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解读金华火腿的香味特征:使用Flavoromics驱动的机器学习模型进行年龄歧视,并实现硬件实现
Wenlu Li1, Xinwei Fan1, Hong Zeng1
1Key Laboratory of Geriatric Nutrition and Health, Ministry of Education, School of Food and Health, Beijing Technology & Business University (BTBU), Beijing 100048, PR China.
精确的金华火腿老化对于质量控制至关重要. 气色谱-质谱法 (GC-MS) 和机器学习通过分析风味化合物,准确地区分火腿年龄,防止伪造.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 农业科学 农业科学
背景情况:
- 珍贵的中国农产品金华火腿在年龄验证方面面临挑战,因为它依赖于主观的人类经验.
- 不准确的年龄歧视对这种高价值的食品产品构成 adulteration 的重大风险.
研究的目的:
- 使用先进的分析技术,开发一个客观的方法来对金华汉姆年龄歧视.
- 为了确定负责金华火腿年龄相关变化的关键风味化合物.
- 建立精确的机器学习模型,用于自动化肉年龄预测.
主要方法:
- 气色谱-质谱 (GC-MS) 和气色谱-离子移动谱 (GC-IMS) 用于分析年龄在0.5至3岁的金华火腿中的挥发性化合物.
- 确定了54种芳香活性化合物,并选择了18种关键的歧视性化合物.
- 为年龄分类构建了机器学习模型 (LR,SVM,NB,DT),并开发了一个便携式评估电路.
主要成果:
- 随着年龄的增长,口味的特征从类演变为类/草类/黄油类,坚果类,最后是子类/桃色类/花类-脂肪类.
- 1-octen-3-ol和decanoic酸被确定为火腿年龄的关键歧视因素.
- 机器学习模型在年龄歧视方面实现了高达100%的准确性,便携式电路展示了实际应用.
结论:
- 与机器学习相结合的GC-MS为金华火腿年龄预测提供了强大而准确的方法.
- 这种方法提供了一种可靠的解决方案,以防止改,并确保老年金华火腿的质量.
- 开发的便携式电路有助于现场质量评估,支持金华火腿行业.
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