[1] Ben-Dor E, Banin A. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties[J]. Soil Science Society of America Journal, 59, 364-372(1995). http://adsabs.harvard.edu/abs/1995SSASJ..59..364B
[2] Wu Q, Yang Y H, Xu Z L et al. Applying local neural network and visible/near-infrared spectroscopy to estimating available nitrogen, phosphorus and potassium in soil[J]. Spectroscopy and Spectral Analysis, 34, 2102-2105(2014).
[3] Li X Y, Fan P P, Hou G L et al. Rapid detection of soil nutrients based on visible and near infrared spectroscopy[J]. Spectroscopy and Spectral Analysis, 37, 3562-3566(2017).
[4] Shao Y N, He Y. Nitrogen, phosphorus, and potassium prediction in soils, using infrared spectroscopy[J]. Soil Research, 49, 166-172(2011). http://www.publish.csiro.au/?paper=SR10098
[5] Jia S Y, Yang X L, Li G et al. Quantitatively determination of available phosphorus and available potassium in soil by near infrared spectroscopy combining with recursive partial least squares[J]. Spectroscopy and Spectral Analysis, 35, 2516-2520(2015).
[6] Gatius F, Miralbés C, David C et al. Comparison of CCA and PLS to explore and model NIR data[J]. Chemometrics and Intelligent Laboratory Systems, 164, 76-82(2017). http://www.sciencedirect.com/science/article/pii/S0169743916305226
[7] Kawamura K, Tsujimoto Y, Rabenarivo M et al. Vis-NIR spectroscopy and PLS regression with waveband selection for estimating the total C and N of paddy soils in Madagascar[J]. Remote Sensing, 9, 1081(2017). http://adsabs.harvard.edu/abs/2017RemS....9.1081K
[8] Genisheva Z, Quintelas C, Mesquita D P et al. New PLS analysis approach to wine volatile compounds characterization by near infrared spectroscopy (NIR)[J]. Food Chemistry, 246, 172-178(2018). http://europepmc.org/abstract/MED/29291836
[9] Sarathjith M C, Das B S, Wani S P et al. Comparison of data mining approaches for estimating soil nutrient contents using diffuse reflectance spectroscopy[J]. Current Science, 110, 1031-1037(2016).
[10] Zhang J J, Guo X, Zhao X M. Hyperspectral characteristics and inversion models of total phosphorus and available phosphorus in paddy fields in southern hilly China[J]. Jiangsu Agricultural Sciences, 44, 522-525(2016).
[11] Qi H J, Li S W, Arnon K et al. Prediction method of soil available phosphorus using hyperspectral data based on PLS-BPNN[J]. Transactions of the Chinese Society for Agricultural Machinery, 49, 166-172(2018).
[12] Wang W C, Li S W, Qi H J et al. The difference analysis of soil available phosphors content imaging and non-imaging spectra prediction[J]. Jiangsu Journal of Agricultural Sciences, 34, 811-817(2018).
[13] Fu Z L. A universal ensemble learning algorithm[J]. Journal of Computer Research and Development, 50, 861-872(2013).
[14] Kaneko H, Funatsu K. Applicability domain based on ensemble learning in classification and regression analyses[J]. Journal of Chemical Information and Modeling, 54, 2469-2482(2014). http://www.ncbi.nlm.nih.gov/pubmed/25119661
[15] Okujeni A, van der Linden S, Suess S et al. . Ensemble learning from synthetically mixed training data for quantifying urban land cover with support vector regression[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 1640-1650(2017). http://ieeexplore.ieee.org/document/7792573/
[16] Mesquita D P P, Gomes J P P, Souza Junior A H. Ensemble of efficient minimal learning machines for classification and regression[J]. Neural Processing Letters, 46, 751-766(2017). http://link.springer.com/article/10.1007/s11063-017-9587-5
[17] Zheng M D, Xiong H G, Qiao J F et al. Remote sensing inversion of soil organic matter based on broad band and narrow band comprehensive spectral index[J]. Laser & Optoelectronics Progress, 55, 072801(2018).
[18] Ying L N, Zhou W D. Comparative analysis of multiple chemometrics methods in application of laser-induced breakdown spectroscopy for quantitative analysis of soil elements[J]. Acta Optica Sinica, 38, 1214002(2018).
[19] Sampaio P S, Soares A, Castanho A et al. Optimization of rice amylose determination by NIR-spectroscopy using PLS chemometrics algorithms[J]. Food Chemistry, 242, 196-204(2018). http://europepmc.org/abstract/MED/29037678
[20] Zou T T, Wang Y, Song H L. Near infrared spectroscopy combined with support vector regression applied for rapid and nondestructive detection of adulterate goat milk powder[J]. Journal of Chinese Institute of Food Science and Technology, 17, 261-267(2017).
