Reflectance Imaging Technique Research Articles

Construction of an upconversion luminescence composite nanoprobe for ratiometric single particle imaging detection of hydrogen peroxide in food

Hydrogen peroxide (H2O2) is associated with diseases and food safety. Thus, it is essential to achieve sensitive and efficient detection of H2O2. Herein, a ratiometric luminescence composite nanoprobe was designed for single particle imaging sensing of H2O2 by combining NaYbF4:Er@NaYbF4:Tm@NaGdF4:Yb upconversion nanoparticles (UCNPs) with cyanine dye IR-628. High brightness NaYbF4:Er@NaYbF4:Tm@NaGdF4:Yb UCNPs with double-peak emission (665 and 812 nm) were synthesised. Cyanine dye IR-628 with an absorption peak at 628 nm was synthesised and served as a recognition unit. More importantly, on the basis of the upconversion luminescence total internal reflection imaging technique, we developed a ratiometric single particle imaging quantitative analysis for sensing H2O2 with a limit of quantitation of 5 nM. This ratiometric single particle imaging method not only greatly eliminates the influence of the probe concentration and instrumental and environmental factors, but also reduces the dosage of the reagent used and improves the sensitivity of detection.

Detection of moisture content and size of pumpkin seeds based on hyperspectral reflection and transmission imaging techniques

Moisture content and size can affect the vigor and weight of pumpkin seeds, where vigor is the key to evaluating seed quality, and weight is an important characteristic of the seeds. Therefore, detecting the moisture content and size of pumpkin seeds contributes to improving seed quality. In this study, hyperspectral reflection and transmission imaging techniques were used to detect the moisture and size of single pumpkin seeds. Linear PLSR and nonlinear LSSVM models were established to predict the moisture content of single pumpkin seeds using reflection and transmission spectral data. Five variable selection and data fusion methods, namely randomization test, variable selection based on C-value, uninformative variable elimination, Monte Carlo-uninformative variable elimination, and competitive adaptive reweighted sampling (CARS), were adopted to optimize the models. Compared with the models based on reflection and transmission spectra, low-level data fusion improved the model performance. The mid-level data fusion with models based on the CARS algorithm achieved the optimal performance, corresponding to R2P and RMSEP of 0.9219, 0.0279%, and 0.9231, 0.0278%, for the PLSR and LSSVM models, respectively. Finally, it was demonstrated that using hyperspectral reflection images to determine the size of pumpkin seeds is feasible, and the length and width of all samples were determined. The results show that hyperspectral imaging techniques and data fusion can effectively detect the moisture content of single pumpkin seeds, and measuring the size of pumpkin seeds is feasible using image processing algorithms.

Detection of early decay in navel oranges by structured-illumination reflectance imaging combined with image enhancement and segmentation

Early detection and removal of decayed fruit is critical for reducing product and economic losses for the fruit industry. It is, however, challenging to detect tissue decay in the early stage, which has no or few visual symptoms. This study was intended to evaluate the feasibility of an emerging structured-illumination reflectance imaging (SIRI) technique for early detection of decay in navel oranges. Pattern images were acquired from navel oranges five days after the fruit had been inoculated with fungal spores of Penicillium digitatum (P. digitatum) (the most important pathogen responsible for citrus decay), using an inhouse assembled multispectral imaging platform under phase-shifted sinusoidal illumination at four spatial frequencies (0.05, 0.10, 0.15 and 0.20 cycles mm−1) and three wavelengths (690, 730 and 810 nm). The pattern images were processed through demodulation to obtain direct component (DC) and amplitude component (AC) images for each wavelength and spatial frequency. The AC images were able to reveal decayed areas in the fruit. The wavelength of 810 nm and a spatial frequency of 0.20 cycles mm−1 were determined to be optimal for decayed tissue detection, based on quantitative assessment of the contrast between decayed and sound tissues in images. Moreover, the ratio image (RDC/AC) between the AC and DC images and the corrected ratio image (Rc), which was obtained by applying the image brightness transform to RDC/AC, have showed improved defect contrast and background uniformity. Three image segmentation methods including watershed, Otsu and global thresholding were applied for segmenting decayed areas in the AC, RDC/AC and RC images. The RC images produced the best detection performance with an overall detection accuracy of over 97 %. This study has demonstrated that when combined with appropriate image processing methods, the SIRI technique is effective for the early detection of decay in citrus fruit.

