Fluorescence Spectroscopy Data Research Articles

Linear Source Apportionment Using Generalized Least Squares

Motivated by applications to water quality monitoring using fluorescence spectroscopy, we develop the source apportionment model for high dimensional profiles of dissolved organic matter (DOM). We describe simple methods to estimate the parameters of a linear source apportionment model, and show how the estimates are related to those of ordinary and generalized least squares. Using this least squares framework, we analyze the variability of the estimates, and we propose predictors for missing elements of a DOM profile. We demonstrate the practical utility of our results on fluorescence spectroscopy data collected from the Neuse River in North Carolina.

Experimental detection of marine plastic litter in surface waters by 405 nm LD-based fluorescence lidar

Plastic pollution has become a global challenge, affecting water quality and health. Plastics including polystyrene (PS), polyvinyl chloride (PVC), polypropylene (PP), polyethylene terephthalate (PET), and high-density polyethylene (HDPE), are significant contributors to environmental pollution. With the growing need for investigation and detection of plastics found in natural waters, we propose the use of a portable laser diode (LD)-based fluorescence lidar system for in-situ detection of plastic litters in surface waters based on excitation-emission fluorescence spectroscopic data. The experiments were carried out in a controlled environment using a fluorescence lidar system with 405 nm excitation wavelength to determine the fluorescence signals of several plastics at 470 nm emission wavelength. Simultaneous detection of PET plastic and Chlorella vulgaris were also observed to determine the fluorescence influence of chlorophyll in surface waters. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy was employed to study the chemical composition of the plastics used before and after being submerged in the water. Scanning electron microscopy (SEM) and high-resolution camera microscopy were used to analyze the morphology of the submerged PET samples. This study provides a basis for a new in-situ technique using a fluorescence lidar system for submerged or transparent plastics in surface waters.

Probabilistic PARAFAC2.

The Parallel Factor Analysis 2 (PARAFAC2) is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example, because of differences in signal sampling or batch sizes. A fully probabilistic treatment of the PARAFAC2 is desirable to improve robustness to noise and provide a principled approach for determining the number of factors, but challenging because direct model fitting requires that factor loadings be decomposed into a shared matrix specifying how the components are consistently co-expressed across samples and sample-specific orthogonality-constrained component profiles. We develop two probabilistic formulations of the PARAFAC2 model along with variational Bayesian procedures for inference: In the first approach, the mean values of the factor loadings are orthogonal leading to closed form variational updates, and in the second, the factor loadings themselves are orthogonal using a matrix Von Mises-Fisher distribution. We contrast our probabilistic formulations to the conventional direct fitting algorithm based on maximum likelihood on synthetic data and real fluorescence spectroscopy and gas chromatography-mass spectrometry data showing that the probabilistic formulations are more robust to noise and model order misspecification. The probabilistic PARAFAC2, thus, forms a promising framework for modeling multi-way data accounting for uncertainty.

Utilizing machine learning algorithms for precise discrimination of glycosuria in fluorescence spectroscopic data

Fluorescence spectroscopy coupled with a random forest machine learning algorithm offers a promising non-invasive approach for diagnosing glycosuria, a condition characterized by excess sugar in the urine of diabetic patients. This study investigated the ability of this method to differentiate between diabetic and healthy control urine samples. Fluorescent spectra were captured from urine samples using a Xenon arc lamp emitting light within the 200 to 950 nm wavelength range, with consistent fluorescence emission observed at 450 nm under an excitation wavelength of 370 nm. Healthy control samples were also analyzed within the same spectral range for comparison. To distinguish spectral differences between healthy and infected samples, the random forest (RF) and K-Nearest Neighbors (KNN) machine learning algorithms have been employed. These algorithms automatically recognize spectral patterns associated with diabetes, enabling the prediction of unknown classifications based on established samples. Principal component analysis (PCA) was utilized for dimensionality reduction before feeding the data to RF and KNN for classification. The model’s classification performance was evaluated using 10-fold cross-validation, resulting in the proposed RF-based model achieving accuracy of 96 %, specificity of 100 %, sensitivity of 93 %, and precision of 100 %. These results suggest that the proposed method holds promise for a more convenient and potentially more accurate method for diagnosing glycosuria in diabetic patients.

