Principal components analysis for quality evaluation of cooled banana 'Nanicão' in different packing

This work aims determinate the evaluation of the quality of 'Nanicão' banana, submitted to two conditions of storage temperature and three different kinds of package, using the technique of the Analysis of Principal Components (ACP), as a basis for an Analysis of Variance. The fruits used were 'Nanicão' bananas, at ripening degree 3, that is, more green than yellow. The packages tested were: "Torito" wood boxes, load capacity: 18 kg; "½ box" wood boxes, load capacity: 13 kg; and cardboard boxes, load capacity: 18 kg. The temperatures assessed were: room temperature (control); and (13±1ºC), with humidity controlled to 90±2,5%. Fruits were discarded when a sensory analysis determined they had become unfit for consumption. Peel coloration, percentages of imperfection, fresh mass, total acidity, pH, total soluble solids and percentages of sucrose were assessed. A completely randomized design with a 2-factorial treatment structure (packing X temperature) was used. The obtained data were analyzed through a multivariate analysis known as Principal Components Analysis, using S-plus 4.2. The conclusion was that the best packages to preserve the fruit were the ½ box ones, which proves that it is necessary to reduce the number of fruits per package to allow better ventilation and decreases mechanical injuries and ensure quality for more time.

Multivariate Analysis for Animal Selection in Experimental Research

Background: Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. Objective: To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. Methods: The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals’ homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. Results: The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. Conclusion: The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate.

Characterization of soil chemical properties of strawberry fields using principal component analysis

One of the largest strawberry-producing municipalities of Rio Grande do Sul (RS) is Turuçu, in the South of the State. The strawberry production system adopted by farmers is similar to that used in other regions in Brazil and in the world. The main difference is related to the soil management, which can change the soil chemical properties during the strawberry cycle. This study had the objective of assessing the spatial and temporal distribution of soil fertility parameters using principal component analysis (PCA). Soil sampling was based on topography, dividing the field in three thirds: upper, middle and lower. From each of these thirds, five soil samples were randomly collected in the 0-0.20 m layer, to form a composite sample for each third. Four samples were taken during the strawberry cycle and the following properties were determined: soil organic matter (OM), soil total nitrogen (N), available phosphorus (P) and potassium (K), exchangeable calcium (Ca) and magnesium (Mg), soil pH (pH), cation exchange capacity (CEC) at pH 7.0, soil base (V%) and soil aluminum saturation(m%). No spatial variation was observed for any of the studied soil fertility parameters in the strawberry fields and temporal variation was only detected for available K. Phosphorus and K contents were always high or very high from the beginning of the strawberry cycle, while pH values ranged from very low to very high. Principal component analysis allowed the clustering of all strawberry fields based on variables related to soil acidity and organic matter content.

REDUÇÃO DE VARIÁVEIS DE ENTRADA DE REDES NEURAIS ARTIFICIAIS A PARTIR DE DADOS DE ANÁLISE DE COMPONENTES PRINCIPAIS NA MODELAGEM DE OXIGÊNIO DISSOLVIDO

The objective of this work is to demonstrate the efficient utilization of the Principal Components Analysis (PCA) as a method to pre-process the original multivariate data, that is rewrite in a new matrix with principal components sorted by it's accumulated variance. The Artificial Neural Network (ANN) with backpropagation algorithm is trained, using this pre-processed data set derived from the PCA method, representing 90.02% of accumulated variance of the original data, as input. The training goal is modeling Dissolved Oxygen using information of other physical and chemical parameters. The water samples used in the experiments are gathered from the Paraíba do Sul River in São Paulo State, Brazil. The smallest Mean Square Errors (MSE) is used to compare the results of the different architectures and choose the best. The utilization of this method allowed the reduction of more than 20% of the input data, which contributed directly for the shorting time and computational effort in the ANN training.

Morphometric variability in populations of Palaemonetes spp. (Crustacea, Decapoda, Palaemonidae) from the Peruvian and Brazilian Amazon Basin

