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A Data-Driven Model Analysis Method in Optimizing Raw Materials Standard for Glutinous Rice Products

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2020)

Abstract

The main method to ensure the stable quality of glutinous rice products is to assure that the raw materials of glutinous rice flour meet the specified quality requirements. Therefore, the prediction about the quality standard of raw materials for glutinous rice flour is extremely important. The existing researches rarely take into account such a prediction method for predicting the quality standard of raw materials for glutinous rice flour. In this paper, we apply the quality standard of high-quality glutinous rice products to predict raw materials for glutinous rice flour which meets requirements. In order to solve the raw material standard formulation problem for the glutinous rice product(RMSP-GRP for short), we propose an analysis method based on a three-stage data-driven model which consists of prediction, modeling and regulation. The first stage is to use the quality index of raw materials of glutinous rice flour to predict the quality index of glutinous rice products, in order to perfect the process of transforming the quality requirements of high-quality glutinous rice into the requirements of raw materials of glutinous rice flour. In the second stage, the multi-objective optimization model with priority objective was established to set the upper and lower boundaries of the quality index of raw materials of glutinous rice flour as decision variables and the correction factors were introduced based on the goodness of fit of the prediction function to improve the reliability of the feasible region. The optimization model mentioned above is to search for an optimal hypercube within the feasible region defined by the constraints of raw materials of glutinous rice flour and the constraints of glutinous rice products. In other words, the hypercube can reach the maximum state of the required shape when all vertices of the hypercube are in the feasible region. Based on the quartile data characteristics of the experimental samples and some conclusions in the second stage, we adjust the optimization model to expand the range of quality index of raw materials of glutinous rice flour in the third stage.

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Acknowledgement

This work is partially supported by subproject of the National Key Research and Development Program of China (Grant No. 2017YFD0401102-02), Key Project of Philosophy and Social Science Research Project of Hubei Provincial Department of Education in 2019(19D59) and Science and Technology Research Project of Hubei Provincial Department of Education (D20191604).

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Correspondence to Zhou Kang .

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He, Z., Kang, Z., Zhou, J., Yang, H., Shang, X., Li, G. (2021). A Data-Driven Model Analysis Method in Optimizing Raw Materials Standard for Glutinous Rice Products. In: Pan, L., Pang, S., Song, T., Gong, F. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2020. Communications in Computer and Information Science, vol 1363. Springer, Singapore. https://doi.org/10.1007/978-981-16-1354-8_14

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  • DOI: https://doi.org/10.1007/978-981-16-1354-8_14

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