Impacts on the quality of surface water in a urban perimeter of the Rio Grande watershed, Brazilian Cerrado.

The aim of this study was to assess the spatiotemporal variation in water quality in the Grande River and the Ondas River, in the city of Barreiras, Bahia, Brazil. Water samples were collected at 11 points along the rivers, and eight physical-chemical parameters (electrical conductivity, pH, alkalinity, apparent and true color, turbidity, dissolved oxygen, and biochemical oxygen demand) and three microbiological indicators (heterotrophic bacteria, total and thermotolerant coliforms) were analyzed. Spatiotemporal variation was assessed using the multivariate techniques of principal component analysis/factorial analysis (PCA/FA) and hierarchical cluster analysis (HCA). The results of the PCA/FA highlighted eight of the eleven parameters as the main ones responsible for the variations in water quality, with the greatest increase in these parameters being observed in the rainy season, especially among the points influenced by sewage discharges and by the influence of the urban area. The CA grouped the results from 11 points into three main groups: group 1 corresponded to points influenced by sewage discharges; group 2 grouped points with mainly urban influences; and group 3 grouped points in rural areas. These groupings showed the negative influence of urbanization and also statistically significant variations between the groups and periods. The most degraded conditions were in group 1, and the least degraded conditions were in group 3. Assessment of the variations between the monitoring periods showed that rainfall had a significant impact on the increase or decrease in the parameters assessed, as a result of surface runoff linked to urbanization and increased river flow.

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.

Monitoring network optimization and impact of fish farming upon water quality in the Três Marias Hydroelectric Reservoir, Brazil.

Hydroelectric power is the main source of electrical energy in Brazil. Electrical energy providers have the duty to monitor water quality in reservoirs to preserve water quality and support best management practices that enable multiple water uses, including fish production. In this context, the objectives of this study were (i) to perform a historical evaluation of water quality in Três Marias Reservoir, (ii) to present an optimization of the water quality monitoring network, and (iii) to evaluate the evolution and impact of fish farming upon surface water quality by using secondary data measured in situ and remote sensing. A systematic approach was applied to analyze historical water quality data. Principal component analysis (PCA) and cluster analysis (CA) were applied to identify the most important parameters and monitoring points. Images obtained from Sentinel 2 were treated by contrast to quantify simple and weighted densities of fish farming activities in the region while regression analysis was performed to verify correlations between these densities and water quality parameters. Results showed that the pH and total suspended solids were the most important parameters for characterizing water quality, especially near tributaries, and that monitoring points could be grouped into three clusters (upstream, central, and downstream regions) with distinct water quality conditions. The PCA indicated that there is no redundance among parameters nor monitoring stations and that areas near tributaries must be prioritized for monitoring as these are important sources of suspended solids. Remote sensing images showed that the area occupied by fish farms has increased in the reservoir from 2016 to 2022 and the methodology used for this purpose in this study may be applied to other bodies of water. Chlorophyll-a showed a direct relationship with the density of fish farms indicating a possible influence of nutrient input to the reservoir by this activity. These results provide valuable information to support decision-making related to water management in the reservoir.

STATIS multivariate three-way method for evaluating quality of life after corneal surgery: Methodology and case study in Costa Rica.

Vision-related quality of life (QoL) analyzes the visual function concerning individual well-being based on activity and social participation. Because QoL is a multivariate construct, a multivariate statistical method must be used to analyze this construct. In this paper, we present a methodology based on STATIS multivariate three-way methods to assess the real change in vision-related QoL for myopic patients by comparing their conditions before and after corneal surgery. We conduct a case study in Costa Rica to detect the outcomes of patients referred for myopia that underwent refractive surgery. We consider a descriptive, observational and prospective study. We utilize the NEI VFQ-25 instrument to measure the vision-related QoL in five different stages over three months. After applying this instrument/questionnaire, a statistically significant difference was detected between the perceived QoL levels. In addition, strong correlations were identified with highly similar structures ranging from 0.857 to 0.940. The application of the dual STATIS method found the non-existence of reconceptualization in myopic patients, but a statistically significant recalibration was identified. Furthermore, a real change was observed in all patients after surgery. This finding has not been stated previously due to the limitations of the existing statistical tools. We demonstrated that dual STATIS is a multivariate method capable of evaluating vision-related QoL data and detecting changes in recalibration and reconceptualization.