Investigation of reflectance, fluorescence, and Raman hyperspectral imaging techniques for rapid detection of aflatoxins in ground maize

Aflatoxins, commonly found in corn and corn-derived products, can cause severe illness in animals and humans if consumed in significant amounts. Early detection is critical to preventing illness, but the most sensitive and effective of commonly used screening tools for aflatoxins are expensive and cumbersome methods based on chromatography or imunoassays that require technical expertise to perform. Multiple hyperspectral imaging techniques, including reflectance in the visible and near-infrared (VNIR) region and short-wave infrared (SWIR) region, fluorescence by 365 nm ultraviolet (UV) excitation, and Raman by 785 nm laser excitation, were used for detection of aflatoxin in ground maize. Four classification models based on linear discriminant analysis (LDA), linear support vector machines (LSVM), quadratic discriminant analysis (QDA), and quadratic support vector machines (QSVM) algorithms were developed for classification with each hyperspectral imaging mode. The multivariate classification models in combination with different preprocessing methods were applied for screening of maize samples naturally contaminated with aflatoxin. The classification accuracies for fluorescence with QSVM, VNIR with QSVM, SWIR with LSVM, and Raman with LSVM were 95.7%, 82.6%, 95.7%, and 87.0%, respectively, with no false-negative error at the cutoff of 10 μg/kg. The SWIR and fluorescence models showed slightly higher performance accuracies, suggesting that they may be more effective and efficient analytical tools for aflatoxin analysis in maize compared to conventional wet-chemical methods. These methods show promise as inexpensive, and easy-to-use screening tools for food safety, to rapidly detect aflatoxins in maize or other food ingredients intended for animal or human consumption.

Probing Single-Molecule Binding Event by the Dynamic Counting and Mapping of Individual Nanoparticles.

Measuring binding processes at the single-molecule level underpin significant functions in understanding biological events. Single-nanoparticle imaging techniques are providing a new concept for mapping the heterogeneous behaviors and characterizations of individual dynamics such as molecule-molecule interactions. Here, we develop the optical imaging techniques for directly counting and monitoring the binding and motion events of single nanoparticles linked to the substrate via the specific and reversible interactions between biomolecules. The one-step digital immunoassay realizes the biomolecular detection based on dynamic counting of the single nanoparticle binding event to substrate with the bright-field imaging. The detection limit achieves 8.4 pg/mL for procalcitonin with detection time of 14 min. Meanwhile, we map the accurate trajectory of single nanoparticle switching between different target molecules among the x-y plane with the total internal reflection imaging technique, which reveals the spatial coordinates of single target molecules on the substrate surface with high spatial and temporal resolutions.

Nature-inspired PDMS cumulonimbus micro-energy-harvesting cloud

Here in this work, nanoenergy-generating films mimicked from cumulonimbus cloud were prepared using deionised water (DI) in polydimethylsiloxane (PDMS). Various characterisation methods have been adopted to verify the content of water as well as the resemblance of the morphological structure of the water-entrapped polymer films with clouds which include scanning electron microscopy and optical reflectance imaging techniques. The water content entrapment in the polymer is verified using the ATR-IR spectroscopy by finding absorbance peaks at 3266 and 1634 cm−1. Polyethylene terephthalate (PET) sheets with a copper coating (Cu-PET) were used as contact electrodes, and the device was fabricated by the simple sandwiching process. Dielectric studies of the device under an applied electric field were used to illustrate the presence of interfacial hopping as well as orientational polarisation in the device. The device fabricated shows an enhancement of 218% and 562% with an increase in water content from 4:1 to 1:1 and a decrease of 26% and 36.5% with a further increase in water for the generated voltage and current measured, respectively. The optimum concentration of polymer to water is selected as 1:1 and the decrease in current and voltage may be due to the condensation of water molecules leading to fewer contact sites between the water and polymer molecules. The devices showed an increment of voltage and current of 171.1 V and 29.16 nA, respectively, with an increase in the force of 1–5 N by using an automated input system. The device was tested under various mechanical inputs, and the generated energy used as sensors in the knock sensor, home doorbell system and seabed vibration detector and as a secondary powering source for charging mobile phones and lighting LED.

