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...
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | JOUR |
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_10942912_v17_n2_p261_Matiacevich |
| Aporte de: |
| id |
todo:paper_10942912_v17_n2_p261_Matiacevich |
|---|---|
| record_format |
dspace |
| spelling |
todo:paper_10942912_v17_n2_p261_Matiacevich2023-10-03T16:05:02Z Oil content fraction in tortilla chips during frying and their prediction by image analysis using computer vision Matiacevich, S.B. Henríquez, O.C. Mery, D. Pedreschi, F. 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. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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, S.B. Henríquez, O.C. Mery, D. Pedreschi, F. 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. |
| format |
JOUR |
| author |
Matiacevich, S.B. Henríquez, O.C. Mery, D. Pedreschi, F. |
| author_facet |
Matiacevich, S.B. Henríquez, O.C. Mery, D. Pedreschi, F. |
| author_sort |
Matiacevich, S.B. |
| 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 |
| url |
http://hdl.handle.net/20.500.12110/paper_10942912_v17_n2_p261_Matiacevich |
| work_keys_str_mv |
AT matiacevichsb oilcontentfractionintortillachipsduringfryingandtheirpredictionbyimageanalysisusingcomputervision AT henriquezoc oilcontentfractionintortillachipsduringfryingandtheirpredictionbyimageanalysisusingcomputervision AT meryd oilcontentfractionintortillachipsduringfryingandtheirpredictionbyimageanalysisusingcomputervision AT pedreschif oilcontentfractionintortillachipsduringfryingandtheirpredictionbyimageanalysisusingcomputervision |
| _version_ |
1782025839392587776 |