为了提高可见-近红外（Vis-NIR）光谱法检测水质pH值的精度和稳定性，基于连续投影算法（SPA）和粒子群优化-最小二乘支持向量机（PSO-LSSVM）建立了多元校正模型。采集60个不同pH值水溶液样品的Vis-NIR光谱数据，运用Savitzky-Golay卷积平滑和标准正态变量变换对原始光谱数据进行预处理。基于SPA筛选的特征波长和PSO算法自动优化LSSVM的建模参数，建立多元非线性校正模型。结果表明，相比于其他对比模型，SPA-PSO-LSSVM模型具有更高的精度与更优的稳定性，验证集的均误差方根为0.67、决定系数为0.91，剩余预测偏差为3.10。

To improve the detecting precision and robustness in the determination of water pH value using visible near-infrared (Vis-NIR) spectroscopy, a multivariate calibration model is constructed based on successive projections algorithm (SPA) and particle swarm optimization-least squares support vector machine (PSO-LSSVM). The Vis-NIR spectra data of 60 water samples with different pH values are collected, and the original spectral data are preprocessed by Savitzky-Golay smoothing and standard normal variate. Based on the characteristic wavelength of SPA screening and PSO algorithm, the modeling parameters of LSSVM are automatically optimized and a multivariate nonlinear calibration model is established. The results show that the SPA-PSO-LSSVM model has higher accuracy and stability than the comparison models. For the verification set, the root mean square error is 0.67, the coefficient of determination is 0.91, and the residual predictive deviation is 3.10.