Efficient Interpretation of Multiparametric Data Using Principal Component Analysis as an Example of Quality Assessment of Microalgae

Multiparametric flow cytometry (FCM) realizes high-throughput measurement, but mul- tiparametric 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 multidimensional 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 pre-senting 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.