To improve the accuracy of laser-induced breakdown spectroscopy (LIBS) in the classification of tea leaves, a modified adaptive mutation probability for genetic algorithm (GA) was proposed to optimize support vector machines (SVM). The implementation process of the GA-SVM algorithm was discussed, and the key parameters were analyzed. The penalty factor and kernel function parameters in SVM were optimized by GA. The spectral line intensities of Mg (279.55 nm), Mn (279.83 nm), Mg (280.27 nm), CN (0–0) (388.34 nm), Ca (393.37 nm), Al (396.15 nm), Ca (396.84 nm), C₂ (0–0) (516.45 nm), Fe (517.46 nm) and K (766.49 nm) compared to C (247.86 nm) were selected as the analysis indexes according to the differences of LIBS spectra. LIBS spectra pre-processed by multiple scattering correction (MSC) with the optimal input feature were used to construct the GA-SVM model for different tea species. The results showed that the average correct classification rate was 99.73% in the training set. In addition, the average accuracy was 98.40% in the test set. The classification accuracy of the improved GA-optimized SVM was obviously higher than that of cross validation-support vector machine (CV-SVM) and particle swarm optimization-support vector machine (PSO-SVM). This work demonstrates that the GA-SVM can avoid the blindness of parameter selection and can effectively increase the accuracy of tea classification. The results indicated that LIBS is an effective technology in identificating tea leaves; moreover, it has a real-time, rapid and reliable measurement prospect.

中文翻译：

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一种改进的遗传算法优化的SVM，用于使用激光诱导击穿光谱法对茶叶进行快速分类

为了提高激光诱导击穿光谱法（LIBS）在茶叶分类中的准确性，提出了一种改进的遗传算法自适应变异概率（GA），以优化支持向量机（SVM）。讨论了GA-SVM算法的实现过程，并分析了关键参数。通过遗传算法优化了支持向量机中的惩罚因子和核函数参数。Mg（279.55 nm），Mn（279.83 nm），Mg（280.27 nm），CN（0-0）（388.34 nm），Ca（393.37 nm），Al（396.15 nm），Ca（396.84 nm）的谱线强度），C ₂根据LIBS光谱的差异，选择（0-0）（516.45 nm），Fe（517.46 nm）和K（766.49 nm）与C（247.86 nm）相比作为分析指标。通过具有最佳输入特征的多重散射校正（MSC）预处理的LIBS光谱用于构建不同茶种的GA-SVM模型。结果表明，训练集的平均正确分类率为99.73％。此外，测试仪的平均准确度为98.40％。改进的遗传算法优化的支持向量机的分类精度明显高于交叉验证支持向量机（CV-SVM）和粒子群优化支持向量机（PSO-SVM）。这项工作表明，GA-SVM可以避免参数选择的盲目性，并可以有效地提高茶分类的准确性。结果表明，LIBS是一种有效的茶叶识别技术。而且，它具有实时，快速，可靠的测量前景。