A supervised data-driven methodology was used to evaluate matrix effect severity and the consequent analytical signal bias caused by easily ionizable elements (EIEs) in inductively coupled plasma optical emission spectrometry (ICP OES). Eight different supervised machine learning models were trained on signals from nine plasma naturally-occurring species of Ar, H and O to predict analyte recoveries for Cd, Co, Cr and Pb. The Ar atomic line at 737.212 nm was then used to correct for analytical signal bias due to matrix effects caused by high concentrations of Ca and Na. Recoveries were accurately predicted for all analytes (R² > 0.9) by optimized generalized linear models (GLMs), regularized GLMs and partial least squares (PLS) regression. Accuracy was improved by employing Ar or Y as internal standards (IS). Although Y outperformed Ar in Cd determinations, both species were comparable for Co, Cr and Pb. In this preliminary study, however, there was no significant accuracy improvement observed when utilizing the Ar 737.212 nm line at the discretion of machine learning models. Therefore, for the analytes and matrices evaluated here, this species should be simply used as an IS when optimized models predict absolute errors < 0.3 (30%). The methodology described in this study may be easily implemented in routine applications, as one simply needs to monitor non-analyte signals while performing external standard calibration (EC). Once a series of models are trained, instrument software can be programmed to monitor plasma native species in real time and alert the analyst to different levels of matrix effect severity. Based on analyte recovery predictions from the optimized models, the analyst can then choose between simple EC, internal standardization with the Ar 737.212 nm line, or a more robust matrix-matching strategy.

中文翻译：

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非分析物信号和监督学习，以评估基质效应并预测电感耦合等离子体光发射光谱法中的分析物回收率

监督数据驱动的方法用于评估矩阵效应的严重性，以及电感耦合等离子体发射光谱法（ICP OES）中易电离元素（EIE）引起的分析信号偏差。对来自9种自然存在的Ar，H和O等离子物种的信号训练了8种不同的监督机器学习模型，以预测Cd，Co，Cr和Pb的分析物回收率。然后使用737.212 nm处的Ar原子线校正由高浓度的Ca和Na引起的基体效应引起的分析信号偏差。准确预测所有分析物的回收率（R ²> 0.9），通过优化的广义线性模型（GLM），正则化GLM和偏最小二乘（PLS）回归。通过使用Ar或Y作为内标（IS）提高了准确性。尽管在镉的测定中Y的性能优于Ar，但是这两种元素在Co，Cr和Pb方面均具有可比性。但是，在此初步研究中，在机器学习模型的判断下，使用Ar 737.212 nm线时没有观察到明显的精度提高。因此，对于此处评估的分析物和基质，当优化模型预测绝对误差<0.3（30％）时，应将该物种简单用作IS。这项研究中描述的方法可以很容易地在常规应用中实施，因为仅需在执行外部标准校准（EC）时监视非分析物信号即可。训练完一系列模型后，实时并提醒分析人员注意不同级别的基质效应严重性。根据优化模型的分析物回收预测，分析人员可以选择简单的EC，使用Ar 737.212 nm谱线进行内部标准化或采用更可靠的基质匹配策略。