Selection of Spectral Resolution and Scanning Speed for Detecting Green Jujubes Chilling Injury Based on Hyperspectral Reflectance Imaging  
[PERSON]  
1 Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China; [EMAIL_ADDRESS] (H.L.); [EMAIL_ADDRESS] (X.Y.); [EMAIL_ADDRESS] (L.Z.) College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-0571-8898-2143  
[PERSON]  
1 Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China; [EMAIL_ADDRESS] (H.L.); [EMAIL_ADDRESS] (X.Y.); [EMAIL_ADDRESS] (L.Z.) College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-0571-8898-2143  
[PERSON]  
22211  
such as ripeness evaluation of strawberry [3], firmness and soluble solids content inspection of blueberry [4], bruise damage detection on pear [5], internal defect identification on cucumbers [6], and hollowness detection in white radish (_Raphanus sativus_ L.) [7]. Since chilling stress often causes an internal defect in agro-product, such as pulp fester and spongy texture of fruit. Hence, much attention was paid to the nondestructive technology for fruit chilling injury detection [8; 9; 10]. [PERSON] et al. (2013) showed the potential ability for backscattering imaging for banana chilling detection [11]. The results have shown relatively low misclassification errors of 6% and 8% for early detection, and 0.67% and 1.33% after chilling symptom development, using images at 660 nm and 785 nm, respectively. [PERSON] et al. (2016) conducted hyperspectral imaging technique by combining the sequential forward selection (SFS) feature selection method and support vector machine (SVM) classifier to distinguish chilled cucumbers from normal ones with the overall accuracy of 100%, and to assess chilling levels with the best overall accuracies of 91.6%, respectively [12]. Additionally, [PERSON] et al. (2009) reported the potential of using hyperspectral reflectance imaging for the identification of chilling stress in apples. They claimed that artificial neural network (ANN) model was more feasible for chilling injury detection based on five optimal wavelengths (717 nm, 751 nm, 875 nm, 960 nm, and 980 nm) with an overall classification accuracy of 98.4% [13]. Although reasonable results were achieved from methods mentioned above, it is still challenging to apply this technique to on-line inspection due to the low scanning speed in hyperspectral images. Therefore, a useful method with appropriate working parameters and feasible algorithm was necessary to achieve on-line chilling injury detection in green jujube production.  
To our knowledge, little research has been done to investigate the effect of spectral resolution and scanning speed on chilling injury detection of green jujube based on hyperspectral imaging. Therefore, the main objective of this research was to select the optimal spectral resolution, scanning speed for green jujube hyperspectral images. To achieve this goal, the specific objectives were (1). To acquire the hyperspectral images of each sample at different scanning speeds and spectral resolutions; (2). Identify the optimal wavelengths for chilling damage detection; and, (3). Develop a feasible classification model for green jujube chilling injury discrimination based on selected features.