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access icon openaccess Performance prediction of tobacco flavouring using response surface methodology and artificial neural network

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      • 2. Li, L., Sai, Z., Qiang, H., et al: ‘Application of response surface method in test design and optimization’, Lab. Res. Explor., 2015, 34, (8), pp. 4145.
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      • 3. Ji, K.: ‘Evaluation of mining engineering technology innovation ability and application based on BP neural network’. Int. Conf. Industrial Technology and Management, Singapore, 2017, pp. 5559.
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      • 4. Aliakbarian, B., Sampaio, F.C., Faria, J.D., et al: ‘Optimization of spray drying microencapsulation of olive pomace polyphenols using response surface methodology and artificial neural network’, LWT, 2018, 93, pp. 220228.
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      • 5. Zhang, J., Peng, L.I., Sun, S.H., et al: ‘Simultaneous determination of 1,2-propylene glycol, glycerol and triethylene glycol of smokeless tobacco products by gas chromatography-mass spectrometry’, Tob. Sci. Technol., 2011, 3, pp. 3642.
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      • 6. Hua, B.H., Qiong, L., Ze, L., et al: ‘The neural network model and its evaluation of the physical quality of tobacco’, J. Yunnan Agric. Univ., Nat. Sci., 2016, 31, (5), pp. 874879.
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