Discrimination of leukemias and non-leukemic cancers in blood serum samples of children and adolescents using a Raman spectral model.

This study aimed to identify the differences presented in the Raman spectrum of blood serum from normal subjects compared to leukemic and non-leukemic subjects and the differences between the leukemics and non-leukemics, correlating the spectral differences with the biomolecules. Serum samples from children and adolescents were subjected to Raman spectroscopy (830 nm, laser power 350 mW; n = 566 spectra, being 72 controls, 269 leukemics, and 225 non-leukemics). Exploratory analysis based on principal component analysis (PCA) of the serum sample's spectra was performed. Classification models based on partial least squares discriminant analysis (PLS-DA) were developed to classify the spectra into normal, leukemic, and non-leukemic, as well as to discriminate spectra of leukemic from non-leukemic. The exploratory analysis showed principal components with peaks related to amino acids, proteins, lipids, and carotenoids. The spectral differences between normal, leukemic, and non-leukemic showed features assigned to proteins (serum features), amino acids, and carotenoids. The PLS-DA model classified the spectra of the normal group versus leukemic and non-leukemic groups with accuracy of 66%, sensitivity of 99%, and specificity of 57%. The PLS-DA discriminated the spectra of the leukemic and non-leukemic groups with accuracy of 67%, sensitivity of 72%, and specificity of 60%. The study showed that Raman spectroscopy is a technique that may be used for the biochemical differentiation of leukemias and other types of cancer in serum samples of children and adolescents. Nevertheless, building an extensive data library of Raman spectra from serum samples of controls, leukemics, and non-leukemics of different age groups is necessary to understand the findings better.

Identifying contamination of heavy metals in soils of Peruvian Amazon plain: use of multivariate statistical techniques.

The Peruvian Amazon plain has abundant natural resources and is home to great biodiversity, which makes it an area with high economic potential. However, the use of its resources through various activities has contributed to the release of heavy metals (HMs) into its soils, generating severe pollution problems which have mainly affected the health of local populations and their ecosystems. Currently, there are no comprehensive studies that have identified the specific sources of contamination by HMs in the soils of this part of the Peruvian territory. In this sense, this research aims to identify the possible sources of contamination by HMs in the soils of the Peruvian Amazon plain to focus efforts on the establishment of adequate measures for the protection of the health of people and the ecosystem. In the present study, samples of topsoils (0-20 cm depth) and subsoils (100-150 cm depth) were collected for the analysis of 11 HMs (Co, Cr, Cu, Fe, Mn, Ni, Pb, V, Zn, Be, and Hg) in 48 sites located in four regions of the Peruvian Amazon plain (Loreto, Amazonas, San Martín, and Ucayali), over the year 2019. The enrichment factor and geoaccumulation index were applied to assess contamination levels of HMs. The results indicated that topsoils and subsoils presented a greater enrichment by the elements Be and Pb, and were classified as moderately contaminated. Likewise, the integral analysis of these indexes together with principal component analysis, hierarchical cluster analysis, correlation analysis, and coefficient of variation allowed the identification of potential sources of contamination by HMs. As a result, Fe, Co, Zn, Ni, V, and Cr were associated with natural or lithogenic sources (parent material, crude oil deposits, and organic matter decomposition). Hg was attributed to anthropogenic sources (illegal gold mining, atmospheric deposition, and vehicle emissions). Be, Pb, Cu, and Mn originated from natural sources (parent material, crude oil deposits, decomposition of organic matter, and forest fires) and anthropogenic (areas degraded by solid waste, illegal gold mining, agriculture, and hydrocarbons). These findings provide essential information to establish regulations and prevent and control HM contamination in soils of the Peruvian Amazon plain.

Differential Chemical Profile of Metabolite Extracts Produced by the Diaporthe citri (G-01) Endophyte Mediated by Varying the Fermented Broth pH.

