Identification of pesticide varieties and concentrations by detecting characteristics of Chlorella pyrenoidosa

To determine the varieties and concentrations of pesticides by detecting the changes of microalgae components using Raman microspectroscopy technology.

Raman microspectroscopy technology was used to detect the internal elements and characteristics of Chlorella pyrenoidosa. Partial least squares (PLS) was used to build the model between Raman signals of C. pyrenoidosa and different varieties or concentrations of pesticides. The PLS models were built with different preprocessing methods and the most optimized preprocessing method was found. Linear discriminant analysis (LDA) was also used to build the models to identify different pesticide varieties or concentrations. Sensitive wavelengths (SWs) corresponding to pigments of microalgae were selected, and the SWs-LDA models were built with the classification accuracy rate up to 96·7 and 90% respectively for pesticide varieties and concentrations identification.

Measuring changes of microalgae components by Raman microspectroscopy techniques could be used to identify the varieties and concentrations of pesticides in water.

Although the low concentration level of pesticides does not have significant impact on the overall water environment, water would become worse due to the pollution accumulation in long-term. The method disclosed in this study can be used as a good example for pesticide detection with the help of microalgae. Raman microspectroscopy technology is a convenient and rapid tool to detect the characteristics of microalgae and contribute for pesticide detection.