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Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks
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 hy...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480044/ https://www.ncbi.nlm.nih.gov/pubmed/30935139 http://dx.doi.org/10.3390/s19071560 |
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author | Ahn, DaeHan Choi, Ji-Young Kim, Hee-Chul Cho, Jeong-Seok Moon, Kwang-Deog Park, Taejoon |
author_facet | Ahn, DaeHan Choi, Ji-Young Kim, Hee-Chul Cho, Jeong-Seok Moon, Kwang-Deog Park, Taejoon |
author_sort | Ahn, DaeHan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6480044 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64800442019-04-29 Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks Ahn, DaeHan Choi, Ji-Young Kim, Hee-Chul Cho, Jeong-Seok Moon, Kwang-Deog Park, Taejoon Sensors (Basel) Article 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. MDPI 2019-03-31 /pmc/articles/PMC6480044/ /pubmed/30935139 http://dx.doi.org/10.3390/s19071560 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ahn, DaeHan Choi, Ji-Young Kim, Hee-Chul Cho, Jeong-Seok Moon, Kwang-Deog Park, Taejoon Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks |
title | Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks |
title_full | Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks |
title_fullStr | Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks |
title_full_unstemmed | Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks |
title_short | Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks |
title_sort | estimating the composition of food nutrients from hyperspectral signals based on deep neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480044/ https://www.ncbi.nlm.nih.gov/pubmed/30935139 http://dx.doi.org/10.3390/s19071560 |
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