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This study investigates the capacity of optical spectroscopy in the visible (VIS) and near-infrared (NIR) spectral ranges for estimating the biomechanical properties of human meniscus. Seventy-two samples obtained from the anterior, central, and posterior locations of the medial and lateral menisci of 12 human cadaver joints were used. The samples were subjected to mechanical indentation, then traditional biomechanical parameters (equilibrium and dynamic moduli) were calculated. In addition, strain-dependent fibril network modulus and permeability strain-dependency coefficient were determined via finite-element modeling. Subsequently, absorption spectra were acquired from each location in the VIS (400 to 750 nm) and NIR (750 to 1100 nm) spectral ranges. Partial least squares regression, combined with spectral preprocessing and transformation, was then used to investigate the relationship between the biomechanical properties and spectral response. The NIR spectral region was observed to be optimal for model development (83.0%≤R2≤90.8%). The percentage error of the models are: Eeq (7.1%), Edyn (9.6%), Eϵ (8.4%), and Mk (8.9%). Thus, we conclude that optical spectroscopy in the NIR range is a potential method for rapid and nondestructive evaluation of human meniscus functional integrity and health in real time during arthroscopic surgery., ((2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).)

The potential of visible-near infrared spectroscopy to predict glucosinolates and total phenolic content in rocket ( Eruca vesicaria ) leaves has been evaluated. Accessions of the E. vesicaria species were scanned by NIRS as ground leaf, and their reference values regressed against different spectral transformations by modified partial least squares (MPLS) regression. The coefficients of determination in the external validation (R²VAL) for the different quality components analyzed in rocket ranged from 0.59 to 0.84, which characterize those equations as having from good to excellent quantitative information. These results show that the total glucosinolates, glucosativin and glucoerucin equations obtained, can be used to identify those samples with low and high contents. The glucoraphanin equation obtained can be used for rough predictions of samples and in case of total phenolic content, the equation showed good correlation. The standard deviation (SD) to standard error of prediction ratio (RPD) and SD to range (RER) were variable for the different quality compounds and showed values that were characteristic of equations suitable for screening purposes or to perform accurate analyses. From the study of the MPLS loadings of the first three terms of the different equations, it can be concluded that some major cell components such as protein and cellulose, highly participated in modelling the equations for glucosinolates., Competing Interests: The authors declare no conflict of interest.

Adulteration of olive oil is not only a major economic fraud but can also have major health implications for consumers. In this study, a combination of visible spectroscopy with a novel multivariate curve resolution method (CR), principal component analysis (PCA) and linear discriminant analysis (LDA) is proposed for the authentication of virgin olive oil (VOO) samples. VOOs are well-known products with the typical properties of a two-component system due to the two main groups of compounds that contribute to the visible spectra (chlorophylls and carotenoids). Application of the proposed CR method to VOO samples provided the two pure-component spectra for the aforementioned families of compounds. A correlation study of the real spectra and the resolved component spectra was carried out for different types of oil samples (n=118). LDA using the correlation coefficients as variables to discriminate samples allowed the authentication of 95% of virgin olive oil samples., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

The identification and quantification of binary blends of refined olive oil with four different extra virgin olive oil (EVOO) varietals (Picual, Cornicabra, Hojiblanca and Arbequina) was carried out with a simple method based on combining visible spectroscopy and non-linear artificial neural networks (ANNs). The data obtained from the spectroscopic analysis was treated and prepared to be used as independent variables for a multilayer perceptron (MLP) model. The model was able to perfectly classify the EVOO varietal (100% identification rate), whereas the error for the quantification of EVOO in the mixtures containing between 0% and 20% of refined olive oil, in terms of the mean prediction error (MPE), was 2.14%. These results turn visible spectroscopy and MLP models into a trustworthy, user-friendly, low-cost technique which can be implemented on-line to characterize olive oil mixtures containing refined olive oil and EVOOs., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Noninvasive examination methods of chemical composition and particle size are presented here based on visible spectroscopy to achieve the identification and recording of mineral pigments used on ancient wall paintings. The normalized spectral curve, slope and curvature extracted from visible spectral reflectance are combined with adjustable weighting coefficients to construct the identification feature space, and Euclid distances between spectral reflectance from wall paintings and a reference database are calculated in the feature space as the discriminant criterion to identify the chemical composition of mineral pigments. A parametric relationship between the integral quantity of spectral reflectance and logarithm of mean particle size is established using a quadratic polynomial to accomplish the noninvasive prediction of mineral pigment particle size used on ancient wall paintings. The feasibility of the proposed methods is validated by the in situ nondestructive identification of the wall paintings in the Mogao Grottoes at Dunhuang. Chinese painting styles and historical evolution are then analyzed according to the identification results of 16 different grottoes constructed from the Sixteen Kingdoms to the Yuan Dynasty., (© The Author(s) 2016.)

