近红外光谱是一种快速、 无损的定量分析工具, 现如今已广泛的应用在各个行业中。 近红外光谱分析技术应用的关键就在于如何建立一个有效而又精确的模型。 目前常用的定量分析方法大多为浅层模型, 深度信念网络(DBN)是一种基于概率的深层模型, 可以自动学习输入的有效特征表示, 且只要设置最后隐层输出节点数低于输入光谱维度, 在对光谱数据完成特征提取的同时即可实现降维。 对于近红外光谱样本量大、 变量多、 维度高等问题, 提出一种基于深度信念网络的近红外光谱建模方法, 定量分析物性浓度。 该方法以近红外光谱数据作为输入信号, 首先对多层受限玻尔兹曼机（RBM）进行无监督学习, 实现光谱自身特征的提取; 然后利用目标理化值对网络进行微调得到最优模型参数。 在建立DBN校正模型的基础下对其进行改进, 建立DBN-PLS校正模型。 通过建立DBN近红外光谱校正模型、 DBN-PLS近红外光谱校正模型, 验证了DBN建模和DBN-PLS建模的可行性, 并引入决定系数（R2）和均方误差（MSE）两个模型评价指标, 对比分析了传统BP建模和DBN建模的精度。 分析结果表明, 相较于传统定量分析方法建模, 利用DBN方法建模和DBN-PLS方法建模可以提高预测精度。

Near infrared NIR spectroscopy is a fast, non-destructive quantitative analysis tool that has been widely used in various industries. How to build an effective and accurate model is of importance to the application of NIR spectroscopy. At present, most commonly used quantitative analysis methods are based on shallow models, while Deep Belief Network (DBN) is a probability-based deep model. It can automatically learn the effective feature representation of the input, and as long as the number of last hidden layer output nodes is lower than the dimension of the input spectrum, the spectral data can be reduced in dimension while the feature extraction is completed on the spectral data. Near-infrared spectroscopy is characterized by a large sample size, large variables, and high dimensionality. This paper proposes a near-infrared spectroscopy modeling method based on a deep belief network to estimate the physical concentration. The method uses near-infrared spectroscopy data as the input layer. Firstly, unsupervised learning of the multi-restricted Boltzmenn Machines (RBM) is employed to achieve the feature extraction of the spectrum itself. Then the target physicochemical value is used to fine tune the network, and optimize model parameters. Based on the DBN calibration model, the final regression layer of the deep belief network is developed by the PLS method, and the DBN-PLS calibration model may avoid the optimal local problem caused by the gradient descent algorithm. In this paper, the feasibility of DBN modeling and DBN-PLS modeling is verified by two model evaluation indexes including decision coefficient (R2) and mean square error (mse), and the traditional BP modeling and DBN modeling are compared and analyzed. The analysis results show thatDBN method modeling and DBN-PLS method modeling can improve the prediction accuracy.