Search Results (138)

Early sex identification is essential for the propagation, cultivation, quality improvement, and germplasm management of dioecious horticultural plants and related functionally dioecious systems, particularly in perennial species with long juvenile phases. However, the reliability and transferability of sex-identification technologies depend strongly on the underlying sex-determining mechanism. Here, we synthesize recent advances in plant sex determination and diagnostic technologies, ranging from morphological and biochemical traits to molecular markers, high-throughput sequencing, structural-variant detection, and emerging non-invasive phenotyping. We propose that sex-identification strategies should be selected according to the biological target generated by each mechanism, including heteromorphic sex chromosomes, homomorphic sex-determining regions (SDRs), functional sex-determining genes, sex chromosome turnover, dosage-dependent systems, and environmentally labile sex expression. We further distinguish genetic, developmental, physiological, and phenotypic layers of plant sex, emphasizing that DNA markers and spectral phenotyping provide complementary information. Genomic markers and non-invasive phenotyping are expected to be consistent when genetic sex is stably expressed, but they may become inconsistent when sex expression is developmentally, hormonally, or environmentally modulated. While molecular markers remain the most reliable tools for confirmatory genotyping, Raman spectroscopy, surface-enhanced Raman scattering (SERS), hyperspectral imaging, and machine learning may serve as rapid prescreening tools in large breeding populations, although their application remains at the proof-of-concept stage. Finally, we present a mechanism-guided decision framework for integrating genomic markers and non-invasive phenotyping to support early sex screening, propagation planning, planting-material optimization, and marker-assisted improvement in dioecious horticultural plants. Full article

As hen eggs are a primary source of high-quality dietary protein, egg freshness is fundamentally linked to biochemical alterations during storage, including moisture redistribution, protein degradation, and fluctuating chemical profiles. Accurate assessment of these internal changes is paramount for quality control; nonetheless, conventional analytical techniques remain predominantly destructive, rendering them impractical for high-throughput industrial monitoring. While existing literature has explored individual spectroscopic methods, the synergistic potential of multi-sensor integration and advanced artificial intelligence (AI) algorithms remains insufficiently synthesized. This review systematically evaluates recent breakthroughs in integrating AI with diverse spectroscopic modalities for non-destructive freshness quantification, including Visible-Near-Infrared (VIS-NIR), Raman, Fluorescence, and Hyperspectral Imaging (HSI). We elucidate the underlying mechanisms of spectral response to internal quality degradation and discuss the evolution of data-driven modeling from traditional chemometrics to sophisticated deep learning architectures. Furthermore, this work identifies critical bottlenecks in real-time industrial implementation and proposes future research trajectories toward intelligent multi-sensor fusion platforms. Full article

Recent advancements in computer vision, particularly in detection, segmentation, and classification, have significantly impacted various domains. However, these advancements are still strongly tied to RGB-based systems, which are insufficient for applications in industries such as waste sorting, pharmaceuticals, and defence, where material characterization beyond shape or visible colour is necessary. Hyperspectral (HS) imaging captures spatial and spectral information for each pixel and therefore offers a promising route for material-level classification. This study evaluates the potential of combining HS imaging with deep learning for plastic material classification. The work includes: (i) the design of an experimental setup with a HS line-scan camera, conveyor, and controlled illumination; (ii) the construction of an object-disjoint dataset of HDPE, PET, PP, and PS samples with semi-automated mask generation and Raman spectroscopy-based labelling; and (iii) the development of P1CH, a lightweight pixel-wise 1D convolutional hyperspectral classifier. On object-disjoint test images, P1CH achieved 97.44% all-pixel accuracy. A boundary sensitivity analysis, reported separately because semi-automated labels are uncertain at material/background interfaces, yielded 99.94% accuracy after excluding a pre-defined two-pixel border band. Additional ablation, baseline, and robustness analyses show that the proposed pixel-wise spectral approach is effective for small fragments, visually similar plastics, and overlapping materials, while black or very dark plastics remain challenging under the present camera and illumination configuration. Full article

