基于高光谱的微藻生物膜生长特性研究

发布时间：2018-05-16 18:01

  本文选题：微藻 + 生物膜量　； 参考：《重庆理工大学》2017年硕士论文

【摘要】：微藻的高效培养是生物能源开发利用的前提及关键,目前关于微藻培养方法有悬浮培养和固定化培养。虽然微藻悬浮态培养模式的操作方便,但采收困难、复杂而且采收的成本比较高;而微藻固定化培养模式的操作稳定、生物量密度高,同时采收便利快捷、能耗低等优势,目前已成为微藻领域研究者关注的焦点。虽然微藻固定化培养具有诸多优点,但是微藻品质,尤其是微藻生物量受生物膜生长因素影响显著。因此,开发出一种快速简便的无损在线检测方法对微藻生物膜生长信息进行监测,以便于及时调节生长条件来提高微藻生物膜量尤为重要。高光谱成像技术作为一种新型的无损、快速、准确的检测技术,该技术结合了计算机视觉和光谱检测两种技术的优点,能够很好的记录生物膜生长的丰富信息,可为微藻生物量的快速无损检测找到了一条简便有效的方法。本文利用高光谱技术与生物测量技术结合,对不同生长环境条件下的微藻生物膜反射光谱特征与生物膜量预测两方面分别进行研究分析,提出了一种基于高光谱的微藻生物膜量的监测方法,为微藻生物膜生长信息的快速获取提供了技术支持以及微藻生物膜的高效培养提供了参考。取得的研究结果如下:(1)培养了不同温度、不同PH值以及不同光强条件下的微藻生物膜。(2)根据高光谱成像仪的理论基础,搭建了高光谱图像采集系统,并对微藻生物膜生长信息进行检测,获取了不同温度、PH值以及光强条件下微藻生物膜的高光谱信息。(3)根据微藻生物膜的原始光谱反射值有三个明显的特征峰,特征波长(789nm、811nm、930nm)处对应的光谱反射值随培养时间的增加而降低。(4)根据采集到微藻生物膜的光谱反射数据,通过衡量生物量的光谱特征变量,提取出了25个与微藻生物膜量有关的光谱特征变量,计算出了每个特征变量的相关性系数。(5)采用相关性系数绝对值大小来衡量,提取出了与微藻生物膜干重关联度前四的光谱特征变量,分别为:SDy)+SDy)/(SDr-(SDr、SDr、OSAVI、NDVI。将这四个光谱特征变量构建了三种数学预测模型,对比分析了BP神经网络预测模型、单一光谱特征变量以及多特征变量融合的预测模型,通过模型预测的耗时以及精确度来衡量,结果表明多特征变量融合模型对微藻生物膜量的综合评价最高。(6)根据原始光谱反射值的变化以及微藻生物膜量的预测模型结果分析,小球藻生物膜培养的最佳生长环境是:温度为28℃、PH值为8、光照强度为3500lx,而且预测模型还可以预测出不同时间的微藻生物膜量。
[Abstract]:The efficient cultivation of microalgae is the premise and key of bioenergy development and utilization. At present, the methods of microalgae culture include suspension culture and immobilized culture. Although the suspension culture model of microalgae is easy to operate, it is difficult to harvest, complex and the cost of harvesting is relatively high, while the immobilized culture model of microalgae has the advantages of stable operation, high biomass density, convenient and fast harvesting, low energy consumption, and so on. At present, microalgae researchers have become the focus of attention. Although the immobilized culture of microalgae has many advantages, the quality of microalgae, especially the biomass of microalgae, is significantly affected by biofilm growth factors. Therefore, it is very important to develop a fast and simple on-line nondestructive detection method to monitor the growth information of microalgae biofilm in order to adjust the growth conditions in time to improve the biofilm quantity of microalgae. As a new nondestructive, fast and accurate detection technology, hyperspectral imaging technology combines the advantages of computer vision and spectral detection, and can well record the rich information of biofilm growth. A simple and effective method can be found for rapid nondestructive detection of microalgae biomass. In this paper, the characteristics of microalgae biofilm reflectance spectrum and biofilm quantity prediction under different growing environment were studied and analyzed by combining hyperspectral technique with biological measurement technology. A hyperspectral monitoring method for microalgae biofilm is proposed, which provides a reference for the rapid acquisition of microalgae biofilm growth information and the efficient cultivation of microalgae biofilm. The results obtained are as follows: (1) the microalgae biofilm was cultivated at different temperatures, different PH values and different light intensities. (2) based on the theoretical basis of the hyperspectral imager, a hyperspectral image acquisition system was built. The hyperspectral information of microalgae biofilm under different temperature and light intensity was obtained by detecting the growth information of microalgae biofilm. There were three distinct characteristic peaks according to the original spectral reflectance of microalgae biofilm. The corresponding spectral reflectance at the characteristic wavelength of 789 nm ~ 811 nm ~ 930 nm decreased with the increase of culture time. (4) according to the spectral reflection data collected from microalgae biofilm, the spectral characteristic variables of biomass were measured. Twenty-five spectral characteristic variables related to biofilm amount of microalgae were extracted, and the correlation coefficient of each characteristic variable was calculated. The spectral characteristic variables of the first four levels of correlation with dry weight of microalgae biofilm were extracted, which were: 1 SDY) SDY / SDrSDr SSAVI / NDVI. The four spectral characteristic variables are used to construct three mathematical prediction models, and the BP neural network prediction model, the single spectral characteristic variable and the multiple characteristic variable fusion prediction model are compared and analyzed. The results show that the multivariate fusion model has the highest comprehensive evaluation of biofilm quantity of microalgae, according to the change of original spectral reflectance and the result of prediction model of biofilm quantity of microalgae. The optimum growth environment for the biofilm culture of Chlorella vulgaris was as follows: the temperature was 28 鈩，

本文编号：1897859