Review of short-wave infrared spectroscopy and imaging methods for biological tissue characterization

Non-destructive prediction of fertility and sex in chicken eggs using the short wave near-infrared region

The objective of this study was to evaluate the ability of a handheld near-infrared device (900-1600 nm) to predict fertility and sex (male and female) traits in-ovo. The NIR reflectance spectra of the egg samples were collected on days 0, 7, 14 and 18 of incubation and the data was analysed using principal component analysis (PCA), linear discriminant analysis (LDA) and support vector machines classification (SVM). The overall classification rates for the prediction of fertile and infertile egg samples ranged from 73 % to 84 % and between 93 % to 95 % using LDA and SVM classification, respectively. The highest classification rate was obtained on day 7 of incubation. The classification between male and female embryos achieved lower classification rates, between 62 % and 68 % using LDA and SVM classification, respectively. Although the classification rates for in-ovo sexing obtained in this study are higher than those obtained by chance (50 %), the classification results are currently not sufficient for industrial in-ovo sexing of chicken eggs. These results demonstrated that short wavelengths in the NIR range may be useful to distinguish between fertile and infertile egg samples at days 7 and 14 during incubation.

Short-Wave Infrared Upconverting Nanoparticles

Optical technologies enable real-time, noninvasive analysis of complex systems but are limited to discrete regions of the optical spectrum. While wavelengths in the short-wave infrared (SWIR) window (typically, 1700-3000 nm) should enable deep subsurface penetration and reduced photodamage, there are few luminescent probes that can be excited in this region. Here, we report the discovery of lanthanide-based upconverting nanoparticles (UCNPs) that efficiently convert 1740 or 1950 nm excitation to wavelengths compatible with conventional silicon detectors. Screening of Ln3+ ion combinations by differential rate equation modeling identifies Ho3+/Tm3+ or Tm3+ dopants with strong visible or NIR-I emission following SWIR excitation. Experimental upconverted photoluminescence excitation (U-PLE) spectra find that 10% Tm3+-doped NaYF4 core/shell UCNPs have the strongest 800 nm emission from SWIR wavelengths, while UCNPs with an added 2% or 10% Ho3+ show the strongest red emission when excited at 1740 or 1950 nm. Mechanistic modeling shows that addition of a low percentage of Ho3+ to Tm3+-doped UCNPs shifts their emission from 800 to 652 nm by acting as a hub of efficient SWIR energy acceptance and redistribution up to visible emission manifolds. Parallel experimental and computational analysis shows rate equation models are able to predict compositions for specific wavelengths of both excitation and emission. These SWIR-responsive probes open a new IR bioimaging window, and are responsive at wavelengths important for vision technologies.

Near-Infrared II Fluorescence Imaging Highlights Tumor Angiogenesis in Hepatocellular Carcinoma with a VEGFR-Targeted Probe

Hepatocellular carcinoma (HCC) is typically characterized by rich vascularity, with angiogenesis playing a crucial role in its growth and invasion. Molecular imaging of specific receptors in blood vessels is crucial in HCC diagnosis. In particular, in vivo imaging utilizing the second near-infrared (NIR-II) window offers improved tissue penetration, reduced light scattering, and lower autofluorescence. Despite the great potential of the NIR-II window, developing safe and effective probes to provide better imaging performance for HCC is urgently needed. In this study, NIR-II imaging integrated with a vascular endothelial growth factor receptor (VEGFR)-targeted probe generated by combining a VEGFR-targeted peptide with indocyanine green (ICG) is used to characterize HCC-related angiogenesis at a resolution of 56.0 µm. For the first time, liver metabolic curves and parameters of liver function reserve (LFR) are obtained by fitting NIR-II fluorescence signals with high spatiotemporal resolution, showing significant differences between HCC mice and controls. Moreover, unlike ICG, the targeting probe has a targeted effect on blood vessels in vivo. The tumor-to-normal (T/N) ratio in NIR-II imaging reaches up to 3.30 after post-injection of the targeting probe. The results indicate that the VEGFR-targeted probe is a powerful tool for NIR-II fluorescence imaging to enhance early diagnosis of HCC.

