Use of smartphone videos and pattern recognition for food authentication

Weiran Song; Nanfeng Jiang; Hui Wang; Jordan Vincent

doi:10.1016/j.snb.2019.127247

Use of smartphone videos and pattern recognition for food authentication

Weiran Song, Nanfeng Jiang, Hui Wang, Jordan Vincent

Research output: Contribution to journal › Article › peer-review

37 Citations (Scopus)

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Abstract

A novel sensor system for food authentication is presented, which is based on computer vision and pattern recognition. The sensor system uses a smartphone to generate a sequence of light with varying colours to illuminate a food sample, and uses the smartphone camera to receive reflected light by way of recording a video. The video is processed using computer vision techniques and transformed into sensor data in the form of a data vector. The sensor data is analysed using pattern recognition techniques. The locally weighted partial least squares regression method is extended for classification to improve the modelling effectiveness and robustness. The sensor system is evaluated on the task of authentication of olive oil and milk – to verify how they are labelled. Large quantities of olive oil and milk were purchased from supermarkets, and sensor videos were created using the sensor system. Test accuracies of 96.2% and 100% were achieved for olive oil and milk authentication respectively. These results suggest the proposed sensor system is effective. Since the sensor system is built in a smartphone, it has the potential to serve as a low-cost and effective solution for food authentication and to empower consumers in food fraud detection.

Original language	English
Article number	127247
Journal	Sensors and Actuators B: Chemical
Volume	304
Early online date	22 Oct 2019
DOIs	https://doi.org/10.1016/j.snb.2019.127247
Publication status	Published - 01 Feb 2020
Externally published	Yes

Bibliographical note

Funding Information: This work is supported byUlster University Global Challenge Fund - Cloud Agri-Food Safety (70642R, PI Hui Wang). Hui Wang came up with the video-based imaging spectrometer concept used in this work, Jordan Vincent wrote the Android app, Weiran Song and Nanfeng Jiang conducted the experiments. Weiran Song wrote the first draft of the manuscript, and other co-authors reviewed the manuscript. Funding Information: This work is supported by Ulster University Global Challenge Fund - Cloud Agri-Food Safety (70642R, PI Hui Wang). Hui Wang came up with the video-based imaging spectrometer concept used in this work, Jordan Vincent wrote the Android app, Weiran Song and Nanfeng Jiang conducted the experiments. Weiran Song wrote the first draft of the manuscript, and other co-authors reviewed the manuscript. Weiran Song received his PhD in Computer Science from Ulster University in 2019, and MSc and BSc in Mathematics from Beijing University of Technology and Hebei University in 2013 and 2010, respectively. His current research focuses on the development of chemometric and machine learning methods for analysing data obtained from spectroscopy and computer vision system. Nanfeng Jiang received BSc in Network Engineering and MSc in Computer Science from Fujian Normal University in 2016 and 2019, respectively. He was a visiting student at School of Computing, Ulster University in 2018. He is currently a research assistant at the College of Physics and Engineering, Fuzhou University. His research interests include computer vision, image processing and deep learning. Hui Wang received his PhD from Ulster University, MSc and BSc both from Jilin University. He is currently Professor of Computer Science at Ulster University. His research interests are machine learning, knowledge representation and reasoning, combinatorial data analysis, and their applications in image, video, spectra and text analysis. He has over 250 publications in these areas. He is an associate editor of IEEE Transactions on Cybernetics, and an associate editor of International Journal of Machine Learning and Cybernetics. He is the Chair of IEEE SMCS Northern Ireland Chapter (2009–2016), and a member of IEEE SMCS Board of Governors (2010–2013). He is principal investigator of a number of regional, national and international projects in the areas of image/video analytics (Horizon 2020 funded DESIREE, FP7 funded SAVASA, Royal Society funded VIAD), text analytics (INI funded DEEPFLOW, Royal Society funded BEACON), and intelligent content management (FP5 funded ICONS); and is co-investigator of several other EU funded projects. Jordan Vincent received his BSc in Computer Science from Ulster University in 2016. He has worked on multiple EU funded projects. He is currently a PhD student at Ulster University studying the application of machine learning methods to spectroscopic data. His research interests include spectroscopy, image processing and deep learning. Publisher Copyright: © 2019 Elsevier B.V. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

Food authentication
Partial least squares
Smartphone based spectrometric sensor

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10.1016/j.snb.2019.127247Licence: Unspecified

Use of smartphone videos and pattern recognition for food authentication
Copyright 2019 Elsevier. This manuscript is distributed under a Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited.
Accepted author manuscript, 820 KBLicence: CC BY-NC-ND

