The reflection and transmission spectral information of Haematococcus pluvialis were collected through hyperspectral imager ,the contents of chlorophyll a and b of H .pluvialis were predicted combined with chemometrics method .For predicting the chlorophyll content of H .pluvialis ,we compared five pretreatment methods [(Raw) ,Baseline Off (BO) ,Savitzky‐Golay smoothing(SG) ,multiplication scatter correction (MSC) ,standard normal variate (SNV)] combined with partial least square (PLS) modeling ,and then chosen better pretreatment and used successive projections algorithm (SPA) Then we compared three modeling algorithm [PLS ,multiple linear regression (MLR) ,least square support vector machine (LS‐SVM)] ,and chosen bet‐ter modeling algorithm .For predicting the chlorophyll a content of H .pluvialis by hyperspectral reflection technology ,the re‐sult shows that SNV pretreatment and SPA‐MLR modeling algorithm work best (RPD value reached 3 .429 6) .For predicting the chlorophyll a content of Haematococcus pluvialis by hyperspectral transmission technology ,the result shows that BO pretreat‐ment and SPA‐LS‐SVM modeling algorithm work best (RPD value reached 3 .156 3) .Both for predicting the chlorophyll b con‐tent of H .pluvialis by hyperspectral reflection and transmission technology ,the result shows that BO pretreatment and SPA‐MLR modeling algorithm are best (RPD value reached 1 .822 1 and 2 .013 2) .The research has shown that it can predict the con‐tents of chlorophyll a and chlorophyll b through some pretreatment algorithms and modeling algorithms by hyperspectral reflec‐tion and transmission technology and it can provide a new approach for detecting the content of chlorophyll a and chlorophyll b .%通过高光谱成像仪采集雨生红球藻（Haematococcus pluvialis）反射和透射的光谱信息，结合化学计量学方法，对雨生红球藻叶绿素a、叶绿素b含量进行预测。比较了5种预处理方法[原始光谱（raw ）、基线校正（baseline correction ，BO ）、卷积平滑（Savitzky‐Golay smoothing ，SG）、多元散射校正（multiplicative scatter correction ，MSC）、变量标准化（standard normal variate ， SNV）]，并结合偏最小二乘回归（partial least square ，PLS）建模的结果，选择较好的预处理方法后采用连续投影算法（successive projections algorithm ，SPA）提取特征波长，并比较3种建模算法[PLS、多元线性回归（multiple linear regression ，MLR）、最小二乘支持向量机（least square support vector machine ，LS‐SVM ）]的预测结果，选择效果较好的建模算法用于预测叶绿素含量。其中，高光谱成像反射法对叶绿素a含量的预测，显示SNV预处理算法和SPA‐MLR建模算法的效果较好，预测的剩余预测偏差（residual predictive deviation ，RPD）达到3．4296；高光谱成像透射法对雨生红球藻叶绿素a含量的预测显示，BO预处理算法和SPA‐LS‐SVM建模算法的效果较好，预测的RPD值达到3．1563；同样地，对于高光谱成像反射法和透射法雨生红球藻叶绿素b含量的预测，显示均是采用BO预处理算法和SPA‐MLR建模算法的效果较好，预测的RPD值分别为1．8221和2．0132。研究表明，采用高光谱成像反射和透射系统，通过一定的预处理结合建模算法可以对雨生红球藻叶绿素a、叶绿素b含量进行预测，为叶绿素a、叶绿素b含量的检测提供了1种新的方法。
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