Instrument Bias Correction With Machine Learning Algorithms: Application to Field-Portable Mass Spectrometry
Instrument Bias Correction With Machine Learning Algorithms: Application to Field-Portable Mass Spectrometry
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DOI：
10.3389/feart.2020.537028
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
B. Loose;R. Short;S. Toler
中科院分区：
文献类型：
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作者：
B. Loose;R. Short;S. Toler
用于环境化学的原位传感器有望实现更全面的观测，这对于地球系统科学中高可信度的预测而言必不可少。然而，这些数据的解读可能颇具挑战，因为传感器会受到温度、湿度、压力或其他并非直接关注的次要环境条件的强烈影响。我们对两种统计学习方法进行了比较，即广义相加模型和长短期记忆神经网络模型，用于对原位传感器数据进行偏差校正。我们探讨了将这两种偏差校正方法应用于潜水器和船载质谱仪数据时的性能及权衡。这两种仪器均测量溶解在水中的含量最为丰富的气体，可用于重建生物化学代谢过程，包括那些调节大气二氧化碳的过程。这两种模型在利用相关环境测量值校正仪器偏差方面都展现出了较高的能力；就均方根误差而言，它们各自性能的差异不到1%。总体而言，与独立的膜式溶解氧仪和激光光谱仪相比，长短期记忆偏差校正对氧气产生的误差为5%，对二氧化碳产生的误差为8.5%。这意味着这两种气体的预测准确率达到了92% - 95%。显然，成功进行偏差校正的最重要因素是对可能与仪器偏差相关的次要环境条件进行测量。这些统计学习方法极为灵活，在寻找最佳偏差校正解决方案时，几乎可以纳入无数个相关因素。
In situ sensors for environmental chemistry promise more thorough observations, which are necessary for high confidence predictions in earth systems science. However, these can be a challenge to interpret because the sensors are strongly influenced by temperature, humidity, pressure, or other secondary environmental conditions that are not of direct interest. We present a comparison of two statistical learning methods—a generalized additive model and a long short-term memory neural network model for bias correction of in situ sensor data. We discuss their performance and tradeoffs when the two bias correction methods are applied to data from submersible and shipboard mass spectrometers. Both instruments measure the most abundant gases dissolved in water and can be used to reconstruct biochemical metabolisms, including those that regulate atmospheric carbon dioxide. Both models demonstrate a high degree of skill at correcting for instrument bias using correlated environmental measurements; the difference in their respective performance is less than 1% in terms of root mean squared error. Overall, the long short-term memory bias correction produced an error of 5% for O2 and 8.5% for CO2 when compared against independent membrane DO and laser spectrometer instruments. This represents a predictive accuracy of 92–95% for both gases. It is apparent that the most important factor in a skillful bias correction is the measurement of the secondary environmental conditions that are likely to correlate with the instrument bias. These statistical learning methods are extremely flexible and permit the inclusion of nearly an infinite number of correlates in finding the best bias correction solution.