Evaluation of the Changes in Optical Properties of Peaches with Different Maturity Levels during Bruising

Abstract

The main objective was to measure the optical coefficients of peaches after bruising at different maturity levels and detect bruises. A spatially resolved method was used to acquire absorption coefficient (μa) and the reduced scattering coefficient (µs’) spectra from 550 to 1000 nm, and a total of 12 groups (3 maturity levels * 4 detection times) were used to assess changes in µa and µs’ resulting from bruising. Maturation and bruising both caused a decrease in µs’ and an increase in µa, and the optical properties of immature peaches changed more after bruising than the optical properties of ripe peaches. Four hours after bruising, the optical properties of most samples were significantly different from those of intact peaches (p < 0.05), and the optical properties showed damage to tissue earlier than the appearance symptoms observed with the naked eye. The classification results of the Support Vector Machine model for bruised peaches showed that μa had the best classification accuracy compared to μs′ and their combinations (µa × µs’, µeff). Overall, based on μa, the average detection accuracies for peaches after bruising of 0 h, 4 h, and 24 h were increased.

Principles, developments, and applications of spatially resolved spectroscopy in agriculture: a review

“…Sun et al. also measured the OP of peaches at different ripeness levels using the SR technique ( Sun Y. et al., 2021 ), with classification accuracies of 85% and 76.25%, respectively, and these results found that this optical property was effective in detecting damage in peaches. Huang et al.…”

1st sensorFINT International Conference

“…Estimating the theoretical light penetration depth could be helpful to validate the non-contact methods, such as hyperspectral imaging, as accurate tools for assessing quality properties based on optical properties. A non-destructive protocol, based on the spatially resolved spectroscopic principle (Sun et al, 2021), was developed to estimate the light penetration depth in persimmons for all bands in the range 450-1050 nm, in steps of 10 nm. Backscattering images of intact persimmons ('Rojo Brillante') of similar size were obtained.…”

Green Plums Surface Defect Detection Based on Deep Learning Methods

“…Raj et al [14] proposed a method based on multi-spectral technology to extract features from defects to achieve apple classification, and the average classification accuracy rate for healthy apples, mildly defective apples and severely defective apples was 94.66%. Sun et al [15] used hyperspectral technology to measure the optical coefficient of peach after injury at different maturity levels. The optical properties showed that tissue damage was earlier than the appearance observed by naked eyes.…”