Revealing the Effect of Stereocontrol on Intermolecular Interactions between Abiotic, Sequence-Defined Polyurethanes and a Ligand.

The development of precision polymer synthesis has facilitated access to a diverse library of abiotic structures wherein chiral monomers are positioned at specific locations within macromolecular chains. These structures are anticipated to exhibit folding characteristics similar to those of biotic macromolecules and possess comparable functionalities. However, the extensive sequence space and numerous variables make selecting a sequence with the desired function challenging. Therefore, revealing sequence-function dependencies and developing practical tools are necessary to analyze their conformations and molecular interactions. In this study, we investigate the effect of stereochemistry, which dictates the spatial location of backbone and pendant groups, on the interaction between sequence-defined oligourethanes and bisphenol A ligands. Various methods are explored to analyze the receptor-like properties of model oligomers and the ligand. The accuracy of molecular dynamics simulations and experimental techniques is assessed to uncover the impact of discrete changes in stereochemical arrangements on the structures of the resulting complexes and their binding strengths. Detailed computational investigations providing atomistic details show that the formed complexes demonstrate significant structural diversity depending on the sequence of stereocenters, thus affecting the oligomer-ligand binding strength. Among the tested techniques, the fluorescence spectroscopy data, fitted to the Stern-Volmer equation, are consistently aligned with the calculations, thus validating the developed simulation methodology. The developed methodology opens a way to engineer the structure of sequence-defined oligomers with receptor-like functionality to explore their practical applications, e.g., as sensory materials.

Serotonin Promotes Vesicular Association and Fusion by Modifying Lipid Bilayers.

The primary event in chemical neurotransmission involves the fusion of a membrane-limited vesicle at the plasma membrane and the subsequent release of its chemical neurotransmitter cargo. The cargo itself is not known to have any effect on the fusion event. However, amphiphilic monoamine neurotransmitters (e.g., serotonin and dopamine) are known to strongly interact with lipid bilayers and to affect their mechanical properties, which can in principle impact membrane-mediated processes. Here, we probe whether serotonin can enhance the association and fusion of artificial lipid vesicles in vitro. We employ fluorescence correlation spectroscopy and total internal reflection fluorescence microscopy to measure the attachment and fusion of vesicles whose lipid compositions mimic the major lipid components of synaptic vesicles. We find that the association between vesicles and supported lipid bilayers is strongly enhanced in a serotonin dose-dependent manner, and this drives an increase in the rate of spontaneous fusion. Molecular dynamics simulations and fluorescence spectroscopy data show that serotonin insertion increases the water content of the hydrophobic part of the bilayer. This suggests that the enhanced membrane association is likely driven by an energetically favorable drying transition. Other monoamines, such as dopamine and norepinephrine, but not other related species, such as tryptophan, show similar effects on membrane association. Our results reveal a lipid bilayer-mediated mechanism by which monoamines can themselves modulate vesicle fusion, potentially adding to the control toolbox for the tightly regulated process of neurotransmission in vivo.

A study of hydrophobic domain formation of polymeric drug precipitation inhibitors in aqueous solution

Despite the widespread use of polymers as precipitation inhibitors in supersaturating drug formulations, the current understanding of their mechanisms of action is still incomplete. Specifically, the role of hydrophobic drug interactions with polymers by considering possible supramolecular conformations in aqueous dispersion is an interesting topic. Accordingly, this study investigated the tendency of polymers to create hydrophobic domains, where lipophilic compounds may nest to support drug solubilisation and supersaturation. Fluorescence spectroscopy with the environment-sensitive probe pyrene was compared with atomistic molecular dynamics simulations of the model drug fenofibrate (FENO). Subsequently, kinetic drug supersaturation and thermodynamic solubility experiments were conducted. As a result, the different polymers showed hydrophobic domain formation to a varying degree and the molecular simulations supported interpretation of fluorescence spectroscopy data. Molecular insights were gained into the conformational structure of how the polymers interacted with FENO in solution phase, which apart from nucleation and crystal growth effects, determined drug concentrations in solution. Notable was that even at the lowest polymer concentration of 0.01 %, w/v, there were polymer-specific solubilisation effects of FENO observed and the resulting reduction in apparent drug supersaturation provided relevant knowledge both from a mechanistic and practical perspective.