Modern cell imaging is increasingly evolving toward high-content, label-free, and spectrally rich analytical approaches capable of resolving biochemical heterogeneity at cellular and subcellular levels. Raman microspectroscopy (µRS) and surface-enhanced Raman scattering (SERS) provide molecularly specific vibrational fingerprints with minimal photobleaching and high multiplexing capability, making them attractive tools for biomedical imaging and cellular analysis. However, broader implementation remains limited by weak intrinsic signals, insufficient targeting specificity, and limited control over nanoscale sensing environments in complex biological systems. Metal–organic framework (MOF) nanoparticles have recently emerged as promising platforms to address these challenges by offering porous, chemically tunable, and structurally well-defined scaffolds for Raman- and SERS-active nanostructures. Their high stability and favourable biocompatibility further support integration into biological applications. This review summarizes recent advances in MOF-assisted µRS and SERS across chemical sensing, bioanalytical detection, and biomedical diagnostics, with particular emphasis on cellular and subcellular imaging. Unlike previous reviews focused primarily on sensing performance, this work highlights the emerging role of MOF-SERS systems in high-content cellular imaging and evaluates their translation toward biologically relevant environments. Key design strategies and current challenges are critically discussed. Full article

L-carnitine plays a central role in mitochondrial fatty acid transport and cellular energy regulation; effects on the biochemical phenotype of brain cancer cells remain insufficiently characterized. Here, we applied confocal Raman spectroscopy and imaging to investigate the biochemical alterations induced by L-carnitine supplementation—administered as its tartrate salt—in human astrocytoma cells. Raman spectral analysis revealed distinct changes in lipid-, protein-, nucleic acid-, and cytochrome-associated vibrational features following 24 h of treatment, suggesting alterations in mitochondrial activity and cellular energy-related processes. Principal component analysis identified PC1 (93.87%) as representing the intrinsic biochemical composition of the cells, whereas PC2 (1.19%) and PC3 (0.59%) captured subtle yet consistent variations in lipid organization, protein conformation, and redox-sensitive vibrational features associated with L-carnitine exposure. Pearson correlation analysis of Raman cluster spectra indicated biochemical differences across cellular compartments, with the most pronounced changes observed in lipid droplets, supporting modifications in lipid-associated cellular processes. These findings demonstrate that Raman imaging provides a sensitive, label-free platform for resolving L-carnitine-induced biochemical heterogeneity at the single-cell level. Overall, this study highlights vibrational spectroscopy as a powerful tool for characterizing cellular responses to metabolic modulators and provides insight into the biochemical impact of exogenous L-carnitine in brain cancer cells. Full article

Historic stone-built heritage is continually exposed to environmental stressors that promote material degradation and surface alteration, often in spatially heterogeneous ways. Rapid, non-destructive diagnostic tools capable of capturing both spectral and spatial information are therefore essential to support preventive conservation strategies. In this study, short-wave infrared hyperspectral imaging (SWIR-HSI), combined with chemometric analysis, three-dimensional (3D) visualisation, and complementary spectroscopic techniques, is investigated as an integrated framework for assessing the conservation state of historical stonework. A field campaign was conducted at the 15th- to 17th-century San Emeterio and San Celedonio Church (Larrabetzu, Spain), a sandstone structure exposed to environmental pollution and adverse conditions. SWIR hyperspectral images (1000–2500 nm) were acquired in situ and analysed using Principal Component Analysis (PCA) and K-Means clustering to explore spectral variability and segment the façade into spectrally homogeneous regions. The resulting chemometric outputs were projected onto a photogrammetry-based 3D RGB model, enabling volumetric visualisation of material heterogeneity and surface alteration patterns. To support the interpretation of hyperspectral features, selected regions were further analysed using X-ray fluorescence (XRF) and Raman spectroscopy. The proposed 3D-SWIR approach enhances the interpretability of hyperspectral data by embedding it within its architectural context and linking spectral variability to underlying physicochemical processes. This integrated methodology demonstrates strong potential as a non-destructive diagnostic and decision-support tool for assessing, monitoring, and conserving cultural heritage stone structures. Full article

