A comparison of machine learning algorithms for predicting consumer responses based on physical, chemical, and physical–chemical data of fruits

Abstract

Machine learning algorithms are widely used for predicting the consumer response to several food products. Recent studies in the literature demonstrated that it is possible to predict the consumer response to fruits using the physical, chemical, and physical–chemical data of fruits as input for the machine learning algorithms. However, a myriad of machine learning algorithms exists, and there is no consensus on which algorithm is the best for this task. This work evaluates and compares the results of six of the most used machine learning algorithms, the Random Forest, the Decision Tree, the Support Vector Machine, the Multilayer Perceptron neural network, the K-Nearest Neighbors, and the Multivariate Linear Regression, in predicting the consumers’ acceptance, expectation, and their ideal of sweetness, succulency, and acidity for three different fruits. The results demonstrated that, indeed, there is no algorithm that outperforms all others for this task. Every algorithm has its advantages and disadvantages and performs differently according to the fruit and the corresponding dataset. Therefore, it highlights the importance of carefully selecting, optimizing, and comparing several algorithms when one is interested in predicting the consumer response to fruits.

Practical applications: Fruits are mostly commercialized without a strong assurance of their quality, which is their physical–chemical and sensory aspects. The loss of control of these aspects may impact the consumer, which can acquire low-quality products and, thus, lead to unsatisfaction. This research shows that machine learning algorithms can be employed to effectively predict the consumer's sensory response to fruits. The use of these algorithms, which are based on easy-to-obtain physical and physical–chemical data, can improve the quality control of fruits in the market. Therefore, fruit producers and markets can commercialize their products based on their quality, thus providing a better experience to the consumer, which, in turn, can improve their satisfaction.