Detection method for the degree of damage to Korla fragrant pears based on electrical properties

Authors

  • Yang Liu 1. Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alaer 843300, Xinjiang, China 3. College of Mechanical and Electrical Engineering, Tarim University, Alaer, Xinjiang 843300, China
  • Yifan Xia 1. Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alaer 843300, Xinjiang, China 3. College of Mechanical and Electrical Engineering, Tarim University, Alaer, Xinjiang 843300, China
  • Jikai Che 1. Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alaer 843300, Xinjiang, China 3. College of Mechanical and Electrical Engineering, Tarim University, Alaer, Xinjiang 843300, China
  • Haipeng Lan 1. Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alaer 843300, Xinjiang, China 3. College of Mechanical and Electrical Engineering, Tarim University, Alaer, Xinjiang 843300, China
  • Hong Zhang 1. Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alaer 843300, Xinjiang, China 2. College of Water Resources and Architectural Engineering, Tarim University, Alaer, Xinjiang 843300, China

DOI:

https://doi.org/10.25165/ijabe.v18i3.8927

Keywords:

Korla fragrant pear, electrical properties, degree of damage, non-destructive detection

Abstract

To solve challenges in the automated and rapid, non-destructive detection of damage to Korla fragrant pears, this study explored the laws influencing the impact of load on damage to fragrant pears during ripening. A comparative analysis of the external features and microscopic structural changes before and after fragrant pear damage was performed. The electrical parameters of fragrant pears were collected using the fruit electrical parameter detection system, including parallel equivalent capacitance (Cp), parallel equivalent resistance (Rp), and complex impedance (Z). The correlations between the electrical parameters of fragrant pears and the degree of damage were analyzed. A detection model for the degree of damage to Korla fragrant pears was constructed using partial least squares regression (PLSR), support vector regression (SVR), and particle swarm optimization-least squares support vector regression (PSO-LSSVR), and the optimal model was determined and screened. The results showed that, in the same ripening, the damaged area of fragrant pears increased as the falling height increased. Given equal impact loads, the damage area of fragrant pears increased as the picking time increased. Cp, Rp, and Z were strongly correlated with the damaged area of fragrant pears. When the test frequency was 1 kHz, the PSO-LSSVR model showed the optimal detection performance (R2 = 0.9172, RMSE = 117.56) for the damaged area of fragrant pears. These research results provide a theoretical reference for the quality assessment and storage regulation of fragrant pears. Keywords: Korla fragrant pear, electrical properties, degree of damage, non-destructive detection DOI: 10.25165/j.ijabe.20251803.8927 Citation: Liu Y, Xia Y F, Che J K, Lan H P, Zhang H. Detection method for the degree of damage to Korla fragrant pears based on electrical properties. Int J Agric & Biol Eng, 2025; 18(3): 215–222.

References

Xia Y F, Liu Y, Zhang H, Che J K, Liang Q. Study on color detection of Korla fragrant pears by near-infrared spectroscopy combined with PLSR. Horticulturae, 2025; 11: 352.

Liu Y, Tang Y R, Zhang H, Niu H, Lan H P. Model for estimating the weight-loss ratio of damaged Korla fragrant pears. Int J Agric & Biol Eng, 2024; 17（1）: 261-266.

Che J K, Liang Q, Xia Y F, Liu Y, Li H S, Hu N G, et al. The study on nondestructive detection methods for internal quality of Korla fragrant pears based on near-infrared spectroscopy and machine learning. Foods, 2024; 13: 3522.

Liu Y, Niu X Y, Tang Y, Li S Y, Lan H P, Niu H. Internal quality prediction method of damaged Korla fragrant pears during storage. Horticulturae, 2023; 9: 666.

Wu J. Study on dynamic viscoelastic property and impact bruise of Korla pear. PhD thesis, Northwest A&F University, Yangling, Shanxi, China, 2011. （in Chinese）

Yu S H, Lan H P, Li X L, Zhang H, Zeng Y, Niu H, et al. Prediction method of shelf life of damaged Korla fragrant pears. Journal of Food Process Engineering, 2021; 44（13）: e13902.

NY/T 585-2002. Agricultural Industry Standard. Ministry of Agriculture of the PRC: Beijing, China, 2002; p. 3. （in Chinese）

Hounhouigan M H, Linnemann A R, Ingenbleek P T M, Soumanou M M, van Trijp H C M, van Boekel M A J S. Effect of physical damage and storage of pineapple fruits on their suitability for juice production. Journal of Food Quality, 2014; 37（4）: 268-273.

Umezuruike L O, Pankaj B P. Bruise damage measurement and analysis of fresh horticultural produce—A review. Postharvest Biology and Technology, 2014; 91: 9-24.

Yousefi S, Farsi H, Kheiralipour K. Drop test of pear fruit: Experimental measurement and finite element modelling. Biosystems Engineering, 2016; 147: 17-25.

Acácio F N, Nelson C O, Erlon R C, Helinando P O. Determination of mango ripening degree by electrical impedance spectroscopy. Computers and Electronics in Agriculture, 2017; 143: 222-226.

Guo W C, Fang L J, Liu D Y, Wang Z W. Determination of soluble solids content and firmness of pears during ripening by using dielectric spectroscopy. Computers and Electronics in Agriculture, 2015; 117; 226-233.

Jha S N, Narsaiah K, Basediya A L, Sharma R, Jaiswal P, Kumar R, et al. Measurement techniques and application of electrical properties for nondestructive quality evaluation of foods—A review. Journal of Food Science and Technology, 2011; 48; 387-411.

