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Food Chem ; 161: 376-82, 2014 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-24837965

RESUMO

Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality.


Assuntos
Cerveja/análise , Máquina de Vetores de Suporte , Algoritmos , Cerveja/normas , Etanol/análise , Análise dos Mínimos Quadrados , Modelos Teóricos , Análise Multivariada , Redes Neurais de Computação
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