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Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer

There has been strong demand for the development of an accurate but simple method to assess the freshness of food. In this study, we demonstrated a system to determine food freshness by analyzing the spectral response from a portable visible/near-infrared (VIS/NIR) spectrometer using the Convolution...

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Detalles Bibliográficos
Autores principales: Moon, Eui Jung, Kim, Youngsik, Xu, Yu, Na, Yeul, Giaccia, Amato J., Lee, Jae Hyung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435377/
https://www.ncbi.nlm.nih.gov/pubmed/32752216
http://dx.doi.org/10.3390/s20154299
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author Moon, Eui Jung
Kim, Youngsik
Xu, Yu
Na, Yeul
Giaccia, Amato J.
Lee, Jae Hyung
author_facet Moon, Eui Jung
Kim, Youngsik
Xu, Yu
Na, Yeul
Giaccia, Amato J.
Lee, Jae Hyung
author_sort Moon, Eui Jung
collection PubMed
description There has been strong demand for the development of an accurate but simple method to assess the freshness of food. In this study, we demonstrated a system to determine food freshness by analyzing the spectral response from a portable visible/near-infrared (VIS/NIR) spectrometer using the Convolutional Neural Network (CNN)-based machine learning algorithm. Spectral response data from salmon, tuna, and beef incubated at 25 °C were obtained every minute for 30 h and then categorized into three states of “fresh”, “likely spoiled”, and “spoiled” based on time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of experimental objects. In addition, a CNN-based machine learning algorithm with a shift-invariant feature can minimize the effect of the variation caused using multiple devices in a real environment. The accuracy of the obtained machine learning model based on the spectral data in predicting the freshness was approximately 85% for salmon, 88% for tuna, and 92% for beef. Therefore, our study demonstrates the practicality of a portable spectrometer in food freshness assessment.
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spelling pubmed-74353772020-08-28 Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer Moon, Eui Jung Kim, Youngsik Xu, Yu Na, Yeul Giaccia, Amato J. Lee, Jae Hyung Sensors (Basel) Letter There has been strong demand for the development of an accurate but simple method to assess the freshness of food. In this study, we demonstrated a system to determine food freshness by analyzing the spectral response from a portable visible/near-infrared (VIS/NIR) spectrometer using the Convolutional Neural Network (CNN)-based machine learning algorithm. Spectral response data from salmon, tuna, and beef incubated at 25 °C were obtained every minute for 30 h and then categorized into three states of “fresh”, “likely spoiled”, and “spoiled” based on time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of experimental objects. In addition, a CNN-based machine learning algorithm with a shift-invariant feature can minimize the effect of the variation caused using multiple devices in a real environment. The accuracy of the obtained machine learning model based on the spectral data in predicting the freshness was approximately 85% for salmon, 88% for tuna, and 92% for beef. Therefore, our study demonstrates the practicality of a portable spectrometer in food freshness assessment. MDPI 2020-08-01 /pmc/articles/PMC7435377/ /pubmed/32752216 http://dx.doi.org/10.3390/s20154299 Text en © 2020 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 Letter
Moon, Eui Jung
Kim, Youngsik
Xu, Yu
Na, Yeul
Giaccia, Amato J.
Lee, Jae Hyung
Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer
title Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer
title_full Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer
title_fullStr Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer
title_full_unstemmed Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer
title_short Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer
title_sort evaluation of salmon, tuna, and beef freshness using a portable spectrometer
topic Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435377/
https://www.ncbi.nlm.nih.gov/pubmed/32752216
http://dx.doi.org/10.3390/s20154299
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