Endophytic microorganisms show great potential for biotechnological exploitation because they are able to produce a wide range of secondary compounds involved in endophyte−plant adaptation, and their interactions with other living organisms that share the same microhabitat. Techniques used to chemically extract these compounds often neglect the intrinsic chemical characteristics of the molecules involved, such as the ability to form conjugate acids or bases and how they influence the solubilities of these molecules in organic solvents. Therefore, in this study, we aimed to evaluate how the pH of the fermented broth affects the process used to extract the secondary metabolites of the Diaporthe citri strain G-01 endophyte with ethyl acetate as the organic solvent. The analyzed samples, conducted by direct-infusion electrospray-ionization mass spectrometry, were grouped according to the pH of the fermented broth (i.e., <7 and ≥7). A more extreme pH (i.e., 2 or 12) was found to affect the chemical profile of the sample. Moreover, statistical analysis enabled us to determine the presence or absence of ions of high importance; for example, ions at 390.7 and 456.5 m/z were observed mainly at acidic pH, while 226.5, 298.3, and 430.1 m/z ions were observed at pH ≥ 7. Extraction at a pH between 4 and 9 may be of interest for exploring the differential secondary metabolites produced by endophytes. Furthermore, pH influences the chemical phenotype of the fungal metabolic extract.

Effect of elemental composition assigned to antrotopic pollution on the quality of the water and sediment of the Marrecas river (PR, Brazil) as highlighted by multivariate statistical analyses.

In recent years, several environmental pollutants have been monitored in surface waters and sediments. However, few studies apply multivariate statistics to identify the main components and correlate them temporally and spatially. In this sense, the present study sought to monitor the quality of water and sediments in the Rio Marrecas/Brazil, through the analysis of physicochemical parameters and trace elements, as well as to identifying sources of contamination, using multivariate statistics. For this purpose, sampling was carried out in nine locations for a period of 12 months. The Total Reflection X-ray Fluorescence (TXRF) technique was used to quantify the 15 elements identified in water and sediment samples. Through multivariate statistical analyses, the most significant elements, their correlations and possible pollutant sources were defined, and the pollution index (HPI) and assessment index (HEI) of heavy metals were applied. The parameters pH and BOD5 do not comply with Brazilian legislation. Based on PCA and Spearman correlation, there was strong evidence of contamination of the water naturally, composed of the elements Ti, V, Mn, Fe, and of anthropogenic origin composed of the elements Ca, Ni, Cu, Zn. These findings provide insights to determine the impacts of heavy metals on human health and the environment.

Self-Perceived Emotional Intelligence Levels in Nursing Students in Times of a Pandemic: Multivariate Representation.

Self-perceived emotional intelligence in healthcare personnel is not just an individual skill but a work tool, which is even more necessary in times of crisis. This article aimed to determine emotional intelligence as perceived by students studying nursing at the University of Colima, Mexico, a year after the start of the COVID-19 pandemic. A cross-sectional survey of an academic year stratified population of 349 students was conducted, using the Trait Meta-Mood Scale-24 instrument. A global descriptive analysis was performed for each school year. Additionally, an ANOVA was performed, and a Multiple Correspondence Analysis was executed. It is essential to highlight the high percentages for emotional attention within the results. However, a large percentage of students required improvement in emotional attention, clarity, and repair. According to their school year, significant differences were observed among student groups within the three emotional intelligence subscales (p < 0.05). Second-year students had low levels in the three subscales of emotional intelligence, while fourth-year students had adequate levels. We established that the scores were different depending on the school year, with a significant decrease in second-year students. The implementation of educational programs could aid in the development of emotional skills in students from the health field, especially in times of crisis.

Evaluation of the physiological potential of Panicum maximum seeds by multivariate analysis

The aim of this work was to identify efficient vigor tests for differentiating the seed lots, forecasting seedling emergence in the field and assessing the physiological quality of Panicum maximum seeds. 12 seed lots from the cultivar Tanzania and 11 seed lots from the cultivar Massai were evaluated for water content, germination, first count and germination speed index, emergence and first emergence count of seedlings in sand, root length and shoot length, analysis of SVIS® images (seedling length, vigor and uniformity index) and seedling emergence in the field. The work was conducted in a completely randomized design for tests performed in the laboratory and in randomized blocks for tests in the field. The data were subjected to analysis of variance and the means compared by Scott Knott's test at 5% probability and statistical multivariate clustering analysis and principal components analysis. The shoot and root length tests are efficient for the evaluation of the physiological potential of P. maximum cv. Massai, while the seedling length, vigor index and growth uniformity index tests using image analysis, seedling emergence in sand and first seedling emergence count in sand are efficient in assessing the physiological potential of seeds of P. maximum cv. Tanzania, and providing information similar to that of seedling emergence in the field.

