Determinação de parâmetros (sólidos solúveis, pH e acidez titulável) em ameixas intactas usando espectroscopia no infravermelho próximo e seleção de comprimento de onda

The aim of this study was to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) as a rapid and non-destructive method to determine the soluble solid content (SSC), pH and titratable acidity of intact plums. Samples of plum with a total solids content ranging from 5.7 to 15%, pH from 2.72 to 3.84 and titratable acidity from 0.88 a 3.6% were collected from supermarkets in Natal-Brazil, and NIR spectra were acquired in the 714 2500 nm range. A comparison of several multivariate calibration techniques with respect to several pre-processing data and variable selection algorithms, such as interval Partial Least Squares (iPLS), genetic algorithm (GA), successive projections algorithm (SPA) and ordered predictors selection (OPS), was performed. Validation models for SSC, pH and titratable acidity had a coefficient of correlation (R) of 0.95 0.90 and 0.80, as well as a root mean square error of prediction (RMSEP) of 0.45ºBrix, 0.07 and 0.40%, respectively. From these results, it can be concluded that NIR spectroscopy can be used as a non-destructive alternative for measuring the SSC, pH and titratable acidity in plums

Signal propagation in Bayesian networks and its relationship with intrinsically multivariate predictive variables

A set of predictor variables is said to be intrinsically multivariate predictive (IMP) for a target variable if all properly contained subsets of the predictor set are poor predictors of the. target but the full set predicts the target with great accuracy. In a previous article, the main properties of IMP Boolean variables have been analytically described, including the introduction of the IMP score, a metric based on the coefficient of determination (CoD) as a measure of predictiveness with respect to the target variable. It was shown that the IMP score depends on four main properties: logic of connection, predictive power, covariance between predictors and marginal predictor probabilities (biases). This paper extends that work to a broader context, in an attempt to characterize properties of discrete Bayesian networks that contribute to the presence of variables (network nodes) with high IMP scores. We have found that there is a relationship between the IMP score of a node and its territory size, i.e., its position along a pathway with one source: nodes far from the source display larger IMP scores than those closer to the source, and longer pathways display larger maximum IMP scores. This appears to be a consequence of the fact that nodes with small territory have larger probability of having highly covariate predictors, which leads to smaller IMP scores. In addition, a larger number of XOR and NXOR predictive logic relationships has positive influence over the maximum IMP score found in the pathway. This work presents analytical results based on a simple structure network and an analysis involving random networks constructed by computational simulations. Finally, results from a real Bayesian network application are provided. (C) 2012 Elsevier Inc. All rights reserved.

A Multivariate Calibration Procedure for UV/VIS Spectrometric Monitoring of BHK-21 Cell Metabolism and Growth

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Experimental evidence for multivariate stabilizing sexual selection

Stabilizing selection is a fundamental concept in evolutionary biology. In the presence of a single intermediate optimum phenotype (fitness peak) on the fitness surface, stabilizing selection should cause the population to evolve toward such a peak. This prediction has seldom been tested, particularly for suites of correlated traits. The lack of tests for an evolutionary match between population means and adaptive peaks may be due, at least in part, to problems associated with empirically detecting multivariate stabilizing selection and with testing whether population means are at the peak of multivariate fitness surfaces. Here we show how canonical analysis of the fitness surface, combined with the estimation of confidence regions for stationary points on quadratic response surfaces, may be used to define multivariate stabilizing selection on a suite of traits and to establish whether natural populations reside on the multivariate peak. We manufactured artificial advertisement calls of the male cricket Teleogryllus commodus and played them back to females in laboratory phonotaxis trials to estimate the linear and nonlinear sexual selection that female phonotactic choice imposes on male call structure. Significant nonlinear selection on the major axes of the fitness surface was convex in nature and displayed an intermediate optimum, indicating multivariate stabilizing selection. The mean phenotypes of four independent samples of males, from the same population as the females used in phonotaxis trials, were within the 95% confidence region for the fitness peak. These experiments indicate that stabilizing sexual selection may play an important role in the evolution of male call properties in natural populations of T. commodus.

