The review of food safety inspection system based on artificial intelligence, image processing, and robotic

被引：29

作者：

Chen, Tzu-Chia ^{[1
]}

Yu, Shu-Yan ^{[1
]}

机构：

[1] Dhurakij Pundit Univ DPU, China Asian Int Coll CAIC, Bangkok, Thailand

来源：

FOOD SCIENCE AND TECHNOLOGY | 2022年 / 42卷

关键词：

food safety; technology; machine; robotics; COMPUTER VISION TECHNOLOGY; AGRICULTURE; ALGORITHMS; INDUSTRY;

D O I：

10.1590/fst.35421

中图分类号：

TS2 [食品工业];

学科分类号：

0832 ;

摘要：

The main target of the current study is to review the latest developments in accurate, reliable, and low-cost non-contact or remote techniques, including the usage of artificial intelligence (AI)-based methods, image processing (IP) system, and sensor technology for quality assessment in the food industry (FI). The IP systems and AI can be used for various purposes, such as classifying products based on size and shape, detecting product defects, the presence of microbes, and grading food quality. The sensor technology is now highly developed to assess food products' quality and safety due to the dramatic growth of nanotechnology and biotechnology. Also, in this paper, it was tried to examine the role of robots in the FI and discusses the advantages and disadvantages of using them in the FI.

引用

页数：7

共 45 条

[1] Computer vision technology for real-time food quality assurance during drying process [J].

Aghbashlo, Mortaza ;

Hosseinpour, Soleiman ;

Ghasemi-Varnamkhasti, Mahdi .

TRENDS IN FOOD SCIENCE & TECHNOLOGY, 2014, 39 (01) :76-84

[2]

[Anonymous], OFFICIAL J EUROPEAN

[3] A comparison of self-reported emotional and implicit responses to aromas in beer [J].

Beyts, C. ;

Chaya, C. ;

Dehrmann, F. ;

James, S. ;

Smart, K. ;

Hort, J. .

FOOD QUALITY AND PREFERENCE, 2017, 59 :68-80

[4] Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles [J].

Bisgin, Halil ;

Bera, Tanmay ;

Ding, Hongjian ;

Semey, Howard G. ;

Wu, Leihong ;

Liu, Zhichao ;

Barnes, Amy E. ;

Langley, Darryl A. ;

Pava-Ripoll, Monica ;

Vyas, Himansu J. ;

Tong, Weida ;

Xu, Joshua .

SCIENTIFIC REPORTS, 2018, 8

[5] Development of food fraud media monitoring system based on text mining [J].

Bouzembrak, Y. ;

Steen, B. ;

Neslo, R. ;

Linge, J. ;

Mojtahed, V. ;

Marvin, H. J. P. .

FOOD CONTROL, 2018, 93 :283-296

[6] Improving quality inspection of food products by computer vision - a review [J].

Brosnan, T ;

Sun, DW .

JOURNAL OF FOOD ENGINEERING, 2004, 61 (01) :3-16

[7] Application of electronic senses to characterize espresso coffees brewed with different thermal profiles [J].

Buratti, Susanna ;

Benedetti, Simona ;

Giovanelli, Gabriella .

EUROPEAN FOOD RESEARCH AND TECHNOLOGY, 2017, 243 (03) :511-520

[8] Development of Noninvasive Classification Methods for Different Roasting Degrees of Coffee Beans Using Hyperspectral Imaging [J].

Chu, Bingquan ;

Yu, Keqiang ;

Zhao, Yanru ;

He, Yong .

SENSORS, 2018, 18 (04)

[9] The development of a simple multispectral algorithm for detection of fecal contamination on apples using a hyperspectral line-scan imaging system [J].

Yang C.-C. ;

Kim M.S. ;

Kang S. ;

Tao T. ;

Chao K. ;

Lefcourt A.M. ;

Chan D.E. .

Sensing and Instrumentation for Food Quality and Safety, 2011, 5 (1) :10-18

[10]

Djekic Ilija., 2018, Food Control and Biosecurity, VJaneiro, P63, DOI [DOI 10.1016/B978-0-12-811445-2.00003-9, 10.1016/B9780-12-811445-2.00003-9, DOI 10.1016/B9780-12-811445-2.00003-9]

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