Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/198474
Type: Artigo
Title: Monitoring the authenticity of low-fat yogurts by an artificial neural network
Author: Cruz, A. G. da
Walter, E. H. M.
Cadena, R. S.
Faria, J. A. F.
Bolini, H. M. A.
Fileti, A. M. Frattini
Abstract: The growing consumption of low- and reduced-fat dairy products demands routine control of their authenticity by health agencies. The usual analyses of fat in dairy products are very simple laboratory methods; however, they require manipulation and use of reagents of a corrosive nature, such as sulfuric acid, to break the chemical bounds between fat and proteins. Additionally, they generate chemical residues that require an appropriate destination. In this work, the use of an artificial neural network based on simple instrumental analyses, such as pH, color, and hardness (inputs) is proposed for the classification of commercial yogurts in the low- and reduced-fat categories (outputs). A total of 108 strawberry-flavored yogurts (48 probiotic low-fat, 36 low-fat, and 24 full-fat yogurts) belonging to several commercial brands and from different batches were used in this research. The statistical analysis showed different features for each yogurt category; thus, a database was built and a neural model was trained with the Levenberg-Marquardt algorithm by using the neural network toolbox of the software MATLAB 7.0.1. Validation with unseen data pairs showed that the proposed model was 100% efficient. Because the instrumental analyses do not require any sample preparation and do not produce any chemical residues, the proposed procedure is a fast and interesting approach to monitoring the authenticity of these products.
The growing consumption of low- and reduced-fat dairy products demands routine control of their authenticity by health agencies. The usual analyses of fat in dairy products are very simple laboratory methods; however, they require manipulation and use of reagents of a corrosive nature, such as sulfuric acid, to break the chemical bounds between fat and proteins. Additionally, they generate chemical residues that require an appropriate destination. In this work, the use of an artificial neural network based on simple instrumental analyses, such as pH, color, and hardness (inputs) is proposed for the classification of commercial yogurts in the low- and reduced-fat categories (outputs). A total of 108 strawberry-flavored yogurts (48 probiotic low-fat, 36 low-fat, and 24 full-fat yogurts) belonging to several commercial brands and from different batches were used in this research. The statistical analysis showed different features for each yogurt category; thus, a database was built and a neural model was trained with the Levenberg-Marquardt algorithm by using the neural network toolbox of the software MATLAB 7.0.1. Validation with unseen data pairs showed that the proposed model was 100% efficient. Because the instrumental analyses do not require any sample preparation and do not produce any chemical residues, the proposed procedure is a fast and interesting approach to monitoring the authenticity of these products
Subject: Controle de qualidade; Iogurte
Country: Estados Unidos
Editor: American Dairy Science Association
Citation: Journal Of Dairy Science. v. 92, n. 10, p. 4797-804, 2009-Oct.
Rights: fechado
Identifier DOI: 10.3168/jds.2009-2227
Address: https://www.journalofdairyscience.org/article/S0022-0302(09)70808-2/abstract
Date Issue: 2009
Appears in Collections:FEA - Artigos e Outros Documentos

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