Non-Destructive Detection of Bone Fragments Embedded in Meat Using Hyperspectral Reflectance Imaging Technique.

Meat consumption has shifted from a quantitative to a qualitative growth stage due to improved living standards and economic development. Recently, consumers have paid attention to quality and safety in their decision to purchase meat. However, foreign substances which are not normal food ingredients are unintentionally incorporated into meat. These should be eliminated as a hazard to quality or safety. It is important to find a fast, non-destructive, and accurate detection technique of foreign substance in the meat processing industry. Hyperspectral imaging technology has been regarded as a novel technology capable of providing large-scale imaging and continuous observation information on agricultural products and food. In this study, we considered the feasibility of the short-wave near infrared (SWIR) hyperspectral reflectance imaging technique to detect bone fragments embedded in chicken meat. De-boned chicken breast samples with thicknesses of 3, 6, and 9-mm and 5 bone fragments with lengths of about 20–30-mm are used for this experiment. The reflectance spectra (in the wavelength range from 987 to 1701-nm) of the 5 bone fragments embedded under the chicken breast fillet are collected. Our results suggested that these hyperspectral imaging technique is able to detect bone fragments in chicken breast, particularly with the use of a subtraction image (corresponding to image at 1153.8-nm and 1480.2-nm). Thus, the SWIR hyperspectral reflectance imaging technique can be potentially used to detect foreign substance embedded in meat.

High-resolution and high-accuracy optical extensometer based on a reflective imaging technique

Optical extensometer is a popular device for strain measurement due to its merits of non-contact measurement over a wide range of strain values compared to conventional contact measurement devices. Telecentric lens is often implemented in optical extensometers to improve the measurement accuracy by alleviating the effect of out-of-plane motion. However, the accuracy of an optical extensometer is always limited by the gauge length, which is generally less than the resolution of a digital camera. In this study, we propose a reflective imaging technique to effectively enhance the gauge length by recording two isolated long-distance regions on the specimen using a telecentric lens. The corresponding reflective imaging device was integrated in a single apparatus in which the installation of telecentric lens was very convenient. Through precise calibration, the gauge length of proposed extensometer could be increased from 1700pixels to 7477.5pixels. The performance of the proposed extensometer is evaluated using self-heating and static tests. Furthermore, the feasibility, repeatability, and reliability of the extensometer are validated by uniaxial tensile tests on a stainless-steel specimen. Experimental results showed that the axial strains obtained using proposed method were in excellent agreement with those obtained using strain gauge, and the root mean square error of strain was below 5με in most cases. Therefore, the proposed extensometer is highly accurate and exhibits immense potential to replace mechanical extensometer and strain gauge for determining the mechanical properties of materials.

Non-Destructive Reflectance Mapping of Collagen Fiber Alignment in Heart Valve Leaflets.

Collagen fibers are the primary structural elements that define many soft-tissue structure and mechanical function relationships, so that quantification of collagen organization is essential to many disciplines. Current tissue-level collagen fiber imaging techniques remain limited in their ability to quantify fiber organization at macroscopic spatial scales and multiple time points, especially in a non-contacting manner, requiring no modifications to the tissue, and in near real-time. Our group has previously developed polarized spatial frequency domain imaging (pSFDI), a reflectance imaging technique that rapidly and non-destructively quantifies planar collagen fiber orientation in superficial layers of soft tissues over large fields-of-view. In this current work, we extend the light scattering models and image processing techniques to extract a critical measure of the degree of collagen fiber alignment, the normalized orientation index (NOI), directly from pSFDI data. Electrospun fiber samples with architectures similar to many collagenous soft tissues and known NOI were used for validation. An inverse model was then used to extract NOI from pSFDI measurements of aortic heart valve leaflets and clearly demonstrated changes in degree of fiber alignment between opposing sides of the sample. These results show that our model was capable of extracting absolute measures of degree of fiber alignment in superficial layers of heart valve leaflets with only general a priori knowledge of fiber properties, providing a novel approach to rapid, non-destructive study of microstructure in heart valve leaflets using a reflectance geometry.

