Leftover cooked food spoilage detection using electronic nose and machine learning algorithms

What is it about?

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.

Featured Image

Photo by Carlos Muza on Unsplash

Photo by Carlos Muza on Unsplash

Why is it important?

This work is important as it helps consumer to identify easily which food has been spoiled. Some of consumers may face difficulties in recognizing the food spoilage due to the factors affecting their sense of smell. Moreover, there is not much studies found that analyse the spoilage of leftover cooked foods.

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