Support vector machine and PCA for the exploratory analysis of Salvia officinalis samples treated with growth regulators based in the agronomic parameters and multielement composition.

The objective of this work was to evaluate the influence of different growth regulators on the mineral and total phenolic contents of Salvia officinalis. The samples received the applications of salicylic acid (AS); gibberellic acid (GA3); abscisic acid (ABA) and solution without regulators (control). The exploratory evaluation of the samples was carried out through the Principal Component Analysis (PCA). In addition, has been used supervised learning methods with support vector machine (SVM) algorithms to classify the samples. The phenolic and total flavonoid contents were higher in the plants treated with the regulators. The element found in the highest concentration in Salvia officinalis was N. Plants sprayed with ABA showed higher concentrations of N, K, and Mn; Fe and Al were higher with ABA and gibberellin application, while the application of AS provided the highest accumulation of P. The application of plant regulators improves the nutraceutical properties of Salvia officinalis.

Multivariate relationships among pH, subcutaneous fat thickness, and color in bovine meat using canonical correlation analysis / Relación multivariada entre pH, espesor de grasa subcutánea y color en carne bovina usando el análisis de correlación canónica / Relação multivariada entre pH, espessura de gordura subcutânea e cor em carne bovina usando a análise de correlação canônica

Abstract Background: pH, subcutaneous fat thickness (SFT), and color are fundamental variables to define the organoleptic characteristics of meat. However, multivariate relationships of those traits remain unexplored in bovine meat. Objective: To investigate the multivariate relationships among pH, subcutaneous fat thickness, and color parameters in bovine meat using canonical correlation analysis. Methods: A dataset containing 173 individual records of pH, SFT, and color parameters (a*: intensity of red color, b*: intensity of yellow color, and L*: lightness) from five Brazilian beef cut types (Breed: Nellore; cuts: acém, contrafilé, fraldinha, patinho and picanha) was constructed. Multivariate relationships between color variables (a*, b*, and L*) and chemical variables (pH and SFT) were explored using the CANCORR procedure of SAS. Results: Two canonical correlations between U (a*, b*, and L*; color variables) and V (pH and SFT; chemical variables) variates were significant (p<0.01). First and second canonical correlations were 0.463 and 0.282, respectively. Canonical weights for variates were for U1: a* = 0.707, b* = 0.406, and L* = -0.039; U2: a* = 0.364, b* = -0.898, and L* = 1.234; V1: pH = -0.376 and SFT = 0.935; V2: pH = 0.927 and STF = 0.356. Conclusion: Subcutaneous fat thickness significantly affected intensity of red and yellow colors, whereas pH significantly affected lightness. The results of this study may be useful for a better understanding of the role of muscle metabolism and its implications on the organoleptic characteristics of bovine meat.

Resumen Antecedentes: El pH, espesor de la grasa subcutánea (SFT) y color, son variables importantes que definen las características organolépticas de la carne de rumiantes. Sin embargo, su relación multivariada en carne bovina permanece inexplorada hasta ahora. Objetivo: Investigar la relación multivariada entre el pH, SFT y parámetros de color en carne bovina mediante el análisis de correlación canónica. Métodos: Se construyó una base de datos con 173 registros individuales de pH, SFT y parámetros de color (a*: intensidad de color rojo, b*: intensidad de color amarillo y L*: luminosidad) de cinco tipos de cortes de carne bovina brasileña (Raza: Nellore; cortes: acém, contrafilé, fraldinha, patinho y picanha). La relación multivariada entre las variables de color (a*, b* y L*) y las variables químicas (pH y SFT) se exploró usando el procedimiento CANCORR de SAS. Resultados: Dos correlaciones canónicas entre las variables U (compuesta por a*, b* y L*; variables de color) y V (compuesta por pH y SFT; variables químicas) fueron significativas (p<0,01). La primera y la segunda correlación canónica fueron 0,463 y 0,282, respectivamente. Los pesos canónicos para las variables canónicas fueron para U1: a* = 0,707, b* = 0,406 y L* = -0,039; U2: a* = 0,364, b* = -0,898 y L* = 1,234; V1: pH = -0,376 y SFT = 0,935; V2: pH = 0,927 y SFT = 0,356. Conclusión: El espesor de grasa subcutánea afectó significativamente la intensidad de los colores rojo y amarillo, mientras que el pH afectó significativamente la luminosidad. Los resultados de este estudio pueden ser útiles para comprender el papel del metabolismo muscular y sus implicaciones en las características organolépticas de la carne bovina.

