Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, United States; Department of Biosystems Engineering, Integrated Major in Global Smart Farm, and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea.
Korea Institute of Ocean Science and Technology, Geoje-si, Gyeongsangnam-do 53201, Republic of Korea.
J Hazard Mater. 2023 Apr 5;447:130806. doi: 10.1016/j.jhazmat.2023.130806. Epub 2023 Jan 16.
We conceived a novel approach to screen oil types on a wax-printed paper-based microfluidic platform. Various oil samples spontaneously flowed through a micrometer-scale channel via capillary action while their components were filtered and partitioned. The resulting capillary flow velocity profile fluctuated during the flow, which was used to screen oil types. Raspberry Pi camera captured the video clips, and a custom Python code analyzed them to obtain the capillary flow velocity profiles. 106 velocity profiles (each with 125 frames for 5 s) were recorded from various oil samples to build a training database. Principal component analysis (PCA), support vector machine (SVM), and linear discriminant analysis (LDA) were used to classify the oil types into heavy-to-medium crude, light crude, marine fuel, lubricant, and diesel oils. The second-order polynomial SVM model with PCA as a pre-processing step showed the highest accuracy: 90% in classifying crude oils and 81% in classifying non-crude oils. The assay took less than 30 s from the sample to answer, with 5 s of the capillary action-driven flow. This simple and effective assay will allow rapid preliminary screening of oil types, enable early tracking, and reduce the number of suspect samples to be analyzed by laboratory fingerprinting analysis.
我们设计了一种新颖的方法，在蜡印纸基微流控平台上筛选油的种类。各种油样通过毛细作用自发地流过微米级通道，同时其成分被过滤和分配。在流动过程中，产生的毛细流动速度分布会发生波动，从而用于筛选油的种类。Raspberry Pi 相机拍摄视频片段，然后使用自定义 Python 代码对其进行分析，以获得毛细流动速度分布。从各种油样中记录了 106 个速度分布（每个分布有 125 帧，持续 5 秒），以构建训练数据库。主成分分析（PCA）、支持向量机（SVM）和线性判别分析（LDA）用于将油的种类分为重质-中质原油、轻质原油、船用燃料、润滑剂和柴油。具有 PCA 作为预处理步骤的二阶多项式 SVM 模型显示出最高的准确性：在分类原油方面准确率为 90%，在分类非原油方面准确率为 81%。从样品到回答的检测时间不到 30 秒，其中 5 秒用于毛细作用驱动的流动。这种简单有效的检测方法将能够快速进行油类的初步筛选，实现早期跟踪，并减少需要通过实验室指纹分析进行分析的可疑样品数量。
