Integration of biosensors and deep learning for assessing food quality
- wei han
, - Zhiyuan Zhu
Abstract
With the rapid expansion of food trade, there is a growing concern about
health issues. As a result, consumer demand for high quality food is
increasing and food quality analysis has become a very important and
interesting area of research. The application of biosensors in food
analysis is promising because specific biosensors can be used to easily
access the nutritional composition inside food products, including
macronutrients, trace elements and other bioactive substances. Deep
learning includes many different types of artificial neural networks,
and convolutional neural networks can be used to extract external
features of food products from images, such as shape, size, color, etc.
Deep neural networks are able to generate predictive models using
different food properties. In this paper, we aim to show how to combine
biosensors and deep learning to assess food quality. We first focus on
the process of generating predictive models by deep neural networks and
the datasets required to train the models. Secondly, we focus on how to
use convolutional neural networks to extract external features of food
products and representative research work on biosensors for food
nutrient content analysis. Finally, the paper summarizes and looks at
the challenges and possible solutions of the approach in the field of
food quality assessment.