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Driven by applications in chemical sensing, biological imaging and material characterisation, Raman spectroscopies are attracting growing interest from a variety of scientific disciplines. The Raman effect originates from the inelastic scattering of light, and it can directly probe vibration/rotational-vibration states in molecules and materials. Despite numerous advantages over infrared spectroscopy, spontaneous Raman scattering is very weak, and consequently, a variety of enhanced Raman spectroscopic techniques have emerged. These techniques include stimulated Raman scattering and coherent anti-Stokes Raman scattering, as well as surface- and tip-enhanced Raman scattering spectroscopies. The present review provides the reader with an understanding of the fundamental physics that govern the Raman effect and its advantages, limitations and applications. The review also highlights the key experimental considerations for implementing the main experimental Raman spectroscopic techniques. The relevant data analysis methods and some of the most recent advances related to the Raman effect are finally presented. This review constitutes a practical introduction to the science of Raman spectroscopy; it also highlights recent and promising directions of future research developments.

The concerns regarding microplastics and nanoplastics pollution stimulate studies on the uptake and biodistribution of these emerging pollutants in vitro. Atomic force microscopy in nanomechanical PeakForce Tapping mode was used here to visualise the uptake and distribution of polystyrene spherical microplastics in human skin fibroblast. Particles down to 500 nm were imaged in whole fixed cells, the nanomechanical characterization allowed for differentiation between internalized and surface attached plastics. This study opens new avenues in microplastics toxicity research.

Several studies suggested that gold nanoparticles (NPs) could be genotoxic in vitro and in vivo. However, gold NPs currently produced present a wide range of sizes and functionalization, which could affect their interactions with the environment or with biological structures and, thus, modify their toxic effects. In this study, we investigated the role of surface charge in determining the genotoxic potential of gold NPs, as measured by the induction of DNA damage (comet assay) and chromosomal damage (micronucleus assay) in human bronchial epithelial BEAS-2B cells. The cellular uptake of gold NPs was assessed by hyperspectral imaging. Two core sizes (~5 nm and ~20 nm) and three functionalizations representing negative (carboxylate), positive (ammonium), and neutral (poly(ethylene glycol) (PEG)ylated) surface charges were examined. Cationic ammonium gold NPs were clearly more cytotoxic than their anionic and neutral counterparts, but genotoxicity was not simply dependent on functionalization or size, since DNA damage was induced by 20-nm ammonium and PEGylated gold NPs, while micronucleus induction was increased by 5-nm ammonium and 20-nm PEGylated gold NPs. The 5-nm carboxylated gold NPs were not genotoxic, and evidence on the genotoxicity of the 20-nm carboxylated gold NPs was restricted to a positive result at the lowest dose in the micronucleus assay. When interpreting the results, it has to be taken into account that cytotoxicity limited the doses available for the ammonium-functionalized gold NPs and that gold NPs were earlier described to interfere with the comet assay procedure, possibly resulting in a false positive result. In conclusion, our findings show that the cellular uptake and cytotoxicity of gold NPs are clearly enhanced by positive surface charge, but neither functionalization nor size can single-handedly account for the genotoxic effects of the gold NPs.

Foodborne pathogens have become ongoing threats in the food industry, whereas their rapid detection and classification at an early stage are still challenging. To address early and rapid detection, hyperspectral microscope imaging (HMI) technology combined with convolutional neural networks (CNN) was proposed to classify foodborne bacterial species at the cellular level. HMI technology can simultaneously obtain both spatial and spectral information of different live bacterial cells, while two CNN frameworks, U-Net and one-dimensional CNN (1D-CNN), were employed to accelerate the data analysis process. U-Net was used for automating cellular regions of interest (ROI) segmentation, which generated accurate cell-ROI masks in a shorter timeframe than the conventional Otsu or Watershed methods. The 1D-CNN was employed for classifying the spectral profiles extracted from cell-ROI and resulted in a higher accuracy (90%) than k-nearest neighbor (81%) and support vector machine (81%). Overall, the CNN-assisted HMI technology showed potential for foodborne bacteria detection.

The Special Issue on hyperspectral imaging (HSI), entitled “The Future of Hyperspectral Imaging”, has published 12 papers. Nine papers are related to specific current research and three more are review contributions: In both cases, the request is to propose those methods or instruments so as to show the future trends of HSI. Some contributions also update specific methodological or mathematical tools. In particular, the review papers address deep learning methods for HSI analysis, while HSI data compression is reviewed by using liquid crystals spectral multiplexing as well as DMD-based Raman spectroscopy. Specific topics explored by using data obtained by HSI include alert on the sprouting of potato tubers, the investigation on the stability of painting samples, the prediction of healing diabetic foot ulcers, and age determination of blood-stained fingerprints. Papers showing advances on more general topics include video approach for HSI dynamic scenes, localization of plant diseases, new methods for the lossless compression of HSI data, the fusing of multiple multiband images, and mixed modes of laser HSI imaging for sorting and quality controls.