Morphometric variability among shrimp populations of the genus Palaemonetes Heller, 1869 from seven lakes (Huanayo and Urcococha, in Peru; Amanã, Mamirauá, Camaleão, Cristalino e Iruçanga, in Brasil) in the Amazon Basin, presumably belonging to Palaemonetes carteri Gordon, 1935 and Palaemonetes ivonicus Holthuis, 1950, were studied. The morphometric studies were carried out from the ratios obtained from the morphometric characters. Multivariated analysis (Principal Components Analysis-PCA, Discriminant Function Analysis and Cluster Analysis) were applied over the ratios. Intra- and interpopulation variations of the rostrum teeth, and the number of spines in the male appendix, were analyzed through descriptive statistics and bivariate analysis (Spearman Rank Correlation test). Results indicated a wide plasticity and overlapping in the studied ratios between populations. The Principal Components Analysis was not able to separate different populations, revealing a large intrapopulation plasticity and strong interpopulation similarity in the studied ratios. Although the Discriminant Functions Analysis was not able to fully discriminate populations, they could be allocated in three subgroups: 1) Cristalino and Iruçanga; 2) Huanayo, Urcococha and Camaleão and 3) Mamirauá and Amanã. The first two groups were morphometrically separated from each other, whereas the third one presented a strong overlap with the former two. The Cluster Analysis confirmed the first two subgroups separation, and indicated that the first and third groups were closely related. Rostrum teeth and number of spines in the appendix masculina showed a large intrapopulation variation and a strong overlapping among the studied populations, regardless of the species.

Oligochaetes (Annelida, Clitellata) in a neotropical stream: a mesohabitat approach

This paper analyses the relationship among mesohabitat and aquatic oligochaete species in the Galharada Stream (Campos do Jordão State Park, state of São Paulo, Brazil). Between August 2005 and May 2006 a total of 192 samples were obtained in areas of four different mesohabitats: riffle leaf litter (RL), pool leaf litter (PL), pool sediment (PS) and interstitial sediment from rocky beds in riffle areas (IS). In the mesohabitats sampled, 2007 specimens were identified, belonging to two families (Naididae and Enchytraeidae). Among the oligochaetes identified Naididae was represented by six genera (Allonais, Chaetogaster, Nais, Pristina, Aulodrilus and Limnodrilus). Principal components analysis (PCA) revealed the first two axes explained 85.1% of the total variance of the data. Limnodrilus hoffmeisteri Claparede, 1862 and Aulodrilus limnobius Bretscher, 1899 were associated with the pool areas (PL and PS). Most species of genera Pristina and Nais demonstrated apparent affinity with the riffle mesohabitats. The Indicator Species Analysis (IndVal) revealed that Nais communis Piguet, 1906, Pristina leidyi Smith, 1896 and Pristina (Pristinella) jenkinae (Stephenson, 1931) are indicative of RL mesohabitat, while family Enchytraeidae was considered indicative of PL mesohabitat.

Aplicação da cromatografia por exclusão e da cromatografia gasosa de alta resolução na análise do café

The combination of high performance exclusion chromatography (HPEC) and gas chromatography (GC) was applied to the analysis of six coffee samples that were previously characterized by sensory tests as of good or poor quality. The data obtained by the two techniques were statistically evaluated by "Principal Components Analysis" (PCA) using selected peak areas. The results showed the potential of the described techniques for coffee analysis. The HPEC technique monitored with the U.V. detector at 272 nm and followed by PCA may be correlated with sensorial data, particularly if a wider group of samples is used.

Classificação periódica: um exemplo didático para ensinar análise de componentes principais

A dataset of chemical properties of the elements is used herein to introduce principal components analysis (PCA). The focus in this article is to verify the classification of the elements within the periodic table. The reclassification of the semimetals as metals or nonmetals emerges naturally from PCA and agrees with the current SBQ/IUPAC periodic table. Dataset construction, basic preprocessing, loading and score plots, and interpretation have been emphasized. This activity can be carried out even when students with distinct levels of formation are together in the same learning environment.

Ácidos orgânicos em aguardentes produzidas em alambique e em coluna

The concentration of 14 organic acids of 50 sugarcane spirits samples was determined by gas chromatography using flame ionization detection. The organic acids analytical quantitative profile in stills and column distilled spirits from wines obtained from the same must were compared. The comparison was also carried in "head", "heart" and "tail fractions of stills distilled spirits. The experimental data were analyzed by Principal Components Analysis (PCA) and pointed out that the distillation process (stills and column) strongly influences the lead spirits' organic acid composition and that producers' operational "cuts off" to produce "tail", "heart" and "head", fractions should be optimized.

Identificação de adulteração de biocombustível por adição de óleo residual ao diesel por espectrofluorimetria total 3D e análise das componentes principais

Total spectrofluorimetry associated to Principal Components Analysis (PCA) were used to classify into different groups the samples of diesel oil, biodiesel, vegetal oil and residual oil, as well as, to identify addition of non-transesterified residual vegetable oil, instead of biodiesel, to the diesel oil. Using this method, the samples of diesel oil, mixtures of biodiesel in diesel and mixtures of residual oil in diesel were separated into well-defined groups.

Fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy and chemometric techniques for the determination of adulteration in petrodiesel/biodiesel blends

We propose an analytical method based on fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy to detect the adulteration of petrodiesel and petrodiesel/palm biodiesel blends with African crude palm oil. The infrared spectral fingerprints from the sample analysis were used to perform principal components analysis (PCA) and to construct a prediction model using partial least squares (PLS) regression. The PCA results separated the samples into three groups, allowing identification of those subjected to adulteration with palm oil. The obtained model shows a good predictive capacity for determining the concentration of palm oil in petrodiesel/biodiesel blends. Advantages of the proposed method include cost-effectiveness and speed; it is also environmentally friendly.

Characterization of biomasses from the north and northeast regions of Brazil for processes in biorefineries

Abstract In search for renewable energy sources, the Brazilian residual biomasses stand out due to their favorable physical and chemical properties, low cost, and their being less pollutant. Therefore, they are likely to be used in biorefineries in the production of chemical inputs to substitute fossil fuels. This substitution is possible due to the high contents of carbohydrates (>40%), low contents of extractives (<20%), ashes (<8%) and moisture (<8%) found in these residual biomasses. High calorific values of all residues also offer them the chance to be used in combustion. A principal components analysis (PCA) was performed for better understanding of the samples and their hysic-chemical properties. Thus, this study aimed to characterize biomasses from the north (babassu residues, such as mesocarp and endocarp; pequi and Brazil nut) and northeast (agave and coconut) regions of Brazil, in order to contribute to the preservation of the environment and strengthen the economy of the region.

Principal components and generalized linear modeling in the correlation between hospital admissions and air pollution

OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to December 2010. For statistical analysis, two techniques were combined: Poisson regression with generalized additive models and principal model component analysis. Those analysis techniques complemented each other and provided more significant estimates in the estimation of relative risk. The models were adjusted for temporal trend, seasonality, day of the week, meteorological factors and autocorrelation. In the final adjustment of the model, it was necessary to include models of the Autoregressive Moving Average Models (p, q) type in the residuals in order to eliminate the autocorrelation structures present in the components. RESULTS For every 10:49 μg/m3 increase (interquartile range) in levels of the pollutant PM10 there was a 3.0% increase in the relative risk estimated using the generalized additive model analysis of main components-seasonal autoregressive – while in the usual generalized additive model, the estimate was 2.0%. CONCLUSIONS Compared to the usual generalized additive model, in general, the proposed aspect of generalized additive model − principal component analysis, showed better results in estimating relative risk and quality of fit.

Avaliação da qualidade do corpo hídrico do rio Tibagi na região de Ponta Grossa utilizando análise de componentes principais (PCA)

The aim of this paper was to evaluate alterations in the quality of the water of the Tibagi River caused by the urban and industrial activities in the region of Ponta Grossa. The study involved the monitoring of physico-chemical and microbiological parameters of the water body, which were evaluated by a principal components analysis routine. Sample collections were carried out monthly during one year (October of 2005 to September of 2006), at 3 sampling points: upstream and downstream of the industrial district and downstream from the city of Ponta Grossa. The principal components analysis showed the effect of point sources associated with industrial activity, which contribute to the rise of total concentration of amoniacal nitrogen and the reduction of dissolved oxygen in the studied region.

An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology

Understanding the different background landscapes in which malaria transmission occurs is fundamental to understanding malaria epidemiology and to designing effective local malaria control programs. Geology, geomorphology, vegetation, climate, land use, and anopheline distribution were used as a basis for an ecological classification of the state of Roraima, Brazil, in the northern Amazon Basin, focused on the natural history of malaria and transmission. We used unsupervised maximum likelihood classification, principal components analysis, and weighted overlay with equal contribution analyses to fine-scale thematic maps that resulted in clustered regions. We used ecological niche modeling techniques to develop a fine-scale picture of malaria vector distributions in the state. Eight ecoregions were identified and malaria-related aspects are discussed based on this classification, including 5 types of dense tropical rain forest and 3 types of savannah. Ecoregions formed by dense tropical rain forest were named as montane (ecoregion I), submontane (II), plateau (III), lowland (IV), and alluvial (V). Ecoregions formed by savannah were divided into steppe (VI, campos de Roraima), savannah (VII, cerrado), and wetland (VIII, campinarana). Such ecoregional mappings are important tools in integrated malaria control programs that aim to identify specific characteristics of malaria transmission, classify transmission risk, and define priority areas and appropriate interventions. For some areas, extension of these approaches to still-finer resolutions will provide an improved picture of malaria transmission patterns.