Wang L, Qin X C, Lin H C, Deng K F, Luo Y W, Sun Q R, Du Q X, Wang Z Y, Tuo Y, Sun J H
School of Forensic Medicine, Shanxi Medical University, Taiyuan 030001, China.
Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
Fa Yi Xue Za Zhi. 2018 Feb;34(1):1-6. doi: 10.3969/j.issn.1004-5619.2018.01.001. Epub 2018 Feb 25.
To analyse the relationship between Fourier transform infrared （FTIR） spectrum of rat's spleen tissue and postmortem interval （PMI） for PMI estimation using FTIR spectroscopy combined with data mining method.
Rats were sacrificed by cervical dislocation, and the cadavers were placed at 20 ℃. The FTIR spectrum data of rats' spleen tissues were taken and measured at different time points. After pretreatment, the data was analysed by data mining method.
The absorption peak intensity of rat's spleen tissue spectrum changed with the PMI, while the absorption peak position was unchanged. The results of principal component analysis （PCA） showed that the cumulative contribution rate of the first three principal components was 96%. There was an obvious clustering tendency for the spectrum sample at each time point. The methods of partial least squares discriminant analysis （PLS-DA） and support vector machine classification （SVMC） effectively divided the spectrum samples with different PMI into four categories （0-24 h, 48-72 h, 96-120 h and 144-168 h）. The determination coefficient （²） of the PMI estimation model established by PLS regression analysis was 0.96, and the root mean square error of calibration （RMSEC） and root mean square error of cross validation （RMSECV） were 9.90 h and 11.39 h respectively. In prediction set, the ² was 0.97, and the root mean square error of prediction （RMSEP） was 10.49 h.
The FTIR spectrum of the rat's spleen tissue can be effectively analyzed qualitatively and quantitatively by the combination of FTIR spectroscopy and data mining method, and the classification and PLS regression models can be established for PMI estimation.
分析大鼠脾脏组织傅里叶变换红外（FTIR）光谱与死后间隔时间（PMI）之间的关系，以便利用FTIR光谱结合数据挖掘方法进行PMI估计。
通过颈椎脱臼法处死大鼠，将尸体置于20℃环境。在不同时间点采集并测量大鼠脾脏组织的FTIR光谱数据。经过预处理后，采用数据挖掘方法对数据进行分析。
大鼠脾脏组织光谱的吸收峰强度随PMI变化，而吸收峰位置不变。主成分分析（PCA）结果显示，前三个主成分的累积贡献率为96%。各时间点的光谱样本有明显的聚类趋势。偏最小二乘判别分析（PLS-DA）和支持向量机分类（SVMC）方法有效地将不同PMI的光谱样本分为四类（0-24小时、48-72小时、96-120小时和144-168小时）。通过PLS回归分析建立的PMI估计模型的决定系数（²）为0.96，校准均方根误差（RMSEC）和交叉验证均方根误差（RMSECV）分别为9.90小时和11.39小时。在预测集中，²为0.97，预测均方根误差（RMSEP）为10.49小时。
结合FTIR光谱和数据挖掘方法可有效对大鼠脾脏组织的FTIR光谱进行定性和定量分析，并可建立用于PMI估计的分类和PLS回归模型。
