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Abstract：
Regression models are constructed to predict glucose and lactate concentrations from near-infrared spectra in culture media. The partial least-squares (PLS) regression technique is employed, and we investigate the improvement in the predictive ability of PLS models that can be achieved using wavelength selection and transfer learning. We combine Boruta, a nonlinear variable selection method based on random forests, with variable importance in projection (VIP) in PLS to produce the proposed variable selection method, VIP-Boruta. Furthermore, focusing on the situation where both culture medium samples and pseudo-culture medium samples can be used, we transfer pseudo media to culture media. Data analysis with an actual dataset of culture media and pseudo media confirms that VIP-Boruta can effectively select appropriate wavelengths and improves the prediction ability of PLS models, and that transfer learning with pseudo media enhances the predictive ability. The proposed method could reduce the prediction errors by about 61% for glucose and about 16% for lactate, compared to the traditional PLS model.
摘要：
构建回归模型以从培养基中的近红外光谱预测葡萄糖和乳酸的浓度。采用偏最小二乘(PLS)回归技术，我们研究了使用波长选择和迁移学习可以实现的PLS模型预测能力的提高。我们结合了Boruta,一种基于随机森林的非线性变量选择方法，在PLS中具有变量的投影重要性(VIP)，以产生所提出的变量选择方法，VIP-Boruta.此外，重点关注培养基样品和伪培养基样品都可以使用的情况，我们将伪媒体转移到文化媒体。用培养基和伪培养基的实际数据集进行数据分析，证实VIP-Boruta可以有效选择合适的波长，提高PLS模型的预测能力，伪媒体迁移学习增强了预测能力。所提出的方法可以将葡萄糖的预测误差降低约61％，乳酸的预测误差降低约16％。与传统的PLS模型相比。
