Mathematical Modeling and Optimization of Ultrasonic Pre-Treatment for Drying of Pumpkin (Cucurbita moschata)
Innovations in food drying processes are usually aimed at reducing drying time and improving the overall properties of dried products. These are important issues from an economic and environmental point of view and can contribute to the sustainability of the whole process. In this study, the effects...
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| Veröffentlicht in: | Processes 2023-02, Vol.11 (2), p.469 |
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| creator | Karlović, Sven Dujmić, Filip Brnčić, Suzana Rimac Sabolović, Marija Badanjak Ninčević Grassino, Antonela Škegro, Marko Šimić, Marko Adrian Brnčić, Mladen |
| description | Innovations in food drying processes are usually aimed at reducing drying time and improving the overall properties of dried products. These are important issues from an economic and environmental point of view and can contribute to the sustainability of the whole process. In this study, the effects of ultrasonic treatment on the drying kinetics of pumpkin pulp are investigated, and mathematical models to predict the drying kinetics are analyzed and optimized. The results show that ultrasonic pretreatment significantly reduces drying time from 451 to 268 min, with optimal processing parameters at 90% of the maximum ultrasonic power and a processing time of 45 min. The total color change of the samples was the lowest at the obtained optimal processing parameters. Based on the values (RMSE and R2) of the investigated mathematical drying models, it was found that the Weibull model is the best fit for the experimental data and is considered suitable for the drying kinetics of ultrasonically pretreated pumpkin samples. In this study, an artificial neural network with 15 neurons in hidden layers was also used to model the drying process in combination with ultrasound pretreatment. The network had a performance of 0.999987 and the mean square error was 8.03 × 10−5, showing how artificial neural networks can successfully predict the effects of all tested process variables on the drying time/moisture ratio. |
| doi_str_mv | 10.3390/pr11020469 |
| format | Article |
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These are important issues from an economic and environmental point of view and can contribute to the sustainability of the whole process. In this study, the effects of ultrasonic treatment on the drying kinetics of pumpkin pulp are investigated, and mathematical models to predict the drying kinetics are analyzed and optimized. The results show that ultrasonic pretreatment significantly reduces drying time from 451 to 268 min, with optimal processing parameters at 90% of the maximum ultrasonic power and a processing time of 45 min. The total color change of the samples was the lowest at the obtained optimal processing parameters. Based on the values (RMSE and R2) of the investigated mathematical drying models, it was found that the Weibull model is the best fit for the experimental data and is considered suitable for the drying kinetics of ultrasonically pretreated pumpkin samples. In this study, an artificial neural network with 15 neurons in hidden layers was also used to model the drying process in combination with ultrasound pretreatment. The network had a performance of 0.999987 and the mean square error was 8.03 × 10−5, showing how artificial neural networks can successfully predict the effects of all tested process variables on the drying time/moisture ratio.</description><identifier>ISSN: 2227-9717</identifier><identifier>EISSN: 2227-9717</identifier><identifier>DOI: 10.3390/pr11020469</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Artificial neural networks ; By products ; Carotenoids ; Cavitation ; Drying ; Energy consumption ; Environmental impact ; Feed industry ; Food ; Food processing ; Fruits ; Kinetics ; Mathematical models ; Moisture effects ; Neural networks ; Optimization ; Pretreatment ; Process parameters ; Process variables ; Pumpkins ; Seeds ; Temperature ; Ultrasonic imaging ; Ultrasonic processing</subject><ispartof>Processes, 2023-02, Vol.11 (2), p.469</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Dujmić, Filip ; Brnčić, Suzana Rimac ; Sabolović, Marija Badanjak ; Ninčević Grassino, Antonela ; Škegro, Marko ; Šimić, Marko Adrian ; Brnčić, Mladen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c334t-337a3f522ad9f2b973cf56468e47af5d94eff88b316104642a98fc465985fe2c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial neural networks</topic><topic>By products</topic><topic>Carotenoids</topic><topic>Cavitation</topic><topic>Drying</topic><topic>Energy consumption</topic><topic>Environmental impact</topic><topic>Feed industry</topic><topic>Food</topic><topic>Food processing</topic><topic>Fruits</topic><topic>Kinetics</topic><topic>Mathematical models</topic><topic>Moisture effects</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Pretreatment</topic><topic>Process parameters</topic><topic>Process variables</topic><topic>Pumpkins</topic><topic>Seeds</topic><topic>Temperature</topic><topic>Ultrasonic imaging</topic><topic>Ultrasonic processing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Karlović, Sven</creatorcontrib><creatorcontrib>Dujmić, Filip</creatorcontrib><creatorcontrib>Brnčić, Suzana Rimac</creatorcontrib><creatorcontrib>Sabolović, Marija Badanjak</creatorcontrib><creatorcontrib>Ninčević Grassino, Antonela</creatorcontrib><creatorcontrib>Škegro, Marko</creatorcontrib><creatorcontrib>Šimić, Marko Adrian</creatorcontrib><creatorcontrib>Brnčić, Mladen</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>ProQuest Biological Science Collection</collection><collection>Biological Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Processes</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Karlović, Sven</au><au>Dujmić, Filip</au><au>Brnčić, Suzana Rimac</au><au>Sabolović, Marija Badanjak</au><au>Ninčević Grassino, Antonela</au><au>Škegro, Marko</au><au>Šimić, Marko Adrian</au><au>Brnčić, Mladen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mathematical Modeling and Optimization of Ultrasonic Pre-Treatment for Drying of Pumpkin (Cucurbita moschata)</atitle><jtitle>Processes</jtitle><date>2023-02-01</date><risdate>2023</risdate><volume>11</volume><issue>2</issue><spage>469</spage><pages>469-</pages><issn>2227-9717</issn><eissn>2227-9717</eissn><abstract>Innovations in food drying processes are usually aimed at reducing drying time and improving the overall properties of dried products. These are important issues from an economic and environmental point of view and can contribute to the sustainability of the whole process. In this study, the effects of ultrasonic treatment on the drying kinetics of pumpkin pulp are investigated, and mathematical models to predict the drying kinetics are analyzed and optimized. The results show that ultrasonic pretreatment significantly reduces drying time from 451 to 268 min, with optimal processing parameters at 90% of the maximum ultrasonic power and a processing time of 45 min. The total color change of the samples was the lowest at the obtained optimal processing parameters. Based on the values (RMSE and R2) of the investigated mathematical drying models, it was found that the Weibull model is the best fit for the experimental data and is considered suitable for the drying kinetics of ultrasonically pretreated pumpkin samples. In this study, an artificial neural network with 15 neurons in hidden layers was also used to model the drying process in combination with ultrasound pretreatment. The network had a performance of 0.999987 and the mean square error was 8.03 × 10−5, showing how artificial neural networks can successfully predict the effects of all tested process variables on the drying time/moisture ratio.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/pr11020469</doi><orcidid>https://orcid.org/0000-0002-2601-7535</orcidid><orcidid>https://orcid.org/0000-0002-1615-3189</orcidid><orcidid>https://orcid.org/0000-0002-8906-4291</orcidid><orcidid>https://orcid.org/0000-0001-7452-3275</orcidid><orcidid>https://orcid.org/0000-0002-6117-2063</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MDPI - Multidisciplinary Digital Publishing Institute; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Artificial neural networks By products Carotenoids Cavitation Drying Energy consumption Environmental impact Feed industry Food Food processing Fruits Kinetics Mathematical models Moisture effects Neural networks Optimization Pretreatment Process parameters Process variables Pumpkins Seeds Temperature Ultrasonic imaging Ultrasonic processing |
| title | Mathematical Modeling and Optimization of Ultrasonic Pre-Treatment for Drying of Pumpkin (Cucurbita moschata) |
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