TrCSL: A Transferred CNN-SE-LSTM Model for High-accuracy Quantitative Analysis of Laser-induced Breakdown Spectroscopy with Small Samples

Abstract

When utilizing the laser-induced breakdown spectroscopy (LIBS) technology for high-precision quantitative analysis, a substantial number of samples is typically required to construct an accurate prediction model. However, in many practical applications, obtaining sufficient samples often faces challenges. The scarcity of samples not only increases the reliability of experiments but also limits the potential and flexibility of LIBS technology in a broader range of applications. In this study, we introduced a transferred convolutional neural network-squeeze and excitation-long short-term memory (TrCSL) model, aimed at achieving high-precision quantitative analysis even with small samples. The TrCSL model combines the strengths of transfer learning, convolutional neural networks (CNN), squeeze and excitation (SE) block mechanisms, and long short-term memory (LSTM) networks to enhance feature extraction and learning capabilities. We trained on 100 sets of steel slag samples to obtain the pre-training model, which was then transferred to the small samples and underwent fine-tuning of its parameters. Compared to the traditional partial least squares regression (PLSR) and support vector regression (SVR) algorithms, the TrCSL model shows an improvement of about 0.4 in R2 value for quantitative analysis results on 20 carbon steel samples. In addition, the experimental results also show that the quantitative analysis accuracy of TrCSL model on only 20 samples is close to that of traditional PLSR and SVR algorithms on 80 samples. The TrCSL model proposed in this paper possesses an enhanced universality and superior prediction accuracy, offering a novel approach to improve the LIBS quantitative analysis precision with small samples.