Reasearch On The Quantitative Detection Method Of Microalgae Cell Concentration

,icroalgae are a kind of low-level phytoplankton,which are widely distributed all over the world.It is the main component of water primary productivity.Because microalgae are rich in protein,amino acid,high unsaturated fatty acid,pigment and many kinds of bioactive substances,they have become an important source of food,medicine,feed and fuel.Therefore,many research institutions are developing microalgae culture.Because microalgae are easy to reproduce in water rich in nitrogen and phosphorus,they can purify water eutrophication.Once explosive growth causes serious water bloom,microalgae can also be used for sewage treatment and water quality monitoring.In microalgae culture and ecological monitoring,it is very important to accurately determine the biomass concentration of microalgae.However,due to the small and large number of microalgae,it is difficult and time-consuming to accurately determine their biomass,which has become one of the problems in research and production practice.In this study,Chlorella and Chlamydomonas reinhardtii were taken as the research objects.The in-situ fluorescence technology,visible near-infrared absorption spectroscopy technology and microscopic imaging technology were combined with a variety of chemometrics algorithms and image processing algorithms to detect the concentration of high concentration microalgae cells,providing information support for the monitoring of microalgae growth information.:(1)The detection method of algal cell concentration based on image processing technology was studied.Firstly,the microscope and the matched CCD camera are used as image acquisition equipment to acquire microalgae images under different concentration conditions and different sampling environments;secondly,different image processing algorithms are selected according to the characteristics of different images,including the combination of HSV image transformation,? transformation and Retinex transformation to eliminate the blood cell counting plate in the microscopic image grid background and using Laplace operator to expand the boundary region of cells;At last,the overlapping microalgae cells are segmented by the improved watershed algorithm,and the microalgae cells are counted by the region labeling method,and the cell concentration of microalgae is obtained by the conversion formula.At the same time,the automatic quantitative software of microalgae cell concentration based on image processing technology was developed.Compared with the manual measurement method,the detection time is reduced from 5 minutes to less than 1 minute,and the detection accuracy is improved from 80%to 99%.(2)The cell concentration of Chlamydomonas reinhardtii was determined by single excitation fluorescence spectrometry combine with artificial neural network(ANN).Light-emitting diodes(LEDs)with a wavelength of 470 nm were used as excitation light sources to electronically excite samples of Chlamydomonas reinhardtii at different concentrations.The results show that with the increase of algal concentration,there is a nonlinear relationship between algal fluorescence intensity and algal cell concentration,and the fluorescence peak moves to the long wave direction.In order to monitor the concentration of microalgae cells quickly and accurately,a GA-BP model was established.The model can realize the rapid,unmarked,rough estimation and monitoring of microalgae cell concentration in the range of 2×105?6.4×106mL-1.At the same time,samples under different growth conditions were used to verify the model.By comparing GA-BP model with other algal cell concentration detection models(BP artificial neural network,PLS model and PCR model),it is found that GA-BP model is more accurate.In addition,the prediction performance of the model was verified by independent data set.The trained model was applied to predict the concentration of microalgae cells under three different culture conditions.The prediction results showed that the Mae and mare of the normal and nitrogen deficient microalgae solutions were 8.96×105mL-1,0.0178 and 6.585×105mL-1,0.039,respectively,which achieved good prediction results.(3)The concept of "reconstructed absorption spectrum" was proposed,and a method for monitoring the concentration of algal cells based on the reconstructed absorption spectrum was proposed.Taking Chlorella as the research object,by measuring the reference solution and algae samples with different integration times,the absorption spectrum of the sample in a larger concentration range can be obtained.Using the conversion relationship between integration time and absorbance,the measured absorption spectrum can be converted into the reconstructed absorption spectrum in the same integration time as the reference solution.Finally,taking the reconstructed absorption spectrum as the research object,the support vector machine regression(SVR)and partial least squares(PLS)were used to establish the algal cell concentration prediction models.The practical application of fiber optic spectrometer as the main body shows that this method can measure the concentration of chlorella cells in the concentration range of 3.5×105?1.4×108 mL-1,covering the actual concentration range of microalgae culture.The coefficient of determination R2 of the evaluation indexes of the two models can reach 0.9762 and 0.9441.Through comparison with previous research results,it is concluded that The method designed in this paper can expand the monitoring range of algal cell concentration in the simplest way,simplify the detection process,improve the working efficiency and detection accuracy,expand the overall dynamic range of the analyzer,reduce the measurement cost,and improve the detection ability of the traditional absorption spectrum technology.