Table. 1.
Table. 1.
Examples of recent e-nose applications and comparisons with sensory evaluation
| E-nose | Sensor array | Application | Sensory evaluation | Statistic | Reference |
|---|---|---|---|---|---|
| Heracles II, Alpha MOS | MS based method | Flavor comparison of cocoa cultivars | Descriptive sensory analysis (n=12) | PCA, PLSR | Rottiers et al., 2019 |
| Sensory quality evaluation of parenica cheese | Acceptance test (n=169) | PCA | Štefániková et al., 2020 | ||
| FOX2000, Alpha MOS | 6 MOS | Changes in aroma profiles of oyster during storage | Preference tests Non-trained panel (n=19) |
PCA, correlation | Kawabe et al., 2019 |
| FOX4000, Alpha MOS | 18 MOS | Sensory changes in gluten-free oat biscuits during storage | Descriptive sensory analysis (n=10) | PCA | Duta et al., 2019 |
| Suitability of tea cultivars for processing oolong tea | Aroma quality by experts (n=3)/acceptance by consumers (n=63) | PCA, correlation | He et al., 2022 | ||
| PEN3.5, Intelligent | 10 MOS | Evaluation of aroma characteristics of sugarcane juice | Triangle test (n=36) | PCA, LDA, PLSR | Wang et al., 2019 |
| Lab made | 10 MOS | Freshness evaluation of meats | Freshness sensory evaluation (experienced assessors, n=18) | PCA, DFA | Chen et al., 2019 |
| 7 MOS | Detection of freshness quality of spinach | Descriptive sensory analysis (n=10) | SVL model | Huang et al., 2019 |
MS, mass spectrometry; MOS, metal oxide semiconductor; PCA, principal component analysis; PLSR, partial least square regression; LDA, linear discriminant analysis; DFA, discriminant factor analysis; SVL, support vector machine.
Prev Nutr Food Sci 2022;27:354~364 https://doi.org/10.3746/pnf.2022.27.4.354
© Prev. Nutr. Food Sci.