Closed-loop, multiobjective optimization of analytical instrumentation: Gas chromatography/time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations
Closed-loop, multiobjective optimization of analytical instrumentation: Gas chromatography/time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations
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DOI：
10.1021/ac049146x
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发表时间：
2005-01-01
影响因子：
7.4
通讯作者：
Kell, DB
中科院分区：
文献类型：
--
作者：
O'Hagan, S;Dunn, WB;Kell, DB
控制现代分析仪器的仪器参数的数量可以是相当多的，并且系统地改变它们以优化特定的色谱分离是不可能的，例如，因为可能的组合的天文数字(即，搜索空间非常大)。然而，启发式方法，例如那些基于进化计算的方法，可以用来有效地探索这样的搜索空间。我们在这里描述了一种完全自动化(闭环)策略的实现，并将其应用于人血清和酵母发酵液代谢物的气相色谱分离的优化。在没有人工干预的情况下，机器人色谱仪系统(I)对仪器上的设置进行初始化，(Ii)控制分析运行，(Iii)提取定义分析性能的变量(特别是峰数、信噪比和运行时间)，(Iv)选择(通过PESA-II多目标遗传算法)，以及(V)对下一系列仪器设置进行编程，整个过程在迭代循环中持续，直到建立了合适的最佳条件集。使用遗传编程来消除噪声峰值，并为观察到的改进奠定基础。该系统显示，可观察到的峰的数量在很大程度上取决于所使用的条件，并有助于将其增加多达3倍(例如，在人血清中达到950以上)，同时在许多情况下保持或减少运行时间，并保持良好的信噪比。我们描述的进化闭环机器学习策略对任何类型的分析优化都是遗传的。
The number of instrumental parameters controlling modem analytical apparatus can be substantial, and varying them systematically to optimize a particular chromatographic separation, for example, is out of the question because of the astronomical number of combinations that are possible (i.e., the "search space" is very large). However, heuristic methods, such as those based on evolutionary computing, can be used to explore such search spaces efficiently. We here describe the implementation of an entirely automated (closed-loop) strategy for doing this and apply it to the optimization of gas chromatographic separations of the metabolomes of human serum and of yeast fermentation broths. Without human intervention, the Robot Chromatographer system (i) initializes the settings on the instrument, (ii) controls the analytical run, (iii) extracts the variables defining the analytical performance (specifically the number of peaks, signal/noise ratio, and run time), (iv) chooses (via the PESA-II multiobjective genetic algorithm), and (v) programs the next series of instrumental settings, the whole continuing in an iterative cycle until suitable sets of optimal conditions have been established. Genetic programming was used to remove noise peaks and to establish the basis for the improvements observed. The system showed that the number of peaks observable depended enormously on the conditions used and served to increase them by as much as 3-fold (e.g., to over 950 in human serum) while in many cases maintaining or reducing the run time and preserving excellent signal/noise ratios. The evolutionary closed-loop machine learning strategy we describe is genetic to any type of analytical optimization.