Impact of Region of Interest Selection for Hyperspectral Imaging and Modeling of Sugar Content in Apple
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Abstract:
Hyperspectral imaging is an effective imaging technique that has been applied to quality and safety inspection of food and agricultural products. However, the selected shape and size of the region of interest (ROI) in hyperspectral imaging directly affects the accuracy and stability of the measurement. In this study, a hyperspectral imaging system was developed for wavelengths spanning from 330 to 1100 nm to acquire hyperspectral images of apple samples. Mean reflectance spectra of round and square ROIs with different sizes were extracted. After the spectral pretreatment to eliminate the impacts of noise and irrelevant information, models for quantitative analysis of the sugar content in apple were developed using partial least squares method. The models were externally verified with the prediction set consisting of independent samples, in order to analyze the impact of the shape and size of ROIs on the accuracy of hyperspectral imaging system modeling. The results showed that the model of sugar content in apple, constructed using a round ROI with 150 pixels diameter, yielded the highest accuracy and predictive capability. The correlation coefficient of calibration set (Rc) was 0.9305, the root mean square error of calibration (RMSEC) was 0.4331, the correlation coefficient of prediction set (Rp) was 0.9232, and the root mean square error of prediction (RMSEP) was 0.4568. These results demonstrate that selecting an ROI with an appropriate shape and size is important to improve the accuracy of modeling and harness the full potential of the hyperspectral imaging technique.
