A predictive model for the sensory aroma characteristics of flue-cured tobacco based on a back-propagation neural network

[21] Han DM(2018)Static/dynamic zoometry concept to design cattle facilities using back propagation neural network (BPNN) Artif Intell Emerg Trends Appl 48 2528-2534

[22] Dai WH(2011)Comparative analysis on chemical components and sensory quality of aging flue-cured tobacco from four main tobacco areas of China Agric Sci China 32 1761-1768

[23] Fang Y(2011)Prediction of formation enthalpies for Al2X-type intermetallics using back-propagation neural network Mater Chem Phys 11 1176-1181

[24] Pang M(2016)Large-scale development of SSR markers in tobacco and construction of a linkage map in flue-cured tobacco Breed Sci 59 1499-1505

[25] Wang B(2013)Neural network models to classify olive oils within the protected denomination of origin framework Int J Food Sci Technol 11 2369-2384

[26] Gu J(2007)Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural networks Energy 62 1172-1180

[27] Yin G(2013)Distribution characteristics of osmiophilic granules’ content in tobacco leaves at different altitudes and the relationship with the content of neutral aroma components J Food Agric Environ undefined undefined-undefined

[28] Huang P(2016)Estimation and visualization of soluble sugar content in oilseed rape leaves using hyperspectral imaging Trans ASABE undefined undefined-undefined

[29] Guo J(2011)Classification and quality evaluation of tobacco leaves based on image processing and fuzzy comprehensive evaluation Sensors undefined undefined-undefined

[30] Chen L(2019)Stochastic seismic inversion based on neural network Chin J Geophys undefined undefined-undefined

[21] Han DM(2018)Static/dynamic zoometry concept to design cattle facilities using back propagation neural network (BPNN) Artif Intell Emerg Trends Appl 48 2528-2534

[22] Dai WH(2011)Comparative analysis on chemical components and sensory quality of aging flue-cured tobacco from four main tobacco areas of China Agric Sci China 32 1761-1768

[23] Fang Y(2011)Prediction of formation enthalpies for Al2X-type intermetallics using back-propagation neural network Mater Chem Phys 11 1176-1181

[24] Pang M(2016)Large-scale development of SSR markers in tobacco and construction of a linkage map in flue-cured tobacco Breed Sci 59 1499-1505

[25] Wang B(2013)Neural network models to classify olive oils within the protected denomination of origin framework Int J Food Sci Technol 11 2369-2384

[26] Gu J(2007)Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural networks Energy 62 1172-1180

[27] Yin G(2013)Distribution characteristics of osmiophilic granules’ content in tobacco leaves at different altitudes and the relationship with the content of neutral aroma components J Food Agric Environ undefined undefined-undefined

[28] Huang P(2016)Estimation and visualization of soluble sugar content in oilseed rape leaves using hyperspectral imaging Trans ASABE undefined undefined-undefined

[29] Guo J(2011)Classification and quality evaluation of tobacco leaves based on image processing and fuzzy comprehensive evaluation Sensors undefined undefined-undefined

[30] Chen L(2019)Stochastic seismic inversion based on neural network Chin J Geophys undefined undefined-undefined