基于三维荧光的产麻痹性贝毒藻浓度监测研究

近年来，我国沿海赤潮发生的次数和面积持续增加，经济损失严重。根据赤潮的毒性特点，通常分为三类，分别为无毒赤潮、鱼毒性赤潮和有毒赤潮。其中有毒赤潮产生的毒素主要是麻痹性贝毒，其由于分布广，毒性强成为危害最大的生物毒素之一。根据麻痹性贝毒的摄入量不同，人类误食染毒的贝类后，身体各部位会出现刺痛或灼热的感觉，然后全身麻痹，严重者甚至在短时间内死亡。近年来，多地出现人类误食染毒的贝类后死亡的事件。麻痹性贝毒的摄入量主要取决于产麻痹性贝毒藻的浓度，因此，对产麻痹性贝毒藻浓度的监测就显着尤为重要。提出了用三维荧光光谱结合化学计量学方法建立产麻痹性贝毒藻定量分析模型。首先，利用F-4600荧光光度计采集微小亚历山大藻（Alexandrium minimum）、链状裸甲藻（Gymnodinium catenatum）和太平洋亚历山大藻（Alexandrium pacificum）三种典型的产麻痹性贝毒藻类三维荧光光谱数据，获取藻类样本的三维荧光光谱等高线图，并进行图谱分析；然后，利用不同激发波长下的发射光谱数据建立产麻痹性贝毒藻三维荧光光谱的串行表示模型，提取新的特征；最后，将新的特征数据分别作为粒子群优化最小二乘支持向量机算法（particle swarm optimization-least squares support vector machine, PSO-LSSVM）和偏最小二乘回归（partial least squares regression, PLSR）的输入，建立产麻痹性贝毒藻的定量分析模型。结果表明，运用粒子群优化最小二乘支持向量机算法建立的产麻痹性贝毒藻的定量分析模型普遍优于偏最小二乘回归算法。当激发波长选择460和530 nm，发射波长选择650～750 nm作为PSO-LSSVM的输入数据，建立的产麻痹性贝毒藻的定量分析模型效果最好，结果显示R_c=0.999 9，RMSEC=0.017 1，R_p=0.949 2，RMSEP=0.291 0。这体现出三维荧光光谱结合PSO-LSSVM定量分析模型可有效地监测活体产麻痹性贝毒藻的浓度数值，为产麻痹性贝毒藻浓度检测提供了一种在线检测的新方法。

Concentration Monitoring of Paralytic Shellfish Poison Producing Algae Based on Three Dimensional Fluorescence Spectroscopy

The frequency and area of red tide in China’s coastal areas continue to increase, resulting in serious economic losses. According to the toxic characteristics of red tide, it is usually classified into three categories: non-toxic red tide, ichthyotoxic red tide and toxic red tide. Among them, paralytic shellfish poison is the main toxin produced by toxic red tide. Because of its wide distribution and strong toxicity have become one of the most harmful biological toxins. According to the different intake of paralytic shellfish poisoning, people will feel tingling or burning in various parts of the body after eating shellfish poisoning, and then they will be paralyzed or even die in a short time. Many people have died after eating shellfish. The intake of paralytic shellfish poisoning mainly depends on the concentration of paralytic shellfish poisoning algae. Therefore, it is particularly important to monitor the concentration of paralytic shellfish poison producing algae. In this paper, a quantitative analysis model of paralytic shellfish poison producing algae was established by three-dimensional fluorescence spectroscopy combined with chemometrics. Firstly, The three-dimensional fluorescence spectrum contour map of algae samples were analyzed by f-4600 fluorophotometer, including Alexandrium minimum, Gymnodinium catenatum and Alexandrium. Then, the new features of the three-dimensional fluorescence spectrum of paralytic shellfish poisoning algae were established using the emission spectrum data under different excitation wavelengths. Finally, the new feature was the input of particle swarm optimization least squares support vector machine and partial least squares regression respectively, and the quantitative analysis model of paralytic shellfish poisoning algae was made. The results showed that the quantitative analysis model established by Particle Swarm Optimization- Least Squares Support Vector Machine algorithm was generally better than the partial least squares regression algorithm when using the emission wavelength of 650~750 nm under an excitation wavelength of 460 and 530 nm. The results show that R_C=0.999 9, RMSEC=0.017 1, R_P=0.949 2, RMSEP=0.291 0. It shows that the three-dimensional fluorescence spectrum combined with the quantitative analysis model of Particle Swarm Optimization- Least Squares Support Vector Machine can effectively monitor the concentration value of paralytic shellfish poison producing algae in vivo, which provides a new online detection method for the concentration detection of paralytic shellfish poison producing algae.