Name
Abstract | ||
|---|---|---|
The aim of this study was to use an electronic nose set up in our lab to detect and predict the freshness of pork, beef and mutton. Three kinds of freshness, including fresh, sub-fresh and putrid, was established by human sensory evaluation and was used as a reference for the electronic nose's discriminant factor analysis. The principal component analysis results showed the electronic nose could distinguish well pork, beef and mutton samples with different storage times. In the PCA figures, three kinds of meats samples all presented an approximate parabola trend during 7 days' storage time. The discriminant factor analysis showed electronic nose could distinguish and judge well the freshness of samples (accuracy was 89.5%, 84.2% and 94.7% for pork, beef and mutton, respectively). Therefore, the electronic nose is promising for meat fresh detection application. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/s19030605 | SENSORS |
Keywords | Field | DocType |
freshness evaluation,meat,electronic nose | Electronic nose,Pattern recognition,Electronic engineering,Artificial intelligence,Engineering,Principal component analysis | Journal |
Volume | Issue | ISSN |
19 | 3.0 | 1424-8220 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Chen | 1 | 730 | 94.14 |
| Juanhong Gu | 2 | 0 | 0.34 |
| Rong Zhang | 3 | 94 | 22.74 |
| Yuezhong Mao | 4 | 0 | 0.34 |
| Shiyi Tian | 5 | 0 | 0.68 |