A comparative analysis of growth kinetics, image analysis, and biofuel potential of different algal strains.

Due to the global population growth and economic development, energy demand has increased worldwide. Countries take steps to improve their alternative and renewable energy sources. Algae is one of the alternative energy sources and can be used to produce renewable biofuel. In this study, nondestructive, practical, and rapid image processing techniques were applied to determine the algal growth kinetics and biomass potential of four algal strains, including C. minutum, Chlorella sorokiniana, C. vulgaris, and S. obliquus. Laboratory experiments were conducted to determine different aspects of biomass and chlorophyll production of those algal strains. Suitable non-linear growth models, including Logistic, modified Logistic, Gompertz, and modified Gompertz models, were employed to determine the growth pattern of algae. Moreover, the methane potential of harvested biomass was calculated. The algal strains were incubated for 18 days, and the growth kinetics were determined. After the incubation, the biomass was harvested and assessed for its chemical oxygen demand content and biomethane potential. Among the tested strains, C. sorokiniana was the best in biomass productivity (111.97 ± 0.9 mg L-1d-1). The calculated vegetation indices, namely; colorimetric difference, color index vegetation, vegetative, excess green, excess green minus excess red, combination, and brown index values showed a significant correlation with biomass and chlorophyll content. Among the tested growth models, the modified Gompertz shows the best growth pattern. Further, the estimated theoretical CH4 yield was highest for C. minutum (0.98 mL g-1) compared to other tested strains. The present findings suggest that image analysis can be used as an alternative method to study the growth kinetics and biomass production potential of different algae during cultivation in wastewater.

Hyperspectral Image Data and Waveband Indexing Methods to Estimate Nutrient Concentration on Lettuce (Lactuca sativa L.) Cultivars.

Lettuce is an important vegetable in the human diet and is commonly consumed for salad. It is a source of vitamin A, which plays a vital role in human health. Improvements in lettuce production will be needed to ensure a stable and economically available supply in the future. The influence of nitrogen (N), phosphorus (P), and potassium (K) compounds on the growth dynamics of four hydroponically grown lettuce (Lactuca sativa L.) cultivars (Black Seeded Simpson, Parris Island, Rex RZ, and Tacitus) in tubs and in a nutrient film technique (NFT) system were studied. Hyperspectral images (HSI) were captured at plant harvest. Models developed from the HSI data were used to estimate nutrient levels of leaf tissues by employing principal component analysis (PCA), partial least squares regression (PLSR), multivariate regression, and variable importance projection (VIP) methods. The optimal wavebands were found in six regions, including 390.57-438.02, 497-550, 551-600, 681.34-774, 802-821, and 822-838 nm for tub-grown lettuces and four regions, namely 390.57-438.02, 497-550, 551-600, and 681.34-774 nm for NFT-system-grown lettuces. These fitted models' levels showed high accuracy (R2=0.85-0.99) in estimating the growth dynamics of the studied lettuce cultivars in terms of nutrient content. HSI data of the lettuce leaves and applied N solutions demonstrated a direct positive correlation with an accuracy of 0.82-0.99 for blue and green regions in 400-575 nm wavebands. The results proved that, in most of the tested multivariate regression models, HSI data of freshly cut leaves correlated well with laboratory-measured data.