Laser-induced fluorescence and Raman spectroscopy are used for the investigation of different genotypes of Thymus daenensis native to the Ilam province of Iran. Different genotypes of $T$. daenensis essential oils, labeled $T_1$ through $T_7$, possess slight differences with regard to the composition of the thymol. The gas chromatography–mass spectrometry (GC–MS) method is performed to determine the concentration of each constituent as a reference method. The Raman spectra of different concentrations of pure thymol dissolved in hexane as standard samples are obtained via a laboratory prototype Raman spectroscopy setup for the calculation of the calibration curve. The regression coefficient and limit of detection are calculated. The possibility of the differentiation of different genotypes of $T$. daenensis is also examined by laser-induced fluorescence spectroscopy, although we do not know the exact amounts of their components. All the fluorescence spectral information is used jointly by cluster analysis to differentiate between 7 genotypes. Our results demonstrate the acceptable precision of Raman spectroscopy with GC–MS and corroborate the capacity of Raman spectroscopy in applications in the quantitative analysis field. Furthermore, the cluster analysis results show that laser-induced fluorescence spectroscopy is an acceptable technique for the rapid classification of different genotypes of $T$. daenensis without having any previous information of their exact amount of constituents. So, the ability to rapidly and nondestructively differentiate between genotypes makes it possible to efficiently select high-quality herbs from many samples.

Despite extensive research efforts, cancer continues to stand as one of the leading causes of death on a global scale. To gain profound insights into the intricate mechanisms underlying cancer onset and progression, it is imperative to possess methodologies that allow the study of cancer cells at the single-cell level, focusing on critical parameters such as cell morphology, metabolism, and molecular characteristics. These insights are essential for effectively discerning between healthy and cancerous cells and comprehending tumoral progression. Recent advancements in microscopy techniques have significantly advanced the study of cancer cells, with Raman microspectroscopy (RM) emerging as a particularly powerful tool. Indeed, RM can provide both biochemical and spatial details at the single-cell level without the need for labels or causing disruptions to cell integrity. Moreover, RM can be correlated with other microscopy techniques, creating a synergy that offers a spectrum of complementary insights into cancer cell morphology and biology. This review aims to explore the correlation between RM and other microscopy techniques such as confocal fluoresce microscopy (CFM), atomic force microscopy (AFM), digital holography microscopy (DHM), and mass spectrometry imaging (MSI). Each of these techniques has their own strengths, providing different perspectives and parameters about cancer cell features. The correlation between information from these various analysis methods is a valuable tool for physicians and researchers, aiding in the comprehension of cancer cell morphology and biology, unraveling mechanisms underlying cancer progression, and facilitating the development of early diagnosis and/or monitoring cancer progression.

Lipid droplets (LDs) are key markers of cellular metabolism, often altered in cancer. While holotomography enables 3D, label‐free imaging of LDs via refractive index, it relies on complex thresholding and lacks biochemical specificity. Here, polarization‐sensitive holotomography (PS‐HT), which leverages the intrinsic birefringence of LDs for high‐contrast, selective identification with a fixed near‐zero threshold is presented. Using prostate cell models (healthy PNT2 and cancer PC3), PS‐HT is validated against fluorescence microscopy and holotomography, showing that it enables accurate quantification of birefringence, LD volume, dry mass, molecular organization, and spatial distribution. Cancer cells show significantly higher birefringence after glucose treatment, reflecting enhanced lipid accumulation. PS‐HT, combined with principal component analysis, achieves near‐perfect classification of cancer versus healthy cells, establishing it as a robust, label‐free tool for studying lipid metabolism and cancer diagnostics.

Assembling metal nanoparticles to provide strong electric field hot spots in their gap spaces for surface-enhanced Raman scattering (SERS) is a great challenge. Therefore, the optimization of assembly parameters is at the heart of design and fabrication of SERS substrates. Herein, we experimentally demonstrate the SERS activity of hierarchical assembled size-asymmetric heterodimers (HDs) of silver nanoparticles (AgNPs) to unveil the effect of size-asymmetry in SERS enhancement factors (EFs). The HDs show an optimum configuration in which maximum EF is achieved. To study the underlying physics of the plasmonic coupling in HDs, we use the discrete dipole approximation method. The theoretical results confirm that the effects of AgNPs on each other through their electric near-fields can be considere as the key point to generate strong hot spots in HDs for SERS.

A rapid, cost-effective, and sample-preparation-free approach is proposed for the quantitative detection of arsenic in drinking water , using surface-enhanced Raman spectroscopy (SERS). This fabrication entailed comprehensive optimization of chemically synthesized silver colloidal nanoparticles , focusing on parameters such as centrifugation time and speed to attain maximal nanoparticle concentration while mitigating interference from trisodium citrate and other chemical agents. Subsequently, SERS substrates were fabricated by depositing a concentrated drop of colloidal silver nanoparticles onto hydrophobic silicon substrates using the drop-coating technique. The drying process induced a coffee-ring effect, resulting in a pronounced spatial variation of nanoparticle concentration, with significantly higher densities observed in the peripheral ring regions compared to the central regions of the substrate. These regions possess the ability to enhance the Raman spectrum of arsenic, enabling the detection of arsenic at concentrations as low as 50 ppb. This method can prove highly valuable for initial field analyses of drinking water.