A new insight into the dynamics of fibre migration in ring spinning

Fibre migration in yarns is thought to take place in the spinning triangle (ST), where high tensioned edge fibres tend to compete for a low tension state by displacing inner fibres outwards to occupy the core position – the tension mechanism. The angular alignment of fibres in the drafted roving – the geometric mechanism – is also considered to contribute to this movement. Although there is an understanding of the mechanisms of fibre migration, the outward movement of core fibres, from a low to high tension state and through a thick web of gripped fibres – moving inwards – has not been fully understood. In this study, we have attempted to reanalyse the classical migration mechanism from a new perspective and establish its relationship with a real ST using a new reflective imaging technique. The study findings suggest that pre-twist region in the ST, which is generally considered to be stable at one location, shows a rather dynamic oscillatory movement – a result of continuous change in ST tension profile. Spinning trials were conducted to confirm a relationship between pre-twist movement and degree of fibre migration.

Comparing the effectiveness of hyperspectral imaging and Raman spectroscopy: a case study on Armenian manuscripts

There is great practical and scholarly interest in the identification of pigments in works of art. This paper compares the effectiveness of the widely used Raman Spectroscopy (RS), with hyperspectral imaging (HSI), a reflectance imaging technique, to evaluate the reliability of HSI for the identification of pigments in historic works of art and to ascertain if there are any benefits from using HSI or a combination of both. We undertook a case study based on six Armenian illuminated manuscripts (eleventh–eighteenth centuries CE) in the Bodleian Library, University of Oxford. RS, and HSI (380–1000 nm) were both used to analyse the same 10 folios, with the data then used to test the accuracy and efficiency of HSI against the known results from RS using reflectance spectra reference databases compiled by us for the project. HSI over the wavelength range 380–1000 nm agreed with RS at best 93% of the time, and performance was enhanced using the SFF algorithm and by using a database with many similarities to the articles under analysis. HSI is significantly quicker at scanning large areas, and can be used alongside RS to identify and map large areas of pigment more efficiently than RS alone. HSI therefore has potential for improving the speed of pigment identification across manuscript folios and artwork but must be used in conjunction with a technique such as RS.

Classification and Discrimination of Different Fungal Diseases of Three Infection Levels on Peaches Using Hyperspectral Reflectance Imaging Analysis.

Peaches are susceptible to infection from several postharvest diseases. In order to control disease and avoid potential health risks, it is important to identify suitable treatments for each disease type. In this study, the spectral and imaging information from hyperspectral reflectance (400~1000 nm) was used to evaluate and classify three kinds of common peach disease. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied to analyse each wavelength image as a whole, and the first principal component was selected to extract the imaging features. A total of 54 parameters were extracted as imaging features for one sample. Three decayed stages (slight, moderate and severe decayed peaches) were considered for classification by deep belief network (DBN) and partial least squares discriminant analysis (PLSDA) in this study. The results showed that the DBN model has better classification results than the classification accuracy of the PLSDA model. The DBN model based on integrated information (494 features) showed the highest classification results for the three diseases, with accuracies of 82.5%, 92.5%, and 100% for slightly-decayed, moderately-decayed and severely-decayed samples, respectively. The successive projections algorithm (SPA) was used to select the optimal features from the integrated information; then, six optimal features were selected from a total of 494 features to establish the simple model. The SPA-PLSDA model showed better results which were more feasible for industrial application. The results showed that the hyperspectral reflectance imaging technique is feasible for detecting different kinds of diseased peaches, especially at the moderately- and severely-decayed levels.

Invisible Security Printing on Photoresist Polymer Readable by Terahertz Spectroscopy.

We experimentally modulate the refractive index and the absorption coefficient of an SU-8 dry film in the terahertz region by UV light (362 nm) exposure with time dependency. Consequently, the refractive index of SU-8 film is increased by approximately 6% after UV light exposure. Moreover, the absorption coefficient also changes significantly. Using the reflective terahertz imaging technique, in addition, we can read security information printed by UV treatment on an SU-8 film that is transparent in the visible spectrum. From these results, we successfully demonstrate security printing and reading by using photoresist materials and the terahertz technique. This investigation would provide a new insight into anti-counterfeiting applications in fields that need security.