A large set of fresh cow milk samples collected from many suppliers over a large geographical area in Russia during a year has been analyzed by optical spectroscopy in the range 400-1100 nm in accordance with previously developed scatter-based technique. The global (i.e. resistant to seasonal, genetic, regional and other variations of the milk composition) models for fat and total protein content, which were built using partial least-squares (PLS) regression, exhibit satisfactory prediction performances enabling their practical application in the dairy. The root mean-square errors of prediction (RMSEP) were 0.09 and 0.10 for fat and total protein content, respectively. The issues of raw milk analysis and multivariate modelling based on the historical spectroscopic data have been considered and approaches to the creation of global models and their transfer between the instruments have been proposed. Availability of global models should significantly facilitate the dissemination of optical spectroscopic methods for the laboratory and in-line quantitative milk analysis., (Copyright © 2016. Published by Elsevier Ltd.)

This study investigates the correlation between the composition of human meniscus and its absorption spectrum in the visible (VIS) and near infrared (NIR) spectral range. Meniscus samples (n = 24) were obtained from nonarthritic knees of human cadavers with no history of joint diseases. Specimens (n = 72) were obtained from three distinct sections of the meniscus, namely; anterior, center, posterior. Absorption spectra were acquired from each specimen in the VIS and NIR spectral range (400-1,100 nm). Following spectroscopic probing, the specimens were subjected to biochemical analyses to determine the matrix composition, that is water, hydroxyproline, and uronic acid contents. Multivariate analytical techniques, including principal component analysis (PCA) and partial least squares (PLS) regression, were then used to investigate the correlation between the matrix composition and it spectral response. Our results indicate that the optical absorption of meniscus matrix is related to its composition, and this relationship is optimal in the NIR spectral range (750-1,100 nm). High correlations (R(2) (uronic)  = 86.9%, R(2) (water)  = 83.8%, R(2) (hydroxyproline)  = 81.7%, p < 0.0001) were obtained between the spectral predicted and measured meniscus composition, thus suggesting that spectral data in the NIR range can be utilized for estimating the matrix composition of human meniscus. In conclusion, optical spectroscopy, particularly in the NIR spectral range, is a potential method for evaluating the composition of human meniscus. This presents a promising technique for rapid and nondestructive evaluation of meniscus integrity in real-time during arthroscopic surgery., (© 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.)

Superoxide reductase (SOR), which is commonly found in prokaryotic organisms, affords protection from oxidative stress by reducing the superoxide anion to hydrogen peroxide. The reaction is catalyzed at the iron centre, which is highly conserved among the prokaryotic SORs structurally characterized to date. Reported here is the first structure of an SOR from a eukaryotic organism, the protozoan parasite Giardia intestinalis (GiSOR), which was solved at 2.0 Å resolution. By collecting several diffraction data sets at 100 K from the same flash-cooled protein crystal using synchrotron X-ray radiation, photoreduction of the iron centre was observed. Reduction was monitored using an online UV-visible microspectrophotometer, following the decay of the 647 nm absorption band characteristic of the iron site in the glutamate-bound, oxidized state. Similarly to other 1Fe-SORs structurally characterized to date, the enzyme displays a tetrameric quaternary-structure arrangement. As a distinctive feature, the N-terminal loop of the protein, containing the characteristic EKHxP motif, revealed an unusually high flexibility regardless of the iron redox state. At variance with previous evidence collected by X-ray crystallography and Fourier transform infrared spectroscopy of prokaryotic SORs, iron reduction did not lead to dissociation of glutamate from the catalytic metal or other structural changes; however, the glutamate ligand underwent X-ray-induced chemical changes, revealing high sensitivity of the GiSOR active site to X-ray radiation damage.

In this research, the detection and quantification of adulterants in one of the most common varieties of extra virgin olive oil (EVOO) have been successfully carried out. Visible absorption information was collected from binary mixtures of Picual EVOO with one of four adulterants: refined olive oil, orujo olive oil, sunflower oil, and corn oil. The data gathered from the absorption spectra were used as input to create an artificial neural network (ANN) model. The designed mathematical tool was able to detect the type of adulterant with an identification rate of 96% and to quantify the volume percentage of EVOO in the samples with a low mean prediction error of 1.2%. These significant results make ANNs coupled with visible spectroscopy a reliable, inexpensive, user-friendly, and real-time method for difficult tasks, given that the matrices of the different adulterated oils are practically alike.

