# Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging
## 1 Introduction  
The Korla pear is characterized by its faint scent, thin skin, delicious flesh and sweet juice. In the international market, it is known as \"the treasure of pears\". Bruising, usually caused by external forces during the process of picking and transporting [1], is one of the most common factors that affect the quality of the pears. However, some light bruises on the pears are difficult to detect by the naked eye or traditional computer vision technology [2] because there is no significant difference in color between lightly bruised and normal areas. So, it is very important to develop a method to detect light bruises on the surface of pears quickly and effectively. Spectral technology is one of the most efficient tools for damage detection. [PERSON] et al. [3] detected the damage degree of pears by visible and near-infrared (vis/NIR) spectroscopy and got a great result. [PERSON] et al. [4] used a hyperspectral infra-red (1000-1700 nm) imaging technique to detect bruise damage underneath the pear skin and the results demonstrated good potential. Compared with the visible and near-infrared spectral analysis, hyperspectral image technology, which combines traditional 2D image technology with spectral technology, has a better ability to detect and show the internal or externalattributes of objects [5]. With the advantage of multi-bands and a high resolution, hyperspectral image technology can meet the needs of slight bruise detection of Korla pears. Hyperspectral imaging technology has been widely used in the damage detection of agricultural products, such as the external damage of tomatoes, freeze damage of mushroom, pest damage of soybeans, and bruising of pears [6; 7; 8; 9; 10]. However, the analysis of spectral data has always been difficult because of the complexity of the data. More and more scholars began to pay attention to the selection of feature bands to improve the efficiency of detection. [PERSON] et al. [11] built a hyperspectral imaging system to acquire reflectance images of orange samples in the spectral region between 400 and 1000 nm. Principal component analysis (PCA) was then used to select six characteristic wavelengths (630, 691, 769, 786, 810 and 875 nm). By developing an algorithm based on images at these six wavelengths, the accuracy rate of damage identification reached 91.5%. [PERSON] et al. [12] detected the external damage of jujubes by acquiring hyperspectral reflectance images at the spectral region between 400 and 720 nm. Then, stepwise discriminant analysis (SDA) was used for modeling and the result showed that the accuracy rate of the damage identification was 97.0%. [PERSON] et al. [13] identified the mechanical damage of mangos by using a 650-1080 nm hyperspectral imaging system. They chose 700-780 nm, 890-900 nm, 1070-1080 nm as characteristic bands using a few methods and the results showed that the recognition rate of models based on those wavelengths was 91.4%. These studies reduced the wavelength range of the model to a certain extent and had ideal results in damage detection. So the extraction of characteristic wavelengths had no significant influence on the detection accuracy but accelerated the detection speed of the system. Receiver operating characteristic (ROC) analysis is one of the methods used for choosing characteristic wavelengths. [PERSON] et al. [14] analyzed the spectra of different kinds of apples. They used ROC analysis to extract wavelength combinations (\(R(\lambda_{1})-R(\lambda_{2})\)) which can identify damaged areas from normal areas for four kinds of apples separately. Comparing the results with that of the partial least squares discriminant analysis (PLS-DA) model based on the full-wave band, they found that the recognition rates were similar. [PERSON] et al. [15] combined ROC analysis with an artificial neural network to identify rot areas of citrus and found the recognition accuracy reached 89%.