Simultaneous spectrometric determination of Cu2+, Mn2+ and Zn2+ in polivitaminic/polimineral drug using SPA and GA algorithms for variable selection

Este artigo apresenta uma aplicação do método para determinação espectrofotométrica simultânea dos íons divalentes de cobre, manganês e zinco à análise de medicamento polivitamínico/polimineral. O método usa 4-(2-piridilazo) resorcinol (PAR), calibração multivariada e técnicas de seleção de variáveis e foi otimizado o empregando-se o algoritmo das projeções sucessivas (APS) e o algoritmo genético (AG), para escolha dos comprimentos de onda mais informativos para a análise. Com essas técnicas, foi possível construir modelos de calibração por regressão linear múltipla (RLM-APS e RLM-AG). Os resultados obtidos foram comparados com modelos de regressão em componentes principais (PCR) e nos mínimos quadrados parciais (PLS). Demonstra-se a partir do erro médio quadrático de previsão (RMSEP) que os modelos apresentam desempenhos semelhantes ao prever as concentrações dos três analitos no medicamento. Todavia os modelos RLM são mais simples pois requerem um número muito menor de comprimentos de onda e são mais fáceis de interpretar que os baseados em variáveis latentes.

Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness

The robustness of multivariate calibration models, based on near infrared spectroscopy, for the assessment of total soluble solids (TSS) and dry matter (DM) of intact mandarin fruit (Citrus reticulata cv. Imperial) was assessed. TSS calibration model performance was validated in terms of prediction of populations of fruit not in the original population (different harvest days from a single tree, different harvest localities, different harvest seasons). Of these, calibration performance was most affected by validation across seasons (signal to noise statistic on root mean squared error of prediction of 3.8, compared with 20 and 13 for locality and harvest day, respectively). Procedures for sample selection from the validation population for addition to the calibration population (‘model updating’) were considered for both TSS and DM models. Random selection from the validation group worked as well as more sophisticated selection procedures, with approximately 20 samples required. Models that were developed using samples at a range of temperatures were robust in validation for TSS and DM.

Selection and Treatment of Data for Radiocarbon Calibration: An Update to the International Calibration (IntCal) Criteria

High-quality data from appropriate archives are needed for the continuing improvement of radiocarbon calibration curves. We discuss here the basic assumptions behind 14C dating that necessitate calibration and the relative strengths and weaknesses of archives from which calibration data are obtained. We also highlight the procedures, problems and uncertainties involved in determining atmospheric and surface ocean 14C/12C in these archives, including a discussion of the various methods used to derive an independent absolute timescale and uncertainty. The types of data required for the current IntCal database and calibration curve model are tabulated with examples.

Espectroscopia no infravermelho próximo e métodos de calibração multivariada aplicados à determinação simultânea de parâmetros bioquímicos em plasma sanguíneo

In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma

Predicting soluble solid content in intact jaboticaba [Myrciaria jaboticaba (Vell.) O. Berg] fruit using near-infrared spectroscopy and chemometrics

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Direct Determination of Sugar Cane Quality Parameters by X-ray Spectrometry and Multivariate Analysis

Current methods for quality control of sugar cane are performed in extracted juice using several methodologies, often requiring appreciable time and chemicals (eventually toxic), making the methods not green and expensive. The present study proposes the use of X-ray spectrometry together with chemometric methods as an innovative and alternative technique for determining sugar cane quality parameters, specifically sucrose concentration, POL, and fiber content. Measurements in stem, leaf, and juice were performed, and those applied directly in stem provided the best results. Prediction models for sugar cane stem determinations with a single 60 s irradiation using portable X-ray fluorescence equipment allows estimating the % sucrose, % fiber, and POL simultaneously. Average relative deviations in the prediction step of around 8% are acceptable if considering that field measurements were done. These results may indicate the best period to cut a particular crop as well as for evaluating the quality of sugar cane for the sugar and alcohol industries.

