使用近红外光谱测定中的碳水化合物成分
Rocío López-Calabozo1, Ângela Liberal1,2,3, Ângela Fernandes2,3
1Food Technology Area, Escuela Politécnica Superior de Zamora, Universidad de Salamanca, Avenida Requejo, 33, 49022 Zamora, Spain.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
近红外光谱学 (NIRS) 有效地预测了中的碳水化合物含量. 土壤样本为粉,糖和拉芬糖量化提供了卓越的模型,证实了NIRS的说法.
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:
- 碳水化合物占豆成分的60%以上,因遗传因素而异,并影响技术功能性质.
- 精确量化的碳水化合物对于了解它们的健康益处和产品应用至关重要.
研究的目的:
- 研究近红外光谱学 (NIRS) 的有效性,以预测中的各种碳水化合物分量.
- 为了比较NIRS分析的性能,对整体与碎样本进行分析.
主要方法:
- 使用NIRS.对六种豆品种 (宏和微) 的分析.
- 对光谱和数学预处理的评估.
- 使用修改部分最小平方回归与交叉验证和外部验证开发校准模型.
主要成果:
- 优秀的预测模型 (RSQ>0.9) 实现了总糖,果糖,糖和拉芬糖.
- 碎的豆样本为粉 (R > 0.8),总糖,糖糖 (R > 0.93) 和拉芬糖 (R > 0.91) 提供了优质的校准模型.
- NIRS光谱数据包含足够的信息,可以准确量化中的碳水化合物.
结论:
- NIRS是一种可行且高效的技术,用于对中的碳水化合物含量进行非破坏性量化.
- 分析碎样本可以提高NIRS基于特定碳水化合物分量的预测的准确性.
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