 1.7%), those intended for the preparation of risotto (> 5%), and a third group having intermediate characteristics. Waxy rice varieties were not united by a certain porosity level, but they shared a low starch granules eccentricity. With reference to morphological features, rice varieties with low GI (<55) seem to be characterized by large starch granules and low porosity values. Our data testify the wide variability of Italian rice cultivation giving interesting information for future breeding programs, finding that the structure of the endosperm can be regarded as a specific characteristic of each variety.

Chemometric Differentiation of Sole and Plaice Fish Fillets Using Three Near-Infrared Instruments.

Fish species substitution is one of the most common forms of fraud all over the world, as fish identification can be very challenging for both consumers and experienced inspectors in the case of fish sold as fillets. The difficulties in distinguishing among different species may generate a "grey area" in which mislabelling can occur. Thus, the development of fast and reliable tools able to detect such frauds in the field is of crucial importance. In this study, we focused on the distinction between two flatfish species largely available on the market, namely the Guinean sole (Synaptura cadenati) and European plaice (Pleuronectes platessa), which are very similar looking. Fifty fillets of each species were analysed using three near-infrared (NIR) instruments: the handheld SCiO (Consumer Physics), the portable MicroNIR (VIAVI), and the benchtop MPA (Bruker). PLS-DA classification models were built using the spectral datasets, and all three instruments provided very good results, showing high accuracy: 94.1% for the SCiO and MicroNIR portable instruments, and 90.1% for the MPA benchtop spectrometer. The good classification results of the approach combining NIR spectroscopy, and simple chemometric classification methods suggest great applicability directly in the context of real-world marketplaces, as well as in official control plans.

Lipids in a Nutshell: Quick Determination of Lipid Content in Hazelnuts with NIR Spectroscopy.

Hazelnuts (Corylus avellana L.) are among the most consumed dry fruits all over the world. Their commercial quality is defined, above all, by origin and dimension, as well as by lipid content. Evaluation of this parameter is currently performed with chemical methods, which are expensive, time consuming, and complex. In the present work, the near-infrared (NIR) spectroscopy, using both a benchtop research spectrometer and a retail handheld instrument, was evaluated in comparison with the traditional chemical approach. The lipid content of hazelnuts from different growing regions of origin (Italy, Chile, Turkey, Georgia, and Azerbaijan) was determined with two NIR instruments: a benchtop FT-NIR spectrometer (Multi Purpose Analyser-MPA, by Bruker), equipped with an integrating sphere and an optic fibre probe, and the pocket-sized, battery-powered SCiO molecular sensor (by Consumer Physics). The Randall/Soxtec method was used as the reference measurement of total lipid content. The collected NIR spectra were inspected through multivariate data analysis. First, a Principal Component Analysis (PCA) model was built to explore the information contained in the spectral datasets. Then, a Partial Least Square (PLS) regression model was developed to predict the percentage of lipid content. PCA showed samples distributions that could be linked to their total crude fat content determined with the Randall/Soxtec method, confirming that a trend related to the lipid content could be detected in the spectral data, based on their chemical profiles. PLS models performed better with the MPA instrument than SCiO, with the highest R2 of prediction (R2PRED = 0.897) achieved by MPA probe, while this parameter for SCiO was much lower (R2PRED = 0.550). Further analyses are necessary to evaluate if more acquisitions may lead to better performances when using the SCiO portable spectrometer.

Monitoring of the Rioja red wine production process by 1 H-NMR spectroscopy.

BACKGROUND: As an inherently quantitative and unbiased analytical technique, proton nuclear magnetic resonance (1 H-NMR) provides an excellent method to monitor the quality of food and beverages, and a sensitive and informative tool to study the winemaking process. RESULTS: By using NMR, it is possible to monitor quantitative changes in wine metabolites (amino acids, organic acids and some phenolic compounds) during the winemaking process, including wine ageing. This study shows an increase in the concentration of the phenols at the beginning of alcoholic fermentation, as well as a stabilization and slight increase in gallic acid and a slight decrease in resveratrol during the oak barrel ageing step. CONCLUSION: This study demonstrates the potential of NMR as a process analytical technology (PAT) tool in the wine industry, by monitoring amino acids, organic acids and three polyphenols - gallic acid, catechin and resveratrol - during the winemaking process. This study of the time course evolution of wine has been conducted in a commercial winery rather than an experimental laboratory, demonstrating the capacity of this technique in commercial winemaking production. © 2021 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A Metabolomic Approach to Beer Characterization.

