| Method | Sensory evaluation by experts | Machine learning, computer vision, and hyperspectral imaging |
| Parameters assessed | Texture, color, aroma, overall appearance | Internal and external characteristics, color patterns, contamination risk |
| Advantages | Provides detailed sensory data | Continuous assessment, high precision, predictive analytics for shelf life |
| Limitations | Human error, inter-inspector inconsistency, assessor fatigue | Requires high-quality data; poor data leads to unreliable results |
| Need for standardization | Challenging due to variability in human perception | More consistent results with repeated, high-quality input |
| Data inputs | Human senses and judgment | Digital images, sensor data, historical data |
| Forecasting ability | Limited | Can predict quality deterioration and enhance shelf life using predictive analytics |