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.

中文翻译：

---

用于食品安全监测的机器学习辅助便携式微等离子体直读光谱仪

为了满足食品安全对简单、快速、低成本分析方法的需求，基于点放电微等离子体光学发射光谱仪（μPD-OES）的便携式设备与机器学习相结合，可实现现场食品新鲜度评估和分析。检测掺假。该装置与两个模块化进样单元（即顶空固相微萃取和顶空吹扫）集成，用于检查各种样品。肉和咖啡的香气首次被引入便携式设备中。由于香气分子的原子和分子结构不同，常温常压微等离子体将香气分子激发成激发态的特定原子和分子碎片。随后，不同的芳香族分子获得了各自特定的分子和原子发射光谱。在机器学习的帮助下，该便携式设备成功应用于肉类新鲜度评估，通过不同储存时香气的光学发射模式，对牛肉、猪肉和鸡肉的准确率分别为 96.0、98.7 和 94.7%次。此外，所开发的程序可以识别含有不同量鸭肉的牛肉样品，准确率达到99.5%，并对两种咖啡品种进行无误分类，展示了其在食品掺假鉴别方面的巨大应用潜力。机器学习和 μPD-OES 的结合为食品香气分析提供了一种简单、便携且经济高效的策略，有可能解决食品安全的现场监测问题。