Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on color measurement and artificial neural networks

• Autores: Leon-Roque, Noemi, Abderrahim, Mohamed, Nunez-Alejos, Luis, Arribas, Silvia M., Condezo-Hoyos, Luis
• Formato: artículo
• Fecha de Publicación: 2016
• Institución: Consejo Nacional de Ciencia Tecnología e Innovación
• Repositorio: CONCYTEC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.concytec.gob.pe:20.500.12390/2863
• Enlace del recurso: https://hdl.handle.net/20.500.12390/2863; https://doi.org/10.1016/j.talanta.2016.08.022
• Nivel de acceso: acceso abierto
• Materia: Analytical Chemistry; http://purl.org/pe-repo/ocde/ford#3.02.18

Several procedures are currently used to assess fermentation index (FI) of cocoa beans (Theobroma cacao L.) for quality control. However, all of them present several drawbacks. The aim of the present work was to develop and validate a simple image based quantitative procedure, using color measurement and artificial neural network (ANNs). ANN models based on color measurements were tested to predict fermentation index (FI) of fermented cocoa beans. The RGB values were measured from surface and center region of fermented beans in images obtained by camera and desktop scanner. The FI was defined as the ratio of total free amino acids in fermented versus non-fermented samples. The ANN model that included RGB color measurement of fermented cocoa surface and R/G ratio in cocoa bean of alkaline extracts was able to predict FI with no statistical difference compared with the experimental values. Performance of the ANN model was evaluated by the coefficient of determination, Bland-Altman plot and Passing-Bablok regression analyses. Moreover, in fermented beans, total sugar content and titratable acidity showed a similar pattern to the total free amino acid predicted through the color based ANN model. The results of the present work demonstrate that the proposed ANN model can be adopted as a low-cost and in situ procedure to predict FI in fermented cocoa beans through apps developed for mobile device. © 2016 Elsevier B.V.