Application of Machine Learning Using Color and Texture Analysis to Recognize Microwave Vacuum Puffed Pork Snacks
The objective of the study was to create artificial neural networks (ANN) capable of highly efficient recognition of modified and unmodified puffed pork snacks for the purposes of obtaining an optimal final product. The study involved meat snacks produced from unmodified and papain modified raw pork...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/10/5071 |
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| author | Tomasz Pawlak Agnieszka A. Pilarska Krzysztof Przybył Jerzy Stangierski Antoni Ryniecki Dorota Cais-Sokolińska Krzysztof Pilarski Barbara Peplińska |
| author_facet | Tomasz Pawlak Agnieszka A. Pilarska Krzysztof Przybył Jerzy Stangierski Antoni Ryniecki Dorota Cais-Sokolińska Krzysztof Pilarski Barbara Peplińska |
| author_sort | Tomasz Pawlak |
| collection | DOAJ |
| description | The objective of the study was to create artificial neural networks (ANN) capable of highly efficient recognition of modified and unmodified puffed pork snacks for the purposes of obtaining an optimal final product. The study involved meat snacks produced from unmodified and papain modified raw pork (Psoas major) by means of microwave-vacuum puffing (MVP) under specified conditions. The snacks were then analyzed using various instruments in order to determine their basic chemical composition, color and texture. As a result of the MVP process, the moisture-to-protein ratio (MPR) was reduced to 0.11. A darker color and reduction in hardness of approx. 25% was observed in the enzymatically modified products. Multi-layer perceptron networks (MLPN) were then developed using color and texture descriptor training sets (machine learning), which is undoubtedly an innovative solution in this area. |
| first_indexed | 2024-03-10T03:23:28Z |
| format | Article |
| id | doaj.art-7c9bb6114f674a1f848c0e18084c0bec |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:23:28Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7c9bb6114f674a1f848c0e18084c0bec2023-11-23T09:57:17ZengMDPI AGApplied Sciences2076-34172022-05-011210507110.3390/app12105071Application of Machine Learning Using Color and Texture Analysis to Recognize Microwave Vacuum Puffed Pork SnacksTomasz Pawlak0Agnieszka A. Pilarska1Krzysztof Przybył2Jerzy Stangierski3Antoni Ryniecki4Dorota Cais-Sokolińska5Krzysztof Pilarski6Barbara Peplińska7Department of Dairy and Process Engineering, Poznań University of Life Sciences, ul. Wojska Polskiego 31, 60-624 Poznań, PolandDepartment of Hydraulic and Sanitary Engineering, Poznań University of Life Sciences, ul. Piątkowska 94A, 60-649 Poznań, PolandDepartment of Dairy and Process Engineering, Poznań University of Life Sciences, ul. Wojska Polskiego 31, 60-624 Poznań, PolandDepartment of Food Quality and Safety Management, Poznań University of Life Sciences, ul. Wojska Polskiego 31, 60-624 Poznań, PolandDepartment of Dairy and Process Engineering, Poznań University of Life Sciences, ul. Wojska Polskiego 31, 60-624 Poznań, PolandDepartment of Dairy and Process Engineering, Poznań University of Life Sciences, ul. Wojska Polskiego 31, 60-624 Poznań, PolandDepartment of Biosystems Engineering, Poznań University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznań, PolandNanoBioMedical Centre, Adam Mickiewicz University, ul. Wszechnicy Piastowskiej 3, 61-614 Poznań, PolandThe objective of the study was to create artificial neural networks (ANN) capable of highly efficient recognition of modified and unmodified puffed pork snacks for the purposes of obtaining an optimal final product. The study involved meat snacks produced from unmodified and papain modified raw pork (Psoas major) by means of microwave-vacuum puffing (MVP) under specified conditions. The snacks were then analyzed using various instruments in order to determine their basic chemical composition, color and texture. As a result of the MVP process, the moisture-to-protein ratio (MPR) was reduced to 0.11. A darker color and reduction in hardness of approx. 25% was observed in the enzymatically modified products. Multi-layer perceptron networks (MLPN) were then developed using color and texture descriptor training sets (machine learning), which is undoubtedly an innovative solution in this area.https://www.mdpi.com/2076-3417/12/10/5071machine learningartificial neural networkspork musclepapainmicrowave-vacuum dryingpuffing |
| spellingShingle | Tomasz Pawlak Agnieszka A. Pilarska Krzysztof Przybył Jerzy Stangierski Antoni Ryniecki Dorota Cais-Sokolińska Krzysztof Pilarski Barbara Peplińska Application of Machine Learning Using Color and Texture Analysis to Recognize Microwave Vacuum Puffed Pork Snacks Applied Sciences machine learning artificial neural networks pork muscle papain microwave-vacuum drying puffing |
| title | Application of Machine Learning Using Color and Texture Analysis to Recognize Microwave Vacuum Puffed Pork Snacks |
| title_full | Application of Machine Learning Using Color and Texture Analysis to Recognize Microwave Vacuum Puffed Pork Snacks |
| title_fullStr | Application of Machine Learning Using Color and Texture Analysis to Recognize Microwave Vacuum Puffed Pork Snacks |
| title_full_unstemmed | Application of Machine Learning Using Color and Texture Analysis to Recognize Microwave Vacuum Puffed Pork Snacks |
| title_short | Application of Machine Learning Using Color and Texture Analysis to Recognize Microwave Vacuum Puffed Pork Snacks |
| title_sort | application of machine learning using color and texture analysis to recognize microwave vacuum puffed pork snacks |
| topic | machine learning artificial neural networks pork muscle papain microwave-vacuum drying puffing |
| url | https://www.mdpi.com/2076-3417/12/10/5071 |
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