Multivariate analysis applied to X-ray fluorescence to assess soil contamination pathways: case studies of mass magnetic susceptibility in soils near abandoned coal and W/Sn mines

Determining the origin and pathways of contaminants in the natural environment is key to informing any mitigation process. The mass magnetic susceptibility of soils allows a rapid method to measure the concentration of magnetic minerals, derived from anthropogenic activities such as mining or industrial processes, i.e., smelting metals (technogenic origin), or from the local bedrock (of geogenic origin). This is especially effective when combined with rapid geochemical analyses of soils. The use of multivariate analysis (MVA) elucidates complex multiple-component relationships between soil geochemistry and magnetic susceptibility. In the case of soil mining sites, X-ray fluorescence (XRF) spectroscopic data of soils contaminated by mine waste shows statistically significant relationships between magnetic susceptibility and some base metal species (e.g., Fe, Pb, Zn, etc.). Here, we show how qualitative and quantitative MVA methodologies can be used to assess soil contamination pathways using mass magnetic susceptibility and XRF spectra of soils near abandoned coal and W/Sn mines (NW Portugal). Principal component analysis (PCA) showed how the first two primary components (PC-1 + PC-2) explained 94% of the sample variability, grouped them according to their geochemistry and magnetic susceptibility in to geogenic and technogenic groups. Regression analyses showed a strong positive correlation (R2 > 0.95) between soil geochemistry and magnetic properties at the local scale. These parameters provided an insight into the multi-element variables that control magnetic susceptibility and indicated the possibility of efficient assessment of potentially contaminated sites through mass-specific soil magnetism.

A Biophysics Toolbox for Reliable Data Acquisition and Processing in Integrated Force-Confocal Fluorescence Microscopy.

Integrated single-molecule force-fluorescence spectroscopy setups allow for simultaneous fluorescence imaging and mechanical force manipulation and measurements on individual molecules, providing comprehensive dynamic and spatiotemporal information. Dual-beam optical tweezers (OT) combined with a confocal scanning microscope form a force-fluorescence spectroscopy apparatus broadly used to investigate various biological processes, in particular, protein:DNA interactions. Such experiments typically involve imaging of fluorescently labeled proteins bound to DNA and force spectroscopy measurements of trapped individual DNA molecules. Here, we present a versatile state-of-the-art toolbox including the preparation of protein:DNA complex samples, design of a microfluidic flow cell incorporated with OT, automation of OT-confocal scanning measurements, and the development and implementation of a streamlined data analysis package for force and fluorescence spectroscopy data processing. Its components can be adapted to any commercialized or home-built dual-beam OT setup equipped with a confocal scanning microscope, which will facilitate single-molecule force-fluorescence spectroscopy studies on a large variety of biological systems.

Components of Mid-Nineteenth- and Mid-Twentieth-Century Cudbears

Analysis of purple dyestuff from a tin labeled “1 oz. Cudbear, No. 1 N. F. Powdered”, marketed by the American business S. B. Penick & Company, “Manufacturers of fine drugs and chemicals”, confirmed that the material was indeed a lichen dyestuff. It contains the same major orcein components identified in several other lichen dyes and dyed samples dating from the mid-19th century to today. These dyestuffs were analyzed using several analytical techniques. Fluorescence and fiber optic reflectance spectroscopic data for all the samples were similar. High performance liquid chromatography with diode array detection coupled to mass spectrometry confirmed that this commercial American cudbear was very similar to the samples from the United Kingdom but rather different from the archil-dyed reference yarns from Europe. The significance of the observations is discussed, and chemical structures are proposed for several of the unknown dye components detected in this study.

Structural and morphological properties of CdSe1-xSx thin films obtained by the method of high-frequency magnetron sputtering

CdSe1-xSx (x= 0.3, 0.4 and 0.6) thin films were deposited on quartz and silicon substrates by the method of high-frequency magnetron sputtering. The chemical composition analysis and crystal structure refinement was examined with using X-ray fluorescence spectroscopy and X-ray diffraction data. CdSe1-xSx thin films crystallizes in hexagonal structure (structure type – ZnO, space group P63mc (No. 186)). The lattice parameters (a, c and V), crystallite size (D), strain (ε), dislocation density (δ) and the texture coefficient TC(hkl) was estimated from X-ray diffraction analysis. Units-cell parameters decrease with increasing S content in CdSe1-xSx thin film.

