Machine learning for predicting industrial performance: Example of the dry matter content of emmental-type cheese.
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| Title: | Machine learning for predicting industrial performance: Example of the dry matter content of emmental-type cheese. |
|---|---|
| Authors: | Perrignon, Manon1 (AUTHOR) manon.perrignon@agrocampus-ouest.fr, Emily, Mathieu2 (AUTHOR), Munch, Mélanie3 (AUTHOR), Jeantet, Romain1 (AUTHOR), Croguennec, Thomas1 (AUTHOR) |
| Source: | International Dairy Journal. Mar2025, Vol. 162, pN.PAG-N.PAG. 1p. |
| Subjects: | Machine learning, Cheesemaking, Cheese industry, Artificial intelligence, Random forest algorithms |
| Abstract: | Controlling the dry matter content of cheese is essential to defining the performance of cheese production. For Emmental-type cheese, dry matter content has to be above but as close as possible to a minimal value that is defined by legislation. The means for achieving the target dry matter content was mostly left to the discretion of the cheese experts, who target a dry matter objective based on his expert knowledge and the deviation of cheese production. To date, the prediction of performance indicators, such as cheese dry matter content, can help cheesemakers to improve their production performance. Several Machine Learning models and classical statistical methods were compared to predict the dry matter of Emmental cheese for a set of data coming from one selected cheese industry. The Random Forest method emerged as the most effective model (RMSE = 0.28 and R2 = 0.67). The weight of variables in explaining the variability of cheese dry matter content was also calculated, helping cheese experts to interpret the model and apply corrective actions to improve cheese production performance. The ability to predict cheese dry matter content and understand its variability from cheese manufacturing data offer new perspectives for the cheese industry. This method can be transferred to other indicators and assist in decision-making to enhance industry performance. [ABSTRACT FROM AUTHOR] |
| Copyright of International Dairy Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 181885699 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine learning for predicting industrial performance: Example of the dry matter content of emmental-type cheese. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Perrignon%2C+Manon%22">Perrignon, Manon</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> manon.perrignon@agrocampus-ouest.fr</i><br /><searchLink fieldCode="AR" term="%22Emily%2C+Mathieu%22">Emily, Mathieu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Munch%2C+Mélanie%22">Munch, Mélanie</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jeantet%2C+Romain%22">Jeantet, Romain</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Croguennec%2C+Thomas%22">Croguennec, Thomas</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Dairy+Journal%22">International Dairy Journal</searchLink>. Mar2025, Vol. 162, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Cheesemaking%22">Cheesemaking</searchLink><br /><searchLink fieldCode="DE" term="%22Cheese+industry%22">Cheese industry</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Controlling the dry matter content of cheese is essential to defining the performance of cheese production. For Emmental-type cheese, dry matter content has to be above but as close as possible to a minimal value that is defined by legislation. The means for achieving the target dry matter content was mostly left to the discretion of the cheese experts, who target a dry matter objective based on his expert knowledge and the deviation of cheese production. To date, the prediction of performance indicators, such as cheese dry matter content, can help cheesemakers to improve their production performance. Several Machine Learning models and classical statistical methods were compared to predict the dry matter of Emmental cheese for a set of data coming from one selected cheese industry. The Random Forest method emerged as the most effective model (RMSE = 0.28 and R2 = 0.67). The weight of variables in explaining the variability of cheese dry matter content was also calculated, helping cheese experts to interpret the model and apply corrective actions to improve cheese production performance. The ability to predict cheese dry matter content and understand its variability from cheese manufacturing data offer new perspectives for the cheese industry. This method can be transferred to other indicators and assist in decision-making to enhance industry performance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Dairy Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.idairyj.2024.106143 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Cheesemaking Type: general – SubjectFull: Cheese industry Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Random forest algorithms Type: general Titles: – TitleFull: Machine learning for predicting industrial performance: Example of the dry matter content of emmental-type cheese. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Perrignon, Manon – PersonEntity: Name: NameFull: Emily, Mathieu – PersonEntity: Name: NameFull: Munch, Mélanie – PersonEntity: Name: NameFull: Jeantet, Romain – PersonEntity: Name: NameFull: Croguennec, Thomas IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09586946 Numbering: – Type: volume Value: 162 Titles: – TitleFull: International Dairy Journal Type: main |
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