微藻高效培养是微藻生物能源开发利用的关键和前提, 而在营养充足的培养条件下生长迅速但较易受到环境污染和影响, 因此微藻生长过程中对其生长状况进行监测意义重大。 高光谱成像技术同时拥有丰富物质品质信号的优点和图像包含丰富品质分布空间信息的优点, 可为微藻的快速无损检测提供新的方法和手段。 分别采集小球藻、 球等鞭金藻和螺旋藻三种微藻各45个样本的高光谱图像, 并提取样本感兴趣区域（ROI）的平均光谱。 利用连续投影算法（SPA）波长优选之后, 取30个建模集样本的光谱数据与其相应的生物量建立多元线性回归（MLR）模型, 对15个预测集样本的生物量进行预测, 小球藻、 球等鞭金藻和螺旋藻预测相关系数（r）分别为0.950, 0.969和0.961, 预测均方根误差（RMSEP）为0.010 2, 0.010 7和0.017 1, 获得了较好的预测精度。 最后, 用所建MLR模型对预测集图像上每个像素点的生物量加以预测, 采用Matlab图像编程处理将不同的生物量用不同的颜色表示, 最终以伪彩图的形式实现藻液生物量的可视化。 研究结果表明, 高光谱成像技术对小球藻和螺旋藻藻液生物量的可视化效果较好, 对球等鞭金藻的预测效果还需要进一步改进。 本研究为实现微藻生长信息的快速获取和进一步开展微藻生物质能源利用奠定了一定的研究基础。

Effective cultivation of the microalgae is the key issue for microalgal bio-energy utilization. In nutrient rich culture conditions, the microalge have a fast growth rate, but they are more susceptible to environmental pollution and influence. So to monitor the the growth process of microalgae is significant during cultivating. Hyperspectral imaging has the advantages of both spectra and image analysis. The spectra contain abundant material quality signal and the image contains abundant spatial information of the material about the chemical distribution. It can achieve the rapid information acquisition and access a large amount of data. In this paper, the authors collected the hyperspectral images of forty-five samples of Chlorella sp., Isochrysis galbana, and Spirulina sp., respectively. The average spectra of the region of interest (ROI) were extracted. After applying successive projection algorithm (SPA), the authors established the multiple linear regression (MLR) model with the spectra and corresponding biomass of 30 samples, 15 samples were used as the prediction set. For Chlorella sp., Isochrysis galbana, and Spirulina sp., the correlation coefficient of prediction (rpre) are 0.950, 0.969 and 0.961, the root mean square error of prediction (RMSEP) for 0.010 2, 0.010 7 and 0.007 1, respectively. Finally, the authors used the MLR model to predict biomass for each pixel in the images of prediction set; images displayed in different colors for visualization based on pseudo-color images with the help of a Matlab program. The results show that using hyperspectral imaging technique to predict the biomass of Chlorella sp. and Spirulina sp. were better, but for the Isochrysis galbana visualization needs to be further improved. This research set the basis for rapidly detecting the growth of microalgae and using the microalgae as the bio-energy.