An autoencoder architecture was adopted for near-infrared (NIR) spectral analysis by extracting the common features in the spectra. Three autoencoder-based networks with different purposes were constructed. First, a spectral encoder was established by training the network with a set of spectra as the input. The features of the spectra can be encoded by the nodes in the bottleneck layer, which in turn can be used to build a sparse and robust model. Second, taking the spectra of one instrument as the input and that of another instrument as the reference output, the common features in both spectra can be obtained in the bottleneck layer. Therefore, in the prediction step, the spectral features of the second can be predicted by taking the reverse of the decoder as the encoder. Furthermore, transfer learning was used to build the model for the spectra of more instruments by fine-tuning the trained network. NIR datasets of plant, wheat, and pharmaceutical tablets measured on multiple instruments were used to test the method. The multi-linear regression (MLR) model with the encoded features was found to have a similar or slightly better performance in prediction compared with the partial least-squares (PLS) model.

中文翻译：

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光谱编码器提取近红外光谱特征进行多元校准

通过提取光谱中的共同特征，采用自动编码器架构进行近红外 (NIR) 光谱分析。构建了三个具有不同目的的基于自动编码器的网络。首先，通过以一组光谱作为输入来训练网络来建立光谱编码器。光谱的特征可以通过瓶颈层中的节点进行编码，进而可以用来构建稀疏且鲁棒的模型。其次，将一台仪器的光谱作为输入，另一台仪器的光谱作为参考输出，可以在瓶颈层获得两种光谱的共同特征。因此，在预测步骤中，可以通过将解码器的反面作为编码器来预测秒的光谱特征。此外，迁移学习用于通过微调训练好的网络来构建更多仪器的光谱模型。在多种仪器上测量的植物、小麦和药片的 NIR 数据集用于测试该方法。与偏最小二乘 (PLS) 模型相比，具有编码特征的多线性回归 (MLR) 模型在预测方面具有相似或略好的性能。