Prediction of human assessments of dairy odor utilizing a fast gas chromatograph and neural networks
文献类型：期刊论文
| 作者 | Chang, Fangle1,2 ; Heinemann, Paul H.3
|
| 刊名 | COMPUTERS AND ELECTRONICS IN AGRICULTURE
 |
| 出版日期 | 2019-02-01 |
| 卷号 | 157页码:541-548 |
| 关键词 | Dairy Human assessments Hedonic tone zNose Artificial Neural Networks |
| ISSN号 | 0168-1699 |
| DOI | 10.1016/j.compag.2019.01.037 |
| 通讯作者 | Heinemann, Paul H.(hzh@psu.edu) |
| 英文摘要 | 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). |
| WOS关键词 | ELECTRONIC NOSE ; HEDONIC TONE ; QUANTIFICATION ; CLASSIFICATION ; INTENSITY |
| 资助项目 | USDA National Institute of Food and Agriculture Federal Appropriations[PEN04547] ; USDA National Institute of Food and Agriculture Federal Appropriations[1001036] |
| WOS研究方向 | Agriculture ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000459358400053 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | USDA National Institute of Food and Agriculture Federal Appropriations |
| 源URL | [http://ir.ia.ac.cn/handle/173211/25044]  |
| 专题 | 自动化研究所_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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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