Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

Eman S. Alamri (), Ghada A. Altarawneh, Hala M. Bayomy and Ahmad B. Hassanat ()
Additional contact information
Eman S. Alamri: Food Science and Nutrition Department, University of Tabuk, Tabuk 47512, Saudi Arabia
Ghada A. Altarawneh: Accounting Department, Mutah University, Karak 61711, Jordan
Hala M. Bayomy: Food Science and Nutrition Department, University of Tabuk, Tabuk 47512, Saudi Arabia
Ahmad B. Hassanat: Computer Science Department, Mutah University, Karak 61711, Jordan

Sustainability, 2023, vol. 15, issue 15, 1-19

Abstract: This study investigates the classification of Arabic coffee into three major variations (light, medium, and dark) using simulated data gathered from the actual measurements of color information, antioxidant laboratory testing, and chemical composition tests. The goal is to overcome the restrictions of limited real-world data availability and the high costs involved with laboratory testing. The Monte Carlo approach is used to generate new samples for each type of Arabic coffee using the mean values and standard deviations of publicly available data. Using these simulated data, multiple machine-learning algorithms are used to classify Arabic coffee, while also investigating the importance of features in identifying the key chemical components. The findings emphasize the importance of color information in accurately recognizing Arabic coffee types. However, depending purely on antioxidant information results in poor classification accuracy due to increased data complexity and classifier variability. The chemical composition information, on the other hand, has exceptional discriminatory power, allowing faultless classification on its own. Notably, particular characteristics like crude protein and crude fiber show high relationships and play an important role in coffee type classification. Based on these findings, it is suggested that a mobile application be developed that uses image recognition to examine coffee color while also providing chemical composition information. End users, especially consumers, would be able to make informed judgments regarding their coffee preferences.

Keywords: smart sustainability; smart food; Arabic coffee; classification; color information; antioxidant information; chemical composition; mobile application (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/15/11561/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/15/11561/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:15:p:11561-:d:1203143

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().