The consumers' interest towards beer consumption has been on the rise during the past decade: new approaches and ingredients get tested, expanding the traditional recipe for brewing beer. As a consequence, the field of "beeromics" has also been constantly growing, as well as the demand for quick and exhaustive analytical methods. In this study, we propose a combination of nuclear magnetic resonance (NMR) spectroscopy and chemometrics to characterize beer. 1H-NMR spectra were collected and then analyzed using chemometric tools. An interval-based approach was applied to extract chemical features from the spectra to build a dataset of resolved relative concentrations. One aim of this work was to compare the results obtained using the full spectrum and the resolved approach: with a reasonable amount of time needed to obtain the resolved dataset, we show that the resolved information is comparable with the full spectrum information, but interpretability is greatly improved.

Differentiation between Fresh and Thawed Cephalopods Using NIR Spectroscopy and Multivariate Data Analysis.

The sale of frozen-thawed fish and fish products, labeled as fresh, is currently one of the most common and insidious commercial food frauds. For this reason, the demand of reliable tools to identify the storage conditions is increasing. The present study was performed on two species, commonly sold in large-scale distribution: Cuttlefish (Sepia officinalis) and musky octopus (Eledone spp.). Fifty fresh cephalopod specimens were analyzed at refrigeration temperature (2 ± 2 °C), then frozen at -20 °C for 10 days and finally thawed and analyzed again. The performance of three near-infrared (NIR) instruments in identifying storage conditions were compared: The benchtop NIR Multi Purpose Analyzer (MPA) by Bruker, the portable MicroNIR by VIAVI and the handheld NIR SCiO by Consumer Physics. All collected spectra were processed and analyzed with chemometric methods. The SCiO data were also analyzed using the analytical tools available in the online application provided by the manufacturer to evaluate its performance. NIR spectroscopy, coupled with chemometrics, allowed discriminating between fresh and thawed samples with high accuracy: Cuttlefish between 82.3-94.1%, musky octopus between 91.2-97.1%, global model between 86.8-95.6%. Results show how food frauds could be detected directly in the marketplace, through small, ultra-fast and simplified handheld devices, whereas official control laboratories could use benchtop analytical instruments, coupled with chemometric approaches, to develop accurate and validated methods, suitable for regulatory purposes.

Human urine 1H NMR metabolomics reveals alterations of protein and carbohydrate metabolism when comparing habitual Average Danish diet vs. healthy New Nordic diet.

OBJECTIVES: The aim of this study was to investigate the alteration of the human urine metabolome by means of diet and to compare the metabolic effects of the nutritionally healthy New Nordic Diet (NND) with an Average Danish Diet (ADD). The NND was designed a decade ago by scientists and chefs, based on local and sustainable foods, including fish, shellfish, vegetables, roots, fruit, and berries. The NND has been proven to lower blood pressure, reduce glycemia, and lead to weight loss. METHODS: The human urine metabolome was measured by untargeted proton nuclear magnetic resonance spectroscopy in samples from 142 centrally obese Danes (20-66 years old), randomized to consume the ADD or the NND. The resulting metabolomics data was processed and analyzed using advanced multivariate data analysis methods to reveal effects related to the design factors, including diet, season, sex, and changes in body weight. RESULTS: Exploration of the nuclear magnetic resonance profiles revealed unique metabolite markers reflecting changes in protein and carbohydrate metabolism between the two diets. Glycine betaine, glucose, trimethylamine N-oxide and creatinine were increased in urine of the individuals following the NND compared with the ADD population, whereas relative concentrations of tartrate, dimethyl sulfone, and propylene glycol were decreased. Propylene glycol had a strong association with the homeostatic model assessment for insulin resistance in the NND group. The food intake biomarkers found in this study confirm the importance of these as tools for nutritional research. CONCLUSIONS: Findings from this study provided new insights into the effects of a healthy diet on glycemia, reduction of inflammation, and weight loss among obese individuals, and alteration of the gut microbiota metabolism.

