Determination of the geographical origin of green coffee beans using NIR spectroscopy and multivariate data analysis

In this work, near infrared (NIR) spectroscopy and multivariate data analysis were investigated as a fast and non disruptive method to classify green coffee beans on continents and countries bases. FT-NIR spectra of 191 coffee samples, origin from 2 continents and 9 countries, were acquired by two different laboratories. Laboratory-independent Partial Least Square-Discriminant Analysis and interval PIS-DA models were developed by following a hierarchical approach, i.e. considering at first the continent and then the country of origin as discrimination rule. The best continent-based classification model was able to identify correctly more than 98% in prediction, whereas 100% of them were correctly predicted by the best country-based classification model. The inter-laboratory reliability of the proposed method was confirmed by McNemar test, since no significant differences (P > 0.05) were found. Furthermore, a validation was performed predicting the spectral test set of a laboratory using the model developed by the other one

Metabolomics as a Powerful Tool for Molecular Quality Assessment of the Fish Sparus aurata

The molecular profiles of perchloric acid solutions extracted from the flesh of Sparus aurata fish specimens, produced according to different aquaculture systems, have been investigated. The 1H-NMR spectra of aqueous extracts are indicative of differences in the metabolite content of fish reared under different conditions that are already distinguishable at their capture, and substantially maintain the same differences in their molecular profiles after sixteen days of storage under ice. The fish metabolic profiles are studied by top-down chemometric analysis. The results of this exploratory investigation show that the fish metabolome accurately reflects the rearing conditions. The level of many metabolites co-vary with the rearing conditions and a few metabolites are quantified including glycogen (stress indicator), histidine, alanine and glycine which all display significant changes dependent on the aquaculture system and on the storage times

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

Tracing the identity of Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” cheese using NMR spectroscopy and multivariate data analysis

Background Nuclear magnetic resonance (NMR) spectroscopy is one of the well-established tools for food metabolomic analysis, as it proved to be very effective in authenticity and quality control of dairy products, as well as to follow product evolution during processing and storage. The analytical assessment of the EU mountain denomination label, specifically for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" (Mountain-CQ) cheese, has received limited attention. Although it was established in 2012 the EU mountain denomination label has not been much studied from an analytical point of view. Nonetheless, tracing a specific profile for the mountain products is essential to support the value chain of this specialty. Results The aim of the study was to produce an identity profile for Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” (Mountain-CQ) cheese, and to differentiate it from Parmigiano Reggiano PDO samples (conventional-PDO) using 1H NMR spectroscopy coupled with multivariate data analysis. Three different approaches were applied and compared. First, the spectra-as-such were analysed after proper preprocessing. For the other two approaches, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used for signals resolution and features extraction, either individually on manually-defined spectral intervals or by reapplying MCR-ALS on the whole spectra with selectivity constraints using the reconstructed “pure profiles” as initial estimates and targets. All approaches provided comparable information regarding the samples’ distribution, as in all three cases the separation between the two product categories conventional-PDO and Mountain-CQ could be highlighted. Moreover, a novel MATLAB toolbox for features extraction via MCR-ALS was developed and used in synergy with the Chenomx library, allowing for a putative identification of the selected features. Significance A first identity profile for Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” obtained by interpreting the metabolites signals in NMR spectroscopy was obtained. Our workflow and toolbox for generating the features dataset allows a more straightforward interpretation of the results, to overcome the limitations due to dimensionality and to peaks overlapping, but also to include the signals assignment and matching since the early stages of the data processing and analysis

Optimization of Supercritical Carbon Dioxide Extraction of Rice Bran Oil and γ-Oryzanol Using Multi-Factorial Design of Experiment

After rice harvesting, the milling processes generate many by-products including husk, bran, germs, and broken rice representing around 40% of the total grain. Bran, one of the external cereal layers, contains proteins, dietary fibers, minerals, and lipids. One of the most common rice bran utilization is the extraction of rice bran oil (RBO). Among all vegetable oils, RBO presents a unique chemical composition rich in antioxidant compounds such as γ-oryzanol that provide several beneficial properties. RBO is generally extracted by exploiting hexane, a solvent toxic to the environment and human health. The growing demand for this oil has led researchers to look for more sustainable extraction techniques. Supercritical carbon dioxide (SC-CO2) has been successfully applied to extract oil and functional compounds from several matrices. In this work, the SC-CO2 extraction of RBO was optimized using a Design of Experiment (DoE) on a pilot scale. "The DoE approach involving multilinear regression allowed modelling the yield in RBO and gamma oryzanol as a function of temperature and pressure, keeping the extraction time constant, as decided by the company. This approach made it possible to optimize the extraction yield and to identify the best temperature (40 °C), while also highlighting that pressure did not play any influential role in the process, at least concerning the analyzed experimental domain on this industrial plant. A model for computing the extraction yield as a function of temperature and pressure was obtained. This study shows that it is possible to obtain good quality RBO, rich in γ-oryzanol and essential fatty acids, using low temperatures and pressures, starting from a rice milling by-product. Graphical Abstract: [Figure not available: see fulltext.

