The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

建议使用酒精类洗手液作为将COVID-19疾病的污染和扩散降至最低的几种策略之一。当前的报道表明，乙醇的潜在杀灭潜力在接近70％的浓度下发生。验证此类产品中乙醇浓度的传统方法会由于化学成分的粘度而引起潜在的误差，或者在大量需求中进行时可能过于昂贵。近红外（NIR）光谱学和化学计量学已用于测定其他基质中的乙醇，并提出了一种替代的快速可靠的方法来控制基于酒精的洗手液的质量。在这项研究中，便携式近红外光谱仪结合了分类化学计量工具，即 偏最小二乘判别分析（PLS–DA）和带有连续算法投影的线性判别分析（SPA–LDA）用于构建模型，以识别合格和不合格的商业和实验室合成的洗手液样品。主成分分析（PCA）用于探索性数据研究。三个主要成分占数据差异的99％，并证明符合和不符合样本的聚类。PLS–DA和SPA–LDA分类模型在交叉/内部验证中分别提供了77％和100％的准确度，在测试样品分类中提供了100％的准确度。使用PLS-DA和SPA-LDA评估的总共43％的商业样品显示乙醇含量不符合洗手液的凝胶要求。