Resumo Antecedentes: O pH, a espessura da gordura subcutânea (SFT) e a cor, são variáveis importantes que definem as características organolépticas da carne de ruminantes. No entanto, sua relação multivariada em carne bovina até o momento permanece inexplorada. Objetivo: Investigar a relação multivariada entre o pH, SFT e os parâmetros de cor em carne bovina, utilizando a análise de correlação canônica. Métodos: Foi construído um banco de dados contendo 173 registros individuais de pH, SFT e parâmetros de cor (a*: intensidade de cor vermelha, b*: intensidade de cor amarela y L*: luminosidade) de cinco tipos de cortes de carne bovina brasileira (Raça: Nellore; cortes: acém, contrafilé, fraldinha, patinho e picanha). A relação multivariada entre variáveis de cor (a *, b * e L*) e variáveis químicas (pH e SFT) foi explorada usando o procedimento CANCORR do SAS. Resultados: Duas correlações canônicas entre as variáveis U (composta de a *, b * e L *, variáveis de cor) e V (composta de pH e SFT, variáveis químicas) foram significativas (p<0,01). A primeira e segunda correlação canônica foram 0,463 e 0,282, respectivamente. Os pesos canônicos para as variáveis canônicas foram para U1: a* = 0,707, b* = 0,406 e L* = -0,039; U2: a* = 0,364, b* = -0,898 e L* = 1,234; V1: pH = -0,376 e SFT = 0,935; V2: pH = 0,927 e SFT = 0,356. Conclusão: A espessura de gordura subcutânea afetou significativamente a intensidade das cores vermelha e amarela, enquanto o pH afetou significativamente a luminosidade, em carne bovina. Os resultados deste estudo podem ser úteis para entender melhor o papel do metabolismo muscular e suas implicações nas características organolépticas da carne bovina.

Leadership, adequate staffing and material resources, and collegial nurse-physician relationships promote better patients, professionals and institutions outcomes.

AIM: To test a theoretical model aiming to understand which characteristics of the professional nursing practice environment most affect patients, professionals and institution outcomes. DESIGN: A cross-sectional and correlational study, using a structural equation model. METHODS: One thousand seven hundred and seventy-three staff nurses were recruited using convenience sampling in five Brazilian hospitals from November 2017 to July 2018. Structural equation modelling was used to assess the relationship between the characteristics of the nursing work environment and patients (climate of safety and quality of care), nursing professionals (job satisfaction and emotional exhaustion) and institutions (intention to leave the job) outcomes. The model was tested using the partial least squares method, considering the bootstrapping technique to estimate the results. The path coefficients and their respective 95% confidence intervals were calculated. The quality of fit of the structural model was assessed by calculating the coefficient of determination (R2 ), the predictive validity coefficient (Q2 ) and the effect size (f2 ). RESULTS: The characteristics that most affected the outcomes for patients were Nurse manager ability, leadership and support of nurses (λ=0.27), and Staffing and resource adequacy (λ=0.26); for nursing professionals, Staffing and resource adequacy (λ=-0.19), and Collegial nurse-physician relations (λ=0.19); and for institutions, Nurse manager ability, leadership and support of nurses (λ=-0.10), and Collegial nurse-physician relations (λ=-0.10). CONCLUSION: The characteristics of the professional nursing practice environment that most contribute to achieving better outcomes include nurse manager ability, leadership and support of nurses, staffing and resource adequacy, and collegial nurse-physician relations. IMPACT: This study allowed us to assess which strategies should be prioritized in the professional nursing practice environment to achieve better results. Thus, investment in the training of leadership, in the adequacy of resources, and in physician-nurse relations will bring better results for patients, nursing professionals, and institutions.