High-throughput and compact volume phase holographic (VPH) grating transmission spectrometers are widely employed in scientific research, agriculture, and industrial applications. Conventional transmission spectrometers generally adopt a fixed configuration and therefore have limitations in simultaneously achieving high spectral resolution and broad wavelength coverage. To address the limited tunability of transmission spectrometers, this work presents the theoretical analysis and experimental validation of a transmission spectrometer incorporating a novel catadioptric grating assembly, which consists of a transmitting VPH and a planar reflector. A catadioptric system is a combination of reflective (catoptric) and refractive (dioptric) elements. In the proposed configuration, a VPH grating and a plane mirror arranged at a fixed 90° angle form the catadioptric dispersion module. Synchronous rotation of this assembly enables wavelength scanning. The structure ensures that the diffracted ray along the optical axis of the imaging lens maintains the Bragg condition across the scanning range, thereby preserving maximum diffraction efficiency. The optical configuration and structural parameters of the spectrometer were theoretically derived, and a prototype spectrometer with an f-number of 1.8 employing a 2400 g/mm grating was constructed. Measurements demonstrate that, when the rotation angle is tuned from 30.5° to 50.5°, the accessible spectral range covers from 410 nm to 650 nm. Spectral response measurements using a tungsten–halogen light source confirm that the spectrometer maintains an acceptable diffraction efficiency across the entire tuning range. The measured spectral resolution is 0.1 nm at 626 nm with a 2400 g/mm grating and 0.18 nm with a 1500 g/mm grating. The spectrometer was further applied to fiber-enhanced gas Raman spectroscopy, where it successfully resolved the closely spaced Raman peaks of CH₄ and C₂H₆ that are difficult to distinguish using conventional compact spectrometers. These results demonstrate that the proposed tunable catadioptric spectrometer simultaneously provides excellent wavelength tunability and high spectral resolution. Full article

Surface-enhanced Raman spectroscopy (SERS) amplifies Raman scattering by placing molecules in the near-field of plasmonic nanostructures, enabling label-free molecular fingerprinting. While attractive for living cell phenotyping, many cellular SERS works rely on internalized colloidal nanoparticles, leading to variable uptake/localization, aggregation-driven hotspot fluctuations, and potential cellular perturbation. Here, we report a chip-like Au/SiO₂ nanolaminate SERS substrate that supports direct culture and label-free measurements of living cells on spatially defined hotspots without nanoparticle uptake. The periodic nanolaminate forms dense nanogaps and is engineered for 785 nm excitation, providing uniform enhancement over a large, culture-compatible area with high hotspot uniformity. By engineering the cell–substrate nano–bio interface, the platform enables reproducible acquisition of intrinsic cellular vibrational fingerprints under physiological conditions without Raman tags. Using MCF-7 and MDA-MB-231 breast cancer cells, we collected hundreds of spectra per line, and MDA-MB-231 exhibited broader spectral variations, indicating greater heterogeneity. Principal component analysis and linear discriminant analysis achieved 99% classification accuracy for MCF-7 and MDA-MB-231, and bright-field imaging confirmed preserved adhesion and canonical morphologies. This chip-based, label-free living cell SERS platform enables scalable, nonperturbative phenotyping and may support rapid malignancy classification and treatment response screening across subtle cancer states. Full article

Nitridation of different materials using ion implantation is of considerable interest for many applications. As electronic components, oxynitride (SiO_xN_y) layers exhibit beneficial properties such as precise compositional variability, refractive index tunability, oxidation resistance, and low mechanical stress. In the present study we investigate nanoscale SiO_xN_y synthesized using ion implantation methods. To introduce N⁺ ions into a shallow Si subsurface region, both conventional ion beam implantation and plasma immersion ion implantation with subsequent high-temperature treatment in dry O₂ are used. The optical and morphological properties and chemical bonding of formed SiO_xN_y layers were studied by applying spectroscopic ellipsometry in the range of VIS-Near IR (SE) and IR (IR-SE), Raman spectroscopy and Atomic Force Microscopy (AFM). Monte Carlo modeling of implant profiles contributed to understanding physical and chemical processes and predicted different influences of the incorporated N⁺ ions on the oxidation mechanism, confirmed by the thickness dependence of SiO_xN_y/Si layers obtained from the SE data analysis. IR-SE spectral analysis established the formation of Si-O, Si-N, Si-N-O and Si-Si chemical bonds in the grown layers. The occurrence of amorphization of the Si crystal lattice due to incorporation of high-energy N⁺ ions into the Si lattice is confirmed by the Raman and ellipsometry results. The free Si atoms can congregate, forming nanocrystalline clusters. AFM imaging revealed that both implantation methods left the surface of the resulting SiO_xN_y layers considerably smooth with similar roughness parameter values. The results of the studies imply that the technological approaches used allow the production of high-quality nanoscale silicon oxynitride films with appropriate tunable composition and properties for possible application in advanced electronic devices for nanoelectronics, optoelectronics and sensor applications. Full article

