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将洗衣粉的振动光谱指纹与消费者体验相关联——与“清洁布尿布”社区的合作调查

Correlating the Vibrational Spectroscopic Fingerprints of Laundry Detergents Against Consumer Experience-A Collaborative Investigation with the Clean Cloth Nappies Community.

作者信息

Muetzel Lea K, Michailov Anastasia, Garagoda Arachchige P Samanali, Gordon Keith C, Laslett Laura, Fraser-Miller Sara J

机构信息

Department of Chemistry and Te Whai Ao-Dodd-Walls Centre for Photonic and Quantum Technologies, University of Otago, Dunedin, 9016, New Zealand.

Clean Cloth Nappies®, South Australia, Australia.

出版信息

Chem Asian J. 2025 Jun;20(11):e202401457. doi: 10.1002/asia.202401457. Epub 2025 Apr 21.

Abstract

Cloth nappies (or "diapers") are one of the most soiled items of laundry that a household will likely encounter, appropriate laundering practices are important to ensure a consistent and thorough clean. One important parameter is appropriate selection of detergent. The "Clean Cloth Nappies" (CCN) community has put considerable time and effort into developing a database of wash performances of laundry detergents based on user experience. We explore the potential for vibrational spectroscopic methods to rapidly predict wash performance for the end user. Raman and infrared spectra were collected from 41 liquid and 37 powder detergents sourced from Australasia, the UK and North America. The spectroscopic variation across the different formulations was assessed using principal component analysis (PCA). Support vector machine (SVM) classification models were developed for predicting wash performance using two thirds of the samples and the CCN performance database was used for reference wash performance classifications. The remaining third of samples were used to assess the accuracy of predictions of wash performance. Reasonable accuracy for distinguishing between poor and adequate performers was obtained with a 90 % test set accuracy for powder samples and an 82 % test set accuracy for the liquid samples. The ability to distinguish between the need for warm or hot wash conditions was less well classified.

摘要

布尿布(或“纸尿裤”)是家庭洗衣中最脏的物品之一,适当的洗涤方法对于确保始终如一地彻底清洁非常重要。一个重要参数是洗涤剂的恰当选择。“清洁布尿布”(CCN)社区投入了大量时间和精力,根据用户体验建立了一个洗衣粉洗涤性能数据库。我们探索了振动光谱法为终端用户快速预测洗涤性能的潜力。从来自澳大拉西亚、英国和北美的41种液体洗涤剂和37种粉末洗涤剂中收集了拉曼光谱和红外光谱。使用主成分分析(PCA)评估不同配方之间的光谱变化。使用三分之二的样品开发了支持向量机(SVM)分类模型来预测洗涤性能,并使用CCN性能数据库进行参考洗涤性能分类。其余三分之一的样品用于评估洗涤性能预测的准确性。对于区分表现差和表现良好的洗涤剂,粉末样品的测试集准确率为90%,液体样品的测试集准确率为82%,获得了合理的准确性。区分需要温水还是热水洗涤条件的能力分类效果较差。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b71f/12182390/45ab8bc20adb/ASIA-20-e202401457-g004.jpg

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