## 1 Introduction  
Near-infrared spectroscopy (NIR) continues to be a developing analytical detection technique, and it is also one of the most effective means for qualitative, quantitative and structural analysis of organic matter. NIR combined with chemometrics and computer technology is fast, highly accuracy, and has low analysis costs. It can be applied to the simultaneous analysis of multiple components. Now, NIR techniques are widely used in medicine, food quality, industry, pharmaceuticals, and other fields [1; 2]. For example, [PERSON] et al. [3] proposed a method to analyze the different stages of apple tree disease. They found the best spectral bands to detect a specific disease and distinguish it from other diseases and healthy trees. [PERSON] et al. [4] used NIR spectroscopy to analyze the contents of apple watercore and soluble solid to detect apple quality. However, NIR techniques usually require a data set large enough to obtain good results. The data sets contain typical NIR spectra and the amount of the target species, which are used as reference samples. Calibration models are established using chemometric methods and then used to provide quantitative analysis of the target species from the samples [5]. The size of the reference samples has a large influence on the quality of the calibration model. One of those chemometrics methods, the deep neural network (DNN), has some obvious advantages over traditional chemometric methods. DNNs can automatically extract features from spectral data [6; 7] to better capture their nonlinear characteristics. Therefore, DNNs have strong, nonlinear modeling ability and can learn complex nonlinear relationships in spectral data. DNN is also suitable for processing high-dimensional data, which effectively reduces computational complexity through a series of operations such as convolution and parameter sharing. Most importantly, DNNs also have the ability to generalize the calibration, improving the generalization performance of models through data augmentation and end-to-end learning. In recent years, many researchers have tried to use DNNs in analytical chemistry. [PERSON] et al. [8] reported a pattern recognition method using NIR spectroscopy for qualitative food analysis using a convolutional neural network architecture. [PERSON] et al. [9] presented a DNN applied to nuclear magnetic resonance spectra using relatively small data sets. [PERSON] et al. [10] established a DNN classifier on an open source platform. By introducing users' labeling of their data set, they developed a machine learning model for the automatic assessment of the quality of NIR signals. [PERSON] et al. [11] studied a deep neural network for fast prediction of X-ray absorption spectra, which can estimate the X-ray absorption spectra of near-edge structures of matter in a very short time without the need for geometric information about the local environment at the absorption location. [PERSON] [12] used a deep-learning stacked sparse autoencoder (SSAE) method to extract advanced features of NIR spectra and used an affine transformation (AT) and an extreme learning machine (ELM) to build prediction models. [PERSON] and [PERSON] [13] constructed the simplest possible DNN architecture. They showed that applying convolutional layers instead of fully connected layers was a good strategy to overcome the overfitting problem of small data sets. In recent work, we followed our previous approach of simplicity in constructing DNN architectures, but the focus was on revising two existing well-known network architectures, the U-Net [14] and the attention mechanism [15]. The former was implemented for image segmentation while the latter was developed for sequence modeling and transduction problems. [PERSON] et al. [16] applied a one-dimensional U-Net to remove artifacts from infrared spectra of complex samples. [PERSON] et al. [17] applied the multiple-head self-attention mechanism to improve the representation and classification performance of this model.