Quantificação de açúcares com uma língua eletrónica: calibração multivariada com seleção de sensores

Este trabalho incide na análise dos açúcares majoritários nos alimentos (glucose, frutose e sacarose) com uma língua eletrónica potenciométrica através de calibração multivariada com seleção de sensores. A análise destes compostos permite contribuir para a avaliação do impacto dos açúcares na saúde e seu efeito fisiológico, além de permitir relacionar atributos sensoriais e atuar no controlo de qualidade e autenticidade dos alimentos. Embora existam diversas metodologias analíticas usadas rotineiramente na identificação e quantificação dos açúcares nos alimentos, em geral, estes métodos apresentam diversas desvantagens, tais como lentidão das análises, consumo elevado de reagentes químicos e necessidade de pré-tratamentos destrutivos das amostras. Por isso se decidiu aplicar uma língua eletrónica potenciométrica, construída com sensores poliméricos selecionados considerando as sensibilidades aos açucares obtidas em trabalhos anteriores, na análise dos açúcares nos alimentos, visando estabelecer uma metodologia analítica e procedimentos matemáticos para quantificação destes compostos. Para este propósito foram realizadas análises em soluções padrão de misturas ternárias dos açúcares em diferentes níveis de concentração e em soluções de dissoluções de amostras de mel, que foram previamente analisadas em HPLC para se determinar as concentrações de referência dos açúcares. Foi então feita uma análise exploratória dos dados visando-se remover sensores ou observações discordantes através da realização de uma análise de componentes principais. Em seguida, foram construídos modelos de regressão linear múltipla com seleção de variáveis usando o algoritmo stepwise e foi verificado que embora fosse possível estabelecer uma boa relação entre as respostas dos sensores e as concentrações dos açúcares, os modelos não apresentavam desempenho de previsão satisfatório em dados de grupo de teste. Dessa forma, visando contornar este problema, novas abordagens foram testadas através da construção e otimização dos parâmetros de um algoritmo genético para seleção de variáveis que pudesse ser aplicado às diversas ferramentas de regressão, entre elas a regressão pelo método dos mínimos quadrados parciais. Foram obtidos bons resultados de previsão para os modelos obtidos com o método dos mínimos quadrados parciais aliado ao algoritmo genético, tanto para as soluções padrão quanto para as soluções de mel, com R²ajustado acima de 0,99 e RMSE inferior a 0,5 obtidos da relação linear entre os valores previstos e experimentais usando dados dos grupos de teste. O sistema de multi-sensores construído se mostrou uma ferramenta adequada para a análise dos iii açúcares, quando presentes em concentrações maioritárias, e alternativa a métodos instrumentais de referência, como o HPLC, por reduzir o tempo da análise e o valor monetário da análise, bem como, ter um preparo mínimo das amostras e eliminar produtos finais poluentes.

Development of procedures for the triazole fungicides determination in fruits and liquid samples using microextraction techniques and chromatographic separation

