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Horticulturae
Volume 11
Issue 10
10.3390/horticulturae11101255
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Variation Law and Predictive Modeling Construction of Internal Quality in Korla Fragrant Pears Under Multi-Type Damage During Storage

by
Yifan Xia
1,2,3,†,
Hong Zhang
1,3,4,†,
Jikai Che
1,2,3,
Qing Liang
1,2,3 and
Yang Liu
1,2,3,*
1
Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alaer 843300, China
2
College of Mechanical Electrification Engineering, Tarim University, Alaer 843300, China
3
Xinjiang Production and Construction Corps Key Laboratory of Utilization and Equipment of Special Agricultural and Forestry Products in Southern Xinjiang, Alaer 843300, China
4
College of Water Resources and Architectural Engineering, Tarim University, Alaer 843300, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Horticulturae 2025, 11(10), 1255; https://doi.org/10.3390/horticulturae11101255
Submission received: 12 September 2025 / Revised: 7 October 2025 / Accepted: 16 October 2025 / Published: 17 October 2025
(This article belongs to the Special Issue Applied Artificial Intelligence in Digital Horticulture: Practices and Innovations)
Versions Notes

Abstract

To enhance the economic benefits of the Korla fragrant pear industry and reduce fruit loss rates, this study investigates changes in pear fruit quality during storage under different damage types. An adaptive neuro-fuzzy inference system (ANFIS), tested with eight different membership functions, was used to predict the hardness and soluble solids content (SSC) of pears stored under various damage conditions. The results showed that both hardness and SSC of damaged pears decreased with prolonged storage time. During identical storage periods, more severe damage led to faster declines in hardness and SSC, among which impact loads and combined impact-compression loads caused the most rapid reductions in pear hardness and SSC. The ANFIS model with the gauss2mf membership function achieved optimal prediction accuracy for pear hardness (RMSE = 0.2207, R2 = 0.9434); the ANFIS model with the trimf membership function yielded the best prediction for pear SSC (RMSE = 0.2016, R2 = 0.9701). This study provides a theoretical basis for optimizing warehouse management and quality control of Korla fragrant pears, and for determining the optimal treatment window during storage.
Keywords: Korla fragrant pears; damage types; internal quality; storage; adaptive neural fuzzy inference system Korla fragrant pears; damage types; internal quality; storage; adaptive neural fuzzy inference system

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MDPI and ACS Style

Xia, Y.; Zhang, H.; Che, J.; Liang, Q.; Liu, Y. Variation Law and Predictive Modeling Construction of Internal Quality in Korla Fragrant Pears Under Multi-Type Damage During Storage. Horticulturae 2025, 11, 1255. https://doi.org/10.3390/horticulturae11101255

AMA Style

Xia Y, Zhang H, Che J, Liang Q, Liu Y. Variation Law and Predictive Modeling Construction of Internal Quality in Korla Fragrant Pears Under Multi-Type Damage During Storage. Horticulturae. 2025; 11(10):1255. https://doi.org/10.3390/horticulturae11101255

Chicago/Turabian Style

Xia, Yifan, Hong Zhang, Jikai Che, Qing Liang, and Yang Liu. 2025. "Variation Law and Predictive Modeling Construction of Internal Quality in Korla Fragrant Pears Under Multi-Type Damage During Storage" Horticulturae 11, no. 10: 1255. https://doi.org/10.3390/horticulturae11101255

APA Style

Xia, Y., Zhang, H., Che, J., Liang, Q., & Liu, Y. (2025). Variation Law and Predictive Modeling Construction of Internal Quality in Korla Fragrant Pears Under Multi-Type Damage During Storage. Horticulturae, 11(10), 1255. https://doi.org/10.3390/horticulturae11101255

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