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.
Journal:
Food research international (Ottawa, Ont.)
PMID:
40086950
Abstract
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 (μ) and reduced scattering (μ') 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 μ' 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 μ and μ' 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.