Cheng Hong, Zhang Zishen, Feng Yunxiao, He Jingang, Wang Jinxiao, Cheng Yudou, Guan Junfeng
Institute of Biotechnology and Food Science, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050051, Hebei, China; Hebei Key Laboratory of Plant Genetic Engineering, Shijiazhuang 050051, Hebei, China.
Institute of Biotechnology and Food Science, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050051, Hebei, China; Hebei Key Laboratory of Plant Genetic Engineering, Shijiazhuang 050051, Hebei, China; College of Horticulture, Xinjiang Agricultural University, Urumqi 830000, Xinjiang, China.
Food Res Int. 2025 May;209:116254. doi: 10.1016/j.foodres.2025.116254. Epub 2025 Mar 16.
Monitoring the yellowing and senescence of postharvest 'Yali' pear is crucial for quality control during storage. This study integrated chlorophyll fluorescence imaging (CFI) and hyperspectral imaging (HSI) to assess the natural senescence of green 'Yali' pear transitioning from green to yellow under different storage conditions. Results demonstrated that common physicochemical parameters, such as firmness and soluble solids content (SSC), exhibited minimal changes throughout storage. Titratable acidity (TA) content showed slight variations under ambient storage conditions but declined significantly after 105 days of cold storage, stabilizing thereafter. Consequently, these parameters proved ineffective in predicting fruit senescence due to their limited changes over storage time. However, changes in the pear peel color (green to yellow) and chlorophyll degradation (evidenced by decreasing I values) effectively reflected the senescence process. Additionally, increased respiration and ethylene production rates further indicated advancing senescence. Among the chlorophyll fluorescence (ChlF) parameters, F and F/F showed a significant decline, correlating strongly with various physicochemical changes, including a*, h, I, as well as respiration and ethylene production rates, thus proving to be reliable indicators of senescence. Notably, HSI was successfully applied to predict ChlF parameters (F, F, and F/F) based on Partial Least Squares Regression (PLSR) and Least-Squares Support Vector Machine (LS-SVM) models, with RPD values exceeding 3.0 for most parameters, indicating high predictive accuracy. In conclusion, the combined use of CFI and HSI offers a robust, non-destructive method for predicting yellowing and senescence in green 'Yali' pear, which provides valuable insights into quality monitoring and postharvest management.