This paper proposes a new Hepatocellular Carcinoma (HCC) classification method utilizing a hyperspectral imaging system (HSI) integrated with a light microscope. Using our custom imaging system, we have captured 270 bands of hyperspectral images of healthy and cancer tissue samples with HCC diagnosis from a liver microarray slide. Convolutional Neural Networks with 3D convolutions (3D-CNN) have been used to build an accurate classification model. With the help of 3D convolutions, spectral and spatial features within the hyperspectral cube are incorporated to train a strong classifier. Unlike 2D convolutions, 3D convolutions take the spectral dimension into account while automatically collecting distinctive features during the CNN training stage. As a result, we have avoided manual feature engineering on hyperspectral data and proposed a compact method for HSI medical applications. Moreover, the focal loss function, utilized as a CNN cost function, enables our model to tackle the class imbalance problem residing in the dataset effectively. The focal loss function emphasizes the hard examples to learn and prevents overfitting due to the lack of inter-class balancing. Our empirical results demonstrate the superiority of hyperspectral data over RGB data for liver cancer tissue classification. We have observed that increased spectral dimension results in higher classification accuracy. Both spectral and spatial features are essential in training an accurate learner for cancer tissue classification.

Fly ash produced during coal combustion is one of the major sources of air and water pollution, but the data on the impact of micrometer-size fly ash particles on human cells is still incomplete. Fly ash samples were collected from several electric power stations in the United States (Rockdale, TX; Dolet Hill, Mansfield, LA; Rockport, IN; Muskogee, OK) and from a metallurgic plant located in the Russian Federation (Chelyabinsk Electro-Metallurgical Works OJSC). The particles were characterized using dynamic light scattering, atomic force, and hyperspectral microscopy. According to chemical composition, the fly ash studied was ferro-alumino-silicate mineral containing substantial quantities of Ca, Mg, and a negligible concentration of K, Na, Mn, and Sr. The toxicity of the fly ash microparticles was assessed in vitro using HeLa cells (human cervical cancer cells) and Jurkat cells (immortalized human T lymphocytes). Incubation of cells with different concentrations of fly ash resulted in a dose-dependent decrease in cell viability for all fly ash variants. The most prominent cytotoxic effect in HeLa cells was produced by the ash particles from Rockdale, while the least was produced by the fly ash from Chelyabinsk. In Jurkat cells, the lowest toxicity was observed for fly ash collected from Rockport, Dolet Hill and Muscogee plants. The fly ash from Rockdale and Chelyabinsk induced DNA damage in HeLa cells, as revealed by the single cell electrophoresis, and disrupted the normal nuclear morphology. The interaction of fly ash microparticles of different origins with cells was visualized using dark-field microscopy and hyperspectral imaging. The size of ash particles appeared to be an important determinant of their toxicity, and the smallest fly ash particles from Chelyabinsk turned out to be the most cytotoxic to Jukart cells and the most genotoxic to HeLa cells.

Research progresses have led to the development of different kinds of nanoplatforms to deliver drugs through different biological membranes. Particularly, nanocarriers represent a precious means to treat skin pathologies, due to their capability to solubilize lipophilic and hydrophilic drugs, to control their release, and to promote their permeation through the stratum corneum barrier. A crucial point in the development of nano-delivery systems relies on their characterization, as well as in the assessment of their interaction with tissues, in order to predict their fate under in vivo administration. The size of nanoparticles, their shape, and the type of matrix can influence their biodistribution inside the skin strata and their cellular uptake. In this respect, an overview of some characterization methods employed to investigate nanoparticles intended for topical administration is presented here, namely dynamic light scattering, zeta potential, scanning and transmission electron microscopy, X-ray diffraction, atomic force microscopy, Fourier transform infrared and Raman spectroscopy. In addition, the main fluorescence methods employed to detect the in vitro nanoparticles interaction with skin cell lines, such as fluorescence-activated cell sorting or confocal imaging, are described, considering different examples of applications. Finally, recent studies on the techniques employed to determine the nanoparticle presence in the skin by ex vivo and in vivo models are reported.

Using dark-field hyperspectral microscopy, we analyzed the distribution of fluorescently-labelled polystyrene microspheres with a size of 2 μm in mosquito larvae and observed the quenching of fluorescence and changes in spectral properties due to histological processing. We propose enhanced dark-field microscopy coupled with hyperspectral imaging as a versatile method for analysing polymer colloids (microplastics) biodistribution in complex tissues.

Hyperspectral imaging, in combination with microscopy, can increase material discrimination compared to standard microscopy. We explored the potential of discriminating pellet microplastic materials using a hyperspectral short-wavelength infrared (SWIR) camera, providing 100 bands in the 1100–1650 nm range, in combination with reflection microscopy. The identification of the most relevant spectral bands helps to increase system cost efficiency. The use of fewer bands reduces memory and processing requirements, and can also steer the development of sustainable, cost-efficient sensors with fewer bands. For this purpose, we present a genetic algorithm to perform band relevance analysis and propose novel algorithm optimizations. The results show that a few spectral bands (between 6 and 9) are sufficient for accurate (>80%) pixel discrimination of all 22 types of microplastic waste, contributing to sustainable development goals (SDGs) such as SDG 6 (‘clean water and sanitation’) or SDG 9 (‘industry, innovation, and infrastructure’). In addition, we study the impact of the classifier method and the width of the spectral response on band selection, neither of which has been addressed in the current state-of-the-art. Finally, we propose a method to steer band selection towards a more balanced distribution of classification accuracy, increasing its applicability in multiclass applications.