Owing to the high pharmacological relevance and multidimensional quality attributes of Panax spp., accurate authentication and quality evaluation of Panax-derived herbal materials remain challenging within traditional Chinese medicine (TCM) quality control systems. Conventional approaches often face trade-offs among analysis speed and throughput, non-destructive measurement, and analytical accuracy, which can limit their suitability for modern, large-scale quality control. This review summarizes recent advances in vibrational and related analytical techniques—infrared (IR) and near-infrared (NIR) spectroscopy, Raman spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging (HSI), and nuclear magnetic resonance (NMR)—for authentication and quality evaluation of Panax materials. We compare the capabilities of each modality in supporting key tasks, including species authentication, geographical origin tracing, age/cultivation-stage discrimination, and quantitative assessment of major chemical markers, with emphasis on the underlying measurement principles. In general, NIR and HSI are well suited to rapid, high-throughput screening of bulk samples, whereas Raman and NMR provide higher chemical specificity for molecular and structural characterization. To mitigate limitations of single-modality analysis, this review discusses a methodological shift from conventional spectral fingerprinting and chemometric approaches toward model-driven, data-enabled sensing strategies for robust quality evaluation. Specifically, we highlight multimodal data fusion frameworks combined with interpretable machine-learning/deep-learning methods to build robust classification and regression models for quality assessment. This perspective aims to support standardized and scalable authentication and quality evaluation of Panax herbal materials and to facilitate the digitization of quality control workflows for Chinese herbal medicines. Full article

Background/Objectives: Breast cancer (BC) has emerged as a significant threat among female malignancies, resulting in approximately 670,000 fatalities. The capacity to identify BC has advanced over the past two decades because of deep learning (DL), machine learning (ML), and artificial intelligence. The early detection of BC is crucial; yet, conventional diagnostic techniques, including MRI, mammography, and biopsy, are costly, time-intensive, less sensitive, incorrect, and necessitate skilled physicians. This narrative review will examine six novel imaging approaches for BC diagnosis. Methods: Optical coherence tomography (OCT) surpasses existing approaches by providing non-invasive, high-resolution imaging. Raman Spectroscopy (RS) offers detailed chemical and structural insights into cancer tissue that traditional approaches cannot provide. Photoacoustic Imaging (PAI) provides superior optical contrast, exceptional ultrasonic resolution, and profound penetration and visualization capabilities. Hyperspectral Imaging (HSI) acquires spatial and spectral data, facilitating non-invasive tissue classification with superior accuracy compared to grayscale imaging. Contrast-Enhanced Spectral Mammography (CESM) utilizes contrast agents and dual energy to improve the visualization of blood vessels, enhance patient comfort, and surpass standard mammography in sensitivity. Multispectral Imaging (MSI) enhances tissue classification by employing many wavelength bands, resulting in high-dimensional images that surpass the ultrasound approach. The imaging techniques studied in this study are very useful for diagnosing tumors, staging them, and guiding surgery. They are not detrimental to morphological or immunohistochemical analysis, which is the gold standard for diagnosing breast cancer and determining molecular characteristics. Results: These imaging modalities provide enhanced sensitivity, specificity, and diagnostic accuracy. Notwithstanding their considerable potential, the majority of these procedures are not employed in standard clinical practices. Conclusions: Validations, standardization, and large-scale clinical trials are essential for the real-time application of these approaches. The analyzed studies demonstrated that the novel modalities displayed enhanced diagnostic efficacy, with reported sensitivities and specificities often exceeding those of traditional imaging methods. The results indicate that they may assist in early detection and surgical decision-making; however, for widespread adoption, they must be standardized, cost-reduced, and subjected to extensive clinical trials. This study offers a concise summary of each methodology, encompassing the methods and findings, while also addressing the many limits encountered in the imaging techniques and proposing solutions to mitigate these issues for future applications. Full article