Fused adjacency matrices to enhance information extraction: The beer benchmark.

Multivariate exploratory data analysis allows revealing patterns and extracting information from complex multivariate data sets. However, highly complex data may not show evident groupings or trends in the principal component space, e.g. because the variation of the variables are not grouped but rather continuous. In these cases, classical exploratory methods may not provide satisfactory results when the aim is to find distinct groupings in the data. To enhance information extraction in such situations, we propose a novel approach inspired by the concept of combining weak classifiers, but in the unsupervised context. The approach is based on the fusion of several adjacency matrices obtained by different distance measures on data from different analytical platforms. This paper is intended to present and discuss the potential of the approach through a benchmark data set of beer samples. The beer data were acquired using three spectroscopic techniques: Visible, near-Infrared and Nuclear Magnetic Resonance. The results of fusing the three data sets via the proposed approach are compared with those from the single data blocks (Visible, NIR and NMR) and from a standard mid-level data fusion methodology. It is shown that, with the suggested approach, groupings related to beer style and other features are efficiently recovered, and generally more evident.

Data fusion approaches in spectroscopic characterization and classification of PDO wine vinegars.

Spain is one of the major producers of high-quality wine vinegars having three protected designations of origin (a.k.a. PDOs): "Vinagre de Jerez", "Vinagre de Condado de Huelva" and "Vinagre de Montilla-Moriles". Their high prices due to their high quality and their high production costs explain the need for developing an adequate quality control technique and the interest in extensive characterization in order to capture the identity of each denomination. In this framework, methodologies based on non-targeted techniques, such as spectroscopies, are becoming popular in food authentication. Thus, for improving vinegar quality assessment, fusion of data blocks obtained from the same samples but different analytical techniques could be a good strategy, since the quantity and quality of sample knowledge could be enhanced providing new insights into the differentiation of vinegars. Therefore, the aim of this manuscript is the development of a multi-platform methodology and a model able to classify the Spanish wine vinegar PDOs. Sixty-five PDO wine vinegars were analyzed by four spectroscopic techniques: Fourier-transform mid-infrared spectroscopy (MIR), near infrared spectroscopy (NIR), multidimensional fluorescence spectroscopy (EEM) and proton nuclear magnetic resonance (1H-NMR). Two different data fusion strategies were evaluated: Mid-level data fusion with different preprocessing, and Common Component and Specific Weights analysis multiblock method. Exploratory and classification analysis on the data from individual techniques were also performed and compared with data fusion models. The data fusion models improved the classification, providing a more efficient differentiation, than the models based on single methods, and supporting the approach to combine these methods to achieve synergies for an optimized PDO differentiation.

Biomarkers of Individual Foods, and Separation of Diets Using Untargeted LC-MS-based Plasma Metabolomics in a Randomized Controlled Trial.

SCOPE: Self-reported dietary intake does not represent an objective unbiased assessment. The effect of the new Nordic diet (NND) versus average Danish diet (ADD) on plasma metabolic profiles is investigated to identify biomarkers of compliance and metabolic effects. METHODS AND RESULTS: In a 26-week controlled dietary intervention study, 146 subjects followed either NND, a predominantly organic diet high in fruit, vegetables, whole grains, and fish, or ADD, a diet higher in imported and processed foods. Fasting plasma samples are analyzed with untargeted ultra-performance liquid chromatography-quadruple time-of-flight. It is demonstrated that supervised machine learning with feature selection can separate NND and ADD samples with an average test set performance of up to 0.88 area under the curve. The NND plasma metabolome is characterized by diet-related metabolites, such as pipecolic acid betaine (whole grain), trimethylamine oxide, and prolyl hydroxyproline (both fish intake), while theobromine (chocolate) and proline betaine (citrus) were associated with ADD. Amino acid (i.e., indolelactic acid and hydroxy-3-methylbutyrate) and fat metabolism (butyryl carnitine) characterize ADD whereas NND is associated with higher concentrations of polyunsaturated phosphatidylcholines. CONCLUSIONS: The plasma metabolite profiles are predictive of dietary patterns and reflected good compliance while indicating effects of potential health benefit, including changes in fat metabolism and glucose utilization.

