School of Information Science and Engineering, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao, China.
Anal Methods. 2023 Nov 16;15(44):6097-6104. doi: 10.1039/d3ay01631e.
A method for measurement of antiepileptic drug concentrations based on Raman spectroscopy and an optimization algorithm for mathematical models are proposed and investigated. This study uses Raman spectroscopy to measure mixed antiepileptic drugs, and an Improved Snake Optimization (ISO)-Convolutional Neural Network (CNN) algorithm is proposed. Raman spectroscopy is widely used in the identification of pharmaceutical ingredients due to its sharp peaks, no pre-treatment of samples and non-destructive detection. To analyze the spectral data precisely, a machine learning method is used in this paper. The ISO algorithm is an improved intelligent swarm algorithm in which the method of generating random solutions is improved, which can ensure that a comprehensive local search of the model is performed, the global search capability is maintained at a later stage, and the convergence speed is accelerated. In this study, 360 groups of oxcarbazepine, carbamazepine, and lamotrigine drug mixtures are measured using Raman spectroscopy, and the raw spectral data after pre-processing are trained and evaluated using ISO-CNN algorithms, and the results are compared and analyzed with those obtained from other algorithms such as the Northern Goshawk Optimization algorithm, Chameleon Swarm Algorithm, and White Shark Optimizer algorithm. The results show that the best ISO-CNN algorithm training is achieved for oxcarbazepine, with a determination coefficient and root mean square error of 0.99378 and 0.0295 for the validation set, and 0.99627 and 0.0278 for the test set. The overall results suggest that Raman spectroscopy combined with machine learning algorithms can be a potential tool for drug concentration prediction.
提出并研究了一种基于拉曼光谱和数学模型优化算法的抗癫痫药物浓度测量方法。本研究使用拉曼光谱法测量混合抗癫痫药物，并提出了一种改进的蛇形优化（ISO）-卷积神经网络（CNN）算法。由于拉曼光谱具有尖锐的峰、无需对样品进行预处理以及非破坏性检测等特点，因此在药物成分的识别中得到了广泛的应用。为了精确地分析光谱数据，本文使用了机器学习方法。ISO 算法是一种改进的智能群算法，其中改进了生成随机解的方法，可以确保对模型进行全面的局部搜索，在后期保持全局搜索能力，并加快收敛速度。在本研究中，使用拉曼光谱法测量了 360 组奥卡西平、卡马西平和拉莫三嗪药物混合物，对预处理后的原始光谱数据进行了 ISO-CNN 算法的训练和评估，并与北方鹰鸮优化算法、变色龙群算法和白鲨优化算法等其他算法的结果进行了比较和分析。结果表明，对于奥卡西平，ISO-CNN 算法的最佳训练结果为验证集的决定系数和均方根误差分别为 0.99378 和 0.0295，测试集分别为 0.99627 和 0.0278。总体结果表明，拉曼光谱结合机器学习算法可能是一种用于预测药物浓度的潜在工具。
