Classification and quantification of sucrose from sugar beetand sugarcane using optical spectroscopy and chemometrics

Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380 nm. Spectral differences were observable in the ultraviolet–visible (UV–Vis) region from 200 to 600 nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV–Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky–Golay were most accept- able with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV–Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries’ agricultural policies.

蔗糖（Sucrose）既可从甜菜（Sugar Beet）与甘蔗（Sugarcane）中提取，亦是食品工业的核心原料之一。由于二者分子结构完全一致，传统化学方法无法区分这两种来源的蔗糖，因此开发更实用且成本可控的鉴别方法具有重要应用价值。本研究从9个国家采集了甜菜源与甘蔗源蔗糖样本，制备得到25%（质量分数，w/w）的水溶液，并记录了其在200~1380 nm波长范围内的吸光度值。由于糖料中存在杂质，在200~600 nm的紫外-可见光（UV-Vis）波段可观测到光谱差异。针对该UV-Vis波段，研究人员测试了线性判别分析（LDA）、分类回归树以及软独立分类建模三种方法，所有方法均表现出优异的分类准确率。其中，线性判别分析在筛选出5个特征波长后，实现了100%的正确分类，且解释逻辑简洁清晰。此外，研究人员制备了糖料样本的二元混合体系，采用偏最小二乘回归与多元线性回归（MLR）开展定量分析。采用一阶导数Savitzky-Golay预处理的多元线性回归模型表现最优，其交叉验证均方根误差、预测均方根误差以及预测标准误差与标准差之比分别为3.92%、3.28%和9.46。基于紫外-可见光光谱与化学计量学技术，本研究结果显示该方法可有效区分两种来源的蔗糖。本研究提出了一种成本低廉、操作快速的鉴别方法，可区分甜菜源与甘蔗源蔗糖。该方法无需复杂的化学分析流程，也无需专业资深人员操作，可应用于科研或工业场景中以鉴别蔗糖来源，这对部分国家的农业政策制定具有重要意义。