Growth and crystal structure of CdTe1−xSex (x ≥ 0.75) thin films prepared by the method of high-frequency magnetron sputtering

CdTe1–xSex (x = 0.75 and 0.95) thin films were deposited on quartz substrate by the method of high-frequency magnetron sputtering. The chemical composition analysis and crystal structure refinement was examined with using X-ray fluorescence spectroscopy and X-ray diffraction data. CdTe1–xSex thin films crystallizes in hexagonal structure [structure type—ZnO, space group P63mc (No. 186)]. Unit cell parameters decrease with increasing Se content in CdTe1–xSex thin films. The value of the optical band gap for CdTe0.25Se0.75 and CdTe0.05Se0.95 was estimating using the Tauc plot and from the maximum position of the first derivative of the transmittance dT/dλ.

Mut (Mersin) Bölgesinde Hasat Edilen Natürel Sızma Zeytinyağlarının Senkron Floresans Spektroskopisi ve Kemometri ile Orijinalliğinin Doğrulanması: LDA ve HCA Model Yaklaşımları

In this work, synchronous fluorescence (SyF) spectroscopy data of extra virgin olive oils (VOOs) harvested from two different harvest terms (2019 and 2020 October) in Mut (Mersin) region and other edible oils were analyzed by linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA) to verify their authenticity. The emission and excitation monochromators were synchronously employed and the data were obtained in the emission wavelength region of 200–800 nm. A robust LDA model was gained to authenticate the extra VOOs and other edible oils: 98.28% of the tested samples were correctly predicted, and HCA dendograms showed successful discriminations with high sensitivity and specificity. The findings show the capability of SyF spectroscopy coupled with chemometrics and offer a promising methodology for confirming the possible adulteration of extra VOOs with inexpensive edible oils. Furthermore, this simple and rapid methodology doesn’t need any chemical usage and laborious procedures through the analysis.

Materials and techniques used for Portrait of Yi Bok Shin oiled paper sketches: scientific analysis and practical application

AbstractDuring the Joseon Dynasty (1392–1897), many silk portraits were made of kings and other public officials. Most of these are now lost. Silk canvases are partially transparent, which allows pigments on both sides of the canvas to be seen. Before working with a silk canvas, artists would create several drafts of their portraits on paper, which was made translucent through the application of oil. In this research, a set of three paper drafts for a portrait from the eighteenth century was analysed via X-ray fluorescence spectroscopic data and microscopic imagery to identify the type of paper and pigments used. Using this analysis, along with surviving historic records, comparisons to other historic portraits, and an artistic analysis of the line and colouring techniques, new insights into the process of making these portraits are presented. Novel features of the three sketches are identified. Furthermore, the step-by-step process used to create these sketches is discussed and illustrated using a reproduction that employs traditional techniques and materials.

The classification of leek seeds based on fluorescence spectroscopic data using machine learning

AbstractThe objective of this study was to distinguish leek seeds belonging to the Starozagorski kamush variety and two breeding lines based on the selected fluorescence spectroscopic data. The classification models were developed for three classes of Starozagorski kamush vs. breeding line 4 vs. breeding line 39 and pairs of classes of Starozagorski kamush vs. breeding line 4, Starozagorski kamush vs. breeding line 39, and breeding line 4 vs. breeding line 39. The traditional machine learning algorithms, such as PART, Logistic, Naive Bayes, Random Forest, IBk, and Filtered Classifier were applied. All three classes were distinguished with an average accuracy of up to 93.33% for models built using IBk and Filtered Classifier. In the case of each model, Starozagorski kamush variety was completely different (accuracy of 100%, precision, and F-measure, MCC (Matthews correlation coefficient), and ROC (receiver operating characteristic) area of 1.000) from breeding lines, and the mixing of cases was observed between breeding line 4 and breeding line 39. The models built for pairs of leek seed classes distinguished Starozagorski kamush and breeding line 4 with an average accuracy reaching 100% (Logistic, Naive Bayes, Random Forest, IBk). The classification accuracy of Starozagorski kamush and breeding line 39 also reached 100% (Logistic, Naive Bayes, Random Forest, IBk), whereas breeding line 4 and breeding line 39 were classified with an average accuracy of up to 80% (Logistic, Naive Bayes, Random Forest, Filtered Classifier). The proposed approach combining fluorescence spectroscopy and machine learning may be used in practice to distinguish leek seed varieties and breeding lines.

