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Abstract:

Deep-fat frying is a unit operation which develops unique sensorial attributes in foods. For instance, texture is the principal quality parameter of tortilla and corn chips. On the other hand, computer vision is a useful tool for quality evaluation and prediction of some physical properties in different either raw or processed foods. The objective of this research was to characterize corn and tortilla chips by using computer vision, and to build proper mathematical models which permit to predict mechanical properties of these chips (maximum force, such as hardness, and distance to maximum force, such as toughness) by using chromatic features extracted from their corresponding digital images. Corn and tortilla chips (thickness of 2 mm; diameter of 37 mm) were made from masa of maize and fried at constant oil temperatures of 160, 175, and 190 °C. A high linear correlation (R 2 < 0. 9400) was obtained between mechanical properties and some image features (Hu, Fourier, and Haralick moments). Cross-validation technique demonstrated the repeatability and good performance (<90%) of the models tested, indicating that can be used to predict the textural properties of the tortilla and corn chips by using selected features extracted from their digital images, without the necessity of measuring them in a texture analyzer. © 2011 Springer Science+Business Media, LLC.

Registro:

Documento: Artículo
Título:Prediction of Mechanical Properties of Corn and Tortilla Chips by Using Computer Vision
Autor:Matiacevich, S.B.; Mery, D.; Pedreschi, F.
Filiación:Departamento de Ciencia y Tecnología de los Alimentos, Facultad Tecnológica, Universidad de Santiago de Chile, Av. Ecuador 3769, 7190200 Santiago, Chile
CONICET-Departamento de Industrias, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Int. Güiraldes s/n, Ciudad Universitaria, EGA1428 Ciudad Autónoma de Buenos Aires, Argentina
Departamento de Ciencia de la Computación, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna, 4860 (143) Santiago, Chile
Departamento de Ingeniería Química y Bioprocesos, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna, 4860 (143) Santiago, Chile
Palabras clave:Computer vision; Frying; Image features; Mechanical properties; Texture of an image; Texture of foods; Tortilla chips; Chromatic features; Cross-validation technique; Deep fat frying; Digital image; Fourier; Frying; Image features; Linear correlation; Oil temperature; Prediction of mechanical properties; Quality evaluation; Quality parameters; Textural properties; Texture analyzers; Tortilla chips; Forecasting; Image texture; Mathematical models; Mechanical properties; Textures; Toughness; Computer vision; Tetragastris balsamifera; Zea mays
Año:2012
Volumen:5
Número:5
Página de inicio:2025
Página de fin:2030
DOI: http://dx.doi.org/10.1007/s11947-011-0662-z
Título revista:Food and Bioprocess Technology
Título revista abreviado:Food. Bioprocess Technol.
ISSN:19355130
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19355130_v5_n5_p2025_Matiacevich

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Citas:

---------- APA ----------
Matiacevich, S.B., Mery, D. & Pedreschi, F. (2012) . Prediction of Mechanical Properties of Corn and Tortilla Chips by Using Computer Vision. Food and Bioprocess Technology, 5(5), 2025-2030.
http://dx.doi.org/10.1007/s11947-011-0662-z
---------- CHICAGO ----------
Matiacevich, S.B., Mery, D., Pedreschi, F. "Prediction of Mechanical Properties of Corn and Tortilla Chips by Using Computer Vision" . Food and Bioprocess Technology 5, no. 5 (2012) : 2025-2030.
http://dx.doi.org/10.1007/s11947-011-0662-z
---------- MLA ----------
Matiacevich, S.B., Mery, D., Pedreschi, F. "Prediction of Mechanical Properties of Corn and Tortilla Chips by Using Computer Vision" . Food and Bioprocess Technology, vol. 5, no. 5, 2012, pp. 2025-2030.
http://dx.doi.org/10.1007/s11947-011-0662-z
---------- VANCOUVER ----------
Matiacevich, S.B., Mery, D., Pedreschi, F. Prediction of Mechanical Properties of Corn and Tortilla Chips by Using Computer Vision. Food. Bioprocess Technol. 2012;5(5):2025-2030.
http://dx.doi.org/10.1007/s11947-011-0662-z