The Prediction of Pectin Viscosity Using Machine Learning Based on Physical Characteristics—Case Study: Aglupectin HS-MR

Przemysław Siejak, Krzysztof Przybył (), Łukasz Masewicz, Katarzyna Walkowiak (), Ryszard Rezler and Hanna Maria Baranowska
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Przemysław Siejak: Department of Physics and Biophysics, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 38/42, 60-637 Poznań, Poland
Krzysztof Przybył: Department of Dairy and Process Engineering, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, 31 Wojska Polskiego St., 60-624 Poznań, Poland
Łukasz Masewicz: Department of Physics and Biophysics, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 38/42, 60-637 Poznań, Poland
Katarzyna Walkowiak: Department of Physics and Biophysics, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 38/42, 60-637 Poznań, Poland
Ryszard Rezler: Department of Physics and Biophysics, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 38/42, 60-637 Poznań, Poland
Hanna Maria Baranowska: Department of Physics and Biophysics, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 38/42, 60-637 Poznań, Poland

Sustainability, 2024, vol. 16, issue 14, 1-12

Abstract: In the era of technology development, the optimization of production processes, quality control and at the same time increasing production efficiency without wasting food, artificial intelligence is becoming an alternative tool supporting many decision-making processes. The work used modern machine learning and physical analysis tools to evaluate food products (pectins). Various predictive models have been presented to estimate the viscosity of pectin. Based on the physical analyses, the characteristics of the food product were isolated, including L*a*b* color, concentration, conductance and pH. Prediction was determined using the determination index and loss function for individual machine learning algorithms. As a result of the work, it turned out that the most effective estimation of pectin viscosity was using Decision Tree (R 2 = 0.999) and Random Forest (R 2 = 0.998). In the future, the prediction of pectin properties in terms of viscosity recognition may be significantly perceived, especially in the food and pharmaceutical industries. Predicting the natural pectin substrate may contribute to improving quality, increasing efficiency and at the same time reducing losses of the obtained final product.

Keywords: pectin viscosity; machine learning; prediction; color L*a*b*; concentration; conductance; pH (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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