Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times

• Autorzy: Al-Mashhadany Sufyan A. , Hasan Haider Ali , Al-Sammarraie Mustafa A.J.

Identyfikatory

DOI

10.12911/22998993/187789

• Języki publikacji: EN

Abstrakty

EN

The consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learning algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying time led to an increase in carbohydrates, sweetness, and CIE-L*a*b levels, while it led to a decrease in the moisture content in dried banana slices. Therefore, there is a direct relationship between CIE-L*a*b levels and sweetness. On the other hand, the RF and CART algorithms gave the highest prediction accuracy of 86% and 0.8 on the Kappa measure. While the other algorithms (SVM, LDA, KNN) gave a prediction accuracy of 80% and 0.7 on the Kappa measure. In terms of testing statistical significance, the null hypothesis (H0) was accepted because there is no relationship between the metric distributions of the algorithms used.

Słowa kluczowe

EN

drying time; machine learning; prediction; sweetness; quality

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2024
• Tom: Vol. 25, nr 6
• Strony: 231--238
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Al-Mashhadany Sufyan A.

• Department of Agricultural Machinery and Equipment, University of Baghdad, College of Agricultural Engineering Sciences, Baghdad, Iraq

autor

Hasan Haider Ali

• Department of Agricultural Machinery and Equipment, University of Baghdad, College of Agricultural Engineering Sciences, Baghdad, Iraq

autor

Al-Sammarraie Mustafa A.J.

• Department of Agricultural Machinery and Equipment, University of Baghdad, College of Agricultural Engineering Sciences, Baghdad, Iraq

• https://orcid.org/0000-0003-3265-2989

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