Novel multi-spectral short-wave infrared imaging for assessment of human burn wound depth

Burn depth determination is critical for patient care but is currently lacking accuracy. Recent animal studies showed that Short Wave Infrared (SWIR) imaging can distinguish between superficial and deep burns. This is a first human study correlating reflectance of multiple SWIR bands using a SWIR assessment tool (SWAT) with burn depth classifications by surgeons and histology. Burns and adjacent normal skin in 11 patients with thermal injuries were imaged with visual and narrow bands centred at 1200, 1650, 1940 and 2250 nm and biopsies were taken from select areas. Reflectance intensities for each band in 273 regions of interest (ROI) were divided by the normal skin reflectance and combined into three Reflectance Indices (RIs). In addition, burns in ROIs and biopsies were classified by five surgeons and three pathologists, respectively, as superficial partial, deep partial, or full thickness. Results show that for burn depth increase classified by the surgeons, reflectance increased at 1200 and 2250, decreased at 1940, and didn't change at 1650 nm. In contrast, all three RIs increase with burn depth and predict the deep and full depths ROIs representing operable regions (Area Under Curve >0.6507, p < 0.0001). Pathologists' classification matched surgeons' classification of burn category only in eight of 21 biopsies (38.1%), but reflectance at all bands and one RI for all deep partial and full thickness biopsies were larger than in non-biopsy normal and superficial partial thickness ROIs (p < 0.0118). In conclusion, multi-spectral imaging with a new SWAT is a promising approach for evaluation of burn wound depth.

Use of bioresorbable fibers for short-wave infrared spectroscopy using time-domain diffuse optics

We demonstrate the usability of bioresorbable phosphate glass fibers for time-domain diffuse optical spectroscopy (TD-DOS) in the short-wave infrared (SWIR) region of 950-1600 nm, with the use of an InGaAs detector. Bioresorbable fibers for diffuse optics present an exciting prospect due to their ability to be left implanted while retrieving optical properties from deeper regions (few cm) for monitoring treatments. Extending TD-DOS to the SWIR region could be useful to better identify biomarkers such as water, lipids and collagen, given their increase in absorption in this range. We attempt to use the bioresorbable fibers to spectrally identify these biomarkers by measuring a series of biological samples known to contain them, such as porcine muscle, porcine fat and bone. We further validate our measurements by comparing the optical properties of high-scattering solid silicone phantoms retrieved with these bioresorbable fibers with those by a standard Si fiber.

Shortwave Infrared Imaging of a Quantum Dot-Based Magnetic Guidewire Toward Non-Fluoroscopic Peripheral Vascular Interventions

Peripheral vascular interventions (PVIs) offer several benefits to patients with lower extremity arterial diseases, including reduced pain, simpler anesthesia, and shorter recovery time, compared to open surgery. However, to monitor the endovascular tools inside the body, PVIs are conducted under X-ray fluoroscopy, which poses serious long-term health risks to physicians and patients. Shortwave infrared (SWIR) imaging of quantum dots (QDs) has shown great potential in bioimaging due to the non-ionizing penetration of SWIR light through tissues. In this paper, a QD-based magnetic guidewire and its system is introduced that allows X-ray-free detection under SWIR imaging and precise steering via magnetic manipulation. The QD magnetic guidewire contains a flexible silicone tube encapsulating a QD polydimethylsiloxane (PDMS) composite, where HgCdSe/HgS/CdS/CdZnS/ZnS/SiO2 core/multi-shell QDs are dispersed in the PDMS matrix for SWIR imaging upon near-infrared excitation, as well as a permanent magnet for magnetic steering. The SWIR penetration of the QD magnetic guidewire is investigated within an artificial tissue model (1% Intralipid) and explore the potential for non-fluoroscopic PVIs within a vascular phantom model. The QD magnetic guidewire is biocompatible in its entirety, with excellent resistance to photobleaching and chemical alteration, which is a promising sign for its future clinical implementation.

Multifunctional Near-Infrared Phosphors Cr3+/Ni2+ Codoped Mg3Ga2GeO8 Based on Energy Transfer from Cr3+ to Ni2

Currently, near-infrared (NIR) light-emitting materials have been widely used in many fields, such as night vision, bioimaging, and nondestructive analysis. However, it is difficult to achieve multifunction in certain NIR light emitting phosphor. Herein, we propose a new near-infrared phosphor Mg3Ga2GeO8:Cr3+,Ni2+ that can be applied to at least three fields, i.e., identification of compounds, temperature sensing, anticounterfeiting, and other applications. The multifunctional material exhibited efficient broadband emission of 650-1650 nm under 420 nm excitation. The emission intensity of Ni2+ in Mg3Ga2GeO8:Cr3+,Ni2+ is enhanced by two times compared with that of Ni2+ in Mg3Ga2GeO8:Ni2+ due to the energy transfer process. Compared with phosphor single doped with Ni2+, Mg3Ga2GeO8:Cr3+,Ni2+ is more convincing in organic compound recognition because it is based on two emission bands: 600-1100 nm and 1100-1650 nm. As a temperature sensor, Mg3Ga2GeO8:Cr3+,Ni2+ is an ideal temperature-sensing material. This work not only provides a super broadband NIR emitting phosphor with multiple functions but also presents a practical approach for the development of high-efficiency and multifunctional NIR phosphors.

