Introduction

Food is closely related to human health and culture. Food-related research has always been a current research hotspot. With the rapid development of economy and society, people pay more attention to their own health problems. Food quality has received widespread attention as an important part of safeguarding human health. At the same time, the rapid growth of the food trade has made high-quality food the basis for success in a highly competitive market. Therefore, simple and effective food quality assessment technology can be effectively applied in food production, transportation, storage and other links. At present, food quality evaluation still relies heavily on manual inspection, which is cumbersome, laborious, and costly, resulting in subjective and inconsistent evaluation results. In order to ensure the quality inspection of food during production, transportation, and storage, and to meet demands for different quality food, researchers have proposed a variety of advanced strategies for food quality inspection, including artificial intelligence methods [1, 2, 3], infrared spectroscopy and biosensors [4, 5, 6, 7, 8]. However, a single biosensor can only extract the content of specific components in food, and cannot comprehensively evaluate food quality.
In recent years, artificial intelligence technology has been used in different fields [9, 10, 11, 12], using artificial neural network to accurately and quickly evaluate food quality. In addition to the advantage in detection speed, artificial neural network can automatically extract the intrinsic features of the target using its own network structure. It constructs stable feature combinations through a process of abstraction from low to high levels, which weakens the subjectivity of manual feature selection and can save a lot of time and workload. The generation of the predictive model is mainly dependent on data collection and algorithm determination. In this method, food quality data is collected from convolutional neural networks and different types of biosensors. Instead of focusing on how feature extraction is performed inside the neural network, we only need to use the collected food quality data and food quality grade as the input and output of the model, respectively. This approach is complex, but it is also flexible. Although artificial intelligence technology can automatically and accurately evaluate food quality through training, the training process requires a large amount of food characteristic data. Therefore, in order to take into account the detection speed and the acquisition speed of food characteristic data, this paper proposes to combine artificial neural network and biosensor to evaluate food quality.
This article will show how to combine biosensors and deep learning to assess food quality. First, we focus on the training of prediction models and the required datasets. Secondly, we present representative research work on convolutional neural networks and biosensors for extracting food quality features. Finally, the paper summarizes and discusses the challenges and possible solutions of the approach in the field of food quality assessment.