Direct radical functionalization of native sugars.

Naturally occurring (native) sugars and carbohydrates contain numerous hydroxyl groups of similar reactivity1,2. Chemists, therefore, rely typically on laborious, multi-step protecting-group strategies3 to convert these renewable feedstocks into reagents (glycosyl donors) to make glycans. The direct transformation of native sugars to complex saccharides remains a notable challenge. Here we describe a photoinduced approach to achieve site- and stereoselective chemical glycosylation from widely available native sugar building blocks, which through homolytic (one-electron) chemistry bypasses unnecessary hydroxyl group masking and manipulation. This process is reminiscent of nature in its regiocontrolled generation of a transient glycosyl donor, followed by radical-based cross-coupling with electrophiles on activation with light. Through selective anomeric functionalization of mono- and oligosaccharides, this protecting-group-free 'cap and glycosylate' approach offers straightforward access to a wide array of metabolically robust glycosyl compounds. Owing to its biocompatibility, the method was extended to the direct post-translational glycosylation of proteins.

Simple demodulation method for optical property extraction in spatial frequency domain imaging.

Different demodulation methods affect the efficiency and accuracy of spatial frequency domain imaging (SFDI). A simple and effective method of sum-to-product identities (STPI) demodulation was proposed in this study. STPI requires one fewer image than conventional three-phase demodulation (TPD) at a spatial frequency. Numerical simulation and phantom experiments were performed. The result proved the feasibility of STPI and showed that STPI combined with subtraction can achieve high-precision demodulation in the low spatial frequency domain. Through extraction of phantom optical properties, STPI had similar accuracy compared with other demodulation methods in extracting optical properties in phantoms. STPI was also used to extract the optical properties of milk, and it had highly consistent results with TPD, which can distinguish milk with different fat content. The demodulation effect of this method in the low spatial frequencies is better than other fast demodulation methods.

A stepwise method for estimating optical properties of two-layer turbid media from spatial-frequency domain reflectance.

This research was conducted to estimate the optical absorption and reduced scattering coefficients of two-layer turbid media using a stepwise method from the spatial-frequency domain reflectance generated by Monte Carlo (MC) simulation. The stepwise method's feasibility for optical property estimations was first investigated by comparing the reflectance generated by the diffusion model and MC simulation for one-layer and two-layer turbid media. The results showed that, with proper frequency selection, the one-layer model could be used for estimating the optical properties of the first layer of the two-layer turbid media. A sample-based calibration method was proposed for calibrating discrepancies of the reflectance between the diffusion model and MC simulation. This significantly improved the parameter estimation accuracy. Results showed that the stepwise method's parameter estimation efficacy and accuracy were much better than that for the one-step method. This was especially true when estimating the absorption coefficient. Absolute error contour maps were generated in order to determine the constraining conditions for the first-layer thickness. It was found that, when each layer's optical properties are within the range of 0.005 mm-1 ≤ µa ≤ 0.04 mm-1 and 0.69 mm-1 ≤ µs'≤ 2.2 mm-1, the first-layer's minimum thickness-for which the first layer's optical properties could be accurately estimated-could be as small as 0.2 mm. Further, the first layer's maximum thickness could not exceed 2.0 mm, in order to have acceptable estimations of the optical properties of the second layer.

Fast estimation of optical properties of pear using a single snapshot technique combined with a least-squares support vector regression model based on spatial frequency domain imaging.

