Response to nutrient variation on lipid productivity in green microalgae captured using second derivative FTIR and Raman spectroscopy

Development of a machine learning model for systematics of Aspergillus section Nigri using synchrotron radiation-based fourier transform infrared spectroscopy

Aspergillus section Nigri (black aspergilli) fungi are economically important food spoilage agents. Some species in this section also produce harmful mycotoxins in food. However, it is remarkably difficult to identify this fungal group at the species level using morphological and chemical characteristics. The molecular approach for classification is preferable; however, it is time-consuming, making it inappropriate for rapid testing of large numbers of samples. To address this, we explored synchrotron radiation-based Fourier transform infrared microspectroscopy (SR-FTIR) as a rapid method for obtaining data suitable for species classification. SR-FTIR data were obtained from the mycelia/conidia of 22 black aspergilli species. The Convolutional Neural Network (CNN) approach, a supervised deep learning algorithm, was used with SR-FTIR data to classify black aspergilli at the species level. A subset of the data was used to train the CNN model, and the model classification performance was evaluated using the validation data subsets. The model demonstrated a 95.97% accuracy in species classification on the testing (blind) data subset. The technique presented herein could be an alternative method for identifying problematic black aspergilli in the food industry.

Salinity-Induced Physiochemical Alterations to Enhance Lipid Content in Oleaginous Microalgae Scenedesmus sp. BHU1 via Two-Stage Cultivation for Biodiesel Feedstock

In the recent past, various microalgae have been considered a renewable energy source for biofuel production, and their amount and extent can be enhanced by applying certain types of stress including salinity. Although microalgae growing under salinity stress result in a higher lipid content, they simultaneously reduce in growth and biomass output. To resolve this issue, the physiochemical changes in microalgae Scenedesmus sp. BHU1 have been assessed through two-stage cultivation. In stage-I, the maximum carbohydrate and lipid contents (39.55 and 34.10%) were found at a 0.4 M NaCl concentration, while in stage-II, the maximum carbohydrate and lipid contents (42.16 and 38.10%) were obtained in the 8-day-old culture. However, under increased salinity, Scenedesmus sp. BHU1 exhibited a decrease in photosynthetic attributes, including Chl-a, Chl-b, Fv/Fm, Y(II), Y(NPQ), NPQ, qP, qL, qN, and ETRmax but increased Y(NO) and carotenoids content. Apart from physiological attributes, osmoprotectants, stress biomarkers, and nonenzymatic antioxidants were also studied to elucidate the role of reactive oxygen species (ROS) facilitated lipid synthesis. Furthermore, elemental and mineral ion analysis of microalgal biomass was performed to evaluate the biomass quality for biofuel and cell homeostasis. Based on fluorometry analysis, we found the maximum neutral lipids in the 8-day-old grown culture at stage-II in Scenedesmus sp. BHU1. Furthermore, the use of Fourier-transform infrared (FT-IR) and nuclear magnetic resonance (NMR) spectroscopy analyses confirmed the presence of higher levels of hydrocarbons and triacylglycerides (TAGs) composed of saturated fatty acids (SFAs) and monounsaturated fatty acids (MUFAs) in the 8-day-old culture. Therefore, Scenedesmus sp. BHU1 can be a promising microalga for potential biodiesel feedstock.

Screening of Spirulina strains for high copper adsorption capacity through Fourier transform infrared spectroscopy

Microalgae have emerged as promising biosorbents for the removal of toxic metals from industrial effluents due to the presence of various free functional groups. While the constitutes are distinct among different algal strains, it needs to screen the algae with high adsorption capacities for heavy metal ions by analyzing the algal components. In this study, a rapid and nondestructive Fourier transform infrared (FTIR) method combined PCA algorithm was used to discriminate algal strains according to their cellular components. With FTIR spectroscopy, we have found that the algal strains for high copper adsorption capacity (RH44, XS58, AH53, and RZ22) can be well differentiated from other strains via assessing the components involved in the biosorption of copper ions at the spectral window range of 1,200–900 cm⁻¹ mainly attributed to polysaccharides. Correspondingly, the copper removal efficiency by different Spirulina strains was also measured by biochemical assay and scanning electron microscopy (SEM) in order to confirm the screening result. Compared with the chemical measurement, the assessment based on spectral features appears fairly good in the evaluation and differentiation of copper adsorption capacity in various Spirulina strains. This study illustrates that FTIR spectroscopy may serve as a fast and effective tool to investigate the functional groups for copper ions binding in the Spirulina cell and it even offers a useful and accurate new approach to rapidly assess potential adsorbents for the high capacity of copper adsorption.