Direct visualization of polarization reversal of organic ferroelectric memory transistor by using charge modulated reflectance imaging

By using the charge modulated reflectance (CMR) imaging technique, charge distribution in the pentacene organic field-effect transistor (OFET) with a ferroelectric gate insulator [P(VDF-TrFE)] was investigated in terms of polarization reversal of the P(VDF-TrFE) layer. We studied the polarization reversal process and the carrier spreading process in the OFET channel. The I-V measurement showed a hysteresis behavior caused by the spontaneous polarization of P(VDF-TrFE), but the hysteresis I-V curve changes depending on the applied drain bias, possibly due to the gradual shift of the polarization reversal position in the OFET channel. CMR imaging visualized the gradual shift of the polarization reversal position and showed that the electrostatic field formed by the polarization of P(VDF-TrFE) contributes to hole and electron injection into the pentacene layer and the carrier distribution is significantly dependent on the direction of the polarization. The polarization reversal position in the channel region is governed by the electrostatic potential, and it happens where the potential reaches the coercive voltage of P(VDF-TrFE). The transmission line model developed on the basis of the Maxwell-Wagner effect element analysis well accounts for this polarization reversal process in the OFET channel.

Discrimination methods for biological contaminants in fresh-cut lettuce based on VNIR and NIR hyperspectral imaging

The rapid detection of biological contaminants such as worms in fresh-cut vegetables is necessary to improve the efficiency of visual inspections carried out by workers. Multispectral imaging algorithms were developed using visible-near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging (HSI) techniques to detect worms in fresh-cut lettuce. The optimal wavebands that can detect worms in fresh-cut lettuce were investigated for each type of HSI using one-way ANOVA. Worm-detection imaging algorithms for VNIR and NIR imaging exhibited prediction accuracies of 97.00% (RI547/945) and 100.0% (RI1064/1176, SI1064-1176, RSI-I(1064-1173)/1064, and RSI-II(1064-1176)/(1064+1176)), respectively. The two HSI techniques revealed that spectral images with a pixel size of 1×1mm or 2×2mm had the best classification accuracy for worms. The results demonstrate that hyperspectral reflectance imaging techniques have the potential to detect worms in fresh-cut lettuce. Future research relating to this work will focus on a real-time sorting system for lettuce that can simultaneously detect various defects such as browning, worms, and slugs.

Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches

Chilling injury is one of physiological disorder in peach fruits, which will reduce its' edible and processing quality. In the work, hyperspectral reflectance imaging (400–1000 nm) combined with chemometrics was used to evaluate chilling injury of peaches. Discriminating models including partial least squares-discriminant analysis (PLS-DA), artificial neural networks (ANN), and support vector machines (SVM), were developed for two-class (“non-chilled” and “chilled”), three-class (“non-chilled”, “semi-chilled” and “heavy-chilled”) and four-class (“non-chilled”, “slight-chilled”, “moderate-chilled”, and “heavy-chilled”) classifications. The results showed that, using full wavelengths, ANN model had the highest classification rates for the prediction set, with accuracies of 85.37%, 96.11%, and 99.29% for four-class, three-class and two-class classifications, respectively. Furthermore, six optimal wavelengths, selected by the successive projections algorithm, were used as the input of PLS-DA, Fisher linear discriminate analysis, ANN and SVM models also presented good performances for two-class classification, with a discriminating accuracies of 92.96%–97.28%. Furthermore, a spatial distribution map of the chilling injury areas was generated by transferring the principal component analysis algorithm of the images. The results showed that the hyperspectral reflectance imaging technique is feasible and useful for the non-destructive detection of peaches' chilling injury, even with several wavelengths, before consumption and processing.

