Application of Visible and Near-Infrared Hyperspectral Imaging for Detection of Defective Features in Loquat

The intent of present work was to develop a valid method for detection of defective features in loquat fruits based on hyperspectral imaging. A laboratorial hyperspectral imaging device covering the visible and near-infrared region of 380–1,030 nm was utilized to acquire the loquat hyperspectral images. The corresponding spectral data were extracted from the region of interests of loquat hyperspectral images. The dummy grades were assigned to the defective and normal group of loquats, separately. Competitive adaptive reweighted sampling (CARS) was conducted to elect optimal sensitive wavelengths (SWs) which carried the most important spectral information on identifying defective and normal samples. As a result, 12 SWs at 433, 469, 519, 555, 575, 619, 899, 912, 938, 945, 970, and 998 nm were selected, respectively. Then, the partial least squares discriminant analysis (PLS-DA) model was established using the selected SWs. The results demonstrated that the CARS-PLS-DA model with the discrimination accuracy of 98.51 % had a capability of classifying two groups of loquats. Based on the characteristics of image information, minimum noise fraction (MNF) rotation was implemented on the hyperspectral images at SWs. Finally, an effective approach for detecting the defective features was exploited based on the images of MNF bands with “region growing” algorithm. For all investigated loquat samples, the developed program led to an overall detection accuracy of 92.3 %. The research revealed that the hyperspectral imaging technique is a promising tool for detecting defective features in loquat, which could provide a theoretical reference and basis for designing classification system of fruits in further work.