Probing interaction of atherogenic lysophosphatidylcholine with functionalized graphene nanosheets: theoretical modelling and experimental validation.

The potential of graphene derivatives for theranostic applications depends on their compatibility with cellular and biomolecular components. Lysophosphatidylcholine (LPC), a lipid component present in oxidized low-density lipoproteins, microvesicles and free circulation in blood, plays a critical role in the pathophysiology of various diseases. Usingdensity functional theory-based methods, we systematically investigated the interaction of atherogenic LPC molecule with different derivatives of graphene, including pristine graphene, graphene with defect, N-doped graphene, amine-functionalized graphene, various graphene oxides and hydroxylated graphene oxides. We observed that the adsorption of LPC on graphene derivatives is highly selective based on the orientation of the functional groups of LPC interacting with the surface of the derivatives. Hydroxylated graphene oxide exhibited the strongest interaction with LPC with adsorption energy of - 2.1 eV due to the interaction between the hydroxyl group on graphene and the phosphate group of LPC. The presence of aqueous medium further enhanced this interaction indicating favourable adsorption of LPC and graphene oxide in biological systems. Such strong interaction leads to substantial change in the electronic structure of the LPC molecule, which results in the activation of this molecule. In contrast, amine-modified graphene showed the least interaction. These theoretical results are in line with our experimental fluorescence spectroscopic data of LPC/1-anilino-8-napthalene sulfonic acid complex. Our present comprehensive investigation employing both theoretical and experimental methods provides a deeper understanding of graphene-lipid interaction, which holds paramount importance in the design and fabrication of graphene-based nanomaterials for biomedical applications. In this study, we employed the density functional theory-based methods to investigate the electronic and structural properties of graphene derivatives and LPC molecule using the Quantum Espresso package. The exchange-correlation functional was described within generalized gradient approximation (GGA) as parameterized by Perdew, Burke and Ernzerhof (PBE). The valence electrons were represented using plane wave basis sets. `The Grimme's dispersion method was used to include the van der Waals dispersion correction.

The application of fluorescence spectroscopy and machine learning as non-destructive approach to distinguish two different varieties of greenhouse tomatoes

AbstractThe application of interdisciplinary non-invasive diagnostic methods combining fluorescence spectroscopy with multiple machine learning algorithms as tools for rapid application in tomato breeding programs is essential when crossing specific genotypes or parental samples to obtain representatives with better performance. Non-destructive distinguishing tomato species is of great importance for the preservation of product quality. This study aimed at combining fluorescence spectroscopic data and machine learning algorithms for distinguishing greenhouse tomatoes. The models for the discrimination of greenhouse tomato samples were built based on selected spectroscopic data using different machine learning algorithms from the groups of Meta, Functions, Bayes, Trees, Rules, and Lazy. The confusion matrices with accuracy for each sample, average accuracy, time taken to build the model, Kappa statistic, mean absolute error, root mean squared error and relative absolute error were determined. The greenhouse tomato samples were discriminated with an accuracy reaching 100% for the models built using Multi-Class Classifier (Meta), Logistic (Function), Bayes Net (Bayes), PART (Rules), and J48 (Trees). In the case of these algorithms, Kappa statistic was 1.0 and mean absolute error, root mean squared error and relative absolute error were equal to 0.

Improving the Bioactivity and Stability of Embedded Enzymes by Covalent Organic Frameworks.

De novo embedding enzymes within reticular chemistry materials have shown the enhancement of physical and chemical stability for versatile catalytic reactions. Compared to metal-organic frameworks (MOFs), covalent organic frameworks (COFs) are usually considered to be the more superior host of enzymes because of their large channels with low diffusion barriers, outstanding chemical/thermal stability, and metal-free nature. However, detailed investigations on the comparison of COFs and MOFs in enhancing biocatalytic performance have not been explored. Here, we de novo encapsulated enzymes within two COFs via a mechanochemical strategy, which avoided the extreme synthetic conditions of COFs and highly maintained the biological activities of the embedded enzymes. The enzymes@COFs biocomposites exhibited a much higher activity (3.4-14.7 times higher) and enhanced stability than those in MOFs (ZIF-8, ZIF-67, HKUST-1, MIL-53, and CaBDC), and the rate parameter (kcat/Km) of enzyme@COFs was 41.3 times higher than that of enzyme@ZIF-8. Further explorations showed that the conformation of enzymes inside MOFs was disrupted, owing to the harmful interfacial interactions between enzymes and metal ions as confirmed by ATR-FTIR, fluorescence spectroscopy, and XPS data. In contrast, enzymes that were embedded in metal-free COFs highly preserved the natural conformation of free enzymes. This study provides a better understanding of the interfacial interactions between reticular supports and enzymes, which paves a new road for optimizing the bioactivities of immobilized enzymes.

