拉曼和NIR光谱:通过两个案例研究,讨论对食品质量测量的校准稳定性
Tiril Aurora Lintvedt1, Petter Vejle Andersen1, Nils Kristian Afseth1
1Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, Tromsø, 9291, Norway.
与近红外 (NIR) 光谱相比,拉曼光谱为食品质量分析提供了更高的稳定性,简化了校准开发和维护. 这一优势对于食品工业的工业决策和流程优化至关重要.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 非破坏性光谱测量对于食品行业的决策支持和流程优化至关重要.
- 选择适当的光谱技术,如拉曼光谱和近红外光谱 (NIR),需要仔细考虑强度.
- 假设拉曼光谱在校准中提供了优势,这是由于详细的光谱信息和较低的水灵敏度.
研究的目的:
- 为了比较拉曼和NIR光谱的稳定性,用于食品质量分析.
- 评估食品成分 (糖,脂肪) 的校准在不同条件下的可转移性.
- 评估光谱细节,频段重叠和水敏感度对校准稳固性的影响.
主要方法:
- 进行了两项案例研究:草糖校准在不同季节的可转移性和肉脂模型在不同研磨度的可转移性.
- 部分最小平方回归 (PLSR) 用于开发化学模型.
- 基于模型复杂性 (PLSR组件数量) 和预测错误来评估性能.
主要成果:
- 拉曼光谱学证明了固有的化学测量优势强度,特别是在草分析.
- 拉曼模型在NIR模型的复杂度的一半的情况下实现了类似的性能,提供了更易于解释的结果.
- 肉类分析显示,强度取决于纹理灵敏度;一个更大的拉曼点大小 (6毫米) 优于NIR,尽管光挑战.
结论:
- 强度是开发和维护拉曼和NIR光谱的工业校准的成本效益的一个关键因素.
- 这些发现鼓励在长期实施食品传感之前,对拉曼和NIR光谱进行批判性比较.
- 考虑到校准成本和实施便利性,应指导拉曼和NIR光谱和采样光学之间的选择.
更多相关视频
10:25Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
Published on: June 28, 2016
06:50O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
Published on: November 8, 2019
相关概念视频
Raman Spectroscopy Instrumentation: Overview
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
Raman Spectroscopy: Overview
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
Instrument Calibration
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
Applications of IR Spectroscopy: Overview
NMR Spectrometers: Resolution and Error Correction
UV–Vis Spectroscopy: Beer–Lambert Law
