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https://hdl.handle.net/11147/10237
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DC Field | Value | Language |
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dc.contributor.author | Söyler, Alper | - |
dc.contributor.author | Çıkrıkçı, Sevil | - |
dc.contributor.author | Çavdaroğlu, Çağrı | - |
dc.contributor.author | Bouillaud, Dylan | - |
dc.contributor.author | Farjon, Jonathan | - |
dc.contributor.author | Giraudeau, Patrick | - |
dc.contributor.author | Öztop, Mecit H. | - |
dc.date.accessioned | 2021-01-24T18:33:11Z | - |
dc.date.available | 2021-01-24T18:33:11Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 0023-6438 | - |
dc.identifier.issn | 1096-1127 | - |
dc.identifier.uri | https://doi.org/10.1016/j.lwt.2020.110557 | - |
dc.identifier.uri | https://hdl.handle.net/11147/10237 | - |
dc.description.abstract | Benchtop NMR systems offers various advantages such as being easy to use, not requiring constant maintenance and being available at affordable prices. In this study, multiple aspects of benchtop NMR spectroscopy were explored to analyze milk in an industrial context, either regarding the quality of production or regarding the differentiation of the final product. The first part focuses on the production conditions of lactose hydrolysis in milk and quantitative online NMR spectroscopy was adapted to follow lactose hydrolysis in milk in continuous flow mode. The second part focuses on differentiating milk samples having different properties. 36 milk samples from France and Turkey were analysed and glycerol, fat and sugar contents were measured from the NMR spectra. Combination of spectroscopic data with a proposed Artificial Neural Network model enabled to classify milk of different origins and different properties. This study shows that benchtop NMR spectroscopy is a versatile non-destructive control method that can help controlling milk quality both during and after production. © 2020 Elsevier Ltd | en_US |
dc.description.sponsorship | Alper Soyler got funding from Short Term Scientific Mission (STSM) program under COST Action CA15209 EURELAX – European Network on NMR Relaxometry ( http://www.cost.eu/COST_Actions/ca/CA15209 ) to conduct experiments at the University of Nantes . Authors from Nantes acknowledge support from the Corsaire metabolomics core facility (Biogenouest). J.F. acknowledges Sandrine Bouchet for a continuous essential assistance. Middle East Technical University , Scientific Research Projects Coordination Unit with grant # BAP-07-02-2017-004-077 is also acknowledged. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Academic Press | en_US |
dc.relation.ispartof | LWT - Food Science and Technology | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Artificial neural network (ANN) | en_US |
dc.subject | Benchtop NMR spectroscopy | en_US |
dc.subject | Classification | en_US |
dc.subject | Flow NMR | en_US |
dc.subject | Lactose hydrolysis | en_US |
dc.subject | Milk | en_US |
dc.title | Multi-scale benchtop 1H NMR spectroscopy for milk analysis | en_US |
dc.type | Article | en_US |
dc.institutionauthor | Çavdaroğlu, Çağrı | - |
dc.department | İzmir Institute of Technology. Food Engineering | en_US |
dc.identifier.wos | WOS:000613931800057 | en_US |
dc.identifier.scopus | 2-s2.0-85096505818 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1016/j.lwt.2020.110557 | - |
dc.relation.doi | 10.1016/j.lwt.2020.110557 | en_US |
dc.coverage.doi | 10.1016/j.lwt.2020.110557 | en_US |
dc.identifier.scopusquality | Q1 | - |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
item.openairetype | Article | - |
crisitem.author.dept | 01. Izmir Institute of Technology | - |
Appears in Collections: | Food Engineering / Gıda Mühendisliği Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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1-s2.0-S0023643820315450-main.pdf | 3.87 MB | Adobe PDF | View/Open |
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