Wine Quality Analysis Using Newton Interpolation: Applying Computational Physics in the Food Industry

Shalom Maria Larasati; Alfian Miftahurrizki; Athallah Dwi Syahputro; Reinal Sihite

doi:10.58797/cser.030304

Authors

Shalom Maria Larasati Department of Physics Education, Faculty of Matehematics and Natural Science, Universitas Negeri Jakarta Jl.R. Mangun Muka Raya No. 11, Rawamangun, Jakarta Timur 13220, Indonesia.
Alfian Miftahurrizki Department of Physics Education, Faculty of Matehematics and Natural Science, Universitas Negeri Jakarta Jl.R. Mangun Muka Raya No. 11, Rawamangun, Jakarta Timur 13220, Indonesia.
Athallah Dwi Syahputro Department of Physics Education, Faculty of Matehematics and Natural Science, Universitas Negeri Jakarta Jl.R. Mangun Muka Raya No. 11, Rawamangun, Jakarta Timur 13220, Indonesia.
Reinal Sihite Department of Physics Education, Faculty of Matehematics and Natural Science, Universitas Negeri Jakarta Jl.R. Mangun Muka Raya No. 11, Rawamangun, Jakarta Timur 13220, Indonesia.

DOI:

https://doi.org/10.58797/cser.030304

Keywords:

computational physics, newton interpolation, wine quality

Abstract

This study aims to analyze wine quality based on computational physics using the Newton Interpolation method. This method is applied to estimate the quality value of wine based on certain values of variables such as sulphates, alcohol, citric acid, and the average of several physicochemical variables. Data were obtained from a red wine dataset commonly used in food product quality studies. Each variable was analyzed separately using strategically selected observation data points, then the interpolation value was calculated to predict the quality of wine at certain variable values. The interpolation results showed that the Newton method was able to produce quality value estimates that were consistent with data trends, such as quality predictions of 4.52 for sulphates = 0.61 and 2.97 for alcohol = 9.2. However, in some cases such as citric acid and the average of the variable, extreme fluctuations appeared in the high-degree divided difference values, indicating the potential for overfitting due to uneven data distribution or outliers. Nevertheless, the Newton interpolation method proved effective in providing initial estimates of wine quality, especially when data is limited.

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