Spatial frequency domain imaging has great potential in agricultural produce quality control due to its advantage of wide-field mapping of absorption (µa) and reduced scattering (µs') parameters. However, it is not widely adopted in real applications due to the large time cost during image acquisition and inversion calculation processes. In this study, a single snapshot technique was used to obtain ac and dc components (Rd_ac, Rd_dc) of diffuse reflectance of turbid media (phantoms and pears). The validation results for the snapshot method indicate that at the spatial frequency of 1000/3 m-1, it achieved the optimal demodulation, by comparison with the results obtained by the commonly used time-domain amplitude demodulation method. Diffusion approximation, artificial neural network, least-squares support vector machine regression (LSSVR), and LSSVR combined with a genetic algorithm (LSSVR+GA) were then used to predict µa and µs' from the obtained Rd_ac, Rd_dc at the fx of 1000/3 m-1. Validation results indicated that the LSSVR method took the least time to calculate µa and µs' with high performance. The proposed imaging system and algorithm were implemented for the inspection of a pear bruise. Results indicated that the bruise, which is not obviously distinguishable in original gray maps, can show obvious contrast in calculated µa and µs' maps, especially in µa maps. Further, the contrast becomes more obvious with the passage of time. In summary, this study developed a low-cost spatial frequency imaging system and matching software that could realize fast detection of optical properties for a pear with the proposed snapshot and LSSVR algorithms.

Feasibility of Laser-Induced Breakdown Spectroscopy and Hyperspectral Imaging for Rapid Detection of Thiophanate-Methyl Residue on Mulberry Fruit.

An effective and rapid way to detect thiophanate-methyl residue on mulberry fruit is important for providing consumers with quality and safe of mulberry fruit. Chemical methods are complex, time-consuming, and costly, and can result in sample contamination. Rapid detection of thiophanate-methyl residue on mulberry fruit was studied using laser-induced breakdown spectroscopy (LIBS) and hyperspectral imaging (HSI) techniques. Principal component analysis (PCA) and partial least square regression (PLSR) were used to qualitatively and quantitatively analyze the data obtained by using LIBS and HSI on mulberry fruit samples with different thiophanate-methyl residues. The competitive adaptive reweighted sampling algorithm was used to select optimal variables. The results of model calibration were compared. The best result was given by the PLSR model that used the optimal preprocessed LIBS-HSI variables, with a correlation coefficient of 0.921 for the prediction set. The results of this research confirmed the feasibility of using LIBS and HSI for the rapid detection of thiophanate-methyl residue on mulberry fruit.

A Review of Hyperspectral Imaging for Chicken Meat Safety and Quality Evaluation: Application, Hardware, and Software.

Food quality and safety issues have received widespread attention around the world. Traditional analytical methods are cumbersome, time consuming, and disruptive. Consumers and businesses are in desperate need of a fast, nondestructive test to evaluate the safety and quality of food. Chicken is an important food source for the human diet and has a high consumption rate. Its quality and safety issues are especially important. The hyperspectral imaging (HSI) technique combines the main characteristics of the spectroscopy technique and the imaging technique to achieve fast, nondestructive testing and demonstrates great potential for evaluating the food safety and quality of chicken. For the past few years, there have been many studies on the HSI technique for the detection and evaluation of chicken meat safety and quality. Therefore, the purpose of this article is to provide a detailed overview of the HSI technique for microbiological safety detection and quality attribute assessments of chicken meat. In addition, the hardware and software used in the HSI systems are also summarized and compared. Finally, some opinions on the focus of future research and its applications in the modern poultry industry are presented.

Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves.

Mulberry trees are an important crop for sericulture. Pests can affect the yield and quality of mulberry leaves. This study aims to develop a hyperspectral imaging system in visible and near-infrared (NIR) region (400⁻1700 nm) for the rapid identification of Diaphania pyloalis larvae and its damage. The extracted spectra of five region of interests (ROI), namely leaf vein, healthy mesophyll, slight damage, serious damage, and Diaphania pyloalis larva at 400⁻1000 nm (visible range) and 900⁻1700 nm (NIR range), were used to establish a partial least squares discriminant analysis (PLS-DA) and least-squares support vector machines (LS-SVM) models. Successive projections algorithm (SPA), uninformation variable elimination (UVE), UVE-SPA, and competitive adaptive reweighted sampling were used for variable selection. The best models in distinguishing between leaf vein, healthy mesophyll, slight damage and serious damage, leaf vein, healthy mesophyll, and larva, slight damage, serious damage, and larva were all the SPA-LS-SVM models, based on the NIR range data, and their correct rate of prediction (CRP) were all 100.00%. The best model for the identification of all five ROIs was the UVE-SPA-LS-SVM model, based on visible range data, which had the CRP value of 97.30%. In summary, visible and near infrared hyperspectral imaging could distinguish Diaphania pyloalis larvae and their damage from leaf vein and healthy mesophyll in a rapid and non-destructive way.