Cite this

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title = "Use of smartphone videos and pattern recognition for food authentication",

abstract = "A novel sensor system for food authentication is presented, which is based on computer vision and pattern recognition. The sensor system uses a smartphone to generate a sequence of light with varying colours to illuminate a food sample, and uses the smartphone camera to receive reflected light by way of recording a video. The video is processed using computer vision techniques and transformed into sensor data in the form of a data vector. The sensor data is analysed using pattern recognition techniques. The locally weighted partial least squares regression method is extended for classification to improve the modelling effectiveness and robustness. The sensor system is evaluated on the task of authentication of olive oil and milk – to verify how they are labelled. Large quantities of olive oil and milk were purchased from supermarkets, and sensor videos were created using the sensor system. Test accuracies of 96.2% and 100% were achieved for olive oil and milk authentication respectively. These results suggest the proposed sensor system is effective. Since the sensor system is built in a smartphone, it has the potential to serve as a low-cost and effective solution for food authentication and to empower consumers in food fraud detection.",

keywords = "Food authentication, Partial least squares, Smartphone based spectrometric sensor",

author = "Weiran Song and Nanfeng Jiang and Hui Wang and Jordan Vincent",

note = "Funding Information: This work is supported byUlster University Global Challenge Fund - Cloud Agri-Food Safety (70642R, PI Hui Wang). Hui Wang came up with the video-based imaging spectrometer concept used in this work, Jordan Vincent wrote the Android app, Weiran Song and Nanfeng Jiang conducted the experiments. Weiran Song wrote the first draft of the manuscript, and other co-authors reviewed the manuscript. Funding Information: This work is supported by Ulster University Global Challenge Fund - Cloud Agri-Food Safety (70642R, PI Hui Wang). Hui Wang came up with the video-based imaging spectrometer concept used in this work, Jordan Vincent wrote the Android app, Weiran Song and Nanfeng Jiang conducted the experiments. Weiran Song wrote the first draft of the manuscript, and other co-authors reviewed the manuscript. Weiran Song received his PhD in Computer Science from Ulster University in 2019, and MSc and BSc in Mathematics from Beijing University of Technology and Hebei University in 2013 and 2010, respectively. His current research focuses on the development of chemometric and machine learning methods for analysing data obtained from spectroscopy and computer vision system. Nanfeng Jiang received BSc in Network Engineering and MSc in Computer Science from Fujian Normal University in 2016 and 2019, respectively. He was a visiting student at School of Computing, Ulster University in 2018. He is currently a research assistant at the College of Physics and Engineering, Fuzhou University. His research interests include computer vision, image processing and deep learning. Hui Wang received his PhD from Ulster University, MSc and BSc both from Jilin University. He is currently Professor of Computer Science at Ulster University. His research interests are machine learning, knowledge representation and reasoning, combinatorial data analysis, and their applications in image, video, spectra and text analysis. He has over 250 publications in these areas. He is an associate editor of IEEE Transactions on Cybernetics, and an associate editor of International Journal of Machine Learning and Cybernetics. He is the Chair of IEEE SMCS Northern Ireland Chapter (2009–2016), and a member of IEEE SMCS Board of Governors (2010–2013). He is principal investigator of a number of regional, national and international projects in the areas of image/video analytics (Horizon 2020 funded DESIREE, FP7 funded SAVASA, Royal Society funded VIAD), text analytics (INI funded DEEPFLOW, Royal Society funded BEACON), and intelligent content management (FP5 funded ICONS); and is co-investigator of several other EU funded projects. Jordan Vincent received his BSc in Computer Science from Ulster University in 2016. He has worked on multiple EU funded projects. He is currently a PhD student at Ulster University studying the application of machine learning methods to spectroscopic data. His research interests include spectroscopy, image processing and deep learning. Publisher Copyright: {\textcopyright} 2019 Elsevier B.V. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.",

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doi = "10.1016/j.snb.2019.127247",

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N2 - A novel sensor system for food authentication is presented, which is based on computer vision and pattern recognition. The sensor system uses a smartphone to generate a sequence of light with varying colours to illuminate a food sample, and uses the smartphone camera to receive reflected light by way of recording a video. The video is processed using computer vision techniques and transformed into sensor data in the form of a data vector. The sensor data is analysed using pattern recognition techniques. The locally weighted partial least squares regression method is extended for classification to improve the modelling effectiveness and robustness. The sensor system is evaluated on the task of authentication of olive oil and milk – to verify how they are labelled. Large quantities of olive oil and milk were purchased from supermarkets, and sensor videos were created using the sensor system. Test accuracies of 96.2% and 100% were achieved for olive oil and milk authentication respectively. These results suggest the proposed sensor system is effective. Since the sensor system is built in a smartphone, it has the potential to serve as a low-cost and effective solution for food authentication and to empower consumers in food fraud detection.

AB - A novel sensor system for food authentication is presented, which is based on computer vision and pattern recognition. The sensor system uses a smartphone to generate a sequence of light with varying colours to illuminate a food sample, and uses the smartphone camera to receive reflected light by way of recording a video. The video is processed using computer vision techniques and transformed into sensor data in the form of a data vector. The sensor data is analysed using pattern recognition techniques. The locally weighted partial least squares regression method is extended for classification to improve the modelling effectiveness and robustness. The sensor system is evaluated on the task of authentication of olive oil and milk – to verify how they are labelled. Large quantities of olive oil and milk were purchased from supermarkets, and sensor videos were created using the sensor system. Test accuracies of 96.2% and 100% were achieved for olive oil and milk authentication respectively. These results suggest the proposed sensor system is effective. Since the sensor system is built in a smartphone, it has the potential to serve as a low-cost and effective solution for food authentication and to empower consumers in food fraud detection.

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KW - Smartphone based spectrometric sensor

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DO - 10.1016/j.snb.2019.127247

M3 - Article

SN - 0925-4005

VL - 304

JO - Sensors and Actuators B: Chemical

JF - Sensors and Actuators B: Chemical

M1 - 127247

ER -

Use of smartphone videos and pattern recognition for food authentication

Abstract

Bibliographical note

Keywords

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