Mackie David M, Jahnke Justin P, Benyamin Marcus S, Sumner James J
U.S. Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD, USA.
MethodsX. 2016 Feb 21;3:128-38. doi: 10.1016/j.mex.2016.02.002. eCollection 2016.
The standard methodologies for quantitative analysis (QA) of mixtures using Fourier transform infrared (FTIR) instruments have evolved until they are now more complicated than necessary for many users' purposes. We present a simpler methodology, suitable for widespread adoption of FTIR QA as a standard laboratory technique across disciplines by occasional users.•Algorithm is straightforward and intuitive, yet it is also fast, accurate, and robust.•Relies on component spectra, minimization of errors, and local adaptive mesh refinement.•Tested successfully on real mixtures of up to nine components. We show that our methodology is robust to challenging experimental conditions such as similar substances, component percentages differing by three orders of magnitude, and imperfect (noisy) spectra. As examples, we analyze biological, chemical, and physical aspects of bio-hybrid fuel cells.
使用傅里叶变换红外（FTIR）仪器对混合物进行定量分析（QA）的标准方法不断发展，如今对于许多用户的目的而言，其复杂性已超出必要程度。我们提出了一种更简单的方法，适合偶尔使用的用户将FTIR QA作为跨学科的标准实验室技术广泛采用。•算法简单直观，同时又快速、准确且稳健。•依赖于组分光谱、误差最小化和局部自适应网格细化。•已在多达九种组分的真实混合物上成功测试。我们表明，我们的方法对于具有挑战性的实验条件具有鲁棒性，例如相似物质、组分百分比相差三个数量级以及不完美（有噪声）的光谱。作为示例，我们分析了生物混合燃料电池的生物学、化学和物理方面。
