近红外光谱仪模型迁移:跨仪器和样品类型的挑战与策略

Model Transfer of Near-Infrared Spectrometers:Challenges and Strategies Across Instruments and Sample Types

近红外光谱技术因其高效、非破坏性的特性广泛应用于分析科学。然而,模型在不同仪器和样品类型间的迁移仍面临诸多挑战,特别是硬件差异、样品类型变化和环境条件对模型精度和稳定性的影响。为应对这些问题,本文提出了多种策略,包括光谱数据预处理、校准转移、多变量校正方法以及机器学习算法的应用,通过对案例的分析,研究揭示了各方法在实际应用中的实际情况。随着人工智能技术的发展,未来模型迁移的鲁棒性和适应性将显著提升,进一步推动光谱技术在更多领域中的广泛应用。

Near-infrared spectroscopy(NIR)is widely used in analytical science due to its efficiency and non-destructive nature.However,model transfer between different instruments and sample types presents significant challenges,especially in terms of hardware differences,variations in sample types,and environmental conditions affecting model accuracy and stability.To address these issues,multiple strategies are proposed,including spectral data preprocessing,calibration transfer,multivariate correction methods,and the application of machine learning algorithms.By analyzing cases,the study highlights the effectiveness of various methods in practical applications.With the development of artificial intelligence and data fusion technologies,the robustness and adaptability of NIR model transfer will be greatly enhanced,further promoting the widespread use of spectroscopy in various fields.