Detoxification of linseed-sunflower meal co-extrudate: Process prediction
For many years, linseed has been attracted a great attention in animal nutrition because of its exceptionally favourable fatty acid composition and high content of essential α-linolenic acid. However, the presence of antinutritive components, cyanogenic glycosides, limits its inclusion in the animal...
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| Format: | Article |
| Language: | English |
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Institute for Food Technology, Novi Sad
2018-01-01
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| Series: | Food and Feed Research |
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| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/2217-5369/2018/2217-53691807193C.pdf |
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| author | Čolović Dušica S. Pezo Lato L. Čolović Radmilo R. Banjac Vojislav V. Đuragić Olivera M. Kavallieratos Nickolas G. Spasevski Nedeljka J. |
| author_facet | Čolović Dušica S. Pezo Lato L. Čolović Radmilo R. Banjac Vojislav V. Đuragić Olivera M. Kavallieratos Nickolas G. Spasevski Nedeljka J. |
| author_sort | Čolović Dušica S. |
| collection | DOAJ |
| description | For many years, linseed has been attracted a great attention in animal nutrition because of its exceptionally favourable fatty acid composition and high content of essential α-linolenic acid. However, the presence of antinutritive components, cyanogenic glycosides, limits its inclusion in the animal's diet. Several ways of linseed detoxification were observed in literature, emphasizing extrusion as one of the most effective processes. In the presented study, the application of Artificial Neural Network (ANN) has been observed, as a tool for prediction of process influence on the deterioration of cyanogenic glycosides during the extrusion process of linseed-sunflower meal co-extrudate. The content of hydrogen cyanide (HCN) was determined according to the AOAC method as an indicator of cyanogenic glycosides in the produced co-extrudate. Extrusion of the material was performed on a laboratory single screw extruder. The performance of ANN model was compared with experimental data in order to develop rapid and accurate method for prediction of HCN content in co-extrudate. According to the experimental results, the highest HCN content (126 mg/kg) was determined at the lowest moisture content (7%) and the lowest screw speed (240 rpm). With the increase of moisture content and temperature during extrusion, the content of HCN drastically decreased. The ANN model showed high prediction accuracy (r2> 0.999), which indicates that the model could be easily and reliably applied in practice. |
| first_indexed | 2024-12-16T14:40:06Z |
| format | Article |
| id | doaj.art-ce3b364e12a14d25a6527905497d0310 |
| institution | Directory Open Access Journal |
| issn | 2217-5369 2217-5660 |
| language | English |
| last_indexed | 2024-12-16T14:40:06Z |
| publishDate | 2018-01-01 |
| publisher | Institute for Food Technology, Novi Sad |
| record_format | Article |
| series | Food and Feed Research |
| spelling | doaj.art-ce3b364e12a14d25a6527905497d03102022-12-21T22:27:58ZengInstitute for Food Technology, Novi SadFood and Feed Research2217-53692217-56602018-01-0145219320110.5937/FFR1802193C2217-53691807193CDetoxification of linseed-sunflower meal co-extrudate: Process predictionČolović Dušica S.0Pezo Lato L.1https://orcid.org/0000-0002-0704-3084Čolović Radmilo R.2https://orcid.org/0000-0002-0689-9147Banjac Vojislav V.3Đuragić Olivera M.4https://orcid.org/0000-0003-3189-5057Kavallieratos Nickolas G.5Spasevski Nedeljka J.6University of Novi Sad, Institute of Food Technology, Novi Sad, SerbiaUniversity of Belgrade, Institute of General and Physical Chemistry, Belgrade, SerbiaUniversity of Novi Sad, Institute of Food Technology, Novi Sad, SerbiaUniversity of Novi Sad, Institute of Food Technology, Novi Sad, SerbiaUniversity of Novi Sad, Institute of Food Technology, Novi Sad, SerbiaAgricultural University of Athens, Athens, GreeceUniversity of Novi Sad, Institute of Food Technology, Novi Sad, SerbiaFor many years, linseed has been attracted a great attention in animal nutrition because of its exceptionally favourable fatty acid composition and high content of essential α-linolenic acid. However, the presence of antinutritive components, cyanogenic glycosides, limits its inclusion in the animal's diet. Several ways of linseed detoxification were observed in literature, emphasizing extrusion as one of the most effective processes. In the presented study, the application of Artificial Neural Network (ANN) has been observed, as a tool for prediction of process influence on the deterioration of cyanogenic glycosides during the extrusion process of linseed-sunflower meal co-extrudate. The content of hydrogen cyanide (HCN) was determined according to the AOAC method as an indicator of cyanogenic glycosides in the produced co-extrudate. Extrusion of the material was performed on a laboratory single screw extruder. The performance of ANN model was compared with experimental data in order to develop rapid and accurate method for prediction of HCN content in co-extrudate. According to the experimental results, the highest HCN content (126 mg/kg) was determined at the lowest moisture content (7%) and the lowest screw speed (240 rpm). With the increase of moisture content and temperature during extrusion, the content of HCN drastically decreased. The ANN model showed high prediction accuracy (r2> 0.999), which indicates that the model could be easily and reliably applied in practice.https://scindeks-clanci.ceon.rs/data/pdf/2217-5369/2018/2217-53691807193C.pdfextrusionantinutritive componentscyanogenic glycosidesartificial neural network |
| spellingShingle | Čolović Dušica S. Pezo Lato L. Čolović Radmilo R. Banjac Vojislav V. Đuragić Olivera M. Kavallieratos Nickolas G. Spasevski Nedeljka J. Detoxification of linseed-sunflower meal co-extrudate: Process prediction Food and Feed Research extrusion antinutritive components cyanogenic glycosides artificial neural network |
| title | Detoxification of linseed-sunflower meal co-extrudate: Process prediction |
| title_full | Detoxification of linseed-sunflower meal co-extrudate: Process prediction |
| title_fullStr | Detoxification of linseed-sunflower meal co-extrudate: Process prediction |
| title_full_unstemmed | Detoxification of linseed-sunflower meal co-extrudate: Process prediction |
| title_short | Detoxification of linseed-sunflower meal co-extrudate: Process prediction |
| title_sort | detoxification of linseed sunflower meal co extrudate process prediction |
| topic | extrusion antinutritive components cyanogenic glycosides artificial neural network |
| url | https://scindeks-clanci.ceon.rs/data/pdf/2217-5369/2018/2217-53691807193C.pdf |
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