用化学计量学方法预测固相微萃取纤维的吸附性能

M. Jafari
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引用次数: 1

摘要

本文首次研究了用人工神经网络(ANN)估计固相微萃取纤维吸附性能的新方法。选择了一种制备简单耐用的蚀刻钢纤维,研究了其对4种不同化学类型的12种分析物的吸附性能。其中9个作为训练,3个作为测试。通过直接萃取、气相色谱分析得到吸附量。用人工神经网络对其吸附进行了分析。结果令人满意,测试的平均绝对百分比误差为18.0%。该方法简便、实用、直接、经济、准确。该方法不需要很多分析物。
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The Prediction of Adsorption Properties of a Solid Phase-Microextraction Fiber by Chemometrics methods
A new method for estimation of adsorption properties of a solid phase microextraction fiber by artificial neural network (ANN) has been studied for the first time ever. An etched steel fiber which is simple prepared and durable was selected and adsorption of 12 analytes that were in four different chemical categories, was studied. 9 of them were selected as the training and 3 as the test. The amount of adsorptions were obtained through the direct extraction from aqueous and then GC analysis. The adsorption were analyzed by ANN. The results are quite satisfactory and the mean absolute percentage error of tests was 18.0 %. The method was simple, practical, straightforward, economical, and accurate. The method did not require many analytes.
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