Supervised and unsupervised machine learning methods are used to evaluate matrix effects caused by carbon and easily ionizable elements (EIEs) on analytical signals of inductively coupled plasma optical emission spectrometry (ICP OES). A simple experimental approach was used to produce a series of synthetic solutions with varying levels of matrix complexity. Analytical lines (n = 29), with total line energies (E_sum) in the 5.0–15.5 eV range, and non-analyte signals (n = 24) were simultaneously monitored throughout the study. Labeled (supervised learning) and unlabeled (unsupervised learning) data on normalized non-analyte signals (from plasma species) were used to train machine learning models to characterize matrix effect severity and predict analyte recoveries. Dimension reduction techniques, including principal component analysis, uniform manifold approximation and projection and t-distributed stochastic neighborhood embedding, were able to provide visual and quantitative representations that correlated well with observed matrix effects on low-energy atomic and high-energy ionic emission lines. Predictive models, including partial least squares regression and generalized linear models fit with the elastic net penalty, were tuned to estimate analyte recovery error when using the external standard calibration method (EC). The best predictive results were found for high-energy ionic analytical lines, e.g. Zn II 202.548 nm (E_sum = 15.5 eV), with accuracy and R² of 0.970 and 0.856, respectively. Two certified reference materials (CRMs) were used for method validation. The strategy described here may be used for flagging compromising matrix effects, and complement method validation based on addition/recovery experiments and CRMs analyses. Because the data analysis workflows feature signals from plasma-based species, there is potential for developing instrument software capable of alerting users in real time (i.e. before data processing) of inaccurate results when using EC.

有监督和无监督机器学习方法用于评估由碳和易电离元素（EIE）引起的矩阵效应对电感耦合等离子体发射光谱（ICP OES）的分析信号的影响。一个简单的实验方法被用来生产一系列具有不同基质复杂度的合成溶液。分析线（n = 29），总线能量（E _sum）在5.0-15.5 eV范围内，并且在整个研究过程中同时监测非分析物信号（n = 24）。归一化非分析物信号（来自血浆物质）的标记（监督学习）和未标记（监督学习）数据用于训练机器学习模型，以表征基质效应的严重性并预测分析物的回收率。降维技术，包括主成分分析，均匀流形逼近和投影以及t分布的随机邻域嵌入，能够提供视觉和定量表示，与在低能原子和高能离子发射线上观察到的基质效应很好地相关。当使用外标校正方法（EC）时，包括偏最小二乘回归的预测模型和与弹性净罚分拟合的广义线性模型都经过调整，以估计分析物的回收误差。对于高能离子分析谱线，例如Zn II 202.548 nm（E _sum  = 15.5 eV），准确度和R ²为最佳预测结果分别为0.970和0.856。两种认证参考材料（CRM）用于方法验证。此处描述的策略可用于标记危害矩阵的效果，并基于添加/恢复实验和CRM分析来补充方法验证。由于数据分析工作流程的特征是来自基于等离子体的物种的信号，因此有可能开发出能够在使用EC时实时（即在数据处理之前）向用户警告不准确结果的仪器软件。