Oil content fraction in tortilla chips during frying and their prediction by image analysis using computer vision

The increasing consumption worldwide of tortilla chips make it relevant to design and optimize their industrial quality analysis. Surface, structural, and total oil content during frying of tortilla chips fried at 160, 175, and 190°C for different times were analyzed. The aim was to obtain a relatio...

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Detalles Bibliográficos
Autor principal: Matiacevich, Silvia Beatriz
Publicado: 2014
Materias:
Computer vision
Image features
Oil content
Oil fraction
Tortilla chips
Cross-validation technique
Frying temperature
Industrial quality
Linear correlation
Oil contents
Oil fractions
Food technology
Image processing
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10942912_v17_n2_p261_Matiacevich
http://hdl.handle.net/20.500.12110/paper_10942912_v17_n2_p261_Matiacevich
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_10942912_v17_n2_p261_Matiacevich
record_format dspace
spelling paper:paper_10942912_v17_n2_p261_Matiacevich2023-06-08T16:06:46Z Oil content fraction in tortilla chips during frying and their prediction by image analysis using computer vision Matiacevich, Silvia Beatriz Computer vision Image features Oil content Oil fraction Tortilla chips Cross-validation technique Frying temperature Image features Industrial quality Linear correlation Oil contents Oil fractions Tortilla chips Computer vision Food technology Image processing The increasing consumption worldwide of tortilla chips make it relevant to design and optimize their industrial quality analysis. Surface, structural, and total oil content during frying of tortilla chips fried at 160, 175, and 190°C for different times were analyzed. The aim was to obtain a relationship between oil content and features from their digital images. The results showed a high linear correlation (R > 0.90) between oil content with image features at each frying temperature, indicating that trustable models can be developed, allowing the prediction of oil content of tortilla chips by using selected features extracted from their digital images, without the necessity of measuring them. Cross-validation technique demonstrated the repeatability of each model and their good performance (>90%). © 2014 Copyright Taylor and Francis Group, LLC. Fil:Matiacevich, S.B. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2014 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10942912_v17_n2_p261_Matiacevich http://hdl.handle.net/20.500.12110/paper_10942912_v17_n2_p261_Matiacevich
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Computer vision
Image features
Oil content
Oil fraction
Tortilla chips
Cross-validation technique
Frying temperature
Image features
Industrial quality
Linear correlation
Oil contents
Oil fractions
Tortilla chips
Computer vision
Food technology
Image processing
spellingShingle Computer vision
Image features
Oil content
Oil fraction
Tortilla chips
Cross-validation technique
Frying temperature
Image features
Industrial quality
Linear correlation
Oil contents
Oil fractions
Tortilla chips
Computer vision
Food technology
Image processing
Matiacevich, Silvia Beatriz
Oil content fraction in tortilla chips during frying and their prediction by image analysis using computer vision
topic_facet Computer vision
Image features
Oil content
Oil fraction
Tortilla chips
Cross-validation technique
Frying temperature
Image features
Industrial quality
Linear correlation
Oil contents
Oil fractions
Tortilla chips
Computer vision
Food technology
Image processing
description The increasing consumption worldwide of tortilla chips make it relevant to design and optimize their industrial quality analysis. Surface, structural, and total oil content during frying of tortilla chips fried at 160, 175, and 190°C for different times were analyzed. The aim was to obtain a relationship between oil content and features from their digital images. The results showed a high linear correlation (R > 0.90) between oil content with image features at each frying temperature, indicating that trustable models can be developed, allowing the prediction of oil content of tortilla chips by using selected features extracted from their digital images, without the necessity of measuring them. Cross-validation technique demonstrated the repeatability of each model and their good performance (>90%). © 2014 Copyright Taylor and Francis Group, LLC.
author Matiacevich, Silvia Beatriz
author_facet Matiacevich, Silvia Beatriz
author_sort Matiacevich, Silvia Beatriz
title Oil content fraction in tortilla chips during frying and their prediction by image analysis using computer vision
title_short Oil content fraction in tortilla chips during frying and their prediction by image analysis using computer vision
title_full Oil content fraction in tortilla chips during frying and their prediction by image analysis using computer vision
title_fullStr Oil content fraction in tortilla chips during frying and their prediction by image analysis using computer vision
title_full_unstemmed Oil content fraction in tortilla chips during frying and their prediction by image analysis using computer vision
title_sort oil content fraction in tortilla chips during frying and their prediction by image analysis using computer vision
publishDate 2014
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10942912_v17_n2_p261_Matiacevich
http://hdl.handle.net/20.500.12110/paper_10942912_v17_n2_p261_Matiacevich
work_keys_str_mv AT matiacevichsilviabeatriz oilcontentfractionintortillachipsduringfryingandtheirpredictionbyimageanalysisusingcomputervision
_version_ 1768545341036036096

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