Advancing DIEP Flap Monitoring with Optical Imaging Techniques: A Narrative Review

OBJECTIVES

This review aims to explore recent advancements in optical imaging techniques for monitoring the viability of Deep Inferior Epigastric Perforator (DIEP) flap reconstruction. The objectives include highlighting the principles, applications, and clinical utility of optical imaging modalities such as near-infrared spectroscopy (NIRS), indocyanine green (ICG) fluorescence angiography, laser speckle contrast imaging (LSCI), hyperspectral imaging (HSI), dynamic infrared thermography (DIRT), and short-wave infrared thermography (SWIR) in assessing tissue perfusion and oxygenation. Additionally, this review aims to discuss the potential of these techniques in enhancing surgical outcomes by enabling timely intervention in cases of compromised flap perfusion.

MATERIALS AND METHODS

A comprehensive literature review was conducted to identify studies focusing on optical imaging techniques for monitoring DIEP flap viability. We searched PubMed, MEDLINE, and relevant databases, including Google Scholar, Web of Science, Scopus, PsycINFO, IEEE Xplore, and ProQuest Dissertations & Theses, among others, using specific keywords related to optical imaging, DIEP flap reconstruction, tissue perfusion, and surgical outcomes. This extensive search ensured we gathered comprehensive data for our analysis. Articles discussing the principles, applications, and clinical use of NIRS, ICG fluorescence angiography, LSCI, HSI, DIRT, and SWIR in DIEP flap monitoring were selected for inclusion. Data regarding the techniques' effectiveness, advantages, limitations, and potential impact on surgical decision-making were extracted and synthesized.

RESULTS

Optical imaging modalities, including NIRS, ICG fluorescence angiography, LSCI, HSI, DIRT, and SWIR offer a non- or minimal-invasive, real-time assessment of tissue perfusion and oxygenation in DIEP flap reconstruction. These techniques provide objective and quantitative data, enabling surgeons to monitor flap viability accurately. Studies have demonstrated the effectiveness of optical imaging in detecting compromised perfusion and facilitating timely intervention, thereby reducing the risk of flap complications such as partial or total loss. Furthermore, optical imaging modalities have shown promise in improving surgical outcomes by guiding intraoperative decision-making and optimizing patient care.

CONCLUSIONS

Recent advancements in optical imaging techniques present valuable tools for monitoring the viability of DIEP flap reconstruction. NIRS, ICG fluorescence angiography, LSCI, HSI, DIRT, and SWIR offer a non- or minimal-invasive, real-time assessment of tissue perfusion and oxygenation, enabling accurate evaluation of flap viability. These modalities have the potential to enhance surgical outcomes by facilitating timely intervention in cases of compromised perfusion, thereby reducing the risk of flap complications. Incorporating optical imaging into clinical practice can provide surgeons with objective and quantitative data, assisting in informed decision-making for optimal patient care in DIEP flap reconstruction surgeries.

Monitoring of the Homogeneity of Primer Layers for Ink Jet Printing on Polyester Fabrics by Hyperspectral Imaging

Untreated polyester films and fibers can be hardly printed or coated, in particular if aqueous inks or lacquers have to be applied. Therefore, an adequate primer layer has to be applied first. A cationic polymer formulation based on poly(dimethylamine-co-epichlorohydrin-co-ethylenediamine) (PDEHED) was used as primer layer for digital printing on polyester fabrics. Because of the exceedingly high requirements on the homogeneity of such layers, hyperspectral imaging was used for qualitative and quantitative monitoring of the distribution of the primer layer on the textiles. Multivariate data analysis methods based on the PLS algorithm were applied for quantification of the NIR reflection spectra using gravimetry as a reference method. Optimization of the calibration method resulted in various models with prediction errors of about 1.2 g/m2. The prediction performance of the models was proven in external validations using independent samples. Moreover, a special ink jet printing technology was tested for application of the aqueous primer formulation itself. Since possible clogging of jet nozzles in the print head might lead to inhomogeneity in the coatings such as missing tracks, the potential of hyperspectral imaging to detect such defects was investigated. It was demonstrated that simulated missing tracks can be clearly detected. Consequently, hyperspectral imaging has been proven to be a powerful analytical tool for in-line monitoring of the quality of printability improvement layers and similar systems.

