Evaluation of the generation of reactive oxygen species and antibacterial activity of honey as a function of its phenolic and mineral composition.

The antibacterial activity (ABA) of honey is associated with the generation of reactive oxygen species (ROS), where polyphenols (PFs) play a key role due to their pro-oxidant action modulated by metallic cations. In this work, the contents of PFs, H2O2, OH radicals, Cu, Fe, Mn, Zn, and ABA against Staphylococcus epidermidis and Pseudomonas aeruginosa were determined in honeys from central Chile. Then, their relationships were evaluated through partial least squares regression. The average contents of phenolic acids, flavonoids and metals in honey ranged from 0.4 to 4 µg/g, 0.3-1.5 µg/g and 3-6 µg/g, respectively. All honeys showed accumulation of H2O2 (1-35 µg/g) and OH radicals. The PLS showed that gallic acid, p-coumaric acid, chrysin, kaempferol, Fe, and Mn stimulate the generation of ROS. Quercetin, Cu, and Zn showed marginal antioxidant effects. PFs favor the ABA of honey against both bacteria and H2O2 against S. epidermidis.

Turbidity shapes shallow Southwestern Atlantic benthic reef communities.

Southwestern Atlantic reefs (Brazilian Province) occur along a broad latitudinal range (∼5°N-27°S) and under varied environmental conditions. We combined large-scale benthic cover and environmental data into uni- and multivariate regression tree analyses to identify unique shallow (<30 m) benthic reef communities and their environmental drivers along the Brazilian Province. Turbidity was the leading environmental driver of benthic reef communities, with the occurrence of two main groups: clear-water (dominated by fleshy macroalgae) and turbid (dominated by turf algae). Seven out of 14 scleractinian coral species were more abundant in the turbid group, thus corroborating the photophobic nature of some Brazilian corals. The most abundant scleractinian in Brazil (Montastraea cavernosa), largely dominated (71-93% of total coral cover) both, the shallow turbid and deeper clear-water reefs. Because these habitat types are widely recognized as potential climate refuges, local threats (e.g. pollution, overfishing) should be averted.

Multivariate Generalized Linear Models for Twin and Family Data.

Multivariate twin and family studies are one of the most important tools to assess diseases inheritance as well as to study their genetic and environment interrelationship. The multivariate analysis of twin and family data is in general based on structural equation modelling or linear mixed models that essentially decomposes sources of covariation as originally suggested by  Fisher. In this paper, we propose a flexible and unified statistical modelling framework for analysing multivariate Gaussian and non-Gaussian twin and family data. The non-normality is taken into account by actually modelling the mean and variance relationship, while the covariance structure is modelled by means of a linear covariance model including the option to model the dispersion components as functions of known covariates in a regression model fashion. The marginal specification of our models allows us to extend classic models and biometric indices such as the bivariate heritability, genetic, environmental and phenotypic correlations to non-Gaussian data. We illustrate the proposed models through simulation studies and six data analyses and provide computational implementation in R through the package mglm4twin.

Evaluation of chemical properties of intact green coffee beans using near-infrared spectroscopy.

BACKGROUND: The chemical compounds in coffee are important indicators of quality. Its composition varies according to several factors related to the planting and processing of coffee. Thus, this study proposed to use near-infrared spectroscopy (NIR) associated with partial least squares (PLS) regression to estimate quickly some chemical properties (moisture content, soluble solids, and total and reducing sugars) in intact green coffee samples. For this, 250 samples produced in Brazil were analyzed in the laboratory by the standard method and also had their spectra recorded. RESULTS: The calibration models were developed using PLS regression with cross-validation and tested in a validation set. The models were elaborated using original spectra and preprocessed by five different mathematical methods. These models were compared in relation to the coefficient of determination, root mean square error of cross-validation (RMSECV), root mean square error of test set validation (RMSEP), and ratio of performance to deviation (RPD) and demonstrated different predictive capabilities for the chemical properties of coffee. The best model was obtained to predict grain moisture and the worst performance was observed for the soluble solids model. The highest determination coefficients obtained for the samples in the validation set were equal to 0.810, 0.516, 0.694 and 0.781 for moisture, soluble solids, total sugar, and reducing sugars, respectively. CONCLUSION: The statistics associated with these models indicate that NIR technology has the potential to be applied routinely to predict the chemical properties of green coffee, and in particular, for moisture analysis. However, the soluble solid and total sugar content did not show high correlations with the spectroscopic data and need to be improved. © 2020 Society of Chemical Industry.

