Multivariate analysis in mathematical model selection to describe Croton urucurana Baill drying kinetics

• Autor(a) principal: LOPES ALVES,Jáliston Júlio
• Data de Publicação: 2022
• Outros Autores: RESENDE,Osvaldo, RIBEIRO NETO,Francisco de Araújo, RIBEIRO AGUIAR,Ana Carolina, FERREIRA VIEIRA BESSA,Jaqueline, QUEQUETO,Wellytton Darci
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Food Science and Technology (Campinas)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000100762
• topic: mathematical modeling; AIC and BIC; plant products; post-harvest

Abstract The Croton urucurana Baill species is known in Brazil as “sangra d’água” and is popular due to its medicinal properties. For better processing of herbal medicines, it is essential that efficient drying and storage techniques are developed and that compounds are preserved. Therefore, this study aimed to select models through multivariate cluster analysis applying Akaike (AIC) and Bayesian information criteria (BIC) to describe Croton urucurana leaves drying kinetics at different temperatures (40-70 °C). The initial moisture content in Croton urucurana leaves was 1.791, 1.841, 2.196 and 2.144 kg water kg dry matter-1, and 8.25, 7.75, 4.25 and 2 hours were required to reach hygroscopic equilibrium, with a final moisture content of 0.134, 0.105, 0.065 and 0.0601 kg water kg dry matter-1, at 40, 50, 60 and 70 °C, respectively. The models with the greatest similarity to the experimental data were Diffusion Approximation; Cavalcanti Mata; Two-term; Two-term Exponential; Modified Henderson & Pabis; Logarithmic; Midilli; Page and Verma. The multivariate cluster technique associated with AIC and BIC criteria during model selection is a great applicability tool to help decision-making when evaluating the drying plant leaves. The Cavalcanti Mata mathematical model was selected to represent the drying kinetics.