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|Title:||Nutritional Clustering of Cookies Developed with Cocoa Shell, Soy, and Green Banana Flours Using Exploratory Methods|
Artificial neural networks
Redes neurais artificiais
|Citation:||BARROS, H. E. A. de et al. Nutritional Clustering of Cookies Developed with Cocoa Shell, Soy, and Green Banana Flours Using Exploratory Methods. Food and Bioprocess Technology, [S. I.], v. 13, p. 1566–1578, Sept. 2020. DOI: https://doi.org/10.1007/s11947-020-02495-w.|
|Abstract:||The aim of this presented study was to develop and cluster cookies made with cocoa shell, a by-product of the chocolate industry, and nutritionally rich raw materials, soy and green banana flours, according to their nutritional characteristics and to evaluate them sensorially, through exploratory methods. The results of proximate composition, phenolic compounds, antioxidant activity, hardness and color were submitted to Kohonen’s self-organizing maps (KSOMs) and the samples trend to form three groups. All treatments showed functional properties due to their high protein content, dietary fiber, phenolic compounds, and antioxidants. All formulations had good sensory acceptance (grades between 6.71 and 7.11) and the simplex-centroid design used to optimize the acceptance was effective. The Check-All-That-Apply (CATA) analysis indicated sensory differences between the treatments. It is concluded that the KSOMs is effective to nutritionally describe and classify samples and cocoa shell, soy, and green banana flours provided nutritional and sensory characteristics acceptable to consumers, making them viable for inclusion in human nutrition.|
|Appears in Collections:||DCA - Artigos publicados em periódicos|
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