Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers

Since consuming gutter oil does great harm to people’s health, the Food Safety Administration has always been seeking for a more effective and timely supervision. As laboratory tests consume much time, and existing field tests have excessive limitations, a more comprehensive method is in great need....

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Autores principales:	Wei Jiang, Yuhanxiao Ma, Ruiqi Chen
Formato:	article
Lenguaje:	EN
Publicado:	PeerJ Inc. 2021
Materias:	Gutter oil detection Machine learning FPGA K-NN Approximate multiplier Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/aaa6a0e47c764b6a84f3619f7c817abc
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spelling	oai:doaj.org-article:aaa6a0e47c764b6a84f3619f7c817abc2021-11-18T15:05:22ZGutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers10.7717/peerj-cs.7742376-5992https://doaj.org/article/aaa6a0e47c764b6a84f3619f7c817abc2021-11-01T00:00:00Zhttps://peerj.com/articles/cs-774.pdfhttps://peerj.com/articles/cs-774/https://doaj.org/toc/2376-5992Since consuming gutter oil does great harm to people’s health, the Food Safety Administration has always been seeking for a more effective and timely supervision. As laboratory tests consume much time, and existing field tests have excessive limitations, a more comprehensive method is in great need. This is the first time a study proposes machine learning algorithms for real-time gutter oil detection under multiple feature dimensions. Moreover, it is deployed on FPGA to be low-power and portable for actual use. Firstly, a variety of oil samples are generated by simulating the real detection environment. Next, based on previous studies, sensors are used to collect significant features that help distinguish gutter oil. Then, the acquired features are filtered and compared using a variety of classifiers. The best classification result is obtained by k-NN with an accuracy of 97.18%, and the algorithm is deployed to FPGA with no significant loss of accuracy. Power consumption is further reduced with the approximate multiplier we designed. Finally, the experimental results show that compared with all other platforms, the whole FPGA-based classification process consumes 4.77 µs and the power consumption is 65.62 mW. The dataset, source code and the 3D modeling file are all open-sourced.Wei JiangYuhanxiao MaRuiqi ChenPeerJ Inc.articleGutter oil detectionMachine learningFPGAK-NNApproximate multiplierElectronic computers. Computer scienceQA75.5-76.95ENPeerJ Computer Science, Vol 7, p e774 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Gutter oil detection Machine learning FPGA K-NN Approximate multiplier Electronic computers. Computer science QA75.5-76.95
spellingShingle	Gutter oil detection Machine learning FPGA K-NN Approximate multiplier Electronic computers. Computer science QA75.5-76.95 Wei Jiang Yuhanxiao Ma Ruiqi Chen Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers
description	Since consuming gutter oil does great harm to people’s health, the Food Safety Administration has always been seeking for a more effective and timely supervision. As laboratory tests consume much time, and existing field tests have excessive limitations, a more comprehensive method is in great need. This is the first time a study proposes machine learning algorithms for real-time gutter oil detection under multiple feature dimensions. Moreover, it is deployed on FPGA to be low-power and portable for actual use. Firstly, a variety of oil samples are generated by simulating the real detection environment. Next, based on previous studies, sensors are used to collect significant features that help distinguish gutter oil. Then, the acquired features are filtered and compared using a variety of classifiers. The best classification result is obtained by k-NN with an accuracy of 97.18%, and the algorithm is deployed to FPGA with no significant loss of accuracy. Power consumption is further reduced with the approximate multiplier we designed. Finally, the experimental results show that compared with all other platforms, the whole FPGA-based classification process consumes 4.77 µs and the power consumption is 65.62 mW. The dataset, source code and the 3D modeling file are all open-sourced.
format	article
author	Wei Jiang Yuhanxiao Ma Ruiqi Chen
author_facet	Wei Jiang Yuhanxiao Ma Ruiqi Chen
author_sort	Wei Jiang
title	Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers
title_short	Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers
title_full	Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers
title_fullStr	Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers
title_full_unstemmed	Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers
title_sort	gutter oil detection for food safety based on multi-feature machine learning and implementation on fpga with approximate multipliers
publisher	PeerJ Inc.
publishDate	2021
url	https://doaj.org/article/aaa6a0e47c764b6a84f3619f7c817abc
work_keys_str_mv	AT weijiang gutteroildetectionforfoodsafetybasedonmultifeaturemachinelearningandimplementationonfpgawithapproximatemultipliers AT yuhanxiaoma gutteroildetectionforfoodsafetybasedonmultifeaturemachinelearningandimplementationonfpgawithapproximatemultipliers AT ruiqichen gutteroildetectionforfoodsafetybasedonmultifeaturemachinelearningandimplementationonfpgawithapproximatemultipliers
_version_	1718420803107684352

Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers

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