Machine and Deep Learning in the Evaluation of Selected Qualitative Characteristics of Sweet Potatoes Obtained under Different Convective Drying Conditions
This paper discusses the use of various methods to distinguish between slices of sweet potato dried in different conditions. The drying conditions varied in terms of temperature, the values were: 60 °C, 70 °C, 80 °C, and 90 °C. Examination methods included instrumental texture analysis using a textu...
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MDPI AG
2022-08-01
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| author | Krzysztof Przybył Franciszek Adamski Jolanta Wawrzyniak Marzena Gawrysiak-Witulska Jerzy Stangierski Dominik Kmiecik |
| author_facet | Krzysztof Przybył Franciszek Adamski Jolanta Wawrzyniak Marzena Gawrysiak-Witulska Jerzy Stangierski Dominik Kmiecik |
| author_sort | Krzysztof Przybył |
| collection | DOAJ |
| description | This paper discusses the use of various methods to distinguish between slices of sweet potato dried in different conditions. The drying conditions varied in terms of temperature, the values were: 60 °C, 70 °C, 80 °C, and 90 °C. Examination methods included instrumental texture analysis using a texturometer and digital texture analysis based on macroscopic images. Classification of acquired data involved the use of machine learning techniques using various types of artificial neural networks, such as convolutional neural networks (CNNs) and multi-layer perceptron (MLP). As a result, in the convective drying, changes in color darkening were found in products with the following temperature values: 60 °C (L = 83.41), 70 °C (L = 81.11), 80 °C (L = 79.02), and 90 °C (L = 75.53). The best-generated model achieved an overall classification efficiency of 77%. Sweet potato dried at 90 °C proved to be completely distinguishable from other classes, among which classification efficiency varied between 61–83% depending on the class. This means that image analysis using deep convolutional artificial neural networks is a valuable tool in the context of assessing the quality of convective-dried sweet potato slices. |
| first_indexed | 2024-03-09T05:34:39Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:34:39Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-9d111cba344e47d88f2b9e8176c550102023-12-03T12:29:44ZengMDPI AGApplied Sciences2076-34172022-08-011215784010.3390/app12157840Machine and Deep Learning in the Evaluation of Selected Qualitative Characteristics of Sweet Potatoes Obtained under Different Convective Drying ConditionsKrzysztof Przybył0Franciszek Adamski1Jolanta Wawrzyniak2Marzena Gawrysiak-Witulska3Jerzy Stangierski4Dominik Kmiecik5Department of Dairy and Process Engineering, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Ul. Wojska Polskiego 31, 60-624 Poznań, PolandDepartment of Dairy and Process Engineering, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Ul. Wojska Polskiego 31, 60-624 Poznań, PolandDepartment of Dairy and Process Engineering, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Ul. Wojska Polskiego 31, 60-624 Poznań, PolandDepartment of Dairy and Process Engineering, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Ul. Wojska Polskiego 31, 60-624 Poznań, PolandDepartment of Food Quality and Safety Management, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Ul. Wojska Polskiego 31, 60-624 Poznań, PolandDepartment of Food Technology od Plant Origin, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Ul. Wojska Polskiego 31, 60-624 Poznań, PolandThis paper discusses the use of various methods to distinguish between slices of sweet potato dried in different conditions. The drying conditions varied in terms of temperature, the values were: 60 °C, 70 °C, 80 °C, and 90 °C. Examination methods included instrumental texture analysis using a texturometer and digital texture analysis based on macroscopic images. Classification of acquired data involved the use of machine learning techniques using various types of artificial neural networks, such as convolutional neural networks (CNNs) and multi-layer perceptron (MLP). As a result, in the convective drying, changes in color darkening were found in products with the following temperature values: 60 °C (L = 83.41), 70 °C (L = 81.11), 80 °C (L = 79.02), and 90 °C (L = 75.53). The best-generated model achieved an overall classification efficiency of 77%. Sweet potato dried at 90 °C proved to be completely distinguishable from other classes, among which classification efficiency varied between 61–83% depending on the class. This means that image analysis using deep convolutional artificial neural networks is a valuable tool in the context of assessing the quality of convective-dried sweet potato slices.https://www.mdpi.com/2076-3417/12/15/7840artificial neural networksconvolutional neural networksmachine learningdeep learningsweet potatoconvective drying |
| spellingShingle | Krzysztof Przybył Franciszek Adamski Jolanta Wawrzyniak Marzena Gawrysiak-Witulska Jerzy Stangierski Dominik Kmiecik Machine and Deep Learning in the Evaluation of Selected Qualitative Characteristics of Sweet Potatoes Obtained under Different Convective Drying Conditions Applied Sciences artificial neural networks convolutional neural networks machine learning deep learning sweet potato convective drying |
| title | Machine and Deep Learning in the Evaluation of Selected Qualitative Characteristics of Sweet Potatoes Obtained under Different Convective Drying Conditions |
| title_full | Machine and Deep Learning in the Evaluation of Selected Qualitative Characteristics of Sweet Potatoes Obtained under Different Convective Drying Conditions |
| title_fullStr | Machine and Deep Learning in the Evaluation of Selected Qualitative Characteristics of Sweet Potatoes Obtained under Different Convective Drying Conditions |
| title_full_unstemmed | Machine and Deep Learning in the Evaluation of Selected Qualitative Characteristics of Sweet Potatoes Obtained under Different Convective Drying Conditions |
| title_short | Machine and Deep Learning in the Evaluation of Selected Qualitative Characteristics of Sweet Potatoes Obtained under Different Convective Drying Conditions |
| title_sort | machine and deep learning in the evaluation of selected qualitative characteristics of sweet potatoes obtained under different convective drying conditions |
| topic | artificial neural networks convolutional neural networks machine learning deep learning sweet potato convective drying |
| url | https://www.mdpi.com/2076-3417/12/15/7840 |
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