References
- Parmar R R, Jain K R, Modi C K. Unified approach in food quality
evaluation using machine vision[C]//International Conference on
Advances in Computing and Communications. Springer, Berlin,
Heidelberg, 2011: 239-248.
- Du L, Zhang R, Wang X. Overview
of two-stage object detection algorithms[C]//Journal of Physics:
Conference Series. IOP Publishing, 2020, 1544(1): 012033.
- Tian Z, Shen C, Chen H, et al.
Fcos: Fully convolutional one-stage object
detection[C]//Proceedings of the IEEE/CVF international conference
on computer vision. 2019: 9627-9636.
- Thakur M S, Ragavan K V.
Biosensors in food processing[J]. Journal of food science and
technology, 2013, 50(4): 625-641.
- Lozano M G, García Y P, Gonzalez J A S, et al. Biosensors for food
quality and safety monitoring: fundamentals and
applications[M]//Enzymes in food biotechnology. Academic Press,
2019: 691-709.
- Narsaiah K, Jha S N, Bhardwaj R, et al. Optical biosensors for food
quality and safety assurance—a review[J]. Journal of food
science and technology, 2012, 49(4): 383-406.
- Lv M, Liu Y, Geng J, et al. Engineering nanomaterials-based biosensors
for food safety detection[J]. Biosensors and Bioelectronics, 2018,
106: 122-128.
- Wang W, Gunasekaran S. Nanozymes-based biosensors for food quality and
safety[J]. TrAC trends in analytical chemistry, 2020, 126: 115841.
- Chen Z, Ai S, Jia C. Structure-aware deep learning for product image
classification[J]. ACM Transactions on Multimedia Computing,
Communications, and Applications (TOMM), 2019, 15(1s): 1-20.
- Liu J, Li J. Research on target tracking algorithm based on YOLO and
KCF[J]. Computer Science and Application, 2020, 10(6): 1113-1121.
- Wu B, Iandola F, Jin P H, et al. Squeezedet: Unified, small, low power
fully convolutional neural networks for real-time object detection for
autonomous driving[C]//Proceedings of the IEEE conference on
computer vision and pattern recognition workshops. 2017: 129-137.
- Ding K, Gunasekaran S. Shape feature extraction and classification of
food material using computer vision[J]. Transactions of the ASAE,
1994, 37(5): 1537-1545.
- Parmar R R, Jain K R, Modi C K.
Unified approach in food quality evaluation using machine
vision[C]//International Conference on Advances in Computing and
Communications. Springer, Berlin, Heidelberg, 2011: 239-248.
- Wu D, Sun D W. Colour
measurements by computer vision for food quality control–A
review[J]. Trends in Food Science & Technology, 2013, 29(1):
5-20.
- Ding K, Gunasekaran S. Shape
feature extraction and classification of food material using computer
vision[J]. Transactions of the ASAE, 1994, 37(5): 1537-1545.
- Gadelmawla E S, Elewa I M.
On-line measurement of product dimensions using computer
vision[C]//Proceedings of 9th IMEKO Symposium metrology for
quality control in production “Surface Metrology for Quality
Assurance”, Cairo, Egypt. 2001: 24-27.
- Pavithra V, Pounroja R, Bama B
S. Machine vision based automatic sorting of cherry
tomatoes[C]//2015 2nd International Conference on Electronics and
Communication Systems (ICECS). IEEE, 2015: 271-275.
- Jiang L, Qiu B, Liu X, et al.
DeepFood: food image analysis and dietary assessment via deep
model[J]. IEEE Access, 2020, 8: 47477-47489.
- Parmar R R, Jain K R, Modi C K.
Unified approach in food quality evaluation using machine
vision[C]//International Conference on Advances in Computing and
Communications. Springer, Berlin, Heidelberg, 2011: 239-248.
- Thakur M S, Ragavan K V. Biosensors in food processing[J]. Journal
of food science and technology, 2013, 50(4): 625-641.
- Kurbanoglu S, Erkmen C, Uslu B.
Frontiers in electrochemical enzyme based biosensors for food and drug
analysis[J]. TrAC Trends in Analytical Chemistry, 2020, 124:
115809.
- Kaçar C, Erden P E. An
amperometric biosensor based on poly (l-aspartic acid), nanodiamond
particles, carbon nanofiber, and ascorbate oxidase–modified glassy
carbon electrode for the determination of l-ascorbic acid[J].
Analytical and Bioanalytical Chemistry, 2020, 412(22): 5315-5327.
- Da Silva W, Ghica M E, Ajayi R
F, et al. Tyrosinase based amperometric biosensor for determination of
tyramine in fermented food and beverages with gold nanoparticle doped
poly (8-anilino-1-naphthalene sulphonic acid) modified
electrode[J]. Food chemistry, 2019, 282: 18-26.
- Lopez M S P, Redondo-Gómez E, López-Ruiz B. Electrochemical enzyme
biosensors based on calcium phosphate materials for tyramine detection
in food samples[J]. Talanta, 2017, 175: 209-216.
- Erden P E, Selvi C K, Kılıç E. A novel tyramine biosensor based on
carbon nanofibers, 1-butyl-3-methylimidazolium tetrafluoroborate and
gold nanoparticles[J]. Microchemical Journal, 2021, 170: 106729.
- Wu L, Lu X, Niu K, et al.
Tyrosinase nanocapsule based nano-biosensor for ultrasensitive and
rapid detection of bisphenol A with excellent stability in different
application scenarios[J]. Biosensors and Bioelectronics, 2020,
165: 112407.
- FERNANDES P M V, CAMPIÑA J M,
SILVA A F. A layered nanocomposite of laccase, chitosan, and Fe3O4
nanoparticles-reduced graphene oxide for the nanomolar electrochemical
detection of bisphenol A[J]. Microchimica Acta, 2020,187(5).
- Mentana A, Nardiello D, Palermo C, et al. Accurate glutamate
monitoring in foodstuffs by a sensitive and interference-free
glutamate oxidase based disposable amperometric biosensor[J].
Analytica Chimica Acta, 2020, 1115: 16-22.
- Rejeb I B, Arduini F, Arvinte
A, et al. Development of a bio-electrochemical assay for AFB1
detection in olive oil[J]. Biosensors and Bioelectronics, 2009,
24(7): 1962-1968.