Nondestructive determination of optical properties of a pear using spatial frequency domain imaging combined with phase-measuring profilometry.

Spatial frequency domain imaging (SFDI), as a rapid, noncontact, and scan-free method, can realize wide-field, quantitative optical property mapping and tomographic imaging for a biological sample. Phase-measuring profilometry (PMP) is a surface profile characterization method. Since the projection of structured light onto an object is the basis for PMP and SFDI, the SFDI system is capable of performing both techniques. In this work, we present the results of a feasibility study with the developed SFDI system to realize acquisition of the optical property information and the surface profile information. The surface profile information was used to correct the absorption (µa) maps and reduced scattering (µs') maps. The evaluation of correction effect of the PMP and the calibration and calculation of detection accuracy of the SFDI system were realized by using a series of self-made hemispheric and homogeneous solid phantoms covering a wide range of absorption and reduced scattering coefficients. The results show that the µa and µs' maps become more uniform after using profilometry correction. The maximum relative errors of the system after profilometry correction and calibration were 8.74% for µa and 4.97% for µs' at the wavelength of 527 nm, respectively. A case study was carried out on a pear to verify the application prospect of the method in the field of agricultural products quality inspection. Results indicate that µa and µs' maps of a pear after profilometry correction and calibration were more uniform and more comparable with the reported values.

Food Safety Evaluation Based on Near Infrared Spectroscopy and Imaging: A Review.

In recent years, due to the increasing consciousness of food safety and human health, much progress has been made in developing rapid and nondestructive techniques for the evaluation of food hazards, food authentication, and traceability. Near infrared (NIR) spectroscopy and imaging techniques have gained wide acceptance in many fields because of their advantages over other analytical techniques. Following a brief introduction of NIR spectroscopy and imaging basics, this review mainly focuses on recent NIR spectroscopy and imaging applications for food safety evaluation, including (1) chemical hazards detection; (2) microbiological hazards detection; (3) physical hazards detection; (4) new technology-induced food safety concerns; and (5) food traceability. The review shows NIR spectroscopy and imaging to be effective tools that will play indispensable roles for food safety evaluation. In addition, on-line/real-time applications of these techniques promise to be a huge growth field in the near future.

[Rapid Discriminantion of Common Leather Varieties by Near Infrared Spectroscopy].

Rapid classification of leather variety means important to product process control, trading process and market surveillance. There is no official detection standard on classification of leather variety for the present. By now the testers use organoleptic method, burning method, chemical dissolution method, microscope method, or combination of them, to give a convincing result. The testers are required to highly sufficiently experienced, and not influenced by subjective factors. It also costs too much time. For the purpose of this research, spectra of five common varieties of leather samples (full-grain leather, split leather, sheep leather, reborn leather and manmade leather) were collected from market. Discriminant analysis combined with pre-processing method, including multiplicative signal correction (MSC), standard normal variate (SNV), first derivative and second derivative were used to classify the spectra above. It shows that the above five varieties of leather overlapped seriously in the same space. But manmade leather can be easily distinguished from the other four leather varieties using rear spectra, with the misclassified percent of 1.2%. The last four leather varieties covered each other partly in the same space, classify of any two of them can reach a lower misclassified percent, about 1.3%～17.9%. Different pre-processing method affected the discriminantion model positively or negatively with no regularity. None of these pre-processing methods was found to give a positive effect in a stable and persistent way. It can be concluded that it is feasible to discriminate the common leather varieties by near infrared Spectroscopy. All of the samples were taken from the finish products in the market (eg, handbag, belt, leather coat), which were processed in different ways (eg. tanning, knurling, dyeing). The different processes of the samples could bring an unforeseeable influence to the model which may be reduced by some method, for example, increasing the number and variety of samples.

[Application of NIR Spectroscopy for Nondestructive Qualitative and Quantitative Analysis of Lotus Seeds].