Agricultural crops can be damaged by funguses, insects, worms and other organisms that cause diseases and decrease the yield of production. The effect of these damaging agents can be reduced using pesticides. Among them, triazole compounds are effective substances against fungus; for example, Oidium. Nevertheless, it has been detected that the residues of these fungicides in foods as well as in derivate products can affect the health of the consumers. Therefore, the European Union has established several regulations fixing the maximum residue of pesticide levels in a wide range of foods trying to assure the consumer safety. Hence, it is very important to develop adequate methods to determine these pesticide compounds. In most cases, gas or liquid chromatographic (GC, LC) separations are used in the analysis of the samples. But firstly, it is necessary to use proper sample treatments in order to preconcentrate and isolate the target analytes. To reach this aim, microextraction techniques are very effective tools; because allow to do both preconcentration and extraction of the analytes in one simple step that considerably reduces the source of errors. With these objectives, two remarkable techniques have been widely used during the last years: solid phase microextraction (SPME) and liquid phase microextraction (LPME) with its different options. Both techniques that avoid the use or reduce the amount of toxic solvents are convenient coupled to chromatographic equipments providing good quantitative results in a wide number of matrices and compounds. In this work simple and reliable methods have been developed using SPME and ultrasound assisted emulsification microextraction (USAEME) coupled to GC or LC for triazole fungicides determination. The proposed methods allow confidently determine triazole concentrations of μg L‐1 order in different fruit samples. Chemometric tools have been used to accomplish successful determinations. Firstly, in the selection and optimization of the variables involved in the microextraction processes; and secondly, to overcome the problems related to the overlapping peaks. Different fractional factorial designs have been used for the screening of the experimental variables; and central composite designs have been carried out to get the best experimental conditions. Trying to solve the overlapping peak problems multivariate calibration methods have been used. Parallel Factor Analysis 2 (PARAFAC2), Multivariate Curve Resolution (MCR) and Parallel Factor Analysis with Linear Dependencies (PARALIND) have been proposed, the adequate algorithms have been used according to data characteristics, and the results have been compared. Because its occurrence in Basque Country and its relevance in the production of cider and txakoli regional wines the grape and apple samples were selected. These crops are often treated with triazole compounds trying to solve the problems caused by the funguses. The peel and pulp from grape and apple, their juices and some commercial products such as musts, juice and cider have been analysed showing the adequacy of the developed methods for the triazole determination in this kind of fruit samples.

Uso da espectroscopia de reflectância do infravermelho próximo (NIRS) para previsão da composição bromatológica de vagens de algaroba e palma forrageira.

