Prediction of human assessments of dairy odor utilizing a fast gas chromatograph and neural networks

doi:10.1016/j.compag.2019.01.037

CASIA OpenIR > 博士后

	Prediction of human assessments of dairy odor utilizing a fast gas chromatograph and neural networks
	Chang, Fangle1,2 ; Heinemann, Paul H.3
发表期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
	2019-02-01
卷号	157 页码:541-548
通讯作者	Heinemann, Paul H.(hzh@psu.edu)
摘要	A method based on hedonic tone was developed and applied to evaluate human assessments of odor emitted from dairy operations using a fast gas chromatograph and neural networks. A general pleasantness scale ranging from -11 (extremely unpleasant) to +11 (extremely pleasant) was used to collect human responses. The panelists were able to identify the difference between various samples, and gave individually consistent responses for the same sample. The measurements of a fast gas chromatograph, called the zNose, were trained using Artificial Neural Networks (ANNs) to predict the human assessments. Three ANNs, Levenberg-Marquardt Back-propagation Neural Network (LMBNN), Scaled Conjugate Gradient Back-propagation (CGBNN), and Resilient Back-propagation Neural Network (RPBNN), were applied to connect human assessments and instrument measurements. In separate validation, zNose-LMBNN model showed superiority in four criteria, Mean Square Error (MSE), Correlation Coefficient (R), probability within 10% range to target, and probability within 5% range to target. The optimal model outputs represented human response as high as 67% within the 10% range and 44% within the 5% range of the targets. In addition, the model outputs have a good linear relationship with the targets (R = 0.53).
关键词	Dairy Human assessments Hedonic tone zNose Artificial Neural Networks
DOI	10.1016/j.compag.2019.01.037
关键词[WOS]	ELECTRONIC NOSE ; HEDONIC TONE ; QUANTIFICATION ; CLASSIFICATION ; INTENSITY
收录类别	SCI
语种	英语
资助项目	USDA National Institute of Food and Agriculture Federal Appropriations[PEN04547] ; USDA National Institute of Food and Agriculture Federal Appropriations[1001036] ; USDA National Institute of Food and Agriculture Federal Appropriations[PEN04547] ; USDA National Institute of Food and Agriculture Federal Appropriations[1001036]
项目资助者	USDA National Institute of Food and Agriculture Federal Appropriations
WOS研究方向	Agriculture ; Computer Science
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:000459358400053
出版者	ELSEVIER SCI LTD
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/25045
专题	博士后
通讯作者	Heinemann, Paul H.
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Qingdao Acad Intelligent Ind, Innovat Ctr Parallel Agr, Qingdao 266109, Peoples R China 3.Penn State Univ, Dept Agr & Biol Engn, 105 Agr Engn Bldg, University Pk, PA 16802 USA
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Chang, Fangle,Heinemann, Paul H.. Prediction of human assessments of dairy odor utilizing a fast gas chromatograph and neural networks[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2019,157:541-548.
APA	Chang, Fangle,&Heinemann, Paul H..(2019).Prediction of human assessments of dairy odor utilizing a fast gas chromatograph and neural networks.COMPUTERS AND ELECTRONICS IN AGRICULTURE,157,541-548.
MLA	Chang, Fangle,et al."Prediction of human assessments of dairy odor utilizing a fast gas chromatograph and neural networks".COMPUTERS AND ELECTRONICS IN AGRICULTURE 157(2019):541-548.