Dynamics of food nutrient loss and prediction of nutrient loss under variable temperature conditions

Qian Wang () and Deepika Koundal ()
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Qian Wang: Zibo Vocational Institute
Deepika Koundal: UPES: University of Petroleum and Energy Studies

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 1, No 23, 225-235

Abstract: Abstract The research-based on nutritionist value of fruits and vegetables has gained importance in the last few decades. In order to better guide the reasonable heat treatment of vegetables and fruits, this work explores the nutrient loss of food under the conditions of heat treatment and variable temperature using asparagus as the research object. Through literature a conceptual framework is proposed on the basis of IoT and Industry 4.0. It establishes an artificial neural network (ANN) model for predicting the vitamin changes, total phenols and total antioxidant, flavonoids activity of asparagus during heat treatment. The experimental analysis presents that the proposed NN model can better predict the loss percentage of vitamin C, total phenols, antioxidant and flavonoids activity during the heat treatment of asparagus, and the correlation coefficient is between 0.8167 and 0.9869. By implementing a single hidden layer NN model, the optimal number of neurons in the hidden layer of the model for the prediction of kinetic parameters of vitamin C degradation in the asparagus bud, upper, middle and lower segments is observed as 24, 26, 26 and 18, respectively. The proposed neural network model established in this article can predict the degradation rate, half-life and D value of asparagus vitamin C during heat treatment, and the correlation coefficients of above 0.99. The ANN model established provides the feasible outcomes in terms of kinetic parameters as well as correlation coefficients providing the viable dynamics of food nutrient loss and prediction of nutrient loss under variable temperature conditions.

Keywords: Asparagus; Variable temperature conditions; Artificial neural network; Nutrient loss (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01370-x

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