Raman spectra-based structural classification analysis of quinoidal and derived molecular systems

Recent research on the physicochemical properties and biological activities of quinones and their practical applications: a comprehensive review

Quinones represent a class of crude organic compounds ubiquitously distributed in nature. Their distinctive quinone-type structure confers upon them unique properties and applications. Quinones demonstrate significant biological activities, including antioxidant, antimicrobial, and antitumor properties. Additionally, they demonstrate noteworthy physicochemical characteristics, including excellent dyeing properties and stability. Given their diverse qualities, quinones hold significant promise for applications in industrial manufacturing, healthcare, and food production, thus garnering considerable attention in recent years. While there is a growing body of research on quinones, the existing literature falls short of providing a comprehensive review encompassing recent advancements in this field along with established knowledge. This paper offers a comprehensive review of research progress for quinones, encompassing structural classification, source synthesis, extraction methods, properties, functions, and specific applications. It serves as a reference and theoretical foundation for the further development and utilization of quinones.

Two-Dimensional Clustering of Spectral Changes for the Interpretation of Raman Hyperspectra

Two-dimensional correlation spectroscopy (2D-COS) is a technique that permits the examination of synchronous and asynchronous changes present in hyperspectral data. It produces two-dimensional correlation coefficient maps that represent the mutually correlated changes occurring at all Raman wavenumbers during an implemented perturbation. To focus our analysis on clusters of wavenumbers that tend to change together, we apply a k-means clustering to the wavenumber profiles in the perturbation domain decomposition of the two-dimensional correlation coefficient map. These profiles (or trends) reflect peak intensity changes as a function of the perturbation. We then plot the co-occurrences of cluster members two-dimensionally in a manner analogous to a two-dimensional correlation coefficient map. Because wavenumber profiles are clustered based on their similarity, two-dimensional cluster member spectra reveal which Raman peaks change in a similar manner, rather than how much they are correlated. Furthermore, clustering produces a discrete partitioning of the wavenumbers, thus a two-dimensional cluster member spectrum exhibits a discrete presentation of related Raman peaks as opposed to the more continuous representations in a two-dimensional correlation coefficient map. We demonstrate first the basic principles of the technique with the aid of synthetic data. We then apply it to Raman spectra obtained from a polystyrene perchlorate model system followed by Raman spectra from mammalian cells fixed with different percentages of methanol. Both data sets were designed to produce differential changes in sample components. In both cases, all the peaks pertaining to a given component should then change in a similar manner. We observed that component-based profile clustering did occur for polystyrene and perchlorate in the model system and lipids, nucleic acids, and proteins in the mammalian cell example. This confirmed that the method can translate to "real world" samples. We contrast these results with two-dimensional correlation spectroscopy results. To supplement interpretation, we present the cluster-segmented mean spectrum of the hyperspectral data. Overall, this technique is expected to be a valuable adjunct to two-dimensional correlation spectroscopy to further facilitate hyperspectral data interpretation and analysis.