Departamento de Engenharia de Produção e Transportes - Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
Departamento de Engenharia de Produção e Transportes - Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil; Instituto Nacional de Ciência e Tecnologia Forense (INCT Forense), Brazil.
J Pharm Biomed Anal. 2018 Apr 15;152:120-127. doi: 10.1016/j.jpba.2018.01.050. Epub 2018 Jan 31.
Street cocaine is typically altered with several compounds that increase its harmful health-related side effects, most notably depression, convulsions, and severe damages to the cardiovascular system, lungs, and brain. Thus, determining the concentration of cocaine and adulterants in seized drug samples is important from both health and forensic perspectives. Although FTIR has been widely used to identify the fingerprint and concentration of chemical compounds, spectroscopy datasets are usually comprised of thousands of highly correlated wavenumbers which, when used as predictors in regression models, tend to undermine the predictive performance of multivariate techniques. In this paper, we propose an FTIR wavenumber selection method aimed at identifying FTIR spectra intervals that best predict the concentration of cocaine and adulterants (e.g. caffeine, phenacetin, levamisole, and lidocaine) in cocaine samples. For that matter, the Mutual Information measure is integrated into a Quadratic Programming problem with the objective of minimizing the probability of retaining redundant wavenumbers, while maximizing the relationship between retained wavenumbers and compounds' concentrations. Optimization outputs guide the order of inclusion of wavenumbers in a predictive model, using a forward-based wavenumber selection method. After the inclusion of each wavenumber, parameters of three alternative regression models are estimated, and each model's prediction error is assessed through the Mean Average Error (MAE) measure; the recommended subset of retained wavenumbers is the one that minimizes the prediction error with maximum parsimony. Using our propositions in a dataset of 115 cocaine samples we obtained a best prediction model with average MAE of 0.0502 while retaining only 2.29% of the original wavenumbers, increasing the predictive precision by 0.0359 when compared to a model using the complete set of wavenumbers as predictors.
街头可卡因通常会与几种化合物混合，这些化合物会增加其有害的健康相关副作用，尤其是抑郁、抽搐和严重损害心血管系统、肺部和大脑。因此，从健康和法医学的角度来看，确定缴获毒品样本中可卡因和掺杂物的浓度非常重要。尽管傅里叶变换红外光谱（FTIR）已被广泛用于识别化合物的指纹和浓度，但光谱数据集通常由数千个高度相关的波数组成，当这些波数用作回归模型中的预测因子时，往往会降低多元技术的预测性能。在本文中，我们提出了一种 FTIR 波数选择方法，旨在识别最佳预测可卡因和掺杂物（例如咖啡因、非那西汀、左咪唑和利多卡因）浓度的 FTIR 光谱区间。为此，互信息测度被整合到一个二次规划问题中，目标是最小化保留冗余波数的概率，同时最大化保留波数与化合物浓度之间的关系。优化输出指导使用基于正向的波数选择方法将波数纳入预测模型的顺序。在纳入每个波数之后，使用三种替代回归模型估计参数，并通过平均平均误差（MAE）测度评估每个模型的预测误差；建议保留的波数子集是最小化预测误差的子集，同时具有最大简约性。在 115 个可卡因样本的数据集上使用我们的提议，我们获得了一个最佳预测模型，平均 MAE 为 0.0502，同时仅保留了原始波数的 2.29%，与使用完整波数作为预测因子的模型相比，预测精度提高了 0.0359。