A systemic approach to the quantification of the phenotypic plasticity of plant physiological traits: the multivariate plasticity index.

The phenotype of an individual emerges from the interaction of its genotype with the environment in which it is located. Phenotypic plasticity (PP) is the ability of a specific genotype to present multiple phenotypes in response to the environment. Past and current methods for quantification of PP present limitations, mainly in what constitutes a systemic analysis of multiple traits. This research proposes an integrative index for quantifying and evaluating PP. The multivariate plasticity index (MVPi) was calculated based on the Euclidian distance between scores of a canonical variate analysis. It was evaluated for leaf physiological traits in two cases using Brazilian Cerrado species and sugarcane varieties, grown under diverse environmental conditions. The MVPi was sensitive to plant behaviour from simple to complex genotype-environment interactions and was able to inform coarse and fine changes in PP. It was correlated to biomass allocation, showing agreement between plant organizational levels. The new method proved to be elucidative of plant metabolic changes, mainly by explaining PP as an integrated process and emergent property. We recommend the MVPi method as a tool for analysis of phenotypic plasticity in the context of a systemic evaluation of plant phenotypic traits.

Insights into chemical and sensorial aspects to understand and manage beer aging using chemometrics.

Beer chemical instability remains, at present, the main challenge in maintaining beer quality. Although not fully understood, after decades of research, significant progress has been made in identifying "aging compounds," their origin, and formation pathways. However, as the nature of aging relies on beer manufacturing aspects such as raw materials, process variables, and storage conditions, the chemical profile differs among beers. Current research points to the impact of nonoxidative reactions on beer quality. The effect of Maillard and Maillard intermediates on the final beer quality has become the focus of beer aging research, as prevention of oxidation can only sustain beer quality to some extent. On the other hand, few studies have focused on tracing a profile of whose compound is sensory relevant to specific types of beer. In this matter, the incorporation of "chemometrics," a class of multivariate statistic procedures, has helped brewing scientists achieve specific correlations between the sensory profile and chemical data. The use of chemometrics as exploratory data analysis, discrimination techniques, and multivariate calibration techniques has made the qualitatively and quantitatively translation of sensory perception of aging into manageable chemical and analytical parameters. However, despite their vast potential, these techniques are rarely employed in beer aging studies. This review discusses the chemical and sensorial bases of beer aging. It focuses on how chemometrics can be used to their full potential, with future perspectives and research to be incorporated in the field, enabling a deeper and more specific understanding of the beer aging picture.

Application of the principal component analysis, cluster analysis, and partial least square regression on crossbreed Angus-Nellore bulls feedlot finished.

Principal component analysis (PCA) and the non-hierarchical clustering analysis (K-means) were used to characterize the most important variables from carcass and meat quality traits of crossbred cattle. Additionally, partial least square (PLS) regression analysis was applied between the carcass measurements and meat quality traits on the classes defined by the cluster analysis. Ninety-seven non-castrated F1 Angus-Nellore bulls feedlot finished were used. After slaughter, hot carcass weight, carcass yield, cold carcass weight, carcass weight losses, pH, and backfat thickness (BFT) were measured. Subsequently, samples of the longissimus thoracis were collected to analyze shear force (SF), cooking loss (CL), meat color (L*, chroma, and hue), intramuscular fat, protein, collagen, moisture, and ashes. Principal component 1 (PC1) was correlated with colorimetric variables, while PC2 was correlated with carcass weights. Afterwards, three clusters (k = 3) were formed and projected in the gradient defined by PC1 and PC2 and allowed distinguishing groups with divergent values for collagen, protein, moisture, CL, SF, and BFT. Animals from high chroma group presented meat with more attractive colors and tenderness (SF = 1.97 to 4.84 kg). Subsequently, the PLS regression on the three chroma groups revealed a good fitness and the coefficients are used to predict the chroma variable from the explanatory variables, which may have practical importance in attempts to predict meat color from carcass and meat quality traits. Thus, PCA, K-means, and PLS regression confirmed the relationship between meat color and tenderness.

Classification of Brazilian roasted coffees from different geographical origins and farming practices based on chlorogenic acid profiles.

