Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, Fujian 361005, PR China.
College of Information Science and Engineering, Huaqiao University, Xiamen, Fujian 361005, PR China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Jan 5;264:120254. doi: 10.1016/j.saa.2021.120254. Epub 2021 Aug 6.
Spectral unmixing algorithm is one of the key technologies for spectral flow cytometer in biology, chemistry and medicine. The proposed algorithm can separate the overlapping spectra automatically without the premeasured single stained or un-stained samples as the basic pure spectra. Genetic algorithm is adopted to search the optimal positions and peak sharps of the basic spectra derived from the unknown components, and then the concentration of each component can be estimated simultaneously by least squares method. Compared with conventional methods, the proposed algorithm has a wider application scope, such as the multi-stained samples with unknown components or the samples with auto-fluorescence. In the simulation, the convergence rate, accuracy and stability of the proposed algorithm are evaluated under the conditions of completely and partly unknown components. In the experiment, the flow spectra of cyanobacteria are processed, and the results demonstrate the feasibility and effectiveness of the proposed algorithm.
光谱分解算法是生物学、化学和医学中光谱流式细胞仪的关键技术之一。该算法可以在没有预先测量的单染色或未染色样本作为基本纯光谱的情况下，自动分离重叠光谱。遗传算法被用来搜索未知成分衍生的基本光谱的最佳位置和峰锐度，然后通过最小二乘法同时估计每个成分的浓度。与传统方法相比，该算法具有更广泛的应用范围，例如具有未知成分的多染色样本或具有自发荧光的样本。在模拟中，在完全和部分未知成分的情况下，评估了所提出算法的收敛速度、准确性和稳定性。在实验中，处理了蓝藻的流谱，结果证明了所提出算法的可行性和有效性。