Espécies forrageiras adaptadas às condições semiáridas são uma alternativa para reduzir os impactos negativos na cadeia produtiva de ruminantes da região Nordeste brasileira devido à sazonalidade na oferta de forragem, além de reduzir custo com o fornecimento de alimentos concentrados. Dentre as espécies, a vagem de algaroba (Prosopis juliflora SW D.C.) e palma forrageira (Opuntia e Nopalea) ganham destaque por tolerarem o déficit hídrico e produzirem em períodos onde a oferta de forragem está reduzida, além de apresentam bom valor nutricional e serem bem aceitas pelos animais. Porém, devido à variação na sua composição, seu uso na alimentação animal exige o conhecimento profundo da sua composição para a elaboração de dietas balanceadas. No entanto, devido ao custo e tempo para análise, os produtores não fazem uso da prática de análise da composição químico-bromatológica dos alimentos. Por isto, a espectroscopia de reflectância no infravermelho próximo (NIRS) representa uma importante alternativa aos métodos tradicionais. Objetivou-se com este estudo desenvolver e validar modelos de predição da composição bromatológica de vagem de algaroba e palma forrageira baseados em espectroscopia NIRS, escaneadas em dois modelos de equipamentos e com diferentes processamentos da amostra. Foram coletadas amostras de vagem de algaroba nos estados do Ceará, Bahia, Paraíba e Pernambuco, e amostras de palma forrageira nos estados do Ceará, Paraíba e Pernambuco, frescas (in natura) ou pré-secas e moídas. Para obtenção dos espectros utilizaram-se dois equipamentos NIR, Perten DA 7250 e FOSS 5000. Inicialmente os alimentos foram escaneados in natura em aparelho do modelo Perten, e, com o auxílio do software The Unscrambler 10.2 foi selecionado um grupo de amostras para o banco de calibração. As amostras selecionadas foram secas e moídas, e escaneadas novamente em equipamentos Perten e FOSS. Os valores dos parâmetros de referência foram obtidos por meio de metodologias tradicionalmente aplicadas em laboratório de nutrição animal para matéria seca (MS), matéria mineral (MM), matéria orgânica (MO), proteína bruta (PB), estrato etéreo (EE), fibra solúvel em detergente neutro (FDN), fibra solúvel em detergente ácido (FDA), hemicelulose (HEM) e digestibilidade in vitro da matéria seca (DIVMS). O desempenho dos modelos foi avaliado de acordo com os erros médios de calibração (RMSEC) e validação (RMSECV), coeficiente de determinação (R2 ) e da relação de desempenho de desvio dos modelos (RPD). A análise exploratória dos dados, por meio de tratamentos espectrais e análise de componentes principais (PCA), demonstraram que os bancos de dados eram similares entre si, dando segurança de desenvolver os modelos com todas as amostras selecionadas em um único modelo para cada alimento, algaroba e palma. Na avaliação dos resultados de referência, observou-se que a variação dos resultados para cada parâmetro corroboraram com os descritos na literatura. No desempenho dos modelos, aqueles desenvolvidos com pré-processamento da amostra (pré-secagem e moagem) se mostraram mais robustos do que aqueles construídos com amostras in natura. O aparelho NIRS Perten apresentou desempenho semelhante ao equipamento FOSS, apesar desse último cobrir uma faixa espectral maior e com intervalos de leituras menores. A técnica NIR, associada ao método de calibração multivariada de regressão por meio de quadrados mínimos (PLS), mostrou-se confiável para prever a composição químico-bromatológica de vagem de algaroba e da palma forrageira. Abstract: Forage species adapted to semi-arid conditions are an alternative to reduce the negative impacts in the feed supply for ruminants in the Brazilian Northeast region, due to seasonality in forage availability, as well as in the reducing of cost by providing concentrated feedstuffs. Among the species, mesquite pods (Prosopis juliflora SW DC) and spineless cactus (Opuntia and Nopalea) are highlighted for tolerating the drought and producion in periods where the forage is scarce, and have high nutritional value and also are well accepted by the animals. However, its use in animal diets requires a knowledge about its composition to prepare balanced diets. However, farmers usually do not use feed composition analysis, because their high cost and time-consuming. Thus, the Near Infrared Reflectance Spectroscopy in the (NIRS) is an important alternative to traditional methods. The objective of this study to develop and validate predictive models of the chemical composition of mesquite pods and spineless cactus-based NIRS spectroscopy, scanned in two different spectrometers and sample processing. Mesquite pods samples were collected in the states of Ceará, Bahia, Paraiba and Pernambuco, and samples of forage cactus in the states of Ceará, Paraíba and Pernambuco. In order to obtain the spectra, it was used two NIR equipment: Perten DA 7250 and FOSS 5000. sSpectra of samples were initially obtained fresh (as received) using Perten instrument, and with The Unscrambler software 10.2, a group of subsamples was selected to model development, keeping out redundant ones. The selected samples were dried and ground, and scanned again in both Perten and FOSS instruments. The values of the reference analysis were obtained by methods traditionally applied in animal nutrition laboratory to dry matter (DM), mineral matter (MM), organic matter (OM), crude protein (CP), ether extract (EE), soluble neutral detergent fiber (NDF), soluble acid detergent fiber (ADF), hemicellulose ( HEM) and in vitro digestibility of dry matter (DIVDM). The performance of the models was evaluated according to the Root Mean Square Error of Calibration (RMSEC) and cross-validation (RMSECV), coefficient of determination (R2 ) and the deviation of Ratio of performance Deviation of the models (RPD). Exploratory data analysis through spectral treatments and principal component analysis (PCA), showed that the databases were similar to each other, and may be treated asa single model for each feed - mesquite pods and cactus. Evaluating the reference results, it was observed that the variation were similar to those reported in the literature. Comparing the preprocessing of samples, the performance ofthose developed with preprocessing (dried and ground) of the sample were more robust than those built with fresh samples. The NIRS Perten device performance similar to FOSS equipment, although the latter cover a larger spectral range and with lower readings intervals. NIR technology associate do multivariate techniques is reliable to predict the bromatological composition of mesquite pods and cactus.