Given the extensive use of antibiotics at present, the identification of antibiotics and production quality monitoring are of high importance. However, conventional antibiotic identification methods have a low sensitivity and a long detection time. Here, we propose an identification method that combines terahertz (THz) spectroscopy and chemometric technology. THz time-domain spectroscopy (THz-TDS) was performed for sixteen types of antibiotics, including β-lactam, cephalosporins, macrolides, and tetracyclines. The absorption spectra within the frequency range of 0.2–1.5 THz were calculated. For dimensionality reduction, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were implemented, respectively. The data after dimensionality reduction were input into a support vector machine (SVM). The model parameters were optimized through grid search (GS), genetic algorithm (GA), and particle swarm optimization (PSO) methods, and the optimal identification results were obtained after comparison across these methods. Experiments indicate a differentiation of the THz absorption spectra among the sixteen types of antibiotics. After dimensionality reduction, the training time of the model significantly decreased. The use of the t-SNE-PSO-SVM model achieved the highest average accuracy on the prediction set, which was 99.91%. Thus, our study does not only confirm that the t-SNE-PSO-SVM model proves to be a reliable method for antibiotics identification, but also confirms that the combination of THz-TDS and chemometric pattern recognition has great potential for drug detection.

中文翻译：

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太赫兹光谱和SVM鉴定抗生素的可靠方法

考虑到目前抗生素的广泛使用，抗生素的鉴定和生产质量的监测非常重要。但是，常规的抗生素鉴定方法灵敏度低，检测时间长。在这里，我们提出了一种结合太赫兹（THz）光谱和化学计量技术的鉴定方法。对16种抗生素（包括β）进行了太赫兹时域光谱（THz-TDS）-内酰胺，头孢菌素，大环内酯类和四环素类。计算了在0.2–1.5 THz频率范围内的吸收光谱。为了降维，分别实现了主成分分析（PCA）和t分布随机邻居嵌入（t-SNE）。降维后的数据输入到支持向量机（SVM）中。通过网格搜索（GS），遗传算法（GA）和粒子群优化（PSO）方法对模型参数进行了优化，并在将这些方法进行比较后获得了最佳识别结果。实验表明，在16种抗生素中，太赫兹吸收光谱有所不同。降维后，模型的训练时间显着减少。t-SNE-PSO-SVM模型的使用在预测集上获得了最高的平均准确度，为99.91％。因此，我们的研究不仅证实了t-SNE-PSO-SVM模型被证明是一种可靠的抗生素鉴定方法，而且还证实了THz-TDS和化学计量学模式识别的结合具有巨大的药物检测潜力。