Jiang Y, Shiina T, Nakamura N, Nakahara A. Electrical conductivity evaluation of postharvest strawberry damage. Journal of Food Science, 2001; 66（9）: 1392-1395.

Sun H, Wan F, Huang Y, Xu Z, Huang X. Evaluation of a new method to assess blueberry bruising based on intracellular and extracellular water ratios. Scientia Horticulturae, 2024; 328: 112896.

Matsumoto S, Sugino N, Watanabe T, Kitazawa H. Bioelectrochemical impedance analysis and the correlation with mechanical properties for evaluating bruise tolerance differences to drop shock in strawberry cultivars. European Food Research and Technology, 2022; 248（3）: 807-813.

An J, Luo X Z, Xiong L J, Tang X Y, Lan H P. Discrimination of inner injury of Korla fragrant pear based on multi-electrical parameters. Foods, 2023; 12: 1805.

Bian H X, Shi P, Tu P. Determination of physicochemical quality of bruised apple using dielectric properties. Journal of Food Measurement and Characterization, 2020; 14: 2590-2599.

Fan X W, Yu S H, Lan H P, Zhang H, Zhang Y C, Liu Y. Study on static pressure damage degree of Korla pear based on electrical properties. Agricultural Mechanization Research, 2021; 9: 194-214. （in Chinese）

Li S Y, Liu Y, Niu X Y, Tang Y R, Lan H P, Zeng Y. Comparison of prediction models for determining the degree of damage to Korla fragrant pears. Agronomy, 2023; 13: 1670.

Guo W C, Shang L, Zhu X H, Nelson S O. Nondestructive detection of soluble solids content of apples from dielectric spectra with ANN and chemometric methods. Food and Bioprocess Technology, 2015; 8: 1126-1138.

Li Y Z, Li C G. The agricultural product wholesale price index forecasting model based on LSSVR optimized by PSO. Journal of Covergence Information Technology, 2012; 7（17）: 531-539.

Zhang R, Li S Y, Liu Y, Li G W, Jiang X, Fan X W. Construction of color prediction model for damaged Korla Pears during storage period. Applied Sciences, 2023; 13: 7885.

Komarnicki P, Stopa R, Kuta L, Szyjewicz D. Determination of apple bruise resistance based on the surface pressure and contact area measurements under impact loads. Computers and Electronics in Agriculture, 2017; 142: 155-164.

Yu S H, Liu Y, Tang Y R, Li X L, Li W, Li C, et al. Non-destructive quality assessment method for Korla fragrant pears based on electrical properties and adaptive neural-fuzzy inference system. Computers and Electronics in Agriculture, 2022; 203: 107492.

Wu L, Ogawa Y, Tagawa A. Electrical impedance spectroscopy analysis of eggplant pulp and effects of drying and freezing-thawing treatments on its impedance characteristics. Journal of Food Engineering, 2007; 87（2）: 274-280.

Zhu H K, Liu F, Ye Y, Chen L, Liu J Y, Gui A H, et al. Application of machine learning algorithms in quality assurance of fermentation process of black tea-based on electrical properties. Journal of Food Engineering, 2019; 263: 165-172.

Ma Q, Yang N, Jin Y M, Zhao J J, Jin Z Y, Xu X M. Evaluating quality indices of pickled garlic based on electrical properties. Journal of Food Process Engineering, 2016; 39（1）: 88-96.

Seo C, Jo H Y, Byun Y, Ryu J H, Joo Y S. Montmorillonite content prediction in bentonite using Vis-NIR spectroscopy and PLSR analysis: Effects of humidity and mineralogical variability. Geoderma, 2024; 448: 116980.

Zhou L J Y, Wu H B, Jing T T, Li T H, Li J S, Kong L J, et al. Estimation of relative chlorophyll content in lettuce （Lactuca sativa L.） leaves under cadmium stress using visible-near-infrared reflectance and machine-learning models. Agronomy, 2024; 14: 427.

Wang X Y, Ling Y, Ling X, Li X H, Li Z C, Hu K P, et al. A particle swarm algorithm optimization-based SVM-KNN algorithm for epileptic EEG recognition. International Journal of Intelligent Systems, 2022; 37: 11233-11249.

Zhang K X, Zuo Z Y, Zhou C A, Chen H, Ding Z T. Research on hyperspectral timely monitoring model of green tea processing quality based on PSO-LSSVR. Journal of Food Composition and Analysis, 2024; 134: 106490.

Li C, Li Z B, Wang X Y, Li W, Lan H P, Liu Y, et al. Using the equivalent static pressure method to assess free fall damage of the Korla fragrant pear. Horticulturae, 2023; 9: 993.

Zhang H, Liu Y, Tang Y R, Lan H P, Niu H, Zhang H. Non-destructive detection of the fruit firmness of Korla fragrant pear based on electrical properties. Int J Agric & Biol Eng, 2022; 15（6）: 216-221

Downloads

Published

2025-06-30

How to Cite

Liu, Y., Xia, Y., Che, J., Lan, H., & Zhang, H. (2025). Detection method for the degree of damage to Korla fragrant pears based on electrical properties. International Journal of Agricultural and Biological Engineering, 18(3), 215–222. https://doi.org/10.25165/ijabe.v18i3.8927

Issue

Vol. 18 No. 3 (2025): IJABE

Section

Information Technology, Sensors and Control Systems

License

IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.


Authors who publish with this journal agree to the following terms:


  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).