The objective of this work was to evaluate multivariate calibration models to predict total lipids, crude protein, and moisture content in grinded soybean grains using near-infrared spectroscopy and partial least squares (PLS). Three hundred samples of grinded soybean, evaluated in duplicate, were used for reference and spectral measurements. The PLS models for total lipids, crude protein, and moisture were validated by figures of merit for accuracy and precision, respectively, of 0.75 and 0.67 for total lipids, 0.51 and 0.46 for crude protein, and 0.97 and 0.99 for moisture. The PLS models developed for total lipids, crude protein, and moisture can be used as an alternative methodology for the determination of physicochemical parameters, and, therefore, they can be applied in quality control in soybean processing industries.
This study successfully applied a potentiometric E‐tongue with 20 cross‐selectivity lipidic polymeric membranes in the discrimination of three semi‐quantitative groups, that represented the following intervals of honey adulteration percentage with cane sugar: 0 %; [0, 10]%; [10, 20]% of adulteration. We analysed five different types of Portuguese honey; five brands of cane sugar were added to the adulterated samples; a comparative analysis was then performed. Linear discriminant analysis coupled with a tabu search algorithm for feature selection was applied to the ETongue's analytical data to select the best model. A discriminant model with 12 sensors was obtained. This model classified correctly all samples in both in internal (train data, 15 samples) and external validation (test data,10 samples). Also, multiple linear regression with tabu search was applied to verify if ETongue's data would allow quantifying the honey's adulteration level. The results showed that it was possible to obtain a quantitative model but with unsatisfactory predictive performance in the test data group (external validation), giving, in general, values below the expected concentrations. E‐tongue is a real‐time green, flexible and low‐cost analytical tool that requires minimum sample preparation and no special technical skills, being a promising tool for everyday application.
As abelhas Apis mellifera coletam um conjunto de substâncias da natureza para assegurarem a sua sobrevivência, nomeadamente o néctar, que é a principal fonte em carboidratos e utilizado para produção de energia, o pólen da onde retiram as proteínas, minerais, vitaminas e lipídeos e a água que atua no controle da temperatura e umidade da colmeia. O consumo de carboidratos ocorre em todas as fases do desenvolvimento das abelhas, porém na fase adulta a dieta é quase exclusiva à base destas substâncias, necessitando uma abelha de aproximadamente 4 mg de açúcar por dia para sobreviver. A suplementação artificial fornecida para as abelhas pelos apicultores surgiu como uma prática de sobrevivência e manutenção das atividades da colmeia. Pastas e xaropes de mel, sacarose, açucares invertidos, xaropes de milho de elevado teor em frutose (HFCS) e outros xaropes de frutas são oferecidos como suplemento energético em períodos de escassez de néctar, como em condições climáticas adversas enquanto que dietas proteicas a base de farinha de soja, albumina, farinha de arroz, farinha de milho, entre outras, servem como estratégia para o apicultor aumentar a criação, produzir rainhas, multiplicar comeias, entre outros fatores de interesse. Dessa forma, o trabalho teve como objetivo revisar o estilo de vida e as necessidades nutricionais das abelhas dessa espécie, assim como exemplificar os tipos de suplementos alimentares existentes e em que circunstâncias melhor se aplicam, auxiliando os apicultores na escolha da alimentação mais adequada.
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