An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data

<p>Abstract</p> <p>Background</p> <p>Nuclear magnetic resonance spectroscopy (NMR) is a powerful technique to reveal and compare quantitative metabolic profiles of biological tissues. However, chemical and physical sample variations make the analysis of the data challenging, and typically require the application of a number of preprocessing steps prior to data interpretation. For example, noise reduction, normalization, baseline correction, peak picking, spectrum alignment and statistical analysis are indispensable components in any NMR analysis pipeline.</p> <p>Results</p> <p>We introduce a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra. For each aligned NMR data point the ratio of the between-group and within-group sum of squares (BW-ratio) is calculated to quantify the difference in variability between and within predefined groups of NMR spectra. This differential analysis is related to the calculation of the F-statistic or a one-way ANOVA, but without distributional assumptions. Statistical inference based on the BW-ratio is achieved by bootstrapping the null distribution from the experimental data.</p> <p>Conclusions</p> <p>The workflow performance was evaluated using a previously published dataset. Correlation maps, spectral and grey scale plots show clear improvements in comparison to other methods, and the down-to-earth quantitative analysis works well for the CluPA-aligned spectra. The whole workflow is embedded into a modular and statistically sound framework that is implemented as an R package called "speaq" ("spectrum alignment and quantitation"), which is freely available from <url>http://code.google.com/p/speaq/</url>.</p

Prognostic value of metabolic response in breast cancer patients receiving neoadjuvant chemotherapy

<p>Abstract</p> <p>Background</p> <p>Today's clinical diagnostic tools are insufficient for giving accurate prognosis to breast cancer patients. The aim of our study was to examine the tumor metabolic changes in patients with locally advanced breast cancer caused by neoadjuvant chemotherapy (NAC), relating these changes to clinical treatment response and long-term survival.</p> <p>Methods</p> <p>Patients (n = 89) participating in a randomized open-label multicenter study were allocated to receive either NAC as epirubicin or paclitaxel monotherapy. Biopsies were excised pre- and post-treatment, and analyzed by high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS). The metabolite profiles were examined by paired and unpaired multivariate methods and findings of important metabolites were confirmed by spectral integration of the metabolite peaks.</p> <p>Results</p> <p>All patients had a significant metabolic response to NAC, and pre- and post-treatment spectra could be discriminated with 87.9%/68.9% classification accuracy by paired/unpaired partial least squares discriminant analysis (PLS-DA) (<it>p </it>< 0.001). Similar metabolic responses were observed for the two chemotherapeutic agents. The metabolic responses were related to patient outcome. Non-survivors (< 5 years) had increased tumor levels of lactate (<it>p </it>= 0.004) after treatment, while survivors (≥ 5 years) experienced a decrease in the levels of glycine (<it>p </it>= 0.047) and choline-containing compounds (<it>p </it>≤ 0.013) and an increase in glucose (<it>p </it>= 0.002) levels. The metabolic responses were not related to clinical treatment response.</p> <p>Conclusions</p> <p>The differences in tumor metabolic response to NAC were associated with breast cancer survival, but not to clinical response. Monitoring metabolic responses to NAC by HR MAS MRS may provide information about tumor biology related to individual prognosis.</p

Diabetes mellitus, maternal adiposity, and insulin-dependent gestational diabetes are associated with COVID-19 in pregnancy: the INTERCOVID study

BACKGROUND: Among nonpregnant individuals, diabetes mellitus and high body mass index increase the risk of COVID-19 and its severity.OBJECTIVE: This study aimed to determine whether diabetes mellitus and high body mass index are risk factors for COVID-19 in pregnancy and whether gestational diabetes mellitus is associated with COVID-19 diagnosis.STUDY DESIGN: INTERCOVID was a multinational study conducted between March 2020 and February 2021 in 43 institutions from 18 countries, enrolling 2184 pregnant women aged >= 18 years; a total of 2071 women were included in the analyses. For each woman diagnosed with COVID-19, 2 nondiagnosed women delivering or initiating antenatal care at the same institution were also enrolled. The main exposures were preexisting diabetes mellitus, high body mass index (overweight or obesity was defined as a body mass index >= 25 kg/m(2)), and gestational diabetes mellitus in pregnancy. The main outcome was a confirmed diagnosis of COVID-19 based on a real-time polymerase chain reaction test, antigen test, antibody test, radiological pulmonary findings, or >= 2 predefined COVID-19 symptoms at any time during pregnancy or delivery. Relationships of exposures and COVID-19 diagnosis were assessed using generalized linear models with a Poisson distribution and log link function, with robust standard errors to account for model misspecification. Furthermore, we conducted sensitivity analyses: (1) restricted to those with a real-time polymerase chain reaction test or an antigen test in the last week of pregnancy, (2) restricted to those with a real-time polymerase chain reaction test or an antigen test during the entire pregnancy, (3) generating values for missing data using multiple imputation, and (4) analyses controlling for month of enrollment. In addition, among women who were diagnosed with COVID-19, we examined whether having gestational diabetes mellitus, diabetes mellitus, or high body mass index increased the risk of having symptomatic vs asymptomatic COVID-19.RESULTS: COVID-19 was associated with preexisting diabetes mellitus (risk ratio, 1.94; 95% confidence interval, 1.55-2.42), overweight or obesity (risk ratio, 1.20; 95% confidence interval, 1.06-1.37), and gestational diabetes mellitus (risk ratio, 1.21; 95% confidence interval, 0.99-1.46). The gestational diabetes mellitus association was specifically among women requiring insulin, whether they were of normal weight (risk ratio, 1.79; 95% confidence interval, 1.06-3.01) or overweight or obese (risk ratio, 1.77; 95% confidence interval, 1.28-2.45). A somewhat stronger association with COVID-19 diagnosis was observed among women with preexisting diabetes mellitus, whether they were of normal weight (risk ratio, 1.93; 95% confidence interval, 1.18-3.17) or overweight or obese (risk ratio, 2.32; 95% confidence interval, 1.82-2.97). When the sample was restricted to those with a real-time polymerase chain reaction test or an antigen test in the week before delivery or during the entire pregnancy, including missing variables using imputation or controlling for month of enrollment, the observed associations were comparable.CONCLUSION: Diabetes mellitus and overweight or obesity were risk factors for COVID-19 diagnosis in pregnancy, and insulin-dependent gestational diabetes mellitus was associated with the disease. Therefore, it is essential that women with these comorbidities are vaccinated