# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
###### Abstract  
Apple core browning not only affects the nutritional quality of apples, but also poses a health risk to consumers. Therefore, there is an urgent need to develop a fast and reliable non-destructive detection method for apple core browning. To deal with the challenges of the long incubation period, strong infectivity, and difficulty in the prevention and control of apple core browning, a novel non-destructive detection method for apple core browning has been developed through combining hyperspectral imaging and dielectric techniques. To reduce the computational complexity of high-dimensional multi-view data, canonical correlation analysis is employed for feature dimensionality reduction. Then, the two low-dimensional vectors extracted from two different sensors are concatenated into one united feature vector; therefore, the information contained in the hyperspectral and dielectric data is fused to improve the detection accuracy of the non-destructive method. At last, five traditional classifiers, such as k-Nearest Neighbors, a support vector machine with radial basis function kernel and polynomial kernel, Decision Tree, and neural network, are trained on the fused feature vectors to discriminate apple core browning. The experimental results on our own constructed dataset have shown that the sensitivity, specificity, and precision of SVM with RBF kernel based on concatenated 70-dimensional feature vectors extracted via canonical correlation analysis reached 99.98%, 99.70%, and 99.70%, respectively, which achieved better results than other models. This study can provide theoretical assurance and technical support for further development of higher accuracy and lower-cost non-destructive detection devices for apple core browning. apple core browning; non-destructive detection; hyperspectral imaging; dielectric parameters; canonical correlation analysis +
Footnote †: journal: Medical  
detection cost. The application of hyperspectral technology in non-destructive detection of apple quality is relatively mature [9; 10; 11]. For example, it has been used for qualitative detection of apple texture quality [12], hardness [12], sugar content [13], acidity [14], and soluble solids [15]. In the detection of apple core browning, [PERSON] et al. [16] achieved effective detection of apple core browning through transmission detection using a combination of a tungsten halogen light source and spectrometer. [PERSON] et al. [17] used principal component analysis to extract 20 principal components and established a discriminant model for apple core browning using the Fisher discriminant analysis. Due to the correlation between apple fruit size and the incidence of core browning, [PERSON] et al. [18] constructed a binary classification discriminant model for apple core browning based on the transmission spectral intensity values between 680 and 735 nm and apple diameter. To further assess the severity of apple core browning, [PERSON] et al. [19] divided the disease severity into four categories. [PERSON] et al. [20] extracted spectral features from the visible/near-infrared spectral data of 140 apples and established an identification model using support vector machines. Another popular detection method is based on dielectric parameters [21; 22; 23; 24; 25]. For example, it can be used for freshness detection [26], variety identification detection of impact and static pressure damage [27; 28], and prediction of soluble solid content [29]. [PERSON] et al. [30] used seven impedance parameters of Fuji apples at frequencies ranging from 100 Hz to 3.98 MHz, with a voltage of 1 V and a constant temperature of (20 \(\pm\) 1) \({}^{\circ}\)C, to build an apple core browning classification model. Although optical and electrical methods have been widely applied in the detection of apple core browning, a method combining hyperspectral image information and electrical characteristics for the non-destructive detection of apple core browning has not been reported.