Background:
Moisture content is one of the most important indicators for the quality of fresh
strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry.
However, these methods are relatively simple and can only be used to detect the moisture content of single
samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is
important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market
circulation of strawberry.
Objective:
This study aims to establish a novel BP neural network prediction model to predict and analyze
strawberry moisture.
Methods:
Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology,
spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural
network technology were combined to predict the moisture content of strawberry.
Results:
The characteristic wavelengths were highly correlated with the strawberry moisture content. The
stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths
are superior to those of the prediction model based on principal components, and the correlation coefficients
of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with
low levels of standard deviations (0.3204 and 0.3010, respectively).
Conclusion:
The BP neural network prediction model of strawberry moisture has certain practicability and can
provide some reference for the on-line and non-destructive detection of fruits and vegetables.
Correction to: Experimental Examination of the Behavior of Shotcrete-Reinforced Masonry Walls and Xgboost Neural Network Prediction Model