Cargando…

Classification of Food Additives Using UV Spectroscopy and One-Dimensional Convolutional Neural Network

Food additives are utilized in countless food products available for sale. They enhance or obtain a specific flavor, extend the storage time, or obtain a desired texture. This paper presents an automatic classification system for five food additives based on their absorbance in the ultraviolet domai...

Descripción completa

Detalles Bibliográficos
Autores principales: Potărniche, Ioana-Adriana, Saroși, Codruța, Terebeș, Romulus Mircea, Szolga, Lorant, Gălătuș, Ramona
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490620/
https://www.ncbi.nlm.nih.gov/pubmed/37687972
http://dx.doi.org/10.3390/s23177517
_version_ 1785103882069737472
author Potărniche, Ioana-Adriana
Saroși, Codruța
Terebeș, Romulus Mircea
Szolga, Lorant
Gălătuș, Ramona
author_facet Potărniche, Ioana-Adriana
Saroși, Codruța
Terebeș, Romulus Mircea
Szolga, Lorant
Gălătuș, Ramona
author_sort Potărniche, Ioana-Adriana
collection PubMed
description Food additives are utilized in countless food products available for sale. They enhance or obtain a specific flavor, extend the storage time, or obtain a desired texture. This paper presents an automatic classification system for five food additives based on their absorbance in the ultraviolet domain. Solutions with different concentrations were created by dissolving a measured additive mass into distilled water. The analyzed samples were either simple (one additive solution) or mixed (two additive solutions). The substances presented absorbance peaks between 190 nm and 360 nm. Each substance presents a certain number of absorbance peaks at specific wavelengths (e.g., acesulfame potassium presents an absorbance peak at 226 nm, whereas the peak associated with potassium sorbate is at 254 nm). Therefore, each additive has a distinctive spectrum that can be used for classification. The sample classification was performed using deep learning techniques. The samples were associated with numerical labels and divided into three datasets (training, validation, and testing). The best classification results were obtained using CNN (convolutional neural network) models. The classification of the 404 spectra with a CNN model with three convolutional layers obtained a mean testing accuracy of 92.38% ± 1.48%, whereas the mean validation accuracy was 93.43% ± 2.01%.
format Online
Article
Text
id pubmed-10490620
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-104906202023-09-09 Classification of Food Additives Using UV Spectroscopy and One-Dimensional Convolutional Neural Network Potărniche, Ioana-Adriana Saroși, Codruța Terebeș, Romulus Mircea Szolga, Lorant Gălătuș, Ramona Sensors (Basel) Article Food additives are utilized in countless food products available for sale. They enhance or obtain a specific flavor, extend the storage time, or obtain a desired texture. This paper presents an automatic classification system for five food additives based on their absorbance in the ultraviolet domain. Solutions with different concentrations were created by dissolving a measured additive mass into distilled water. The analyzed samples were either simple (one additive solution) or mixed (two additive solutions). The substances presented absorbance peaks between 190 nm and 360 nm. Each substance presents a certain number of absorbance peaks at specific wavelengths (e.g., acesulfame potassium presents an absorbance peak at 226 nm, whereas the peak associated with potassium sorbate is at 254 nm). Therefore, each additive has a distinctive spectrum that can be used for classification. The sample classification was performed using deep learning techniques. The samples were associated with numerical labels and divided into three datasets (training, validation, and testing). The best classification results were obtained using CNN (convolutional neural network) models. The classification of the 404 spectra with a CNN model with three convolutional layers obtained a mean testing accuracy of 92.38% ± 1.48%, whereas the mean validation accuracy was 93.43% ± 2.01%. MDPI 2023-08-30 /pmc/articles/PMC10490620/ /pubmed/37687972 http://dx.doi.org/10.3390/s23177517 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Potărniche, Ioana-Adriana
Saroși, Codruța
Terebeș, Romulus Mircea
Szolga, Lorant
Gălătuș, Ramona
Classification of Food Additives Using UV Spectroscopy and One-Dimensional Convolutional Neural Network
title Classification of Food Additives Using UV Spectroscopy and One-Dimensional Convolutional Neural Network
title_full Classification of Food Additives Using UV Spectroscopy and One-Dimensional Convolutional Neural Network
title_fullStr Classification of Food Additives Using UV Spectroscopy and One-Dimensional Convolutional Neural Network
title_full_unstemmed Classification of Food Additives Using UV Spectroscopy and One-Dimensional Convolutional Neural Network
title_short Classification of Food Additives Using UV Spectroscopy and One-Dimensional Convolutional Neural Network
title_sort classification of food additives using uv spectroscopy and one-dimensional convolutional neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490620/
https://www.ncbi.nlm.nih.gov/pubmed/37687972
http://dx.doi.org/10.3390/s23177517
work_keys_str_mv AT potarnicheioanaadriana classificationoffoodadditivesusinguvspectroscopyandonedimensionalconvolutionalneuralnetwork
AT sarosicodruta classificationoffoodadditivesusinguvspectroscopyandonedimensionalconvolutionalneuralnetwork
AT terebesromulusmircea classificationoffoodadditivesusinguvspectroscopyandonedimensionalconvolutionalneuralnetwork
AT szolgalorant classificationoffoodadditivesusinguvspectroscopyandonedimensionalconvolutionalneuralnetwork
AT galatusramona classificationoffoodadditivesusinguvspectroscopyandonedimensionalconvolutionalneuralnetwork