Application of the expert algorithm for substance identification (EASI) to the electron ionization (EI) mass spectra of fentanyl isomers and analogs

IF 2.6 3区医学 Q2 CHEMISTRY, ANALYTICAL

Forensic Chemistry Pub Date : 2025-04-09 DOI:10.1016/j.forc.2025.100660

Alexandra I. Adeoye , Glen P. Jackson

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引用次数: 0

Abstract

Fentanyl analogs (fentalogs) share many structural and mass spectral similarities that make them difficult to differentiate and accurately identify without chromatographic data. In such situations, the expert algorithm for substance identification (EASI) provides superior classification relative to conventional approaches. Using a database of >57,000 replicate electron-ionization mass spectra of 76 fentalogs from ten laboratories, three challenging sets of isomers were studied in detail. To maximize limits of detection, only the 20 most abundant ions were considered. In each case, 50 % of the data from one laboratory served as the training set. On average, the mean absolute residuals between measured and modeled abundances of known positives were five times smaller using EASI than the consensus approach, which used the means of training sets as the exemplar spectra to which all query spectra were compared. With a conservative threshold of zero false positives, EASI identified isovalerylfentanyl from its two closest isomers with an accuracy of 96.7 %, which was ∼10 % better than the consensus approach. The associated positive likelihood ratios increased from 366 for the consensus approach to more than 4,200 for EASI. When discriminating isovalerylfentanyl spectra from the other 72 fentalogs, EASI provided errorless results with a positive likelihood ratio exceeding 50,000. For all 9 fentalogs, EASI outperformed the consensus approach and the use of Mahalanobis distance as a metric for identifying outliers. In the absence of retention time information, EASI improves confidence in drug identifications, enables inter-laboratory identifications, and reduces the need for acquiring concomitant spectra of standards.

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物质鉴定专家算法（EASI）在芬太尼异构体和类似物电子电离质谱中的应用

芬太尼类似物（fentalogs）在结构和质谱上有许多相似之处，因此在没有色谱数据的情况下难以区分和准确识别。在这种情况下，物质鉴定专家算法（EASI）提供了优于传统方法的分类方法。利用来自十个实验室的 76 种芬太尼的 57,000 份重复电子电离质谱数据库，对三组具有挑战性的异构体进行了详细研究。为了最大限度地提高检测限，只考虑了 20 个最丰富的离子。在每种情况下，来自一个实验室的 50% 的数据作为训练集。使用 EASI 方法，已知阳性物的测量丰度与模型丰度之间的平均绝对残差比共识方法小五倍，共识方法使用训练集的平均值作为示范光谱，所有查询光谱都与之进行比较。在假阳性为零的保守阈值下，EASI 从两种最接近的异构体中识别出异戊酰芬太尼的准确率为 96.7%，比共识方法高出 10%。相关的阳性似然比从共识方法的 366 增加到 EASI 的 4,200 以上。在将异戊酰基芬太尼光谱与其他 72 种芬太尼进行鉴别时，EASI 提供了无差错的结果，其阳性似然比超过了 50,000 。在所有 9 种芬太尼中，EASI 的表现均优于共识方法和使用马哈拉诺比距离（Mahalanobis distance）作为识别异常值的指标的方法。在没有保留时间信息的情况下，EASI 提高了药物鉴定的可信度，实现了实验室之间的鉴定，并减少了获取标准品相关光谱的需要。

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来源期刊

Forensic Chemistry CHEMISTRY, ANALYTICAL-

CiteScore

5.70

自引率

14.80%

发文量

审稿时长

46 days

期刊介绍： Forensic Chemistry publishes high quality manuscripts focusing on the theory, research and application of any chemical science to forensic analysis. The scope of the journal includes fundamental advancements that result in a better understanding of the evidentiary significance derived from the physical and chemical analysis of materials. The scope of Forensic Chemistry will also include the application and or development of any molecular and atomic spectrochemical technique, electrochemical techniques, sensors, surface characterization techniques, mass spectrometry, nuclear magnetic resonance, chemometrics and statistics, and separation sciences (e.g. chromatography) that provide insight into the forensic analysis of materials. Evidential topics of interest to the journal include, but are not limited to, fingerprint analysis, drug analysis, ignitable liquid residue analysis, explosives detection and analysis, the characterization and comparison of trace evidence (glass, fibers, paints and polymers, tapes, soils and other materials), ink and paper analysis, gunshot residue analysis, synthetic pathways for drugs, toxicology and the analysis and chemistry associated with the components of fingermarks. The journal is particularly interested in receiving manuscripts that report advances in the forensic interpretation of chemical evidence. Technology Readiness Level: When submitting an article to Forensic Chemistry, all authors will be asked to self-assign a Technology Readiness Level (TRL) to their article. The purpose of the TRL system is to help readers understand the level of maturity of an idea or method, to help track the evolution of readiness of a given technique or method, and to help filter published articles by the expected ease of implementation in an operation setting within a crime lab.