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Outline
With improvements in living standards, the influence of food flavour on food quality and consumer decision-making has become increasingly important. While traditional detection methods are subject to limitations (e.g., strong subjectivity and poor repeatability), machine learning (ML) technology has demonstrated unique advantages in this field. This review focuses on common ML algorithms used in the field of flavour analysis-based food detection, including traditional ML (e.g., support vector machine and random forest) and deep learning (e.g., convolutional neural network and recurrent neural network) algorithms. Moreover, it explores ML applications in flavour analysis-based food quality prediction and food type recognition. ML has reportedly achieved good results in the field of flavour analysis-based food detection, effectively constructing prediction models and improving the efficiency of food classification and authenticity identification. Nevertheless, ML application in the field of flavour analysis-based food detection could still be improved. Further ML development could be promoted by optimizing algorithms, integrating technologies, establishing standard systems, and building high-quality databases, which could strongly support innovation and quality improvement in the food industry.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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