Relationships between the optical and Raman behavior of van Hove singularity in twisted bi- and fewlayer graphenes and environmental effects

Twisted bilayer graphene (tBLG) provides an opportunity to control the optoelectronic properties of graphene owing to the relative orientation (θ)-induced van Hove singularities (vHs). However, how different environments affect vHs behaviors of various tBLGs and their Raman resonance window is not clear. A study of the optical vHs properties of tBLGs on a quartz substrate, prepared by chemical vapor deposition, was carried out using simultaneous reflection and Raman imaging techniques according to the presence of ubiquitous residual amorphous carbon (RAC). The results show that the presence of RAC exhibits a narrower vHs peak width and resonance Raman windows of tBLG as compared to that without RAC, due to the absence of charge-inhomogeneous interactions from bare substrate. In addition, the background-subtracted vHs peak reflectances from various θ values at a specific laser energy are proportional to the measured G-band enhancement factor (GEF). The comparison reveals the detailed optical and Raman resonance windows of various tBLGs in both environments. Extension of the approach to twisted fewlayer graphenes reveals the different vHs peak behaviors including broadening, intensification, and splitting governed by θ-, along with layer number-dependent band structure hybridization.

POLARIZED SPATIAL FREQUENCY DOMAIN IMAGING OF HEART VALVE FIBER STRUCTURE.

Our group previously introduced Polarized Spatial Frequency Domain Imaging (PSFDI), a wide-field, reflectance imaging technique which we used to empirically map fiber direction in porcine pulmonary heart valve leaflets (PHVL) without optical clearing or physical sectioning of the sample. Presented is an extended analysis of our PSFDI results using an inverse Mueller matrix model of polarized light scattering that allows additional maps of fiber orientation distribution, along with instrumentation permitting increased imaging speed for dynamic PHVL fiber measurements. We imaged electrospun fiber phantoms with PSFDI, and then compared these measurements to SEM data collected for the same phantoms. PHVL was then imaged and compared to results of the same leaflets optically cleared and imaged with small angle light scattering (SALS). The static PHVL images showed distinct regional variance of fiber orientation distribution, matching our SALS results. We used our improved imaging speed to observe bovine tendon subjected to dynamic loading using a biaxial stretching device. Our dynamic imaging experiment showed trackable changes in the fiber microstructure of biological tissue under loading. Our new PSFDI analysis model and instrumentation allows characterization of fiber structure within heart valve tissues (as validated with SALS measurements), along with imaging of dynamic fiber remodeling. The experimental data will be used as inputs to our constitutive models of PHVL tissue to fully characterize these tissues' elastic behavior, and has immediate application in determining the mechanisms of structural and functional failure in PHVLs used as bio-prosthetic implants.

Evaluation of drought and UV radiation impacts on above-ground biomass of mountain grassland by spectral reflectance and thermal imaging techniques

Drought represents one of the major factors limiting productivity of managed and natural ecosystems. Under natural field conditions drought is often associated with other stress factors such as high temperature and UV radiation, which may result in enhancement or vice versa alleviation of drought impact. Remote sensing methods have a large potential to evaluate impacts of drought on plant production at regional scale. The main objective of this study was to analyse the potential of ground-based measurement of spectral reflectance and thermal imaging for monitoring the impacts of drought and UV radiation on above-ground biomass production of mountain grassland ecosystem. Experimental rain-out shelters were used to manipulate incident precipitation and UV radiation for 7 weeks (May–July). A canopy spectral reflectance, thermal images, and total above-ground biomass were determined at the end of drought and UV treatment. Results show that drought led to a significant reduction of above-ground biomass, particularly under ambient UV radiation. In contrary, UV had only negligible effect on biomass production. Canopy temperature as well as selected spectral reflectance indices showed significant response to drought stress and also significant relationships to above-ground biomass. However, the relationship between canopy temperature and above-ground biomass is modified by UV radiation. Best prediction of changes in biomass caused by drought stress was provided by vegetation index NDVI.

Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging.

Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to discrimination between sound and discolored lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectral reflectance images obtained in the 400–1000 nm wavelength range. The optimal wavebands for discriminating between discolored and sound lettuce surfaces were determined using one-way analysis of variance. Multi-spectral imaging algorithms developed using ratio and subtraction functions resulted in enhanced classification accuracy of above 99.9% for discolored and sound areas on both adaxial and abaxial lettuce surfaces. Ratio imaging (RI) and subtraction imaging (SI) algorithms at wavelengths of 552/701 nm and 557–701 nm, respectively, exhibited better classification performances compared to results obtained for all possible two-waveband combinations. These results suggest that hyperspectral reflectance imaging techniques can potentially be used to discriminate between discolored and sound fresh-cut lettuce.