Rapid quality determination of cherry fruit (Prunus spp.) using artificial olfactory technique as combined with non-linear data extraction model
In this article, quality rapid determination of cherry fruit (Prunus spp.) using artificial olfactory technique (AOT) combined with non-linear data extraction model was studied. AOT system was developed and used for cherry quality detection. AOT system responses to cherry samples stored at 4°C were...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | International Journal of Food Properties |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10942912.2022.2106999 |
| Summary: | In this article, quality rapid determination of cherry fruit (Prunus spp.) using artificial olfactory technique (AOT) combined with non-linear data extraction model was studied. AOT system was developed and used for cherry quality detection. AOT system responses to cherry samples stored at 4°C were recorded. At the same time, physical/chemical indexes, such as human sensory evaluation (HSE), firmness, color, pH, total soluble solids (TSS), and reducing sugar content (RSC), were examined to provide quality references to the cherry samples. AOT data was analyzed by principal component analysis (PCA), and bilayer stochastic resonance (BSR) models. PCA only partially discriminated the cherry samples. The signal-to-noise ratio (SNR) maximum values (SNR-Max) generated by BSR successfully discriminated all the samples. Multiple variable regression (MVR) between cherry physical/chemical indexes and BSR SNR-Max values was conducted. Results indicated that BSR was suitable for cherry quality rapid evaluation. Cherry quality examination model was built based on linear fitting regression on BSR eigen values. Validation tests results indicated that the developed model has good forecasting accuracy. The proposed method had some advantages, such as rapid responses, high accuracy, easy operation, etc. |
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| ISSN: | 1094-2912 1532-2386 |