Evaluation of the freshness of food products by predictive models and neural networks - a comparative analysis
| Autor: | Stanislav Penchev, Martin Dejanov, Miroljub Mladenov |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Artificial neural network
Frequency band 010401 analytical chemistry Feature extraction Hyperspectral imaging 04 agricultural and veterinary sciences computer.software_genre 040401 food science 01 natural sciences 0104 chemical sciences Set (abstract data type) 0404 agricultural biotechnology Kernel (statistics) Principal component analysis Range (statistics) Data mining Biological system computer Mathematics |
| Zdroj: | IEEE Conf. on Intelligent Systems |
| Popis: | The paper presents a comparative analysis of possibilities for assessment of the freshness of widespread foodstuffs like white brined cheese, yellow cheese, meat and bacon. The freshness is represented by the time of storage in specific conditions (dark room with temperature of 20°C). The time of storage is assessed using regression predictive models of features, related to the freshness product and through neural networks, which represent the product quality by set of features. The quality features are extracted from the spectral characteristics obtained from the overall measuring range of the spectrophotometer and from the selected frequency band of the hyperspectral characteristics. They are represented by the first three Principal Components. The possibility for distinct assessment of the time of storage is evaluated by the separation accuracy of the spectral data for different days of storage. It is found that the error of separation of spectral data decreases nearly two orders of magnitude when we use spectral data from selected frequency bands, instead of data obtained from the overall measuring range. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|