Determination of the isotopic composition of enriched materials using laser ablation molecular isotopic spectrometry: partial least squares and multivariate curve resolution for the determination of 15N content in enriched urea.

A quantitative analytical method based on laser ablation molecular isotopic spectrometry (LAMIS) and multivariate analysis was developed and evaluated for the determination of the isotopic composition of enriched materials. The method consists preparing a concentrated solution of the enriched material, using small quantities of a sample (125 mg), and ensuring the economic efficiency of the analysis. Standard solutions of known isotopic contents are prepared by employing mixtures of urea highly enriched in 15N and urea of natural isotopic ratio and analyzed by mass spectrometry. A small volume (30 µL) of these solutions is delivered to a filter paper disc (3 cm diameter). After drying, the disc, offering a homogeneously distributed analyte, is presented to a LAMIS equipment to acquire the vibronic emission spectra containing information about the isotopologues of interest. To illustrate the proposed method, the content of 15N is determined in enriched samples of urea. In this case, each spectrum is normalized by the intensity of emission of the CN isotopologues for the electronic (Δν = 0) emission band at 387.1 nm, ensuring better accuracy. Selected regions and single wavelengths of the vibronic emission spectrum (Δν = + 1 or - 1) related to CN species were employed to construct multivariate partial least squares (PLS) and univariate regression models to predict the isotopic content of new samples. Besides, the LAMIS data set was evaluated by multivariate curve resolution (MCR) algorithm. The best MCR and PLS models presented similar results regarding the accuracy to determine 15N content in enriched urea. MCR is capable of identifying spectral interferences and minimizing its effect. The results show that the proposed method based on LAMIS and PLS or MCR multivariate analysis can determine the 15N content in the range 5-50% with a root mean square error of prediction (RMSEP) respectively equal to 0.5 or 0.7% (m/m) in comparison with reference results obtained by mass spectrometry. Graphical abstract.

Prediction of the total acid number (TAN) of colombian crude oils via ATR-FTIR spectroscopy and chemometric methods.

Reactions as the attack by naphthenic and hydrogen sulfide have caused corrosion problems in the petroleum industry due to they affect the crude oil heating furnaces and distillation towers at temperatures between 220 and 400 °C. The total acid number (TAN) measurement has been used as a test to quantify the acid compounds in crude oils and has shown to be a reliable indicator of their corrosion degree. However, the standard method for the TAN measurement, ASTM D-644, involves long times, environment unfriendly wastes and high costs for each analysis. A more appropriate method for the TAN determination is implemented in this paper, by correlating Fourier transform infrared spectroscopy (FTIR) spectral data of the samples with the standard method measurements using multivariate regression models. In particular, the intensities and frequencies of their mid-infrared attenuated total reflectance (MIR-ATR) spectra (4000 - 400 cm-1) are used as independent variables of several principal component regression (PCR) and partial least squares regression (PLSR) models. The latter are employed to correlate the spectra with their respective TAN values so as to obtain a suitable prediction model. Twenty-six (26) samples of Colombian crude oils are used for the study with a TAN ranging from 0.1 to 6.8 mg KOH/g crude oil (ASTM D-664). The models are evaluated according to the coefficient of determination (R2), the root mean square error of calibration (RMSEC) and of prediction (RMSEP). The best model is obtained via PLSR using as few as four components (i.e. factors), which attains a calibration R2 of 0.981 and an RMSEC of 0.317 mg KOH/g crude oil, while for prediction it attains an R2 of 0.996 and an RMSEP of 0.160 mg KOH/g crude oil. It is observed that the functional groups COOH, CH3 and CH2 contribute the most to the prediction models. The designed methodology is faster and environmentally friendly since it does not require sample pretreatment and the use of toxic reagents, and of low-cost compared with the standard procedure since FTIR measurements can be easily taken anywhere using a hand-held or portable spectrometer and a laptop.

