Machine Learning-Assisted Portable Microplasma Optical Emission Spectrometer for Food Safety Monitoring

微等离子体 化学 食品安全 分光计 芳香 分析化学(期刊) 色谱法 等离子体 食品科学 量子力学 物理
作者
Tian Ren,Yao Lin,Yubin Su,Simin Ye,Chengbin Zheng
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:96 (13): 5170-5177
标识
DOI:10.1021/acs.analchem.3c05332
摘要

To meet the needs of food safety for simple, rapid, and low-cost analytical methods, a portable device based on a point discharge microplasma optical emission spectrometer (μPD-OES) was combined with machine learning to enable on-site food freshness evaluation and detection of adulteration. The device was integrated with two modular injection units (i.e., headspace solid-phase microextraction and headspace purge) for the examination of various samples. Aromas from meat and coffee were first introduced to the portable device. The aroma molecules were excited to specific atomic and molecular fragments at excited states by room temperature and atmospheric pressure microplasma due to their different atoms and molecular structures. Subsequently, different aromatic molecules obtained their own specific molecular and atomic emission spectra. With the help of machine learning, the portable device was successfully applied to the assessment of meat freshness with accuracies of 96.0, 98.7, and 94.7% for beef, pork, and chicken meat, respectively, through optical emission patterns of the aroma at different storage times. Furthermore, the developed procedures can identify beef samples containing different amounts of duck meat with an accuracy of 99.5% and classify two coffee species without errors, demonstrating the great potential of their application in the discrimination of food adulteration. The combination of machine learning and μPD-OES provides a simple, portable, and cost-effective strategy for food aroma analysis, potentially addressing field monitoring of food safety.
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