Deep learning combined Monte Carlo simulation reveal the fundamental light propagation in apple puree: Monitoring the quality changes from different cultivar, storage period and heating duration
文献类型： 外文期刊
作者： Yang, Yucan 1 ; Ma, Chen 3 ; Chen, Mingrui 4 ; Chen, Xiao 1 ; Xu, Zhi 5 ; Pan, Leiqing 1 ; Lan, Weijie 1 ;
作者机构： 1.Nanjing Agr Univ, Coll Food Sci & Technol, 1 Weigang Rd, Nanjing 210095, Jiangsu, Peoples R China
2.Nanjing Agr Univ, Sanya Inst, Sanya 572024, Hainan, Peoples R China
3.Jiangsu Acad Agr Sci, Xuzhou Inst Agr Sci Jiangsu Xuhuai Area, Xuzhou 221131, Peoples R China
4.Sichuan Agr Univ, Coll Food Sci, Yaan 625014, Sichuan, Peoples R China
5.Chinese Acad Trop Agr Sci, Anal & Test Ctr, Haikou 571100, Peoples R China
关键词： Puree quality; Optical property; Monte Carlo simulation; Deep learning regression
期刊名称：FOOD RESEARCH INTERNATIONAL （ 影响因子：8.0； 五年影响因子：8.5 ）
ISSN： 0963-9969
年卷期： 2025 年 207 卷
页码：
收录情况： SCI
摘要： This work explored the light propagation of purees from a large variability of apple cultivar, storage period and heating duration based on their optical absorption (mu(a)) and reduced scattering (mu(s)') properties at 900-1650 nm, in order to better monitor the chemical, structural and rheological parameters. The prolonged heating duration modified intensively on puree structure and rheology, and resulted significant increases of mu(s)' at 900-1350 nm. Based on Monte Carlo simulation, the maximum light attenuation distance at 1050 nm of 'Golden Delicious' and 'Red Delicious' apple puree increased intensively from 16.22 mm to 17.60 mm and from 16.19 mm to 17.41 mm respectively while thermal processing duration from 10 min to 20 min. Back propagation neural network models based on mu(a) and mu(s)' can monitor their dry matter content, titratable acidity, apparent viscosity and viscoelasticity, with the RPD > 2.53. These provided fundamental knowledge on light propagation of puree matrix and the potential strategy to monitor their quality.
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