This review summarizes the application of spectroscopic techniques in pesticide residue analysis, with a focus on the principles, advancements, and challenges of surface-enhanced Raman spectroscopy (SERS), infrared spectroscopy, fluorescence spectroscopy, ultraviolet-visible (UV-Vis) spectroscopy, and hyperspectral imaging (HSI). Nanomaterials, serving as key enhancing substrates, significantly improve the sensitivity and selectivity of these detection methods. This article critically evaluates the strengths and limitations of each technique in practical applications—such as the exceptional sensitivity of SERS versus its dependence on substrate reproducibility, and the non-destructive nature of hyperspectral imaging against the complexity of data processing. Future research directions should emphasize the development of intelligent nanosubstrates, the construction of cross-modal spectral databases, and the miniaturization of integrated spectroscopic-mass spectrometric instruments. These advancements are essential for enhancing the efficiency and reliability of agricultural and food safety monitoring. Full article

The findings of non-invasive, multi-analytical research on two wall paintings located in the Santuario della Beata Vergine dei Miracoli in Saronno (Varese, Italy)—The Marriage of the Virgin and The Adoration of the Christ Child—are presented in this paper. The authorship of the latter is up for controversy, while the former is unquestionably attributed to Bernardino Luini. The objective was to assess the compatibility of their color palettes through material comparison. A complementary suite of non-invasive techniques, including X-ray fluorescence (XRF), external reflection FTIR, Raman, visible reflectance spectroscopy and hyperspectral imaging, were employed to characterize pigments and surface materials without sampling. Results confirm the use of historically consistent pigments such as calcium carbonate, ochres, Naples yellow, smalt, azurite and lapis lazuli. Differences in the application of blue pigments—lapis lazuli in The Marriage of the Virgin and azurite in The Adoration of the Christ Child—may reflect workshop variation rather than separate authorship. Spectral imaging revealed pigment mixing and layering strategies, especially in skin tones and shadow modeling. This study underscores the significance of diagnostics as an interpretive instrument, capable of contextualizing Luini’s paintings within the context of Renaissance creative practice, providing a framework relevant to analogous inquiries. Full article

Plant gums have long served as essential binding media in polychrome cultural heritage, contributing to pigment adhesion, surface cohesion, and long-term stability. This review evaluates recent advances in analytical technologies, including FTIR, Raman spectroscopy, GC-MS, LC-MS/MS, MALDI-TOF MS, hyperspectral imaging, and immunological assays, for the identification of gums such as gum arabic, peach gum, and tragacanth in diverse cultural contexts. Drawing on case studies from 19th-century watercolours, ancient Egyptian coffins, and Maya murals, the paper demonstrates how these methods enable precise chemical characterization even in complex, aged, and mineral-rich matrices. Such information directly aids conservators in selecting compatible restoration materials, tailoring treatment protocols, and assessing deterioration mechanisms. Persistent challenges remain, including gum degradation, spectral interference from pigments and restoration materials, sample heterogeneity, and limited reference libraries, particularly for non-European species. Future research directions emphasize multi-modal, non-invasive workflows that integrate hyperspectral imaging with spectroscopic and chromatographic methods, drone-assisted micro-Raman for inaccessible surfaces, machine learning-assisted spectral databases, and bio-inspired adhesives replicating historical rheology. By linking molecular identification to conservation decision-making, plant gum analysis not only deepens our understanding of historical material practices but also strengthens the scientific basis for sustainable heritage preservation strategies. Full article

Multimodal imaging techniques have the potential to enhance the interpretation of histology by offering additional molecular and structural information beyond that accessible through hematoxylin and eosin (H&E) staining alone. Here, we present a quantitative approach for comparing the information content of different image modalities, such as H&E and multimodal imaging. We used a combination of deep learning and radiomics-based feature extraction with different information markers, implemented in Python 3.12, to compare the information content of the H&E stain, multimodal imaging, and the combined dataset. We also compared the information content of individual channels in the multimodal image and of different Coherent Anti-Stokes Raman Scattering (CARS) microscopy spectral channels. The quantitative measurements of information that we utilized were Shannon entropy, inverse area under the curve (1-AUC), the number of principal components describing 95% of the variance (PC95), and inverse power law fitting. For example, the combined dataset achieved an entropy value of 0.5740, compared to 0.5310 for H&E and 0.5385 for the multimodal dataset using MobileNetV2 features. The number of principal components required to explain 95 percent of the variance was also highest for the combined dataset, with 62 components, compared to 33 for H&E and 47 for the multimodal dataset. These measurements consistently showed that the combined datasets provide more information. These observations highlight the potential of multimodal combinations to enhance image-based analyses and provide a reproducible framework for comparing imaging approaches in digital pathology and biomedical image analysis. Full article