Comprehensive new approaches for variable selection using ordered predictors selection.

New strategies of ordered predictors selection (OPS) were developed in this work, making this method more versatile and expanding its worldwide use and applicability. OPS is a recognized method to select variables in multivariate regression and is used by analytical chemists and chemometrists. It shows high ability to improve the prediction of models after the selection of a few and important variables. At the core of OPS is sorting variables from informative vectors and systematically investigating the regression models to identify the most relevant set of variables by comparing the cross-validation parameters of the models. Nevertheless, the first version of the OPS method performs variable selection using only one informative vector at a time and is limited to just one variable selection run. Then, three new strategies were proposed. First, an automatic method was developed to perform variable selection using several informative vectors and their combinations. Second, the feedback OPS is presented, in this new strategy the pre-selected variables would return to a new selection. Last, a method to apply OPS in full array subdivisions called OPS intervals was established. Initially, the new strategies were applied in the six datasets used in the original OPS paper to compare the prediction performance with the new OPS algorithms. After that, twelve new datasets were used to test and compare the new OPS approaches with other variable selection methods, genetic algorithm (GA), the interval successive projections algorithm for PLS (iSPA), and recursive weighted partial least squares (rPLS). The new OPS approaches outperformed the first OPS version and the other variable selection methods. Results showed that in addition to greater predictive capacity, the accuracy in the selection of expected variables is highly superior with the new OPS approaches. Overall, the new OPS provided the best set of selected variables to build more predictive and interpretative regression models, proving to be efficient for variable selection in different types of datasets.

Systematic study of hydroxyl radical production in white wines as a function of chemical composition.

The chemical oxidation of white wines through the Fenton reaction has been widely studied, wherein the primary species is the hydroxyl radical (OH). This radical possesses high oxidising power and may oxidise the primary components in wines. The Fenton reaction in wines is complex and includes the participation of phenolic compounds, organic acids, and metals. However, there has been no systematic study to date that relates wine composition to OH production in real white wines. In this paper, a multivariate model was generated using the partial least squares (PLS) algorithm, and linear correlations were established between wine composition and OH production. Based on the results, the variables were classified as either pro-oxidants (malic acid, p-coumaric acid, and caffeic acid, % prodelphinidins, procyanidins B1, Mn, Cu, and Zn) or antioxidants (cis-piceid, ellagic acid, gallic acid, proanthocyanins, glucose, and proton concentration). Finally, the pathways by which wine compounds participate in OH production are discussed.

Quick analysis of organic matter in soil by energy-dispersive X-ray fluorescence and multivariate analysis.

The rapid, simple and accurate determination of soil quality indicators is fundamental for improvements in precision agriculture and consequently in production efficiency. The objectives of this study were to determine the organic matter (OM) and total organic carbon (TOC) concentrations in agricultural soil and to discriminate soil provenance by energy-dispersive X-ray fluorescence (EDXRF) combined with principal component analysis and partial least square regression. The conventional methods used for the determination of OM and TOC concentrations are the gravimetric and Walkley-Black methods, respectively. Figures of merit such as sensitivity, detection and quantification limits, accuracy and precision were evaluated. Samples were differentiated by their provenance, and the quality of the prediction model shows that EDXRF combined with multivariate analysis is a promising methodology to fulfil the lack of rapid and accurate analytical methods for the assessment of OM and TOC concentrations in agricultural soils.

Parasite abundance and its determinants in fishes from Brazil: an eco-epidemiological approach / Abundância parasitária e seus determinantes em peixes do Brasil: uma abordagem ecoepidemiológica

The variability in parasite abundance has an ecological basis; however, from an epidemiological point of view, the contribution of factors inherent to the host to the variability in parasite abundance remains an open question. A database consisting of 3,746 specimens of 73 fish species was used to verify the relation between the distribution of parasite abundance in fishes and a set of biotic factors inherent to the hosts. Classical and mixed Poisson regression models were constructed. Prevalence ratios (PR) and their respective 95% confidence intervals were estimated. The parasite abundance was significantly higher in female hosts, nonschooling species, species from benthopelagic and pelagic habitats, and fishes with greater body length. Overall, these results suggest that the variability in the abundance of infection is an attribute of the parasite species. Although the results are biologically plausible, important gaps may still exist and should be explored to better understand the variations in parasite abundance, which has great relevance in epidemiological studies. We reinforce the importance of choosing the statistical model most appropriate for the nature of the data to avoid spurious results, especially when the autocorrelation in the data is not taken into account.(AU)

