LIBS技术结合BO优化后的机器学习算法对口香糖分类  

Classification of Chewing Gum Using LIBS Technology Combined with BO-Optimized Machine Learning Algorithm

作　　者：张涛[1] 李春宇[1] 白文哲 金维正 Zhang Tao;Li Chunyu;Bai Wenzhe;Jin Weizheng(Institite of Criminal Investigation,People’s Public Security University of China,Beijing 100038,China)

机构地区：[1]中国人民公安大学侦查学院,北京100038

出　　处：《应用激光》2024年第10期136-146,共11页Applied Laser

基　　金：中国人民公安大学刑事科学技术双一流创新研究专项(2023SYL06)。

摘　　要：口香糖是犯罪现场常见的物证之一,为建立一种高效识别口香糖类别的方法,利用激光诱导击穿光谱技术对34种口香糖样品进行检测,获取样本光谱数据。首先对样本光谱数据进行归一化处理,再使用主成分分析法对光谱数据进行降维,选取累计解释率达92.32%的前100个主成分。预处理后,将全部数据划分为70%的训练集和30%的测试集,分别输入与贝叶斯优化算法相结合的随机森林、支持向量机和K最近邻三种机器学习模型。经历100轮迭代,三种模型得到最优超参数组合,此时分类准确率分别为98.03%、88.72%、89.21%。其中,贝叶斯-随机森林模型的分类准确度最高,达到98.03%。最后,使用K折交叉验证评估贝叶斯-随机森林模型的分类精度和稳定性。Chewing gum is frequently found as physical evidence at crime scenes,necessitating an efficient method for gum category identification.The spectral data were normalized,followed by Principal Component Analysis for dimensionality reduction,selecting the top 100 principal components that accounted for a cumulative explained variance of 92.32%.After preprocessing,the entire dataset was divided into a 70%training set and a 30%test set.These sets were input into three machine learning models—Random Forest,Support Vector Machine,and K-Nearest Neighbors-combined with the Bayesian Optimization.After 100iterations,the three models obtained optimal hyperparameter combinations,achieving classification accuracies of 98.03%,88.72%,and 89.21%,respectively.Notably,the Bayesian Optimization-Random Forest model exhibited the highest classification accuracy,reaching 98.03%.K-fold Cross-Validation was subsequently applied to evaluate the classification accuracy and stability of the Bayesian Optimization-Random Forest model.

关 键 词：激光诱导击穿光谱 主成分分析 贝叶斯优化算法 机器学习 

分 类 号：O433.4[机械工程—光学工程]