Prediction method for nutritional quality of Korla pear during storage
Abstract
Keywords: Korla fragrant pear, harvest maturity, storage time, nutritional quality, prediction method
DOI: 10.25165/j.ijabe.20211403.5990
Citation: Liu Y, Zhang Q, Niu H, Zhang H, Lan H P, Zeng Y, et al. Prediction method for nutritional quality of Korla pear during storage. Int J Agric & Biol Eng, 2021; 14(3): 247–254.
Keywords
Full Text:
PDFReferences
Wu J, Guo K Q. Dynamic viscoelastic behaviour and microstructural changes of Korla pear (Pyrus bretschneideri rehd) under varying turgor levels. Biosyst Eng, 2010; 106(4): 485–492.
Wang B H, Sun X X, Dong F Y, Zhang F, Niu, J X. Cloning and expression analysis of an MYB gene associated with calyx persistence in Korla fragrant pear. Plant Cell Rep, 2014; 33(8): 1333–1341.
Liu J, Zhang X, Li Z, Zhang X S, Jemric T, Wang X. Quality monitoring and analysis of Xinjiang 'Korla' fragrant pear in cold chain logistics and home storage with multi-sensor technology. Appl Sci, 2019; 9(18): 3895.
Yu X J, Lu H D, Wu D. Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging. Postharvest Biol Technol, 2018; 141: 39‒49.
Andrea C G S, Angel G, María I G. Comparative study of six pear cultivars in terms of their phenolic and vitamin C contents and antioxidant capacity. J Sci Food Agric, 2003; 83(10): 995–1003.
Wang Z H, Wang W H, Jiang Y B, Bao A M, Tong W, Wang B X. Effects of different harvesting periods on the storage quality and senescence of apple at room temperature. Transactions of the CSAE, 2020; 36(7): 300–306. (in Chinese )
Rizzolo A, Grassi M, Vanoli M. Influence of storage (time, temperature, atmosphere) on ripening, ethylene production and texture of 1-MCP treated ‘Abbé Fétel’ pears. Postharvest Biol Technol, 2015; 109: 20–29.
Jia X H, Wang W H, Du Y M, Tong W, Wang Z H, Hera G. Optimal storage temperature and 1-MCP treatment combinations for different marketing times of Korla Xiang pears. J Integr Agric, 2018; 17(03): 693–703.
Saquet A A. Storage of pears. Sci Hortic, 2019; 246: 1009–1016.
Boonyakiat D, Chen P M, Spotts R A, Richardson D G. Effect of harvest maturity on decay and post-harvest life of ‘d’ Anjou’ pear. Sci Hortic, 1987; 31: 131–139.
Blanckenberg A, Muller M, Theron K I, Crouch E M, Steyn W J. Harvest maturity and ripeness differentially affects consumer preference of ‘Forelle’, ‘Packham’s Triumph’ and ‘Abate Fetel’ pears (Pyrus communis L.). Sci Hortic, 2016; 207: 131–139.
Dong Y, Zhang S Y, Wang Y. Compositional changes in cell wall polyuronides and enzyme activities associated with melting/mealy textural property during ripening following long-term storage of ‘Comice’ and ‘d’Anjou’ pears. Postharvest Biol Technol, 2018; 135: 131–140.
Nordey T, Davrieux F, Léchaudel M. Predictions of fruit shelf life and quality after ripening: Are quality traits measured at harvest reliable indicators? Postharvest Biol Technol, 2019; 153: 52–60.
Huang Y B, Lan Y B, Thomson S J, Fang A, Hoffmann W C, Lacey R E. Development of soft computing and applications in agricultural and biological engineering. Comput Electron Agr, 2010; 71: 107–127.
Guo W C, Shang L, Zhu X H, Shang L, Nelson S O. Nondestructive detection of soluble solids content of apples from dielectric spectra with ANN and chemometric methods. Food Bioprocess Tech, 2015; 8(5): 1126–1138.
Jiang Z D, Zheng H, Mantri N, Qi Z C, Zhang X D, Hou Z N, et, al. Prediction of relationship between surface area, temperature, storage time and ascorbic acid retention of fresh-cut pineapple using adaptive neuro-fuzzy inference system (ANFIS). Postharvest Biol Technol, 2016; 113: 1–7.
Zheng H, Jiang B, Lu H F. An adaptive neural-fuzzy inference system (ANFIS) for detection of bruises on Chinese bayberry (Myrica rubra) based on fractal dimension and RGB intensity color. J Food Eng, 2011; 104: 663–667.
National Standard of the People’s Republic of China. Korla fragrant pear, 2002; NY/T 585-2002. (in Chinese)
National Standard of the People’s Republic of China. Production technical rules for Korla fragrant pear, 2004; NY/T 881-2004. (in Chinese)
Lan H P, Jia F G, Tang Y R, Zhang Q, Han Y L, Liu Y. Quantity evaluation method of maturity for Korla fragrant pear. Transactions of the CSAE, 2015; 31(5): 325–330. (in Chinese)
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. Comput Electron Agr, 2015; 117: 226–233.
Nicolaï B M, Verlinden B E, Desmet M, Saevels S, Saeys W, Theron K, et al. Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear. Postharvest Biol Technol, 2008; 47(1): 68–74.
Peiris K H S, Dull G G, Leffler R G, Kays S J. Spatial variability of soluble solids or dry-matter content within individual fruits, bulbs, or tubers: implications for the development and use of NIR spectrometric techniques. HortScience, 1999; 34(1): 114–118.
Paz P, Sánchez M T, Pérez-Marín D, Guerrero J E, Garrido-Varo A. Instantaneous quantitative and qualitative assessment of pear quality using near infrared spectroscopy. Comput Electron Agr, 2009; 69(1): 24–32.
Wang Z H, Wang W H, Jiang Y B, Bao A M, Tong W, Wang B X. Effects of different harvesting periods on the storage quality and senescence of apple at room temperature. Transactions of the CSAE, 2020; 36(7): 300–306. (in Chinese )
Tijskens L M M, Konopacki P, Simcic M. Biological variance, burden or benefit? Postharvest Biol Technol, 2003; 27(1): 15–25.
Heddam S. Generalized regression neural network based approach as a new tool for predicting Total Dissolved Gas (TDG) downstream of spillways of dams: a case study of Columbia river basin dams, USA. Environ Process, 2017; 4: 235‒253.
Li H. Statistic learning method (second edition). Beijing: Tsinghua University Press, 2019; 24p. (in Chinese)
Rooki R. Application of general regression neural network (GRNN) for indirect measuring pressure loss of Herschel-Bulkley drilling fluids in oil drilling. Measurement, 2017; 85: 184–191.
Jang J S. ANFIS: Adaptive-network-based fuzzy inference system. IEEE T Syst Man Cy-s, 1993; 23(3): 665−685.
Liu X. Studies on mechanism of fruit growth and development of different ripening-season of pears in China. PhD dissertation. Ya’an: Sichuan Agricultural University, 2008; 6. 48p. (in Chinese)
Shao M C. Studies on technique for ‘Hosui’ pear storage in China. Master dissertation. Nanjing: Nanjing Agricultural University, 2005; 12. 16 p. (in Chinese)
Li G L. Study on the preservation technique of Huanghua pear in China. Master dissertation. Fuzhou: Fujian Agricultural and Forestry University, 2013; 4. 27p. (in Chinese)
Lee S K, Kader A A. Preharvest and postharvest factors influencing Vitamin C content of horticultural crops. Postharvest Biol Technol, 2000; 20(3): 207–220.
Copyright (c) 2021 International Journal of Agricultural and Biological Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.