Journal of Zhejiang Agricultural Sciences ›› 2026, Vol. 67 ›› Issue (8): 1977-1981.DOI: 10.16178/j.issn.0528-9017.20250426

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Texture identification and evaluation of different fruit-shaped peppers based on texture profile analysis

ZHEN Junqi(), LI Junli, TAN Xiufang, ZOU Ming, LIU Yicheng, WEI Fang, DONG Qi, CHEN Xi   

  1. Xinxiang Academy of Agricultural Sciences,Xinxiang 453000,Henan
  • Received:2025-06-16 Online:2026-08-11 Published:2026-08-20

Abstract:

To systematically explore the texture differences in pepper fruits with different fruit shapes,the texture profile analysis(TPA)method was used to measure the main texture parameters of three commonly marketed pepper types,namely linear peppers,screw peppers,and horn peppers. Correlation analysis and principal component analysis(PCA)were conducted to reveal the internal relationships among the texture parameters. The results showed significant (p<0.05)differences in texture parameters among pepper fruits with different fruit shapes. Xinke 26(horn pepper) significantlyoutperformed screw peppers and linear peppers in hardness,elasticity,and chewiness. The adhesiveness presented an order of horn peppers>screw peppers>linear peppers,while the differences in fruit cohesiveness among the three types were relatively small. Correlation analysis indicated significant or highly significant(p<0.01) positive correlations among the texture parameters. The correlation coefficients between chewiness and key parameters such as adhesiveness,hardness,and elasticity were all at or above 0.88,demonstrating a highly synergistic change trend. PCA showed that the first two principal components accounted for 91.6% of the cumulative variance contribution rate of pepper fruit texture,with chewiness and adhesiveness having the highest loadings,which can serve as core indicators for evaluating pepper fruit texture. This study provides a scientific basis for pepper variety breeding,quality comprehensive evaluation,and processing.

Key words: pepper, texture profile analysis, correlation analysis, principal component analysis, texture evaluation

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