Dielectric spectroscopy with machine learning integrated approach to analysis the influence of N:P ratio on algae growth modeling for forecasting

Abstract

The task of monitoring algal biomass is a significant challenge in contemporary environmental conservation. A novel technique called dielectric spectroscopy with machine learning integration is an intelligent approach to address this challenge. This work optimized a machine learning supervised PCA, Hierarchical cluster analysis & linear regression model -strengthen the real-time dielectric spectroscopy approach to study the influence of N:P ratios on a model algae Desmodesmus sp. growth for forecasting. As a primary feature, different N:P concentrations (1:1, 2:1, 3:1, 1:2, and 1:3) were utilized to characterize the relationship between dielectric (S11(dB) & ε'') and conventional UV–vis spectroscopy properties (OD686 & Chl-a). The dielectric measurements of S11(dB) & ε'' are consistent with the OD686 & Chl-a value. Highest Desmodesmus sp. OD686 0.87, 0.76 recorded at N:P 3:1, 2:1 ratio respectively. The machine learning based PCA, Hierarchical cluster analysis and linear regression model were applied and found at 0–8 days incubation Desmodesmus sp. growth biomass negatively corelated with S11(dB) & ε“ and positively correlatedly with OD686 & Chl-a respectively, with R2 values > 0.95. While for the 0&8days measured ε”& S11(dB) were −6.169&-0.4765; −7.956 &-0.6775 respectively, the linear regression model was applied to predicted OD686 for 0&8 days was 0.299, 0.234; 0.817, 0.716 respectively, at the ratios N:P (3:1) and the RMES were 0.0047 & 0.0319 respectively. This analysis revealed that the machine learning based integrated approach has high accuracy. Thus, Dielectric spectroscopy and machine learning-integrated method could strengthen to the real-time monitoring of algal biomass or predicting to control the N:P ratios to protecting aquatic ecosystem.