According to the difficulty in selecting parameter of least squaresupport vector machine (LS-SVM) when modeling on the gas mixture, and the highcomputational complexity of the infrared spectrum data, LS-SVM optimized byparticle swarm optimization (PSO) algorithm was proposed to build an infraredspectrum quantitative analysis model with feature extracted by principalcomponent analysis (PCA). Firstly, seven feature variables were extracted by PCAas the input of the model from 550 infrared spectrum data of the main absorptionapex field, so the computational complexity was reduced. This model aimed atthree components of gas mixture, in which methane, ethane and propane gases areincluded. The concentration of each component ranged from 0.1% to 1%, 0.1% to 1%and 0.1% to 1.5% respectively. Each component quantitative analysis model wasbuilt by LS-SVM and the parameters were optimized by PSO algorithm, then theregression model would be reconstructed according to the optimal parameters. Thismethod replaced the traditional ergodic optimization. The experiment results showthat the time of offline modeling by PSOwas reduced to one fortieth of that ofergodic optimizing. The precision of the model was corresponsive. It can meet therequirement of the measure. PSO algorithm has more superior performance on globaloptimization and convergence speed. So it is feasible to combine PSO algorithmwith LS-SVM to create the infrared spectrum quantitative analysis model. It hasdefinite practice significance and application value.square support vector machine; Quantitative analysis; Gas mixture; Principalcomponent analysis