接近红外光谱 (NIRS) 的潜力是基于收获后储存时间,种类和子水中的成熟度进行高效分类
Xiaojun Shen1,2, Tao Wang3, Jingyi Wei2
1Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang 571339, China.
Foods (Basel, Switzerland)
|June 28, 2023
概括
近红外光谱 (NIRS) 和化学测量可以分析子水 (CW) 的质量. 虽然预测模型显示了局限性,但分类模型通过储存,种类和成熟度准确地区分了CW,确保了产品完整性.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
背景情况:
- 子水 (CW) 是全球流行的饮料,需要强有力的质量控制措施.
- 确保CW的质量和真实性对于消费者信任和市场完整性至关重要.
- 收获后储存,种类和成熟度的变化对CW的组成和质量产生重大影响.
研究的目的:
- 调查近红外光谱学 (NIRS) 与化学计量相结合的CW质量评估的有效性.
- 为关键CW质量参数 (糖,TSS,pH) 开发预测模型.
- 建立基于储存时间,品种和成熟度的CW分类的歧视性模型.
主要方法:
- 从Wenye No.的CW样本中获得了近红外光谱. 2号和温耶号的交叉路口 有4种种类. 有4种种类.
- 部分最小平方回归 (PLSR) 用于构建糖含量,TSS和pH的预测模型.
- 在样本分类中使用了正交部分最小平方差分分析 (OPLS-DA).
主要成果:
- PLSR模型在预测可减少和可溶糖方面显示出适度的适用性 (RPD 1.541.83).
- 总溶性固体 (TSS),pH和TSS/pH比率的模型显示预测性能差 (RPD <1.4).
- OPLS-DA模型实现了超过95%的正确分类,有效地根据储存,品种和成熟度区分CW.
结论:
- 与OPLS-DA相结合的NIRS是一个强大的,非破坏性的工具,用于根据关键因素对CW进行分类.
- 虽然对某些参数的直接预测是有限的,但基于NIRS的分类可以提高质量控制和真实性验证.
- 这些技术为确保子水质量和消费者满意度提供了有价值的方法.
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