By extracting the Near Infrared (NIR) diffuse reflectance spectral characteristics from the post-harvest lotus seeds in different storage periods, the quantitative and qualitative analysis were applied to lotus seeds with the Soluble Solids Content (SSC) and dry matter content (DM) as criteria. The results of the Partial Least Squares Regression (PLSR) and distance discrimination (DA) models showed that the absorption spectra of lotus seeds and lotus kernels has clear relations to their SSC and DM. The PLSR models of SSC and DM of lotus seeds had the best performance in 5 941-12 480 cm(-1) spectral region in this study. Their correlation coefficients of prediction were 0.74 and 0.82, and the correlation coefficients of calibration were 0.82 and 0.84, and the correlation coefficients of leave one out cross validation were 0.72 and 0.71. The PLSR model of SSC of lotus kernels was better in 7 891-9 310 cm(-1) spectral region. Its correlation coefficient of prediction was 0.79, and the correlation coefficient of calibration was 0.84, and the correlation coefficient of leave one out cross validation was 0.77. The PLSR model of DM of lotus kernels is better in the full spectral region. Its correlation coefficient of prediction was 0.92, and the correlation coefficient of calibration was 0.89, and the correlation coefficient of leave one out cross validation was 0.82. For lotus seeds, the DA model in 5 400-7 885 cm(-1) spectral region is the best with a correctness of 84.2%. And for lotus kernels, the DA model in 9 226-12 480 cm(-1) spectral region is the best with a correctness of 90.8%. For dry lotus kernels, the discriminant accuracy of the DA model is 98.9% in the optimal spectral region. All kernels with membrane and plumule were correctly discriminated. This research shows that the NIR spectroscopy technique can be used to determine SSC and DM content of lotus seeds and lotus kernels, as well as to discriminate their freshness and also to discriminate dry lotus kernels of different age and the kernels with membrane and plumule.

[Investigation of light penetration depth and distribution inside citrus tissue].

Experiment was carried out to explore the light intensity change inside citrus samples in the present study. An experimental platform was set up, including a light box, a spectrometer, a sample stage, an optic fiber probe, light sources, etc. The sample stage is adjustable in three dimensions. The optic fiber probe was used to measure the light changes by observing the light attenuation and intensity variation within the citrus tissues. A 632 nm laser source and a 50 W tungsten halogen lamp light source were used. Light intensity and transmittance were investigated at different positions within the citrus fruit. The band with most significant intensity difference was selected to analyze the light intensity and transmittance trends in different positions inside the citrus fruit In order to examine the influence significance of the sample factor on test results, SPSS software was used to do the analysis of variation (ANOVA) of different samples. The results showed that light intensity and transmittance have a positive correlation with puncture depth, while citrus peel and stone have a more obvious attenuation effect than citrus flesh, and the influence of the sample factor on the test results is not significant. Further research can be carried out by improving the experimental device. The method used and results obtained in this study are valuable for studies on light transmission properties inside fruit tissue, not only for citrus but also for other kinds of fruits.

Deoxynivalenol Detection beyond the Limit in Wheat Flour Based on the Fluorescence Hyperspectral Imaging Technique.

Deoxynivalenol (DON) is a harmful fungal toxin, and its contamination in wheat flour poses a food safety concern globally. This study proposes the combination of fluorescence hyperspectral imaging (FHSI) and qualitative discrimination methods for the detection of excessive DON content in wheat flour. Wheat flour samples were prepared with varying DON concentrations through the addition of trace amounts of DON using the wet mixing method for fluorescence hyperspectral image collection. SG smoothing and normalization algorithms were applied for original spectra preprocessing. Feature band selection was carried out by applying the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the random frog algorithm on the fluorescence spectrum. Random forest (RF) and support vector machine (SVM) classification models were utilized to identify wheat flour samples with DON concentrations higher than 1 mg/kg. The results indicate that the SG-CARS-RF and SG-CARS-SVM models showed better performance than other models, achieving the highest recall rate of 98.95% and the highest accuracy of 97.78%, respectively. Additionally, the ROC curves demonstrated higher robustness on the RF algorithm. Deep learning algorithms were also applied to identify the samples that exceeded safety standards, and the convolutional neural network (CNN) model achieved a recognition accuracy rate of 97.78% for the test set. In conclusion, this study demonstrates the feasibility and potential of the FHSI technique in detecting DON infection in wheat flour.

