Abstract Hydrocolloids such as natural gums and carrageenans are used extensively in the food industry in various mixtures that are difficult to be characterised due to their similar chemical structure. The aim of this study was to develop an analytical framework for the identification and quantification of these compounds in complex mixtures using Near-infrared (NIR) spectroscopy and chemometrics. Partial Least Squares (PLS) regression accompanied by Continuous Locality Preserving Projections (CLPP) dimensionality reduction technique is proposed as chemometric framework. Four different analytical models based on this framework are developed and compared for the analysis of spectral fingerprints of food hydrocolloids mixtures. Classification results showed that this method allowed the discrimination of hydrocolloids in blends with a 100% of correct classification. The same scheme also allows the quantitative determination of the different types of food hydrocolloids (3 types) and/or their individual compounds (8 different compounds) with a relative low root mean square error of prediction (RMSEP) of 0.028 and 0.038 respectively.

中文翻译：

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使用近红外光谱表征复杂水胶体混合物的连续统计建模

摘要 天然树胶和角叉菜胶等亲水胶体以各种混合物形式广泛用于食品工业，由于其化学结构相似，难以表征。本研究的目的是开发一个分析框架，用于使用近红外 (NIR) 光谱和化学计量学来识别和量化复杂混合物中的这些化合物。偏最小二乘法 (PLS) 回归伴随着连续局部性保留投影 (CLPP) 降维技术被提议作为化学计量学框架。开发并比较了基于此框架的四种不同分析模型，用于分析食品水解胶体混合物的光谱指纹。分类结果表明，该方法允许以 100% 的正确分类区分混合物中的水胶体。相同的方案还允许定量测定不同类型的食品水解胶体（3 种）和/或其单个化合物（8 种不同的化合物），预测均方根误差 (RMSEP) 分别为 0.028 和 0.038，相对较低。