Investigation of Variations in the Human Urine Metabolome amongst European Populations: An Exploratory Search for Biomarkers of People at Risk-of-Poverty.

SCOPE: According to Eurostat 2016, approximately 119 million European citizens live at-risk-of-poverty (ROP). This subpopulation is highly diverse by ethnicity, age, and culture in the different EU states, but they all have in common a low income that could represent an increased risk of nutrient deficiencies due to poor nutritional habits. This study aims to investigate the human urine metabolome in the search of common biomarkers representing dietary deficiencies amongst European populations at ROP. METHODS AND RESULTS: 2732 urine samples were collected from 1391 subjects across five different European countries, including the United Kingdom, Finland, Italy, Lithuania, and Serbia, and analyzed using 1 H-NMR spectroscopy. The resulting urine metabolome data were explored according to study design factors including economic status, country, and gender. CONCLUSION: Partitioning of the effects derived from the study design factors using ANOVA-simultaneous component analysis (ASCA) revealed that country and gender effects were responsible for most of the systematic variation. The effect of economic status was, as expected, much weaker than country and gender, but more pronounced in Lithuania than in other countries. Citrate and hippurate were among the most powerful ROP biomarkers. The possible relationship between these markers and nutritional deficiencies amongst the ROP population is discussed.

Simultaneous classification of multiple classes in NMR metabolomics and vibrational spectroscopy using interval-based classification methods: iECVA vs iPLS-DA.

Interval based chemometric algorithms have proven to be very powerful for spectral alignments, spectral regressions and spectral classifications. The interval-based methods may not only improve the performance, but also reduce model complexity and enhance the spectral interpretation. Extended Canonical Variate Analysis (ECVA) is a powerful method for multiple group classifications of multivariate data and can easily be extended to an interval approach, iECVA. This study outlines the iECVA method and compares its performance to interval Partial Least Squares Discriminant Analysis (iPLS-DA) on three spectroscopic datasets from Nuclear Magnetic Resonance (NMR), Near Infrared (NIR) and Infrared (IR) spectroscopy, respectively. The results invariantly show that the interval-based classification methods greatly enhance the interpretability of the models by identifying important spectral regions, which facilitate interpretation and biomarker discovery. Although the results for the two methods are similar regarding the number of misclassifications and identified important regions, the model complexity of the PLS-DA proved to consistently lower than the ECVA. The Matlab source codes for both iECVA and iPLS-DA are made freely available at www. MODELS: life.ku.dk.

The Effect of Season on the Metabolic Profile of the European Clam Ruditapes decussatus as Studied by ¹H-NMR Spectroscopy.

In this study, the metabolome of Ruditapes decussatus, an economically and ecologically important marine bivalve species widely distributed in the Mediterranean region, was characterized by using proton Nuclear Magnetic Resonance (¹H-NMR) spectroscopy. Significant seasonal variations in the content of carbohydrates and free amino acids were observed. The relative amounts of alanine and glycine were found to exhibit the same seasonal pattern as the temperature and salinity at the harvesting site. Several putative sex-specific biomarkers were also discovered. Substantial differences were found for alanine and glycine, whose relative amounts were higher in males, while acetoacetate, choline and phosphocholine were more abundant in female clams. These findings reveal novel insights into the baseline metabolism of the European clam and represent a step forward towards a comprehensive metabolic characterization of the species. Besides providing a holistic view on the prominent nutritional components, the characterization of the metabolome of this bivalve represents an important prerequisite for elucidating the underlying metabolic pathways behind the environment-organism interactions.

Forecasting Chronic Diseases Using Data Fusion.