Quantitative phase image stitching guided by reconstructed intensity images in one-shot double field of view multiplexed digital holographic microscopy.

In digital holographic microscopy (DHM), achieving large field of view (FOV) imaging while maintaining high resolution is critical for quantitative phase measurements of biological cell tissues and micro-nano structures. We present a quantitative phase image stitching guided by reconstructed intensity images in one-shot double FOV multiplexed DHM. Double FOVs are recorded simultaneously through frequency division multiplexing; intensity feature pairs are accurately extracted by multi-algorithm fusion; aberrations and non-common baselines are effectively corrected by preprocessing. Experimental results show that even if phase images have coherent noise, complex aberrations, low overlap rate and large size, this method can achieve high-quality phase stitching.

Site-selective S-gem-difluoroallylation of unprotected peptides with 3,3-difluoroallyl sulfonium salts.

Bench-stable 3,3-difluoroallyl sulfonium salts (DFASs), featuring tunable activity and their editable C-ß and gem-difluoroallyl group, proved to be versatile fluoroalkylating reagents for site-selective S-gem-difluoroallylation of cysteine residues in unprotected peptides. The reaction proceeds with high efficiency under mild conditions (ambient temperature and aqueous and weak basic conditions). Various protected/unprotected peptides, especially bioactive peptides, are site-selectively S-gem-difluoroallylated. The newly added gem-difluoroallyl group and other functional groups derived from C-ß of DFASs are poised for ligation with bio-functional groups through click and radical chemistry. This stepwise "doubly orthogonal" modification of peptides enables the construction of bioconjugates with enhanced complexity and functionality. This proof of principle is successfully applied to construct a peptide-saccharide-biotin chimeric bioconjugate, indicating its great potential application in medicinal chemistry and chemical biology.

Late-stage gem-difluoroallylation of phenol in bioactive molecules and peptides with 3,3-difluoroallyl sulfonium salts.

An efficient method for the late-stage selective O-fluoroalkylation of tyrosine residues with a stable yet highly reactive fluoroalkylating reagent, 3,3-difluoroallyl sulfonium salts (DFASs), has been developed. The reaction proceeds in a mild basic aqueous buffer (pH = 11.6) with high efficiency, high biocompatibility, and excellent regio- and chemoselectivity. Various oligopeptides and phenol-containing bioactive molecules, including carbohydrates and nucleosides, could be selectively O-fluoroalkylated. The added vinyl and other functional groups from DFASs can be valuable linkers for successive modification, significantly expanding the chemical space for further bioconjugation. The synthetic utility of this protocol has been demonstrated by the fluorescently labeled anti-cancer drug and the synthesis of O-link type 1,4,7,10-tetraazacyclododecane-N,N',N,N'-tetraacetic acid-tyrosine3-octreotate (DOTA-TATE), showing the prospect of the method in medicinal chemistry and chemical biology.

Spectral Reflectance Reconstruction from Red-Green-Blue (RGB) Images for Chlorophyll Content Detection.

Chlorophyll is one of the most important pigments in plants, and the measurement of chlorophyll levels enables real-time monitoring of plant growth, which is of great importance to the vegetation monitoring. Compared with the high cost and time-consuming operation of hyperspectral imaging technique, the spectral reflectance reconstruction technique based on RGB images has the advantages of being inexpensive and fast. In this study, using the example of ginkgo leaves, the spectra were reconstructed from red-green-blue (RGB) images taken by smartphones based on a back propagation (BP) neural network and pseudo-inverse method. Based on a BP neural network, the maximum absolute error between the reconstructed spectra and the reference spectra acquired by the hyperspectral camera was less than 0.038. A partial least squares regression (PLSR) prediction model for chlorophyll content estimation was established using the reconstructed spectra. The R2 and root mean square error (RMSE) of the validation set were 0.8237 and 1.1895%, respectively, there was a high correlation between predicted and measured values. Compared with the pseudo-inverse method, the maximum absolute error of the reconstructed spectra was reduced by 10.9%, the R2 in the chlorophyll prediction results was improved by 12.7%, and the RMSE was reduced by 19.3%. This research showed that reconstructing spectral reflectance based on RGB images can realize real-time measurement of chlorophyll content. It provided a reliable tool for fast and low-cost monitoring of plant physiology and growth conditions.

