Abstract
The rum aging process shows volume losses, called wastage. The numerical operation variables: product, boardwalk, horizontal and vertical positions, date, volume, alcoholic degree, temperature, humidity and aging time, recorded in databases, contain valuable information to study the process. MATLAB 2017 software was used to estimate volume losses. In the modeling of the rum aging process, the multilayer perceptron neuronal network with one and two hidden layers was used, varying the number of neurons in these between 4 and 10. The Levenberg-Marquadt (LM) and Bayesian training algorithms were compared (Bay) The increase in 6 consecutive iterations of the validation error and 1,000 as the maximum number of training cycles were the criteria used to stop the training. The input variables to the network were: numerical month, volume, temperature, humidity, initial alcoholic degree and aging time, while the output variable was wastage. 546 pairs of input/output data were processed. The statistical Friedman and Wilcoxon tests were performed to select the best neural architecture according to the mean square error (MSE) criteria. The selected topology has a 6-4-4-1 structure, with an MSE of 2.1∙10-3 and a correlation factor (R) with experimental data of 0.9898. The neural network obtained was used to simulate thirteen initial aging conditions that were not used for training and validation, detecting a coefficient of determination (R2) of 0.9961.
Highlights
In fresh rum, as in most distilled alcoholic beverages, the aroma reminds of the used raw material
The predictive models obtained by data mining techniques constitute an alternative to the mathematical models and at the same time, a tool to analyze the information stored in the rum aging processes to predict the percentage of volume losses based on the variables that are registered
Determination of Noise, Cleaning and Selection of the Data to be Used A data with 900 instances and ten variables were obtained, five of them qualitative: product, track, date and horizontal and vertical positions; while the remaining: aging time, volume, temperature, humidity and initial alcoholic degree are quantitative
Summary
As in most distilled alcoholic beverages, the aroma reminds of the used raw material. Aroma varies when the fresh rum rests in oak containers for a certain time, commonly known as "aging" or aging time During this time, reactions that cause a transformation of the original organoleptic properties of the distillates occur naturally. The existing technology in the aging cellars allowed the study of the wastages during 13 months; measuring the liquid level of the barrels, alcoholic strength, temperature and humidity All this stored memory constitutes a valuable source of information that can be useful in understanding the present and predicting the future. Data mining (DM) is the process of extracting useful and understandable knowledge, previously unknown, from large amounts of data stored in different formats [3] It allows prediction, classification, association, grouping and correlation tasks based on statistical techniques such as the analysis of principal and computational components such as artificial neural networks [4]. The predictive models obtained by data mining techniques constitute an alternative to the mathematical models and at the same time, a tool to analyze the information stored in the rum aging processes to predict the percentage of volume losses based on the variables that are registered
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