[1] [1] Chu X. L. and Lu W. Z., Research and application progress of near infrared spectroscopy analytical technology in China in the past five years, Spectrosc. Spect. Anal. 34 (10) (2014) 2595–2605. ISI,

[2] [2] Jarvinen K., Hoehe W., Jarvinen M. and Poutiainen S., In-line monitoring of the drug content of powder mixtures and tablets by near-infrared spectroscopy during the continuous direct compression tableting process, Eur. J. Pharm. Sci 48 (4–5) (2013) 680–688. ISI,

[3] [3] Miyano T., Kano M. and Tanabe H., Spectral fluctuation dividing for efficient wavenumber selection: Application to estimation of water and drug content in granules using near infrared spectroscopy, Int. J. Pharm 475 (1–2) (2014) 504–513. ISI,

[4] [4] Deconinck E., Sacré P. Y. and Coomans D., Classification trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medicines, J. Pharma. Biomed. Anal. 57 (1) (2012) 68–75. ISI,

[5] [5] Storme P. I., Rebiere H. and Matoga M., Challenging near infrared spectroscopy discriminating ability for counterfeit pharmaceuticals detection, Anal. Chim. Acta 658 (2) (2010) 163–174. ISI,

[6] [6] Yu K. and Cheng Y., Discriminating the genuineness of Chinese medicines with least squares support vector machines, Chin. J. Anal. Chem. 34 (4) (2006) 561–564. ISI,

[7] [7] Hinton G. E. and Salakhutdinov R. R., Reducing the dimensionality of data with neural networks, Science 313 (5786) (2006) 504–507. ISI,

[8] [8] Langkvist M., Karlsson L. and Loutfi A., A review of unsupervised feature learning and deep learning for time-series modeling, Pattern Recognit. Lett. 42 (1) (2014) 11–24. ISI,

[9] [9] Srivastava N. and Salakhutdinov R., Multimodal learning with deep Boltzmann machines, H. Mach. Learn. Res. 15 (8) (2014) 1967–2006.

[10] [10] Variani E., Lei X. and Mcdermott E., Deep neural networks for small footprint text-dependent speaker verification, IEEE Int. Conf. Acoustics Speech and Signal Processing, Florence, 4–9 May 2014, (2014) 4052–4056.

[11] [11] Luo Z. C., Research on auto-encoder model and model transfer in near infrared spectroscopy, IEEE Trans. Neural Netw (2015).

[12] [12] Hinton G. E., Osindero S. and Teh T. W., A fast learning algorithm for deep belief nets, Neural Comput. 18 (7) (2006) 1527–1554. ISI,

[13] [13] Mohamed A. R., Dahl D. E. and Hinton G. E., Acoustic modeling using deep belief networks, IEEE. Trans. Audio, Speech, Lang. Process. 20 (1) (2012) 14–22.

[14] [14] Sarikaya R., Hinton G. E. and Deoras A., Application of deep belief nets for natural language understanding, IEEE. Trans. Audio, Speech, Lang. Process. 22 (4) (2014) 778–784.

[15] [15] Hinton G. E., Training products of experts by minimizing contrastive divergence, Neural Comput. 14 (8) (2002) 1711–1800. ISI,

[16] [16] Kuremoto T., Kimura S., Kobayashi K. and Obayashi M., Time series forecasting using a deep belief network with restricted Boltzmann machines, Neurocomputing 137 (15) (2014) 47–56. ISI,

[17] [17] Srivastava N., Hinton G. E. and Krezhevsky A., Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res. 15 (1) (2014) 1929–1958.