Abstract
At present, China is the world’s largest apple producer. After being picked, apples rely on its own nutrients to maintain life activities, and with the consumption of energy, the freshness will gradually decrease. Nowadays, the storage and preservation awareness, facilities and methods of the apple processing industry and households are weak and lacking. Apples are prone to decay and deterioration, which seriously affects the flavor, nutritional value and food safety. This paper studies the artificial intelligence recognition technology based on sensors array apple freshness characteristics. The main research contents include: artificial intelligence odor recognition theory research and construction of sensor array information acquisition system. Combined with theoretical experiments, the research establishes apple odor characteristic parameter evaluation model, uses dielectric features, odor recognition technology and node location information to achieve apple freshness artificial intelligence recognition. This project has important significance in both theoretical research and practical application.
Main lab open fund of North university of China (DXMBJJ2018-08) is funding.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, H., Wang, J., Ye, S., et al.: Application of electronic nose and statistical analysis to predict quality indices of peach. Food Bioprocess Technol. 5, 65–72 (2012)
Shi, B., Zhao, L., Zhi, R., et al.: Quality recognition of Xihu Longjing tea based on intelligent olfactory. Trans. Chin. Soc. Agric. Mach. 43(12), 130–135 (2012)
Nelson, S., Forbus, W., Lawrence, K.: Permittivities of fresh fruits and vegetables at 0.2 to 20 GHz. J. Microw. Power Electromagn. Energy: Publ. Int. Microw. Power Inst. 29(2), 81–93 (1994)
Kuang, W., Nelson, S.: Dielectric relaxation characteristics of fresh fruits and vegetables from 3 to 20 GHz. J. Microw. Power 32(2), 114–122 (1997)
Ma, D., Ding, Q., Li, Z., et al.: Prototype of an aquacultural information system based on internet of things E-Nose. Intell. Autom. Soft Comput. 18(5), 569–579 (2012)
Byoung-Kwon, H., Hyochin, K., Atul, K.S.: Pathogen enrichment device (PED) enables one-step growth, enrichment and separation of pathogen from food matrices for detection using bioanalytical platforms. J. Microbiol. Methods 117, 64–73 (2015)
Castro, J., Ramanathan, A., Hennubhotla, C.: Categorical dimensions of human odor descriptor space revealed by non-negative matrix factorization. PLoS One 8(9), 78–89 (2013)
Hong, X., Wang, J., Hai, Z.: Discrimination and prediction of multiple beef freshness indexes based on electronic nose. Sens. Actuators: B Chem. 161(1), 381–389 (2012)
Adak, M., Nejat, Y.: Classification of E-Nose aroma data of four fruit types by ABC-based neural network. Sensors 16(3), 304–310 (2016)
Brezmes, J., Fructuoso, L., Llobet, E., et al.: Evaluation of an electronic nose to assess fruit ripeness. IEEE Sens. J. 5(1), 97–108 (2005)
Baietto, M., Wilson, D.: Electronic-nose applications for fruit identification, ripeness and quality grading. Sensors 15(1), 899–931 (2015)
Guohua, H., Yuling, W., Dandan, Y., et al.: Study of peach freshness predictive method based on electronic nose. Food Control 28(1), 260–271 (2012)
Guohua, H., Wu, Y., Ye, D.: Study of peach freshness predictive method based on electronic nose. Food Control 28, 1848–1852 (2012)
Barba, A., Lamberti, G.: Dielectric properties of pineapple as function of temperature and water content. Int. J. Food Sci. Technol. 48(6), 1334–1338 (2013)
Birla, S.L., Wang, S., Tang, J., et al.: Characterization of radio frequency heating of fresh fruits influenced by dielectric properties. J. Food Eng. 89(4), 390–398 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, W., Guo, Z., Li, M., Liu, Y. (2020). Apple Freshness Recognition Technology Based on Gas Sensors. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-32456-8_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32455-1
Online ISBN: 978-3-030-32456-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)