Research On Rapid And Non-invasive Determination Techniques For Microalgal Growth Information And Qualities Of Algal Products

Microalgae have broad application prospects in many fields such as fuel, energy, food industry, biotechnology, pharmaceutical industry, animal feed, environmental monitoring, and pollution control. Microalgae have excellent qualities of fast growth and reproduction, high photosynthetic efficiency, short doubling time, high production per acre, low greenhouse gas emission and little or no competition with food production. Because of the growing threats of energy, food and environmental issues, microalgal biorescoure industry has gained more attention worldwide. At present, one of the key questions is how to improve the growing efficiency, increase the value of algal products and reduce the production cost. This thesis proposed a new concept of DIGITAL MICROALGAE, which means using high-tech methods to rapidly and non-invasively measure the microalgal growth information and qualities of algal products in the processes of microalgal breeding, cultivation, harvesting, processing and sales. The obtained information can be used for the optimal decision-making control and system operation, and ultimately optimizes the production and management of microalgae, and improves production efficiency, ensures the quality of products, reduces costs and increases economic benefits. The foundation and key in the process of Digital Microalgae is the rapid and non-invasive determination of microalgal growth information and qualities of algal products. Digital Microalgae will become the research priorities and hot spots of the world. At present, however, conventional off-line analyses are rather time-consuming and inefficient which can not meet the needs of modern production of microalgal biorescoure industry.In this work, according to the requirements of relevant quality information of microalgal in the processes of breeding, cultivation, harvesting, processing and sales, microscopy imaging technique, spectral analysis technique, hyperspectral imaging technique, nuclear magnetic resonance (NMR) technique combined with image process algorithms and chemometrics were used for rapid and non-invasive determination of comprehensive quality information of microalgal such as microscopic morphological features, growth information, qualities of algal oils and algal powders to further provide information support for the high efficient production and system optimization of microalgal biorescoure industry. The main research contents and results are shown as follow:(1) Microscopy image processing algorithms were proposed to rapid measure morphological features of Spirulina microalga filaments. After the image preprocessing, binary images of each filament at vertical position were obtained. The filament rotation was applied to make the filament characteristic parameters extraction easier. The vertices of filaments were determined based on discrete contour evolution (DCE) algorithm. Because traditional algorithm of skeleton extraction has the disadvantage of bringing in branches, a new algorithm was proposed to reduce the branches and obtain the Spirulina length based on the main skeleton. Morphological features such as length, diameter of helix, degree of spiralization, width of filament, pitch, helix number, and helix pitch were obtained based on the vertices of filaments. The errors between manually obtained and automatic calculated values were 4.7%for degree of spiralization,5.6%for diameter of helix and 6.2%for width of filament, respectively. Furthermore, a Spirulina Morphological Feature Extraction System Software was developed. The feature extraction time is about 30 seconds and the measurement accuracy was 99%by using the software, while manual measurement usually takes five minutes with the accuracy of 93%.(2) Spectroscopy and hyperspectral imaging techniques were for the first time applied to establish the quantitative relationship models between the spectra and hyperspectral image information and algal growing information respectively and realized the rapid determination of algal growing information. In the spectral analysis, transmittance model and transflectance model were better than reflectance model and reflectance measured outside the bioreactor model. The best coefficients of determination of prediction (rpre2e)values of the models were 0.9836,0.9777 and 0.9487 for the spectral analysis of the dry weight, lipid content per unit volume and lipid content per unit weight. There were average 9.41 variables for the spectral efficient variable models. Compared to the whole spectral models,99.62%of the variables were eliminated, while the average rpre2 values of the spectral efficient variable models only decreased 4.19%. In all the twelve spectral efficient variable models, for eight models, uninformative variable elimination combined with successive projections algorithm (UVE-SPA) performed better than using SPA directly in the variable selection processes. It shows that UVE could effectively improve the variable selection accuracy of SPA. The rpre2 values of the hyperspectral image models for the measurement of dry weight, chlorophyll content per unit volume, chlorophyll content per unit weight, chlorophyll a content per unit volume, chlorophyll a content per unit weight, chlorophyll b content per unit volume and chlorophyll b content per unit weight were 0.9891,0.9882,0.9242,0.9895,0.9444, and 0.9780 respectively. There were avaerage 7.41 variables for the hyperspectral image efficient variable models. Compared to the whole spectral models,99.7% variables were eliminated, while the average rpre2 value of the spectral efficient variable models was 0.9550 (0.9573 for the whole spectral model). In all the seven hyperspectral image efficient variable models, for five models, UVE-SPA was better than using SPA directly in the variable selection processes. It shows that UVE could improve the hyperspectral image variable selection accuracy compared to using SPA directly. Moreover, the quality distribution maps of microalgal slurry were obtained based on the algal hyperspectral images. The results show that hyperspectral imaging technique is better than RGB images to measure the growing information of algae.(3) Rapid determination method and system of the content of w-3 PUFAs in algal oil were proposed. NMR obtained the best DHA and EPA prediction models. The best coefficients of determination of validation (rpre2) values of two models were 0.9625 and 0.9674 respectively. The best rpre2, values of visible and short-wave near infrared spectral model, long-wave near infrared spectral model, and mid-infrared spectral model were 0.9790,0.9232 and 0.8748 for DHA analysis,0.9213,0.8757 and 0.8857 for EPA analysis respectively. However their results were not as good as those of NMR models. The Raman spectroscopy with 514 nm light laser didn't perform well for DHA and EPA prediction. The RMSECV of their models were 21.2707 and 1.8529, which were 1.67 and 2.68 times of those of NMR models. The effective variable selection can improve the DHA and EPA prediction accuracy. Models'average RMSECV values decreased 18.70%and 29.03%respectively. In all the ten efficient variable models, for nine models, UVE-SPA was better than using SPA directly in the variable selection processes. It shows that UVE could improve the variable selection accuracy compared to using SPA directly in the algal oil analysis.(4) Rapid determination method and models of the quality of algal powders were established. Based on visible and near infrared spectroscopy, SPA-least square support vector machine (SPA-LS-SVM) with seven effective variables reached 100%correct answer rate for the algal powder classification. Visible and near infrared spectroscopy was used to predict the protein content in algal powders. UVE-SPA-multiple linear regression (UVE-SPA-LS-SVM) obtained the best result with the rpre2, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of 0.9750,0.2344, and 6.2206, respectively. The detection accuracy can meet the practical requirements. Spectroscopy technique was used to determine the irradiation dose of algal powders. UVE-SPA-Back-Propagation Artificial Neural Network (UVE-SPA-BP-ANN) obtained the best result with the rpre2, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of 0.9850,0.6414, and 8.1366, respectively. The detection accuracy can meet the practical requirements. Visible and near infrared spectroscopy was used to predict the adulterant contents in algal powder. The design value analysis (DVA) indicated that for quantification of adulterants in algal powder, short-wave near infrared spectroscopy outweighs full spectra, and LS-SVM models outweigh PLS and PLS2 models. The rpre2 values of LS-SVM models based on NIR spectra were 0.9966,0.9430, and 0.9740 for flour detection and 0.9903,0.9474, and 0.9705 for mung-bean detection, while single adulterant, two adulterants and multiple adulterants were considered.