基于1H-NMR光谱的多变量预测测定水溶液中的糖浓度

IF 6.1 Q1 CHEMISTRY, MULTIDISCIPLINARY
MSc. Kristoffer Mega Herdlevær, MSc. Kasper Strandengen, Assoc. Prof. Dr. Camilla Løhre, Prof. Dr. Tanja Barth
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引用次数: 0

摘要

从富含碳水化合物的废物中提取的可再生化学品,如糠醛和5-羟甲基糠醛(HMF),作为石油基资源的替代品正日益受到重视。评估生物质作为糠醛和HMF生产原料的适用性需要了解其组成。本研究的重点是利用定量1H NMR数据开发和验证水解物中单个糖浓度的预测模型。利用偏最小二乘(PLS)回归,该数据集包括137个多组分糖标准(阿拉伯糖、果糖、半乳糖、葡萄糖、甘露糖、麦芽糖、蔗糖和木糖)的核磁共振光谱。表现最好的模型R2为0.987-0.999,RMSECV为0.37-1.56 mM,该模型基于核磁共振谱的非重叠区域。实际样品用于验证,结果预测的糖浓度平均标准差为0.5 mM。这种高精度和简化的分析过程使这些模型适用于量化大样本集,展示了从H-NMR数据中提取统计信息的可靠性和可访问性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Determination of Sugar Concentrations in Aqueous Solution Using Multivariate Predictions Based on 1H-NMR Spectroscopy

Determination of Sugar Concentrations in Aqueous Solution Using Multivariate Predictions Based on 1H-NMR Spectroscopy

Renewable chemicals from carbohydrate-rich wastes, like furfural and 5-hydroxymethylfurfural (HMF), are gaining prominence as alternatives to petroleum-based resources. Assessing the suitability of biomass as feedstock for furfural and HMF production requires knowledge of its composition. This study focuses on developing and validating predictive models for individual sugar concentrations in hydrolysates using quantitative 1H NMR data. Utilizing partial least square (PLS) regression, the dataset includes 137 NMR spectra of multi-component sugar standards (arabinose, fructose, galactose, glucose, mannose, maltose, sucrose, and xylose). The best-performing model achieved an R2 of 0.987–0.999 and RMSECV of 0.37–1.56 mM and is based on the non-overlapping area of the NMR spectrum. Real-world samples were used for validation, resulting in predicted sugar concentrations with a mean standard deviation of 0.5 mM. This high accuracy and streamlined analysis process make these models practical for quantifying large sample sets, showcasing the reliability and accessibility of extracting statistical information from H-NMR data.

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CiteScore
7.30
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