Extracting Tissue Optical Properties and Detecting Bruised Tissue in Pears Quickly and Accurately Based on Spatial Frequency Domain Imaging and Machine Learning.

Recently, Spatial Frequency Domain Imaging (SFDI) has gradually become an alternative method to extract tissue optical properties (OPs), as it provides a wide-field, no-contact acquisition. SFDI extracts OPs by least-square fitting (LSF) based on the diffuse approximation equation, but there are shortcomings in the speed and accuracy of extracting OPs. This study proposed a Long Short-term Memory Regressor (LSTMR) solution to extract tissue OPs. This method allows for fast and accurate extraction of tissue OPs. Firstly, the imaging system was developed, which is more compact and portable than conventional SFDI systems. Next, numerical simulation was performed using the Monte Carlo forward model to obtain the dataset, and then the mapping model was established using the dataset. Finally, the model was applied to detect the bruised tissue of 'crown' pears. The results show that the mean absolute errors of the absorption coefficient and the reduced scattering coefficient are no more than 0.32% and 0.21%, and the bruised tissue of 'crown' pears can be highlighted by the change of OPs. Compared with the LSF, the speed of extracting tissue OPs is improved by two orders of magnitude, and the accuracy is greatly improved. The study contributes to the rapid and accurate extraction of tissue OPs based on SFDI and has great potential in food safety assessment.

Stereoretentive Post-Translational Protein Editing.

Chemical post-translational methods allow convergent side-chain editing of proteins without needing to resort to genetic intervention. Current approaches that allow the creation of constitutionally native side chains via C-C bond formation, using off-protein carbon-centered C· radicals added to unnatural amino acid radical acceptor (SOMOphile, singly occupied molecular orbital (SOMO)) "tags" such as dehydroalanine, are benign and wide-ranging. However, they also typically create epimeric mixtures of d/l-residues. Here, we describe a light-mediated desulfurative method that, through the creation and reaction of stereoretained on-proteinl-alanyl Cß· radicals, allows Cß-Hγ, Cß-Oγ, Cß-Seγ, Cß-Bγ, and Cß-Cγ bond formation to flexibly generate site-selectively edited proteins with full retention of native stereochemistry under mild conditions from a natural amino acid precursor. This methodology shows great potential to explore protein side-chain diversity and function and in the construction of useful bioconjugates.

Spatial Frequency Domain Imaging System Calibration, Correction and Application for Pear Surface Damage Detection.

Spatial frequency domain imaging (SFDI) is a non-contact wide-field optical imaging technique for optical property detection. This study aimed to establish an SFDI system and investigate the effects of system calibration, error analysis and correction on the measurement of optical properties. Optical parameter characteristic measurements of normal pears with three different damage types were performed using the calibrated system. The obtained absorption coefficient µa and the reduced scattering coefficient µ's were used for discriminating pears with different surface damage using a linear discriminant analysis model. The results showed that at 527 nm and 675 nm, the pears' quadruple classification (normal, bruised, scratched and abraded) accuracy using the SFDI technique was 92.5% and 83.8%, respectively, which has an advantage compared with the conventional planar light classification results of 82.5% and 77.5%. The three-way classification (normal, minor damage and serious damage) SFDI technique was as high as 100% and 98.8% at 527 nm and 675 nm, respectively, while the classification accuracy of conventional planar light was 93.8% and 93.8%, respectively. The results of this study indicated that SFDI has the potential to detect different damage types in fruit and that the SFDI technique has a promising future in agricultural product quality inspection in further research.