Algae are becoming a strategic source of fuels, food, feedstocks, and biologically active compounds. This potential has stimulated the development of innovative analytical methods focused on these microorganisms. Algal lipids are among the most promising potential products for fuels as well as for nutrition. The crucial parameter characterizing the algal lipids is the degree of unsaturation of the constituent fatty acids quantified by the iodine value. Here we demonstrate the capacity of the spatially resolved Raman microspectroscopy to determine the effective iodine value in lipid storage bodies of individual living algal cells. The Raman spectra were collected from three selected algal species immobilized in an agarose gel. Prior to immobilization, the algae were cultivated in the stationary phase inducing an overproduction of lipids. We employed the characteristic peaks in the Raman scattering spectra at 1,656 cm⁻¹ (cis C=C stretching mode) and 1,445 cm⁻¹ (CH₂ scissoring mode) as the markers defining the ratio of unsaturated-to-saturated carbon-carbon bonds of the fatty acids in the algal lipids. These spectral features were first quantified for pure fatty acids of known iodine value. The resultant calibration curve was then used to calculate the effective iodine value of storage lipids in the living algal cells from their Raman spectra. We demonstrated that the iodine value differs significantly for the three studied algal species. Our spectroscopic estimations of the iodine value were validated using GC-MS measurements and an excellent agreement was found for the Trachydiscus minutus species. A good agreement was also found with the earlier published data on Botryococcus braunii. Thus, we propose that Raman microspectroscopy can become technique of choice in the rapidly expanding field of algal biotechnology.

Antibiotics cure infections by influencing bacterial growth or viability. Antibiotics can be divided to two groups on the basis of their effect on microbial cells through two main mechanisms, which are either bactericidal or bacteriostatic. Bactericidal antibiotics kill the bacteria and bacteriostatic antibiotics suppress the growth of bacteria (keep them in the stationary phase of growth). One of many factors to predict a favorable clinical outcome of the potential action of antimicrobial chemicals may be provided using in vitro bactericidal/bacteriostatic data (e.g., minimum inhibitory concentrations—MICs). Consequently, MICs are used in clinical situations mainly to confirm resistance, and to determine the in vitro activities of new antimicrobials. We report on the combination of data obtained from MICs with information on microorganisms’ “fingerprint” (e.g., DNA/RNA, and proteins) provided by Raman spectroscopy. Thus, we could follow mechanisms of the bacteriostatic versus bactericidal action simply by detecting the Raman bands corresponding to DNA. The Raman spectra of Staphylococcus epidermidis treated with clindamycin (a bacteriostatic agent) indeed show little effect on DNA which is in contrast with the action of ciprofloxacin (a bactericidal agent), where the Raman spectra show a decrease in strength of the signal assigned to DNA, suggesting DNA fragmentation.

Algal biomass that is represented mainly by commercially grown algal strains has recently found many potential applications in various fields of interest. Its utilization has been found advantageous in the fields of bioremediation, biofuel production and the food industry. This paper reviews recent developments in the analysis of algal biomass with the main focus on the Laser-Induced Breakdown Spectroscopy, Raman spectroscopy, and partly Laser-Ablation Inductively Coupled Plasma techniques. The advantages of the selected laser-based analytical techniques are revealed and their fields of use are discussed in detail.

Raman spectroscopy is a fast-growing and increasingly powerful analytical technique applied across diverse disciplines such as materials science, chemistry, biology and medicine. This growth is driven by advances in Raman instrumentation and greatly supported by the flourishing of chemometrics and artificial intelligence (AI). However, the full potential of this technique is often hampered by challenges related to data acquisition, processing, interpretation, and sharing. This review paper addresses how a concerted effort toward digitalization, incorporating principles of Open Science and FAIR data (Findable, Accessible, Interoperable, and Reusable), is essential to develop and implement robust, standardized, and accessible digital workflows. These workflows are key to unlock the full power of Raman spectroscopy in combination with AI. We explore the current landscape of digital tools and open resources in Raman spectroscopy, highlighting both existing solutions as well as critical gaps. Despite these advances, the field remains fragmented, with many initiatives developed in isolation, limiting interoperability and slowing progress. In this regard, we assess the trends in Raman spectroscopy hardware and control software as well as the role of AI in improving data collection, automating data analysis, extracting meaningful insights, and enabling predictive modeling. We review challenges such as data quality and model interpretability that constrain the effectiveness and applicability of AI in Raman spectroscopy. Furthermore, we emphasize the importance of standardized data formats, metadata schemas, and domain-specific ontologies to ensure machine-actionability, database federation and interoperability as well as to facilitate collaborative research. We provide curated lists of existing open hardware, databases and standards relevant to Raman spectroscopy. Finally, we propose a roadmap toward an open and FAIR ecosystem for Raman spectroscopy, emphasizing the need for sustainable infrastructure, collaborative development, and community involvement.