Porter Sarah E G, Stoll Dwight R, Paek Changyub, Rutan Sarah C, Carr Peter W
Virginia Commonwealth University, Department of Chemistry, 1001 W. Main Street, Box 842006, Richmond, VA 23294-2006, USA.
J Chromatogr A. 2006 Dec 29;1137(2):163-72. doi: 10.1016/j.chroma.2006.10.024. Epub 2006 Oct 27.
In Part I of this work, we developed a method for the detection of drugs of abuse in biological samples based on fast gradient elution liquid-chromatography coupled with diode array spectroscopic detection (LC-DAD). In this part of the work, we apply the chemometric method of target factor analysis (TFA) to the chromatograms. This algorithm identifies the target compounds present in chromatograms based on a spectral library, resolves nearly co-eluting components, and differentiates between drugs with similar spectra. The ability to resolve highly overlapped peaks using the spectral data afforded by the DAD is what distinguishes the present method from conventional library searching methods. Our library has a mean list length (MLL) of 1.255 and a discriminating power of 0.997 when both retention index and spectral factors are considered. The algorithm compares a library of 47 different compounds of toxicological relevance to unknown samples and identifies which compounds are present based on spectral and retention index matching. The application of a corrected retention index for identification rather than raw retention times compensates for long-term and column-to-column retention time shifts and allows for the use of a single library of spectral and retention data. Training data sets were used to establish the search and identification parameters of the method. A validation data set of 70 chromatograms was used to calculate the sensitivity (correct identification of positives) and specificity (correct identification of negatives) of the method, which were found to be 92% and 94%, respectively.
在本研究的第一部分,我们开发了一种基于快速梯度洗脱液相色谱与二极管阵列光谱检测(LC-DAD)联用的生物样品中滥用药物检测方法。在本研究的这一部分中,我们将目标因子分析(TFA)的化学计量学方法应用于色谱图。该算法基于光谱库识别色谱图中存在的目标化合物,解析几乎同时洗脱的组分,并区分光谱相似的药物。利用DAD提供的光谱数据解析高度重叠峰的能力是本方法与传统库搜索方法的区别所在。当同时考虑保留指数和光谱因子时,我们的库平均列表长度(MLL)为1.255,鉴别能力为0.997。该算法将一个包含47种具有毒理学相关性的不同化合物的库与未知样品进行比较,并根据光谱和保留指数匹配识别出存在哪些化合物。使用校正后的保留指数进行鉴定而非原始保留时间,可补偿长期和柱间保留时间的偏移,并允许使用单一的光谱和保留数据库。训练数据集用于建立该方法的搜索和识别参数。使用一个包含70个色谱图的验证数据集来计算该方法的灵敏度(阳性的正确识别率)和特异性(阴性的正确识别率),结果分别为92%和94%。