Next Article in Journal
Tuning Multiple Fano Resonances for On-Chip Sensors in a Plasmonic System
Previous Article in Journal
Modelling and Laboratory Tests of the Temperature Influence on the Efficiency of the Energy Harvesting System Based on MFC Piezoelectric Transducers
Open AccessArticle

Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks

1
Department of Robotics Engineering, Hanyang University, 55 Hanyang daehak-ro, Ansan 15588, Korea
2
School of Food Science and Biotechnology, Kyungpook National University, 80 daehak-ro, bukgu, Daegu 41566, Korea
3
Food and Bio-industry Research Institute, Kyungpook National University, 80 daehak-ro, bukgu, Daegu 41566, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(7), 1560; https://doi.org/10.3390/s19071560
Received: 3 February 2019 / Revised: 20 March 2019 / Accepted: 28 March 2019 / Published: 31 March 2019
(This article belongs to the Section Intelligent Sensors)
There is an increasing demand for acquiring details of food nutrients especially among those who are sensitive to food intakes and weight changes. To meet this need, we propose a new approach based on deep learning that precisely estimates the composition of carbohydrates, proteins, and fats from hyperspectral signals of foods obtained by using low-cost spectrometers. Specifically, we develop a system consisting of multiple deep neural networks for estimating food nutrients followed by detecting and discarding estimation anomalies. Our comprehensive performance evaluation demonstrates that the proposed system can maximize estimation accuracy by automatically identifying wrong estimations. As such, if consolidated with the capability of reinforcement learning, it will likely be positioned as a promising means for personalized healthcare in terms of food safety. View Full-Text
Keywords: food analysis; hyperspectral signals; deep neural networks; multimodal learning; autoencoders food analysis; hyperspectral signals; deep neural networks; multimodal learning; autoencoders
Show Figures

Figure 1

MDPI and ACS Style

Ahn, D.; Choi, J.-Y.; Kim, H.-C.; Cho, J.-S.; Moon, K.-D.; Park, T. Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks. Sensors 2019, 19, 1560.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop