Multi-classification of freshness from leftover-cooked food in Malaysian foods using machine learning.

The objective of this study is to implement machine learning (ML) to identify and classify the level of contamination in leftover cooked foods based on its aroma. An evaluation on the smell profiles using as a model local Malaysian lunch or evening foods that have always been stored as leftover cooked food is done in this study. To capture the data, a simple e-nose application is built and affixed to the food containers, which will accommodate four types of sensors sensitive to different gases and is programmed using the Arduino platform. To determine the aroma categorization of leftover Malaysian cuisine, samples are examined using RStudio. The results in this study demonstrated satisfactory performances by k-Nearest Neighbours (k-NN), Support Vector Machines (SVM), and Random Forest (RF) with accuracies ranging from 87.5% to 100% using the oversampling and undersampling techniques. Unfortunately, Linear Discriminant Analysis (LDA) gave poor performances (19.64% – 58.93%) in classifying the contamination level of the samples. Hence, the results obtained gave an indication that the electronic nose presented in this research was a promising for classification of contamination level for leftover cooked foods, allowing food to be better anticipated as to whether it is still edible or not. [ABSTRACT FROM AUTHOR]