连续小波变换特征提取用于LIBS血浆样本新冠阳性分类
Continuous wavelet-based feature extraction for laser-induced breakdown spectroscopy classification of human plasma samples
用LIBS光谱加小波变换和SVM分类新冠血浆样本，181例做到93%准确率，光谱医学诊断方向可以看看。
研究用连续小波变换（CWT）从LIBS光谱中提取峰值特征，区分新冠阳性与阴性人体血浆样本。数据来自181名个体（53例阳性、128例阴性），样本以5μL液滴沉积在硅基底上，用1064nm Nd:YAG激光采集光谱。CWT峰值检测搭配SVM分类器，留一法评估得到93.00%准确率、94.67%灵敏度和91.61%特异度。识别出的特征谱线包括Ca II、K I和N I，作者指出结果仍需独立验证。
Continuous wavelet-based feature extraction for laser-induced breakdown spectroscopy classification of human plasma samples
Extracting discriminative features from laser-induced breakdown spectroscopy (LIBS) spectra is important for the classification of complex biological samples. This study evaluates continuous wavelet transform (CWT)-based spectral peak extraction for distinguishing human plasma samples labeled as SARS-CoV-2-positive or negative. Plasma specimens from 181 individuals, comprising 53 positive samples and 128 negative controls, were deposited as 5 μL droplets on silicon substrates and dried before analysis. Spectra were acquired using a nanosecond Nd:YAG laser operating at 1064 nm, with 100 single-shot measurements collected per specimen and averaged after intensity-based outlier rejection. CWT-based peak detection was coupled with a support vector machine (SVM) classifier. The reported leave-one-out evaluation yielded an accuracy of 93.00%, sensitivity of 94.67%, and specificity of 91.61%; these estimates remain provisional pending reconciliation with specimen-level predictions. Features identified by the CWT pipeline included emission lines assigned to Ca II, K I, and N I. The workflow provides a basis for examining spectral features associated with the studied plasma groups. Independent validation and assessment of specimen, substrate, and acquisition-related variability are required to establish generalizability and diagnostic relevance.