Seaweed has long been an important kitchen ingredient and a functional food material. Microalgae have attracted the same attention as seaweed from food, pharmaceutical and cosmetic companies because several algae contain unique functional materials. Industry application of algae requires the selection of useful algal species, evaluation of their features and monitoring of their quality in culture. Taking Chlorella for example, this chapter presents a method using flow cytometry (FCM) to assess not only the number of algae but also algal quality. First, Chlorella was cultured in media containing eluate from steel slag as an experimental factor and trace metals. After the treatment of algae with eluate, the number and physiological features of algae were evaluated, respectively, using hemocytometry and FCM. Results show that eluate from slag induced neither lethality nor growth inhibition. Coupled with hemocytometry, FCM was used to estimate vigorous and aberrant algal status. Consequently, the eluate did not give rise to algae stresses. Interestingly, the addition of slag eluate increased the amounts of the carbonate species. The increase in the carbonate species actually triggered the potential increase in aqueous CO2 for photosynthesis, eventually inducing algal proliferation. These analyses can support evaluation of algal features and maintenance of their quality for industry application.
Part of the book: Superfood and Functional Food
Multiparametric flow cytometry (FCM) realizes high-throughput measurement, but multiparametric data make it difficult to interpret the complicated information. To present clear patterning graphs from FCM data, one must grasp the essence of the data. This study estimated the usefulness of principal component analysis (PCA), which reduces multi-dimensional information to arbitrary one-dimensional information. Recently, microalgae have attracted the attention of pharmaceutical, cosmetic, and food companies. Taking alga Chlorella as an example, this chapter presents the usefulness of PCA for the evaluation of algal quality using FCM. To evaluate the algal status effectively, Chlorella (control), heated algae, and metallic-treatment algae were prepared and quantified using FCM. FCM data were subjected to PCA analysis. To interpret correlativity among parameters, FCM data are generally expressed as histograms and scatter or contour plots. An operator using multiple parameters has difficulty finding high correlativity among parameters and presenting an effective graph. The PCA method produced new comprehensive axes with different inclination factors among parameters. Scatter plots using new axes showed patterns treatment dependently with different vectors. Results show that the PCA method can extract information of test objects from data and that it can contribute to effective interpretation of cell characteristics, even if data include multiparameters from FCM.