This study describes two applications of a variant of the multivariate curve resolution alternating least squares (MCR-ALS) method with a correlation constraint. The first application describes the use of MCR-ALS for the determination of biodiesel concentrations in biodiesel blends using near infrared (NIR) spectroscopic data. In the second application, the proposed method allowed the determination of the synthetic antioxidant N,N'-Di-sec-butyl-p-phenylenediamine (PDA) present in biodiesel mixtures from different vegetable sources using UV-visible spectroscopy. Well established multivariate regression algorithm, partial least squares (PLS), were calculated for comparison of the quantification performance in the models developed in both applications. The correlation constraint has been adapted to handle the presence of batch-to-batch matrix effects due to ageing effects, which might occur when different groups of samples were used to build a calibration model in the first application. Different data set configurations and diverse modes of application of the correlation constraint are explored and guidelines are given to cope with different type of analytical problems, such as the correction of matrix effects among biodiesel samples, where MCR-ALS outperformed PLS reducing the relative error of prediction RE (%) from 9.82% to 4.85% in the first application, or the determination of minor compound with overlapped weak spectroscopic signals, where MCR-ALS gave higher (RE (%)=3.16%) for prediction of PDA compared to PLS (RE (%)=1.99%), but with the advantage of recovering the related pure spectral profile of analytes and interferences. The obtained results show the potential of the MCR-ALS method with correlation constraint to be adapted to diverse data set configurations and analytical problems related to the determination of biodiesel mixtures and added compounds therein., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Background: Graphene is a single-atom thick, two-dimensional sheet of hexagonally arranged carbon atoms isolated from its three-dimensional parent material, graphite. One of the most common methods for preparation of graphene is chemical exfoliation of graphite using powerful oxidizing agents. Generally, graphene is synthesized through deoxygenation of graphene oxide (GO) by using hydrazine, which is one of the most widespread and strongest reducing agents. Due to the high toxicity of hydrazine, it is not a promising reducing agent in large-scale production of graphene; therefore, this study focused on a green or sustainable synthesis of graphene and the biocompatibility of graphene in primary mouse embryonic fibroblast cells (PMEFs)., Methods: Here, we demonstrated a simple, rapid, and green chemistry approach for the synthesis of reduced GO (rGO) from GO using triethylamine (TEA) as a reducing agent and stabilizing agent. The obtained TEA reduced GO (TEA-rGO) was characterized by ultraviolet (UV)-visible absorption spectroscopy, X-ray diffraction (XRD), particle size dynamic light scattering (DLS), scanning electron microscopy (SEM), Raman spectroscopy, and atomic force microscopy (AFM)., Results: The transition of graphene oxide to graphene was confirmed by UV-visible spectroscopy. XRD and SEM were used to investigate the crystallinity of graphene and the surface morphologies of prepared graphene respectively. The formation of defects further supports the functionalization of graphene as indicated in the Raman spectrum of TEA-rGO. Surface morphology and the thickness of the GO and TEA-rGO were analyzed using AFM. The presented results suggest that TEA-rGO shows significantly more biocompatibility with PMEFs cells than GO., Conclusion: This is the first report about using TEA as a reducing as well as a stabilizing agent for the preparation of biocompatible graphene. The proposed safe and green method offers substitute routes for large-scale production of graphene for several biomedical applications.

Non-symbiotic hemoglobins (nsHbs) are O(2)-binding proteins widely distributed in land plants, including primitive bryophytes. Little is known about the properties of bryophyte nsHbs. Here, we report the spectroscopic characterization of two moss recombinant nsHbs, CerpurnsHb of Ceratodon purpureus and PhypatnsHb of Physcomitrella patens. Spectra showed that the absorption maxima of the ferrous and ferric forms of recombinant CerpurnsHb are located at 418, 531 and 557 nm and 407, 537, 569 (shoulder) and 632 (shoulder) nm, respectively, and of PhypatnsHb are located at 422, 529 and 557 nm and 407, 531, 571 (shoulder) and 647 (shoulder) nm, respectively. These absorption maxima are similar to those of rice Hb1. Also, the absorption maxima of the oxygenated ferrous form of recombinant CerpurnsHb and PhypatnsHb are located at 412, 541 and 575 nm and 414, 541 and 574 nm, respectively, similar to those of oxygenated rice Hb1 and cowpea leghemoglobin II. This evidence indicates that CerpurnsHb and PhypatnsHb are mostly hexacoordinate and that they bind O(2).