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Fast Gradient Elution Reversed-Phase Liquid Chromatography with Diode-Array Detection as a High-throughput Screening Method for Drugs of Abuse II. Data Analysis

机译:二极管阵列检测快速梯度洗脱反相液相色谱法作为滥用药物的高通量筛选方法II。数据分析

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摘要

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 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.
机译:在这项工作的第一部分中,我们开发了一种基于快速梯度洗脱液相色谱结合二极管阵列光谱检测(LC-DAD)的生物样品中滥用药物的检测方法。在这部分工作中,我们将目标因子分析(TFA)的化学计量方法应用于色谱图。该算法基于光谱库识别色谱图中存在的目标化合物,分离几乎共洗脱的组分,并区分具有相似光谱的药物。使用DAD提供的光谱数据解析高度重叠的峰的能力使本方法与常规库搜索方法区别开来。当同时考虑保留指数和光谱因子时,我们的库的平均列表长度为1.255,判别力为0.997。该算法将毒理学相关的47种不同化合物的文库与未知样品进行比较,并根据光谱和保留指数匹配确定存在哪些化合物。校正后的保留指数用于鉴定而不是原始保留时间的应用可补偿长期和色谱柱至色谱柱的保留时间偏移,并允许使用单个光谱和保留数据库。训练数据集用于建立该方法的搜索和识别参数。使用70个色谱图的验证数据集计算该方法的灵敏度(正确鉴定阳性)和特异性(正确鉴定阴性),分别为92%和94%。

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