Development of qualitative or quantitative models is essential to exploit the full potential of near infrared (NIR) spectroscopy. In tandem with one-dimensional convolutional neural network (1D-CNN), a data-driven model is developed using NIR spectroscopy to estimate organic contents. First, the 1D-CNN model is designed to capture the features of the NIR spectra by means of several convolutional and pooling operations. Then, the suitable hyper-parameters of 1D-CNN are obtained by using the grid search algorithm to achieve the optimal performance. Furthermore, the dropout operation is added into the 1D-CNN to suppress the overfitting problem by means of removing some neurons, and the probability distribution of throwing follows the Bernoulli distribution. The developed framework is validated by the application in the sugar content estimation of Huangshan Maofeng tea. The experimental results demonstrate that the key features of the NIR spectra are successfully extracted by the proposed strategy; thereby, a new effective scheme for analyzing NIR spectra is provided for food analysis.

定性或定量模型的开发对于充分发挥近红外 (NIR) 光谱的潜力至关重要。与一维卷积神经网络 (1D-CNN) 相结合，使用 NIR 光谱开发了一种数据驱动模型来估计有机物含量。首先，1D-CNN 模型旨在通过几个卷积和池化操作来捕获 NIR 光谱的特征。然后，通过网格搜索算法获得合适的一维CNN超参数，以达到最佳性能。此外，在 1D-CNN 中加入了 dropout 操作，通过去除一些神经元来抑制过拟合问题，并且抛出的概率分布遵循伯努利分布。所开发的框架在黄山毛峰茶含糖量估算中的应用得到了验证。实验结果表明，该策略成功提取了近红外光谱的关键特征；从而为食品分析提供了一种新的有效的近红外光谱分析方案。