E-nose based rapid prediction of early mouldy grain using probabilistic neural networks
作者： Xiaoguo Ying , Wei Liu , Guohua Hui , Jun Fu
DOI: 10.1080/21655979.2015.1022304
关键词:
摘要: In this paper, early mouldy grain rapid prediction method using probabilistic neural network (PNN) and electronic nose (e-nose) was studied. E-nose responses to rice, red bean, oat samples with different qualities were measured recorded. data analyzed principal component analysis (PCA), back propagation (BP) network, PNN, respectively. Results indicated that PCA BP could not clearly discriminate status showed poor predicting accuracy. PNN satisfying discriminating abilities an accuracy of 93.75%. combined is effective for prediction.
sci-hub.se 参考文章(25)
M. Laureati, S. Buratti, A. Bassoli, G. Borgonovo, E. Pagliarini, Discrimination and characterisation of three cultivars of Perilla frutescens by means of sensory descriptors and electronic nose and tongue analysis Food Research International. ,vol. 43, pp. 959- 964 ,(2010) , 10.1016/J.FOODRES.2010.01.024
Bo Yang, Xiaoshan Li, Jing Luo, A novel multi-human location method for distributed binary pyroelectric infrared sensor tracking system: Region partition using PNN and bearing-crossing location Infrared Physics & Technology. ,vol. 68, pp. 35- 43 ,(2015) , 10.1016/J.INFRARED.2014.10.006
H.M. Solis-Solis, M. Calderon-Santoyo, P. Gutierrez-Martinez, S. Schorr-Galindo, J.A. Ragazzo-Sanchez, Discrimination of eight varieties of apricot (Prunus armeniaca) by electronic nose, LLE and SPME using GC–MS and multivariate analysis Sensors and Actuators B: Chemical. ,vol. 125, pp. 415- 421 ,(2007) , 10.1016/J.SNB.2007.02.035
E. Delivopoulos, J.B. Theocharis, A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method Information Sciences. ,vol. 168, pp. 133- 170 ,(2004) , 10.1016/J.INS.2004.02.001
Yanxi Zhang, Xiangdong Gao, Seiji Katayama, Weld appearance prediction with BP neural network improved by genetic algorithm during disk laser welding Journal of Manufacturing Systems. ,vol. 34, pp. 53- 59 ,(2015) , 10.1016/J.JMSY.2014.10.005
Cheng-Hang Lan, Yu-Li Huang, Sheng-Huei Ho, Chiung-Yu Peng, Volatile organic compound identification and characterization by PCA and mapping at a high-technology science park. Environmental Pollution. ,vol. 193, pp. 156- 164 ,(2014) , 10.1016/J.ENVPOL.2014.06.014
Guolin Jing, Wenting Du, Yingying Guo, Studies on prediction of separation percent in electrodialysis process via BP neural networks and improved BP algorithms Desalination. ,vol. 291, pp. 78- 93 ,(2012) , 10.1016/J.DESAL.2012.02.002
Lin Huang, Jiewen Zhao, Quansheng Chen, Yanhua Zhang, Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chemistry. ,vol. 145, pp. 228- 236 ,(2014) , 10.1016/J.FOODCHEM.2013.06.073
S. Balasubramanian, S. Panigrahi, B. Kottapalli, C.E. Wolf-Hall, Evaluation of an artificial olfactory system for grain quality discrimination Lwt - Food Science and Technology. ,vol. 40, pp. 1815- 1825 ,(2007) , 10.1016/J.LWT.2006.12.016
Hande Kaya-Celiker, P. Kumar Mallikarjunan, David Schmale, Maria Elisa Christie, Discrimination of moldy peanuts with reference to aflatoxin using FTIR-ATR system Food Control. ,vol. 44, pp. 64- 71 ,(2014) , 10.1016/J.FOODCONT.2014.03.045