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A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

机译:使用便携式近红外光谱仪和化学测量学使用便携式洗手液质量控制的快速和低成本方法

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摘要

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.
机译:2019冠状病毒疾病的预防和使用是一种减少酒精污染和传播的策略。目前的报告表明,乙醇在浓度接近70%时具有杀灭病毒的潜力。由于化学成分的粘度,验证此类产品中乙醇浓度的传统方法会产生潜在误差,或者在需求量大的情况下,成本可能过高。近红外(NIR)光谱和化学计量学已被用于测定其他基质中的乙醇,为酒精洗手液的质量控制提供了一种快速可靠的替代方法。在本研究中,使用便携式近红外光谱仪结合分类化学计量学工具,即偏最小二乘判别分析(PLS-DA)和线性判别分析逐次算法投影(SPA-LDA),构建模型来识别合格和不合格的商用和实验室合成洗手液样品。主成分分析(PCA)应用于探索性数据研究。三个主成分占数据方差的99%,并证明了合格和不合格样本的聚类。PLS-DA和SPA-LDA分类模型在交叉/内部验证中的准确率分别为77%和100%,在测试样本分类中的准确率为100%。使用PLS-DA和SPA-LDA评估的商业样品中,共有43%的乙醇含量不符合洗手液凝胶的要求。这些结果表明,使用近红外光谱和化学计量学是一种很有前途的策略,产生了一种快速、便携且可靠的方法,用于区分酒精基洗手液的合格和不合格乙醇浓度。

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