Insights into the binding of half-sandwich phosphino Ir(III) and Ru(II) complexes to deoxyribonucleic acid, albumin and apo-transferrin: Experimental and theoretical investigation

A group of cytotoxic half-sandwich iridium(III) (Ir(η5-Cp*)Cl2PPh2CH2OH (IrPOH)), (Ir(η5-Cp*)Cl2P(p-OCH3Ph)2CH2OH (IrMPOH)), and ruthenium(II) (Ru(η6-p-cymene)Cl2PPh2CH2OH (RuPOH), Ru(η6-p-cymene)Cl2P(p-OCH3Ph)2CH2OH (RuMPOH)) complexes with phosphine ligands exhibit the ability to (i) slow hydrolysis which is reversed by adding a high NaCl concentration; (ii) oxidation of NADH to NAD+; (iii) induction of cytotoxicity towards various cancer cell lines. Furthermore, we found that RuPOH and RuMPOH selectively inhibit the proliferation of skin cancer cells (WM266-4) while Ir(III) complexes were found to be moderate against prostate cancer cells (DU-145). Herein, to elucidate the cytotoxic effects, we investigated the interaction of these complexes with DNA and serum proteins by gel electrophoresis, fluorescence spectroscopy, and molecular docking studies. Fluorescence spectroscopic data (calf thymus DNA: CT-DNA titration), together with analysis of DNA fragmentation (gel electrophoresis) and molecular docking provided evidence for the multimodal interaction of Ir(III) and Ru(III) complexes with DNA with predominance of intercalation and minor groove binding. All examined complexes caused single-stranded cleavage of the sugar–phosphate backbone of plasmid DNA. The affinity of the complexes for apo-transferrin (apo-Tf) and human serum albumin (HSA) was evaluated by fluorescence emission spectroscopy to calculate the binding constants which suggested a tight and reversible binding. Moreover, ruthenium complexes can mimic the binding of iron compounds to specific biomolecules such as albumin and transferrin better than iridium complexes. In silico study indicate that complexes mostly bind to (i) apo-Tf with a preference for a single binding site and/or (ii) to dock within all the four predicted binding sites of HSA with the predominance of site I which include tryptophan residues of HSA. This class of ruthenium(II) and iridium(III) complexes has unusual features worthy of further exploration in the design of novel anticancer drugs.

Mandarin orange (Citrus reticulata Blanco cv. Batu 55) ripeness level prediction using combination reflectance-fluorescence spectroscopy

Optical characteristics of Mandarin Orange cv. Batu 55 of different maturity level has been obtained using reflectance (Vis-NIR) and fluorescence spectroscopy. Spectra features of both reflectance and fluorescence spectroscopy have been evaluated to develop a ripeness prediction model. Spectra dataset and reference measurements were subject to the partial least square regression (PLSR) analysis. The best prediction models were using reflectance spectroscopy data showing the coefficient of determination R2 up to 0.89 and root mean square error (RMSE) of 2.71. On the other hand, it was found that fluorescence spectroscopy showed interesting spectra change in correlation with the accumulation of bluish and reddish fluorescence compounds in the lenticel spots on the fruit surface. The best prediction model using fluorescence spectroscopy data showed the R2 of 0.88 and RMSE of 2.81. Besides that, it wa found that combining spectra of reflectance and fluorescence features could increase the R2 of the partial least square regression (PLSR) model with Savitzky-Golay smoothing, up to 0.91 for brix-acid ratio prediction with RMSE 2.46. These results show the potential of the combined reflectance-fluorescence spectroscopy system for Mandarin ripeness assessment.