利用一种基于760.88 nm处近红外吸收光谱的氧气浓度探测方法，可以实现开放环境中西林瓶内氧气残留浓度在灯检机上的原位、非接触测量检测。该方法采用基于TDLAS的波长调制光谱技术（WMS），在开放光路环境下利用主成分提取法（PCA）对WMS的二次谐波进行主特性提取，在抑制噪声的同时可降低数据量，提高了后期数据处理速度，然后利用遗传算法（GA）优化的BP神经网络建立浓度反演模型。实验结果证明：该方法相对利用半峰值面积的最小二乘拟合方法其平均相对误差从8.32%减少到1.12%，决定系数提升了8.86%，相比单独PCA-BP神经模型的平均相对误差从3.80%减少到1.12%，决定系数提升了2.81%，该方法有效地抑制了开放光路环境所致测量仪器的信号随机扰动，提高了西林瓶内氧气残留浓度检测的准确度和稳定性。

The oxygen concentration detection method based on near infrared absorption spectrum at 760.88nm was used to realize in-situ, non-contact detection of the residual oxygen concentration in the vial in the open environment on the lamp detector. The method based on Wavelength-modulated tunable diode laser absorption spectroscopy technology （TDLAS/WMS） uses the principal component extraction method （PCA） to extract the main characteristics of the second harmonic in the open light path, and then utilizes the genetic algorithm （GA） to optimize the BP neural network to build a concentration inversion model. This method can reduce the data required for calculation, suppress noise and improve the processing speed of post-processing data. The experimental results show that the average relative error of this method is reduced from 8.32% to 1.12%, and the coefficient of determination is increased by 8.86%, compared with the least square fitting method using semi-peak area. Compared with the average relative error of the single PCA-BP neural model, the mean relative error is reduced from 3.80% to 1.12%, and the coefficient of determination is increased by 2.81%.This method can effectively suppress the signal random disturbance caused by the open light path environment and improve the accuracy and stability of the detection of oxygen residual concentration in the vial.