Sixty-seven roasted coffee samples from different regions of Brazil cultivated using organic, conventional and biodynamic farming practices were analysed and quantified using high performance liquid chromatography coupled with mass spectrometry, and treated with supervised (PLS-DA) and unsupervised (PCA) multivariate statistical tools. The profile of the chlorogenic acids constituents were analysed by high resolution and tandem mass spectrometry, which allowed the identification of mono- caffeoyl-, feruloyl-, para-Coumaroylquinic acids and their respective regio-isomers. This study provides a comprehensive analysis of absolute quantitative data set of chlorogenic acids constituents (CQA, FQA and pCoQA isomers) in Brazilian coffee beans produced from different regions of the country. Variations in the chlorogenic acids compositions were observed if organic and conventional roasted coffee beans were compared. The use of multivariate statistical tools allowed the identification of suitable biomarkers for determining significant differences between the three coffee agricultural practices, while coffees produced from the diverse geographical regions showed no significant difference.

A multi-approach assessment of land use effects on groundwater quality in a karstic aquifer.

Groundwater represents almost half of the drinking water worldwide and more than one third of water used for irrigation. Agro-industrial activities affect water resources in several manners; one of the most important is leaching of agrochemical residues. This research identifies the major contributors of changes in groundwater quality comparing two contrasting land uses in a karstic area of the Yucatan peninsula as case study. Using a multiple approach, we assess the impact of land use with physicochemical data, multivariate analyses, hydrogeochemistry and nitrate isotopic composition. We confirmed that agricultural land use has a greater impact on groundwater quality, observed in higher concentration of nitrates, ammonium, potassium and electrical conductivity. Seasonality has an influence on phosphates and the chemical composition of the groundwater, increasing the concentration of dissolved substances in the rainy season. There was a clear effect of manure application in the agricultural zone and the nitrate isotopic composition of groundwater points toward recharge in certain areas. We consider that seasonality and land use effects are intertwined and sometimes difficult to separate, likely because of land use intensity and hydrogeochemical process at a local scale. Finally, we observed poor groundwater quality in the agricultural area during the wet season; thus, it is desirable to maintain non-agricultural areas that provide groundwater of appropriate quality.

Relationship between reproductive and productive traits in Holstein cattle using multivariate analysis.

Multivariate procedures are used for the extraction of variables from the correlation matrix of phenotypes in order to identify those traits that explain the largest proportion of phenotypic variation and to evaluate the relationship structure between these traits. The reproductive traits (days from calving to first estrus [CFE], days from calving to last service [CLS], calving interval [CI] and gestation length [GL]) and milk production traits (milk yield at 305 days of lactation [MY305], peak yield [PY] and milk yield per day of calving interval [MYCI]) of 5,217 Holstein females (primiparous and multiparous) were measured. Principal component analysis (PCA) and factor analysis of the correlation matrix were used to estimate the correlation between traits. Analysis grouped the seven traits into three principal components and four latent factors that retained approximately 81.5% and 88.9% of the total variation of the data, respectively. The production variables exhibited positive phenotypic correlation coefficients of high magnitude (>.67). The phenotypic correlation estimates between the productive and reproductive traits were low, ranging from .13 to .22. A strong association (.99) was observed between CLS and CI. Our results indicate that multivariate analysis was effective in generating correlations between the traits studied, grouping the seven traits in a smaller number of variables that retained approximately 81% of the total variation of the data.

Sensory attributes of coffee beverages and their relation to price and package information: A case study of Colombian customers' preferences.

Sensory analysis is a powerful tool for creating profiles of food and beverages based on information perceived by the human senses. This paper investigates 18 of the most popular Colombian coffees. Individuals from nine different cities assessed products in two different ways: degree of presence (absence) of sensory properties and degree of acceptance (liking). The results focused on identifying variations in sensory evaluations due to the city, as well as classification of the products according to their degree of acceptance or rejection, and investigating associations between sensory attributes, price, and label-package information. A correspondence analysis allowed us to investigate the variation introduced by the factor city. The most preferred/rejected products were identified through preference mapping. The level of intensity of the smelling sensory attribute positively affects the price and the information presented at the product´s label-package. However, tasting attributes negatively affects price and perceptions of the product´s label-package information. We conclude that smelling sensory attributes has greater impact on purchase intentions than tasting attributes. Decision-makers should manage scent, price, and label-package characteristics wisely because they are part of the first experience of the customer.