以高光谱数据有效预测苹果可溶性固形物含量
黄文倩, 李江波, 陈立平, 郭志明. 以高光谱数据有效预测苹果可溶性固形物含量[J]. 光谱学与光谱分析, 2013, 33(10): 2843.
HUANG Wen-qian, LI Jiang-bo, CHEN Li-ping, GUO Zhi-ming. Effectively Predicting Soluble Solids Content in Apple Based on Hyperspectral Imaging[J]. Spectroscopy and Spectral Analysis, 2013, 33(10): 2843.
[1] HONG Tian-sheng, QIAO Jun, NING Wang Michael , et al（洪添胜, 乔军, Ning Wang Michael, 等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2007, 23(2): 151.
[2] Zhao J W, Vittayapadung S, Chen Q S, et al. Maejo International Journal of Science and Technology, 2009, 3(1): 130.
[3] Huang M, Lu R. Transactions of the ASABE, 2010, 53(4): 1175.
[5] Kawano S, Abe H, Iwamoto M. Journal of Near Infrared Spectroscopy, 1995, 3(4): 211.
[6] Macho S, Iusa R, Callao M P, et al. Analytica Chimica Acta, 2001, 445(2): 213.
[7] Sun D. Hyperspectral Imaging for Food Quality Analysis and Control. Massachusetts: Academic Press, 2010. 56.
[8] Riccardo L, Amparo L, Gonzalez. Chemometrics and Intelligent Laboratory Systems, 1998, 41(2): 195.
[9] Mário C U A, Teresa C B S, Roberto K H G, et al. Chemometrics and Intelligent Laboratory Systems, 2001, 57(2): 65.
[10] Svante W, Michael S, Lennart E. Chemometrics and Intelligent Laboratory Systems, 2001, 58(2): 109.
[11] Vapnik V N. The nature of statistical learning theory. New York: Springer-Verlag, 1995.
黄文倩, 李江波, 陈立平, 郭志明. 以高光谱数据有效预测苹果可溶性固形物含量[J]. 光谱学与光谱分析, 2013, 33(10): 2843. HUANG Wen-qian, LI Jiang-bo, CHEN Li-ping, GUO Zhi-ming. Effectively Predicting Soluble Solids Content in Apple Based on Hyperspectral Imaging[J]. Spectroscopy and Spectral Analysis, 2013, 33(10): 2843.
PDF全文
