The feasibility of early detection and grading of apple bruises using hyperspectral imaging

The ability to determine if an apple is bruised and to provide quantitative and objective descriptions of the degree of bruising is not the only important basis for assessing apple quality but also has significance for improving the postharvest handling of apples. In this study, segmented principal component analysis for hyperspectral images in the spectral range of 401 to 1037nm was carried out, and seven characteristic wavelengths were selected based on the weight coefficients of the principal component images. By using the principal component analysis operations with the selected wavelengths and image processing methods, an accurate recognition algorithm for apple bruises was proposed. For 40 intact samples and 160 bruised samples, the average correct recognition rate was 99.1%. Moreover, this paper obtained the average spectra of 157 segmented bruised regions by applying a binary mask. A characteristic wavelength selection method that combines competitive adaptive reweighted sampling with correlation coefficient methods and supports vector machine modeling methods based on grid parameter optimization was put forward for the classification and identification of the bruising degrees of apples. The results showed that the classification accuracy was as high as 97.5% for the test set. Overall, this study demonstrated that hyperspectral imaging technology can be used to accurately and effectively identify early bruises and determine the bruising degree of apples, which provides a new method for on-line, nondestructive detection, and grading of early bruises in apples. This paper conducted a hyperspectral image acquisition of normal and bruised apples; developed a bruise detection algorithm based on segmented principal component analysis and image-processing methods; and, on this basis, performed the classification of bruising degrees of apples. The results demonstrated that hyperspectral imaging technology can be used to accurately and effectively identify early bruises and bruising degrees of apples.