Artificial Neural Networks for the Evaluation of Physicochemical Properties of Carrots (Daucus carota L.) Subjected to Different Cooking Conditions as an Alternative to Traditional Statistical Methods

• Published in: ACS food science & technology Vol. 2; no. 1; pp. 143 - 150
• Main Authors: Abreu, Danilo José Machado de, Lorenço, Mario Sérgio, Ferreira, Aline Norberto, Scalice, Henrique Kovacs, Vilas Boas, Eduardo Valério de Barros, Piccoli, Roberta Hilsdorf, Carvalho, Elisângela Elena Nunes
• Format: Journal Article
• Language: English
• Published: American Chemical Society 21.01.2022
• Subjects: sous vide; steam; self-organizing maps; boiled; HCA; PCA
• ISSN: 2692-1944; 2692-1944
• DOI: 10.1021/acsfoodscitech.1c00375

The study aimed to evaluate the impact of different cooking methods (sous vide, boiling, and steamed) on the physicochemical properties of carrots (Daucus carota L.). The colorimetric parameters, texture, carotenoid content, and antioxidant capacity of carrots were observed. The steam cooking method proved to be the best method to preserve the concentration of carotenoids and showed a protection of about 40%, regarding the antioxidant capacity, a property also observed in the sous vide method, independent of the time. In terms of texture, the steam cooking method rendered them a greater softness. Moreover, this study corroborates that artificial neural networks (ANNs) can be used as an effective tool for data treatments by grouping according to their similarities. The results obtained with ANN provided the same information when compared to those of the commonly used traditional multivariate statistical techniques considering that the self-organizing maps proved to be easier to visualize and analyze.