A variabilidade na abundância parasitária tem embasamento na perspectiva ecológica, entretanto, do ponto de vista epidemiológico, permanece em aberto a possibilidade da contribuição de fatores inerentes aos hospedeiros para essa variabilidade. Foram analisados 3.746 espécimes, pertencentes a 73 espécies de peixes, para verificar a relação entre a distribuição da abundância parasitária em peixes e um conjunto de fatores bióticos inerentes aos hospedeiros. Modelos de Regressão de Poisson clássico e misto foram ajustados. As razões de prevalência (RP) e seus respectivos intervalos, com 95% de confiança, foram estimados. A abundância parasitária foi significativamente maior em hospedeiros fêmeas, não formadoras de cardumes, de hábitats bentopelágico e pelágico e com maior comprimento do corporal. De um modo geral, esses resultados sugerem que a abundância de infecção é um atributo da espécie de parasitos que pode ser variável. Apesar dos resultados apresentarem plausibilidade biológica, ainda pode haver lacunas importantes a serem exploradas para o melhor entendimento das variações da abundância parasitária que, por sua vez, tem grande relevância nos estudos epidemiológicos. Reforça-se a importância da escolha de um modelo estatístico mais adequado à natureza dos dados, evitando-se resultados espúrios, principalmente quando não se leva em conta a autocorrelação entre os dados.(AU)

Parasite abundance and its determinants in fishes from Brazil: an eco-epidemiological approach / Abundância parasitária e seus determinantes em peixes do Brasil: uma abordagem ecoepidemiológica

Abstract The variability in parasite abundance has an ecological basis; however, from an epidemiological point of view, the contribution of factors inherent to the host to the variability in parasite abundance remains an open question. A database consisting of 3,746 specimens of 73 fish species was used to verify the relation between the distribution of parasite abundance in fishes and a set of biotic factors inherent to the hosts. Classical and mixed Poisson regression models were constructed. Prevalence ratios (PR) and their respective 95% confidence intervals were estimated. The parasite abundance was significantly higher in female hosts, nonschooling species, species from benthopelagic and pelagic habitats, and fishes with greater body length. Overall, these results suggest that the variability in the abundance of infection is an attribute of the parasite species. Although the results are biologically plausible, important gaps may still exist and should be explored to better understand the variations in parasite abundance, which has great relevance in epidemiological studies. We reinforce the importance of choosing the statistical model most appropriate for the nature of the data to avoid spurious results, especially when the autocorrelation in the data is not taken into account.

Resumo A variabilidade na abundância parasitária tem embasamento na perspectiva ecológica, entretanto, do ponto de vista epidemiológico, permanece em aberto a possibilidade da contribuição de fatores inerentes aos hospedeiros para essa variabilidade. Foram analisados 3.746 espécimes, pertencentes a 73 espécies de peixes, para verificar a relação entre a distribuição da abundância parasitária em peixes e um conjunto de fatores bióticos inerentes aos hospedeiros. Modelos de Regressão de Poisson clássico e misto foram ajustados. As razões de prevalência (RP) e seus respectivos intervalos, com 95% de confiança, foram estimados. A abundância parasitária foi significativamente maior em hospedeiros fêmeas, não formadoras de cardumes, de hábitats bentopelágico e pelágico e com maior comprimento do corporal. De um modo geral, esses resultados sugerem que a abundância de infecção é um atributo da espécie de parasitos que pode ser variável. Apesar dos resultados apresentarem plausibilidade biológica, ainda pode haver lacunas importantes a serem exploradas para o melhor entendimento das variações da abundância parasitária que, por sua vez, tem grande relevância nos estudos epidemiológicos. Reforça-se a importância da escolha de um modelo estatístico mais adequado à natureza dos dados, evitando-se resultados espúrios, principalmente quando não se leva em conta a autocorrelação entre os dados.