Intelligent modelling of sugarcane juice quality characteristics based on microfluidization processing conditions
Ayon Tarafdar1,2, Barjinder Pal Kaur1
1Food Engineering Lab, Department of Food Engineering, National Institute of Food Technology Entrepreneurship and Management, Kundli, Sonipat, Haryana 131 028 India.
Journal of Food Science and Technology
|October 14, 2024
Summary
Artificial neural network (ANN) models accurately predicted sugarcane juice quality after microfluidization. The GDX+tansigmoid model achieved 96% accuracy, optimizing processing parameters for enhanced juice characteristics.
Area of Science:
- Food Science and Technology
- Biotechnology
- Agricultural Engineering
Background:
- Microfluidization is an emerging technology for processing food products like sugarcane juice.
- The impact of microfluidization parameters on sugarcane juice quality has not been extensively studied.
- Predictive modeling can optimize food processing and ensure product quality.
Purpose of the Study:
- To explore the use of different artificial neural network (ANN) architectures for predicting sugarcane juice quality under microfluidization.
- To investigate the effects of varying microfluidization pressures and cycles on key sugarcane juice characteristics.
- To identify the optimal ANN model for predicting sugarcane juice quality attributes.
Main Methods:
- Employed various ANN infrastructures with different hidden layer activation functions (tansigmoid, logsigmoid) and learning algorithms (LM, GDX).
- Tested models with varying hidden layer neurons (HLNs) to predict multiple quality parameters including color, phenolic content, sugars, enzyme activities, microbial counts, particle size, sensory score, and sedimentation rate.
- Evaluated prediction accuracy on training and testing datasets to select the best performing model.
Main Results:
- Several ANN models, including LM+logsigmoid, GDX+logsigmoid, and GDX+tansigmoid, achieved over 90% prediction accuracy.
- The GDX+tansigmoid model demonstrated the highest prediction accuracy (91.7% training, 96% testing) with a lower number of hidden layer neurons (10 HLNs).
- This model effectively predicted diverse quality characteristics of sugarcane juice subjected to microfluidization.
Conclusions:
- ANNs are effective tools for predicting sugarcane juice quality under microfluidization.
- The GDX+tansigmoid ANN model offers a reliable method for optimizing microfluidization parameters for sugarcane juice.
- This predictive approach can guide processing conditions to achieve desired juice quality attributes.
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