[1] Goetz A F H, Vane G, Solomon J E et al. Imaging spectrometry for earth remote sensing[J]. Science, 228, 1147-1153(1985).

[2] Tagesson T, Fensholt R, Guiro I et al. Ecosystem properties of semiarid savanna grassland in West Africa and its relationship with environmental variability[J]. Global Change Biology, 21, 250-264(2015).

[3] Amodio M L, Capotorto I. Chaudhry M M A, et al. The use of hyperspectral imaging to predict the distribution of internal constituents and to classify edible fennel heads based on the harvest time[J]. Computers and Electronics in Agriculture, 134, 1-10(2017).

[4] Li C, Fan P, Jiang K et al. Melon seed variety identification based on hyperspectral technology combined with discriminant analysis[J]. Bangladesh Journal of Botany, 46, 1153-1160(2017).

[5] Ravikanth L, Jayas D S. White N D G, et al. Extraction of spectral information from hyperspectral data and application of hyperspectral imaging for food and agricultural products[J]. Food and Bioprocess Technology, 10, 1-33(2017).

[6] Goto A, Nishikawa J, Kiyotoki S et al. Use of hyperspectral imaging technology to develop a diagnostic support system for gastric cancer[J]. Journal of Biomedical Optics, 20, 016017(2015).

[7] Markgraf W. Janssen M W W, Lilienthal J, et al. Hyperspectral imaging for ex-vivo organ characterization during normothermic machine perfusion[J]. European Urology Supplements, 17, e767(2018).

[8] Liu L X, Li M Z, Zhao Z G et al. Recent advances of hyperspectral imaging application in biomedicine[J]. Chinese Journal of Lasers, 45, 0207017(2018).

[9] Chander S, Gujrati A, Abdul Hakeem K et al. Water quality assessment of river ganga and chilika lagoon using AVIRIS-NG hyperspectral data[J]. Current Science, 116, 1172-1181(2019).

[10] Yu J W, Cheng Z Q, Zhang J S et al. An approach to distinguishing between species of trees and crops based on hyperspectral information[J]. Spectroscopy and Spectral Analysis, 38, 3890-3896(2018).

[11] Li J B, Tian X, Huang W Q et al. Application of long-wave near infrared hyperspectral imaging for measurement of soluble solid content (SSC) in pear[J]. Food Analytical Methods, 9, 3087-3098(2016).

[12] Zhang C, Guo C T, Liu F et al. Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine[J]. Journal of Food Engineering, 179, 11-18(2016). http://www.sciencedirect.com/science/article/pii/S0260877416300024

[13] Ma T, Li X Z, Inagaki T et al. Noncontact evaluation of soluble solids content in apples by near-infrared hyperspectral imaging[J]. Journal of Food Engineering, 224, 53-61(2018).

[14] Rao L B, Pang T, Ji R S et al. Firmness detection for apples based on hyperspectral imaging technology combined with stack autoencoder-extreme learning machine method[J]. Laser & Optoelectronics Progress, 56, 113001(2019).

[15] Deng X L, Kong C, Wu W B et al. Detection of citrus HuangLongBing based on principal component analysis and back propagation neural network[J]. Acta Photonica Sinica, 43, 0430002(2014).

[16] Sun Y, Gu X Z, Sun K et al. Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches[J]. LWT-Food Science and Technology, 75, 557-564(2017). http://www.sciencedirect.com/science/article/pii/S0023643816306004

[17] Fan Y Y, Qiu Z J, Chen J et al. Identification of varieties of dried red jujubes with near-infrared hyperspectral imaging[J]. Spectroscopy and Spectral Analysis, 37, 836-840(2017).

[18] Pan X Y, Sun L J, Li Y S et al. Non-destructive classification of apple bruising time based on visible and near-infrared hyperspectral imaging[J]. Journal of the Science of Food and Agriculture, 99, 1709-1718(2019). http://onlinelibrary.wiley.com/doi/abs/10.1002/jsfa.9360