References
- Afanador, N., T. Tran, and L. Buydens, 2013. Use of the bootstrap and permutation methods for a more robust variable importance in the projection metric for partial least squares regression, Analytica chimica acta, 768: 49-56. https://doi.org/10.1016/j.aca.2013.01.004
- Bae, H., Y. Seo, D. Kim, S. Lohumi, E. Park, and B. Cho, 2016. Development of non-destructive sorting technique for viability of watermelon seed by using hyperspectral image processing, Journal of the Korean Society for Nondestructive Testing, 36(1): 35-44. https://doi.org/10.7779/JKSNT.2016.36.1.35
- Chong, I. and C. Jun, 2005. Performance of some variable selection methods when multicollinearity is present, Chemometrics and Intelligent Laboratory Systems, 78(1): 103-112. https://doi.org/10.1016/j.chemolab.2004.12.011
- Dong, J. and W. Guo, 2015. Nondestructive determination of apple internal qualities using near-infrared hyperspectral reflectance imaging, Food Analytical Methods, 8(10): 2635-2646. https://doi.org/10.1007/s12161-015-0169-8
- Fan, S., B. Zhang, J. Li, C. Liu, W. Huang, and X. Tian, 2016. Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data, Postharvest Biology and Technology, 121: 51-61. https://doi.org/10.1016/j.postharvbio.2016.07.007
- Gomez, C., P. Lagacherie, and G. Coulouma, 2008. Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements, Geoderma, 148(2): 141-148. https://doi.org/10.1016/j.geoderma.2008.09.016
- Guo, W., Y. Du, Y. Zhou, S. Yang, J. Lu, Y. Zhao, and L. Teng, 2012. At-line monitoring of key parameters of nisin fermentation by near infrared spectroscopy, chemometric modeling and model improvement, World Journal of Microbiology and Biotechnology, 28(3): 993-1002. https://doi.org/10.1007/s11274-011-0897-x
- Karpouzli, E. and T. Malthus, 2003. The empirical line method for the atmospheric correction of IKONOS imagery, International Journal of Remote Sensing, 24(5): 1143-1150. https://doi.org/10.1080/0143116021000026779
- Kim, D., B. Cho, and Y. Kim, 2012. Non-destructive quality prediction of truss tomatoes using hyperspectral reflectance imagery, Journal of Agriculutral Science, 39(3): 413-420.
- Liu, D., X. Zeng, and D. Sun, 2015. Recent developments and applications of hyperspectral imaging for quality evaluation of agricultural products: a review, Critical reviews in food science and nutrition, 55(12): 1744-1757. https://doi.org/10.1080/10408398.2013.777020
- Mendoza, F., R. Lu, D. Ariana, H. Cen, and B. Bailey, 2011. Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content, Postharvest Biology and Technology, 62(2): 149-160. https://doi.org/10.1016/j.postharvbio.2011.05.009
- Mo, C., M. Kim, G. Kim, J. Lim, S. Delwiche, K. Chao, H. Lee, and B. Cho, 2017. Spatial assessment of soluble solid contents on apple slices using hyperspectral imaging, Biosystems Engineering, 159: 10-21. https://doi.org/10.1016/j.biosystemseng.2017.03.015
- Noh, S. and D. Ryu, 2002. Preprocessing of Transmitted Spectrum Data for Development of a Robust Non-destructive Sugar Prediction Model of Intact Fruits, Journal of the Korean Society for Nondestructive Testing, 22(4): 361-368.
- Noh, H. and R. Lu, 2007. Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality, Postharvest Biology and Technology, 43(2): 193-201. https://doi.org/10.1016/j.postharvbio.2006.09.006
- Peng, Y. and R. Lu, 2008, Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content, Postharvest Biology and Technology, 48(1): 52-62. https://doi.org/10.1016/j.postharvbio.2007.09.019
- Pu, Y., Y. Feng, and D. Sun, 2015. Recent progress of hyperspectral imaging on quality and safety inspection of fruits and vegetables: a review, Comprehensive Reviews in Food Science and Food Safety, 14(2): 176-188. https://doi.org/10.1111/1541-4337.12123
- Qin, J., R. Lu, and Y. Peng, 2009. Prediction of apple internal quality using spectral absorption and scattering properties, Transactions of the ASABE, 52(2): 499-486. https://doi.org/10.13031/2013.26807
- Rinnan, A., L. Norgaard, F. van den Berg, J. Thygesen, R. Bro, and S. Engelsen, 2009. Data preprocessing. Infrared spectroscopy for food quality analysis and control, 29-50.
- Saeys, W., A. Mouazen, and H. Ramon, 2005. Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy, Biosystems Engineering, 91(4): 393-402 https://doi.org/10.1016/j.biosystemseng.2005.05.001
- Stellacci, A., A. Castrignano, A. Troccoli, B. Basso, and G. Buttafuoco, 2016. Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches. Environmental Monitoring and Assessment, 188(3): 199. https://doi.org/10.1007/s10661-016-5171-0
- Wang, Z, Q. He, and J. Wang, 2014. Comparison of different variable selection methods for partial least squares soft sensor development, Proc. of 2014 IEEE In American Control Conference (ACC), OR, USA, June. 4-6, pp. 3116-3121.
- Xu, L., Y. Zhou, L. Tang, H. Wu, J. Jiang, G. Shen, and R. Yu, 2008. Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration, Analytica Chimica Acta, 616(2): 138-143. https://doi.org/10.1016/j.aca.2008.04.031
- Zhao, J., S. Vittayapadung, Q. Chen, S. Chaitep, and R. Chuaviroj, 2009. Nondestructive measurement of sugar content of apple using hyperspectral imaging technique, Maejo International Journal of Science and Technology, 3(1): 130-142.
- Zhang, C., F. Liu, W. Kong, and Y. He, 2015. Application of visible and near-infrared hyperspectral imaging to determine soluble protein content in oilseed rape leaves, Sensors, 15(7): 16576-16588. https://doi.org/10.3390/s150716576
- Zhu, Q., M. Huang, X. Zhao, and S. Wang, 2013. Wavelength selection of hyperspectral scattering image using new semi-supervised affinity propagation for prediction of firmness and soluble solid content in apples, Food Analytical Methods, 6(1): 334-342. https://doi.org/10.1007/s12161-012-9442-2