School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, PR China.
Bozhou University, 2266 Tangwang Avenue, Bozhou 236800, PR China.
Int J Biol Macromol. 2024 Jun;269(Pt 2):132147. doi: 10.1016/j.ijbiomac.2024.132147. Epub 2024 May 7.
Lignin in biomass plays significant role in substitution of synthetic polymer and reduction of energy expenditure, and the lignin content was usually determined by wet chemical methods. However, the methods' heavy workload, low efficiency, huge consumption of chemicals and use of toxic reagents render them unsuitable for sustainable development and environmental protection. Chinese fir, a prevalent angiosperm tree, holds immense importance for various industries. Since our previous work found that Raman spectroscopy could accurately predict the lignin content in poplar, we propose that the lignin content of Chinese fir can be estimated by similar strategy. The results suggested that the peak at 2895 cm is the optimal choice of internal standard peak and algorithm of XGBoost demonstrates the highest accuracy among all algorithms. Furthermore, transfer learning was successfully introduced to enhance the accuracy and robustness of the model. Ultimately, we report that a machine learning algorithm, combining transfer learning with XGBoost or LightGBM, offers an accurate, high-efficiency and environmental friendly method for predicting the lignin content of Chinese fir using Raman spectra.
生物质中的木质素在替代合成聚合物和降低能源消耗方面发挥着重要作用，木质素含量通常通过湿法化学方法来确定。然而，这些方法工作量大、效率低、化学试剂消耗大且使用有毒试剂，因此不适合可持续发展和环境保护。杉木是一种常见的被子植物，对各种工业具有重要意义。由于我们之前的工作发现拉曼光谱可以准确预测杨树中的木质素含量，因此我们提出可以通过类似的策略来估算杉木中的木质素含量。结果表明，2895cm-1 处的峰是内部标准峰的最佳选择，XGBoost 算法在所有算法中表现出最高的准确性。此外，还成功地引入了迁移学习来提高模型的准确性和鲁棒性。最终，我们报告了一种机器学习算法，将迁移学习与 XGBoost 或 LightGBM 相结合，为使用拉曼光谱预测杉木中的木质素含量提供了一种准确、高效和环保的方法。