Data fusion, that is, extracting information through the fusion of complementary data sets, is a topic of great interest in metabolomics because analytical platforms such as liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy commonly used for chemical profiling of biofluids provide complementary information. In this study, with a goal of forecasting acute coronary syndrome (ACS), breast cancer, and colon cancer, we jointly analyzed LC-MS, NMR measurements of plasma samples, and the metadata corresponding to the lifestyle of participants. We used supervised data fusion based on multiple kernel learning and exploited the linearity of the models to identify significant metabolites/features for the separation of healthy referents and the cases developing a disease. We demonstrated that (i) fusing LC-MS, NMR, and metadata provided better separation of ACS cases and referents compared with individual data sets, (ii) NMR data performed the best in terms of forecasting breast cancer, while fusion degraded the performance, and (iii) neither the individual data sets nor their fusion performed well for colon cancer. Furthermore, we showed the strengths and limitations of the fusion models by discussing their performance in terms of capturing known biomarkers for smoking and coffee. While fusion may improve performance in terms of separating certain conditions by jointly analyzing metabolomics and metadata sets, it is not necessarily always the best approach as in the case of breast cancer.

New Nordic Diet versus Average Danish Diet: A Randomized Controlled Trial Revealed Healthy Long-Term Effects of the New Nordic Diet by GC-MS Blood Plasma Metabolomics.

A previous study has shown effects of the New Nordic Diet (NND) to stimulate weight loss and lower systolic and diastolic blood pressure in obese Danish women and men in a randomized, controlled dietary intervention study. This work demonstrates long-term metabolic effects of the NND as compared with an Average Danish Diet (ADD) in blood plasma and reveals associations between metabolic changes and health beneficial effects of the NND including weight loss. A total of 145 individuals completed the intervention and blood samples were taken along with clinical examinations before the intervention started (week 0) and after 12 and 26 weeks. The plasma metabolome was measured using GC-MS, and the final metabolite table contained 144 variables. Significant and novel metabolic effects of the diet, resulting weight loss, gender, and intervention study season were revealed using PLS-DA and ASCA. Several metabolites reflecting specific differences in the diets, especially intake of plant foods and seafood, and in energy metabolism related to ketone bodies and gluconeogenesis formed the predominant metabolite pattern discriminating the intervention groups. Among NND subjects, higher levels of vaccenic acid and 3-hydroxybutanoic acid were related to a higher weight loss, while higher concentrations of salicylic, lactic, and N-aspartic acids and 1,5-anhydro-d-sorbitol were related to a lower weight loss. Specific gender and seasonal differences were also observed. The study strongly indicates that healthy diets high in fish, vegetables, fruit, and whole grain facilitated weight loss and improved insulin sensitivity by increasing ketosis and gluconeogenesis in the fasting state.

Metabolic responses of clams, Ruditapes decussatus and Ruditapes philippinarum, to short-term exposure to lead and zinc.

This study investigated the effects of 48h heavy metal exposure upon the metabolic profiles of Ruditapes decussatus and Ruditapes philippinarum using (1)H NMR metabolomics. Both species were exposed to increasing concentrations of lead nitrate (10, 40, 60 and 100µg/L) and zinc chloride (20, 50, 100 and 150µg/L), under laboratory conditions. ICP-OES analysis was further performed on the clams' samples in order to verify the occurrence of heavy metal bioaccumulation. With respect to the controls, the metabolic profiles of treated R. decussatus exhibited higher levels of organic osmolytes and lower contents of free amino acids. An opposite behavior was shown by R. philippinarum. In terms of heavy metal, the exposure effects were more evident in the case of Pb rather than Zn. These findings show that NMR-based metabolomics has the required sensitivity and specificity for the identification of metabolites that can act as sensitive indicators of contaminant-induced stress.

Data on the changes of the mussels׳ metabolic profile under different cold storage conditions.

One of the main problems of seafood marketing is the ease with which fish and shellfish undergo deterioration after death. (1)H NMR spectroscopy and microbiological analysis were applied to get in depth insight into the effects of cold storage (4 °C and 0 °C) on the spoilage of the mussel Mytilus galloprovincialis. This data article provides information on the average distribution of the microbial loads in mussels׳ specimens and on the acquisition, processing, and multivariate analysis of the (1)H NMR spectra from the hydrosoluble phase of stored mussels. This data article is referred to the research article entitled "Metabolomics analysis of shucked mussels' freshness" (Aru et al., 2016) [1].