Microalgae lipid and its degree of fatty acid unsaturation (DU) have the ability to undergo programmatic changes in response to different environmental and culture conditions. A real-time and reliable approach to monitor the change patterns of lipid-accumulation and DU is crucial for microalgal culture and bioprocess control. In this study, a fast visual and nondestructive method based on visible/short-wave near-infrared hyperspectral imaging (V/SNIR HSI) at 400–1000 nm region was developed to investigate the dynamic changes and content distribution of lipids and DU of Scenedesmus obliquus treated by nitrogen-low stress (0.3 g/L NaNO₃, NL) and nitrogen-free stress (0 g/L NaNO₃, NF). After collecting the average spectra from the region of interests (ROIs), the competitive adaptive reweighted sampling (CARS) algorithm and the successive projection algorithm (SPA) were applied to pick effective wavebands from the spectral data for the establishment of the lipid concentration and DU estimation models including partial least squares regression (PLSR) and multiple linear regression (MLR). The prediction results show the CARS-PLSR (Rp = 0.9762, RMSEp = 0.0143) and CARS-MLR (Rp = 0.9477, RMSEp = 0.5718) performed the best for lipids and DU of S. obliquus, respectively. Based on the optimal models, the spatial distribution of lipid abundance and DU in liquid suspension were visualized, which could be helpful in exploring the dynamic process of lipid synthesis and DU of S. obliquus. The overall results show that V/SNIR HSI is a promising technique for fast and noninvasively monitoring the lipid-accumulation and its characteristics i.e. DU in situ, and is expected to be applied for efficient and high-quality lipid production in microalgae industries.

微藻脂质及其脂肪酸不饱和度（DU）具有响应不同环境和培养条件而经历程序性变化的能力。实时可靠的方法来监测脂质蓄积和DU的变化模式对于微藻培养和生物过程控制至关重要。在这项研究中，基于近红外在400-1000 nm范围高光谱成像（V / SNIR HSI）可见/短波快速视觉和非破坏性方法的开发是为了调查的动态变化和脂质的内容分发和DU栅藻斜由含氮低应力（0.3克/ L的NaNO处理₃，NL）和无氮的应力（0克/升的NaNO ₃，NF）。在从目标区域（ROIs）收集平均光谱后，应用竞争性自适应加权加权采样（CARS）算法和连续投影算法（SPA）从光谱数据中选择有效波段，以建立脂质浓度和DU估计模型包括偏最小二乘回归（PLSR）和多元线性回归（MLR）。预测结果表明，CARS-PLSR（R p =  0.9762，RMSE p =  0.0143）和CARS-MLR（R p =  0.9477，RMSE p =  0.5718）对斜生链球菌的脂质和DU表现最佳。， 分别。在最佳模型的基础上，对液体悬液中脂质丰度和DU的空间分布进行了可视化，这有助于探索斜生链球菌脂质合成和DU的动态过程。总体结果表明，V / SNIR HSI是一种有前途的技术，可以快速，无创地监测脂质积累及其特性，即原位DU，并有望将其用于微藻行业中高效，高质量的脂质生产。