School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
Anal Chem. 2023 Apr 11;95(14):5976-5984. doi: 10.1021/acs.analchem.2c05714. Epub 2023 Mar 29.
Similar to smartphones, smart or automatic level is also a critical feature for a miniature mass spectrometer. Compared to large-scale instruments, miniature mass spectrometers often have a lower mass resolution and larger mass drift, making it challenging to identify molecules with close mass-charge ratios. In this work, a miniature mass spectrometer (the Brick-V model) was combined with intelligent algorithms to realize rapid and accurate identification. This Brick-V mass spectrometer developed in our lab was equipped with a vacuum ultraviolet photoionization (VUV-PI) source, which ionizes volatile organic compounds (VOCs) with minor fragments. Machine learning would be especially helpful when analyzing samples with multiple characteristic peaks. Four machine learning algorithms were tested and compared in terms of precision, recall, balanced F score (F1 score), and accuracy. After optimization, the multilayer perceptron (MLP) method was selected and first applied for the automatic identification and differentiation of ten different fruits. By recognizing the pattern of multiple VOCs diffused from fruits, an average accuracy of 97% was achieved. This system was further applied to determine the freshness of strawberries, and strawberry picking at different times (especially during the first 24 h at room temperature of winter) could be well discriminated. After building a database of 63 VOCs, a rapid method to identify compounds in the database was established. In this method, molecular ions, fragment ions, and dimer ions in the full mass spectrum were all utilized in the machine learning program. A satisfactory prediction accuracy for the 63 VOCs could be achieved (>99%).
类似于智能手机，智能或自动调谐也是微型质谱仪的关键特性。与大型仪器相比，微型质谱仪通常质量分辨率较低，质量漂移较大，因此难以识别质量电荷比接近的分子。在这项工作中，将微型质谱仪（Brick-V 型）与智能算法相结合，实现了快速准确的识别。我们实验室开发的这种 Brick-V 质谱仪配备了真空紫外光电离（VUV-PI）源，可对挥发性有机化合物（VOC）进行微量碎片电离。当分析具有多个特征峰的样品时，机器学习将特别有帮助。测试并比较了四种机器学习算法在精度、召回率、平衡 F 分数（F1 分数）和准确性方面的性能。经过优化，选择了多层感知机（MLP）方法，并首先应用于十种不同水果的自动识别和区分。通过识别从水果中扩散的多种 VOC 的模式，实现了平均 97%的准确率。该系统进一步应用于确定草莓的新鲜度，并且可以很好地区分草莓在不同时间（特别是在冬季室温下的头 24 小时内）的采摘情况。在建立了 63 种 VOC 数据库之后，建立了一种快速识别数据库中化合物的方法。在该方法中，在机器学习程序中利用了全质谱中的分子离子、碎片离子和二聚体离子。可以实现对 63 种 VOC 的令人满意的预测精度（>99%）。