[21] Ni W D, Nørgaard L, Mørup M. Non-linear calibration models for near infrared spectroscopy[J]. Analytica Chimica Acta, 813, 1-14(2014). http://www.ncbi.nlm.nih.gov/pubmed/24528654
[22] Nie P C, Wu D, Yang Y et al. Fast determination of boiling time of yardlong bean using visible and near infrared spectroscopy and chemometrics[J]. Journal of Food Engineering, 109, 155-161(2012). http://www.sciencedirect.com/science/article/pii/S0260877411005127
[23] Ting J A. D'Souza A, Vijayakumar S, et al. Efficient learning and feature selection in high-dimensional regression[J]. Neural Computation, 22, 831-886(2010).
[24] Kalika D, Morton K D, Collins L M et al. Hyperbolic and PLSDA filter algorithms to detect buried threats in GPR data[J]. Proceedings of SPIE, 9072, 90720U(2014). http://spie.org/Publications/Proceedings/Paper/10.1117/12.2050502
[25] Jain A, Smarra F, Mangharam R. Data predictive control using regression trees and ensemble learning. [C]∥2017 IEEE 56th Annual Conference on Decision and Control (CDC), December 12-15, 2017, Melbourne, VIC, Australia. New York: IEEE, 4446-4451(2017).
[26] Kaneko H. Automatic outlier sample detection based on regression analysis and repeated ensemble learning[J]. Chemometrics and Intelligent Laboratory Systems, 177, 74-82(2018). http://www.sciencedirect.com/science/article/pii/S0169743917305919
[27] Kabir A, Ruiz C, Alvarez S A et al. Regression, classification and ensemble machine learning approaches to forecasting clinical outcomes in ischemic stroke[M]. ∥Peixoto N, Silveira M, Ali H, et al. Biomedical engineering systems and technologies. Cham: Springer, 881, 376-402(2018).
[28] Alazzam I, Alsmadi I, Akour M. Software fault proneness prediction: a comparative study between bagging, boosting, and stacking ensemble and base learner methods[J]. International Journal of Data Analysis Techniques and Strategies, 9, 1-16(2017).
[29] Li S F, Jia M Z, Dong D M. Fast measurement of sugar in fruits using near infrared spectroscopy combined with random forest algorithm[J]. Spectroscopy and Spectral Analysis, 38, 1766-1771(2018).
[30] Ge X Y, Ding J L, Wang J Z et al. Estimation of soil moisture content based on competitive adaptive reweighted sampling algorithm coupled with machine learning[J]. Acta Optica Sinica, 38, 1030001(2018).
[31] Kong Q Q, Ding X Q, Gong H L. Application of improved random forest pruning algorithm in tobacco origin identification of near infrared spectrum[J]. Laser & Optoelectronics Progress, 55, 013006(2018).
[32] Gao Y, Cui L J, Lei B et al. Estimating soil organic carbon content with visible-near-infrared (Vis-NIR) spectroscopy[J]. Applied Spectroscopy, 68, 712-722(2014). http://www.ncbi.nlm.nih.gov/pubmed/25014837
[33] Chang C W, Laird D A, Mausbach M J et al. Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties[J]. Soil Science Society of America Journal, 65, 480-490(2001). http://www.tandfonline.com/servlet/linkout?suffix=CIT0012&dbid=16&doi=10.1080%2F03650340.2017.1373185&key=10.2136%2Fsssaj2001.652480x
[34] Shi Y F, Chang S P. A study of determining the available phosphorus in high orgnaic soils by means of NaHCO3 extraction, ammonium molybdate-tartaric emetic-ascrbic acid colorimetry[J]. Journal of Gansu Agricultural University, 19, 108-111(1984).
[35] Claeys D D, Verstraelen T, Pauwels E et al. Conformational sampling of macrocyclic alkenes using a Kennard-Stone-based algorithm[J]. The Journal of Physical Chemistry A, 114, 6879-6887(2010). http://europepmc.org/abstract/MED/20527925
[36] Liu G S, Guo H S, Pan T et al. Vis-NIR spectroscopic pattern recognition combined with SG smoothing applied to breed screening of transgenic sugarcane[J]. Spectroscopy and Spectral Analysis, 34, 2701-2706(2014).
[37] Bayer A, Bachmann M, Müller A et al. A comparison of feature-based MLR and PLS regression techniques for the prediction of three soil constituents in a degraded South African ecosystem[J]. Applied and Environmental Soil Science, 2012, 971252(2012).
[38] Rossel R A V, Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra[J]. Geoderma, 158, 46-54(2010). http://www.cabdirect.org/abstracts/20103239570.html
[39] Peng J, Zhang Y Z, Zhou Q et al. The progress on the relationship physics-chemistry properties with spectrum characteristic of the soil[J]. Chinese Journal of Soil Science, 40, 1204-1208(2009).
[40] Ji W. Viscarra Rossel R A, Shi Z. Accounting for the effects of water and the environment on proximally sensed Vis-NIR soil spectra and their calibrations[J]. European Journal of Soil Science, 66, 555-565(2015).