预测营养含量和确定香的地理原产地,通过将高光谱成像与化学成像相结合
Honghui Xiao1,2, Chunlin Li2,3, Mingyue Wang4
1College of Food Science and Engineering, Ningbo University, Ningbo 315211, China.
Foods (Basel, Switzerland)
|November 27, 2024
概括
超光谱成像提供了一种快速,非破坏性的方法来评估香的营养质量并验证香的来源. 这项技术准确预测可溶性固体含量,含量,并对地理来源进行分类,使贸易和消费者受益.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
背景情况:
- 准确评估香的营养质量和地理来源对于国际贸易至关重要.
- 目前用于质量和原产地验证的方法通常耗时且具有破坏性.
- 需要快速,非破坏性的技术来确保香的真实性和质量.
研究的目的:
- 开发和验证超光谱成像方法,以非破坏性预测香的营养含量 (可溶性固体含量和).
- 建立一种可靠的方法来确定香的地理来源.
- 提高香贸易的质量保证.
主要方法:
- 从各种生产国收集了99个香样本.
- 使用过光谱数据与化学测量方法 (第二导数分析,CARS,RF) 相结合.
- 构建定量 (PLS) 和定性 (PLS-DA) 模型来预测营养含量和来源.
主要成果:
- 优化预处理方法 (第二导数,CARS,RF) 进行准确的预测.
- 对于溶性固体含量 (R2p=0.8012) 和含量 (R2p=0.8606) 实现了高预测准确度.
- 通过使用PLS-DA和RF,成功地以95.83%的准确率对中国国内和进口香进行分类.
结论:
- 超光谱成像是一种高效的技术,用于对香营养质量的非破坏性分析.
- 这种技术可以准确地确定香的地理来源.
- 未来的应用可能会扩展到评估来自全球其他地区的香.
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