使用近红外光谱学对个别葡萄树果的关键品质特征进行非破坏性量化
Lucie Cornehl1, Pascal Gauweiler2, Xiaorong Zheng1
1Institute for Grapevine Breeding Geilweilerhof, Julius Kühn-Institute, Federal Research Centre of Cultivated Plants, Siebeldingen, Germany.
Frontiers in plant science
|July 22, 2024
概括
近红外光谱学可以准确预测葡萄中的糖和酸含量,帮助葡萄种植者. 这项技术为葡萄栽培质量评估的传统方法提供了更快,更无损的替代方案.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 优化葡萄收获对于优质葡萄酒生产至关重要.
- 气候变化影响葡萄糖和酸度,影响葡萄酒的质量.
- 传统的果采样和果汁分析是耗时且昂贵的.
研究的目的:
- 为了预测葡萄的关键品质参数 (糖和酸含量) 在Vitis vinifera.
- 开发和验证用于非破坏性质量评估的化学测量模型.
- 探索近红外 (NIR) 光谱在葡萄栽培中的潜力.
主要方法:
- 在葡萄果上的非破坏性NIR光谱 (1100-1350nm).
- 使用高性能液态染色学 (HPLC) 的参考分析.
- 部分最小平方回归 (PLSR) 用于不同葡萄品种和颜色的化学模型开发.
主要成果:
- 在预测果糖和葡萄糖 (R2 > 93%),酸 (R2 > 86%) 和酸 (R2 > 73%) 的高准确性.
- 准确预测糖含量在±6.97~±10.08g/L和酸在±2.01~±3.69g/L之间.
- 通过结合多种葡萄品种和果颜色来实现坚固的模型.
结论:
- 与化学测量相结合的NIR光谱学为葡萄质量预测提供了快速而准确的方法.
- 开发的模型显示了手持NIR传感器的广泛采用潜力,可能集成到智能手机中.
- 服务器端的基于云的解决方案可以提高不同葡萄酒地区和葡萄品种的模型稳定性.
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