Department of Biological Systems Engineering, 1757Virginia Tech, Blacksburg, Virginia, USA.
Department of Biomedical Engineering and Mechanics, 1757Virginia Tech, Blacksburg, Virginia, USA.
Appl Spectrosc. 2022 Mar;76(3):273-283. doi: 10.1177/00037028211060853. Epub 2022 Feb 1.
Hematuria refers to the presence of blood in urine. Even in small amounts, it may be indicative of disease, ranging from urinary tract infection to cancer. Here, Raman spectroscopy was used to detect and quantify macro- and microhematuria in human urine samples. Anticoagulated whole blood was mixed with freshly collected urine to achieve concentrations of 0, 0.25, 0.5, 1, 2, 6, 10, and 20% blood/urine (v/v). Raman spectra were obtained at 785 nm and data analyzed using chemometric methods and statistical tests with the Rametrix toolboxes for Matlab. Goldindec and iterative smoothing splines with root error adjustment (ISREA) baselining algorithms were used in processing and normalization of Raman spectra. Rametrix was used to apply principal component analysis (PCA), develop discriminate analysis of principal component (DAPC) models, and to validate these models using external leave-one-out cross-validation (LOOCV). Discriminate analysis of principal component models were capable of detecting various levels of microhematuria in unknown urine samples, with prediction accuracies of 91% (using Goldindec spectral baselining) and 94% (using ISREA baselining). Partial least squares regression (PLSR) was then used to estimate/quantify the amount of blood (v/v) in a urine sample, based on its Raman spectrum. Comparing actual and predicted (from Raman spectral computations) hematuria levels, a coefficient of determination (R) of 0.91 was obtained over all hematuria levels (0-20% v/v), and an R of 0.92 was obtained for microhematuria (0-1% v/v) specifically. Overall, the results of this preliminary study suggest that Raman spectroscopy and chemometric analyses can be used to detect and quantify macro- and microhematuria in unprocessed, clinically relevant urine specimens.
血尿是指尿液中存在血液。即使血液量很少，也可能表明存在疾病，范围从尿路感染到癌症。在这里，拉曼光谱被用于检测和定量人尿液样本中的宏观血尿和微观血尿。将抗凝全血与新采集的尿液混合，以达到 0、0.25、0.5、1、2、6、10 和 20%的血液/尿液（v/v）浓度。在 785nm 处获得拉曼光谱，并使用化学计量方法和统计检验（使用 Rametrix 工具箱 for Matlab）对数据进行分析。在处理和归一化拉曼光谱时，使用了 Goldindec 和具有根误差调整（ISREA）的迭代平滑样条基线算法。Rametrix 用于应用主成分分析（PCA）、开发主成分判别分析（DAPC）模型，并使用外部留一法交叉验证（LOOCV）验证这些模型。主成分判别分析模型能够检测未知尿液样本中不同程度的微观血尿，预测准确率为 91%（使用 Goldindec 光谱基线）和 94%（使用 ISREA 基线）。然后，使用偏最小二乘回归（PLSR）根据其拉曼光谱估计/定量尿液样本中的血液量（v/v）。比较实际和预测（来自拉曼光谱计算）的血尿水平，在所有血尿水平（0-20%v/v）上获得了 0.91 的决定系数（R），在微观血尿（0-1%v/v）上获得了 0.92 的决定系数。总的来说，这项初步研究的结果表明，拉曼光谱和化学计量分析可用于检测和定量未经处理的、临床上相关的尿液标本中的宏观血尿和微观血尿。