Quantifying tissue temperature changes induced by infrared neural stimulation: numerical simulation and MR thermometry

Infrared neural stimulation (INS) delivered via short pulse trains is an innovative tool that has potential for us use for studying brain function and circuitry, brain machine interface, and clinical use. The prevailing mechanism for INS involves the conversion of light energy into thermal transients, leading to neuronal membrane depolarization. Due to the potential risks of thermal damage, it is crucial to ensure that the resulting local temperature increases are within non-damaging limits for brain tissues. Previous studies have estimated damage thresholds using histological methods and have modeled thermal effects based on peripheral nerves. However, additional quantitative measurements and modeling studies are needed for the central nervous system. Here, we performed 7 T MRI thermometry on ex vivo rat brains following the delivery of infrared pulse trains at five different intensities from 0.1-1.0 J/cm2 (each pulse train 1,875 nm, 25 us/pulse, 200 Hz, 0.5 s duration, delivered through 200 µm fiber). Additionally, we utilized the General BioHeat Transfer Model (GBHTM) to simulate local temperature changes in perfused brain tissues while delivering these laser energies to tissue (with optical parameters of human skin) via three different sizes of optical fibers at five energy intensities. The simulation results clearly demonstrate that a 0.5 second INS pulse train induces an increase followed by an immediate drop in temperature at stimulation offset. The delivery of multiple pulse trains with 2.5 s interstimulus interval (ISI) leads to rising temperatures that plateau. Both thermometry and modeling results show that, using parameters that are commonly used in biological applications (200 µm diameter fiber, 0.1-1.0 J/cm2), the final temperature increase at the end of the 60 sec stimuli duration does not exceed 1°C with stimulation values of 0.1-0.5 J/cm2 and does not exceed 2°C with stimulation values of up to 1.0 J/cm2. Thus, the maximum temperature rise is consistent with the thermal damage threshold reported in previous studies. This study provides a quantitative evaluation of the temperature changes induced by INS, suggesting that existing practices pose minimal major safety concerns for biological tissues.

High-frequency photoacoustic and ultrasound imaging for skin evaluation: Pilot study for the assessment of a chemical burn

Skin architecture and its underlying vascular structure could be used to assess the health status of skin. A non-invasive, high resolution and deep imaging modality able to visualize skin subcutaneous layers and vasculature structures could be useful for determining and characterizing skin disease and trauma. In this study, a multispectral high-frequency, linear array-based photoacoustic/ultrasound (PAUS) probe is developed and implemented for the imaging of rat skin in vivo. The study seeks to demonstrate the probe capabilities for visualizing the skin and its underlying structures, and for monitoring changes in skin structure and composition during a 5-day course of a chemical burn. We analayze composition of lipids, water, oxy-hemoglobin, and deoxy-hemoglobin (for determination of oxygen saturation) in the skin tissue. The study successfully demonstrated the high-frequency PAUS imaging probe was able to provide 3D images of the rat skin architecture, underlying vasculature structures, and oxygen saturation, water, lipids and total hemoglobin.

Overtone photothermal microscopy for high-resolution and high-sensitivity vibrational imaging

Photothermal microscopy is a highly sensitive pump-probe method for mapping nanostructures and molecules through the detection of local thermal gradients. While visible photothermal microscopy and mid-infrared photothermal microscopy techniques have been developed, they possess inherent limitations. These techniques either lack chemical specificity or encounter significant light attenuation caused by water absorption. Here, we present an overtone photothermal (OPT) microscopy technique that offers high chemical specificity, detection sensitivity, and spatial resolution by employing a visible probe for local heat detection in the C-H overtone region. We demonstrate its capability for high-fidelity chemical imaging of polymer nanostructures, depth-resolved intracellular chemical mapping of cancer cells, and imaging of multicellular C. elegans organisms and highly scattering brain tissues. By bridging the gap between visible and mid-infrared photothermal microscopy, OPT establishes a new modality for high-resolution and high-sensitivity chemical imaging. This advancement complements large-scale shortwave infrared imaging approaches, facilitating multiscale structural and chemical investigations of materials and biological metabolism.

Tutorial on methods for estimation of optical absorption and scattering properties of tissue

Significance

The estimation of tissue optical properties using diffuse optics has found a range of applications in disease detection, therapy monitoring, and general health care. Biomarkers derived from the estimated optical absorption and scattering coefficients can reflect the underlying progression of many biological processes in tissues.

Aim

Complex light-tissue interactions make it challenging to disentangle the absorption and scattering coefficients, so dedicated measurement systems are required. We aim to help readers understand the measurement principles and practical considerations needed when choosing between different estimation methods based on diffuse optics.

Approach

The estimation methods can be categorized as: steady state, time domain, time frequency domain (FD), spatial domain, and spatial FD. The experimental measurements are coupled with models of light-tissue interactions, which enable inverse solutions for the absorption and scattering coefficients from the measured tissue reflectance and/or transmittance.

Results

The estimation of tissue optical properties has been applied to characterize a variety of ex vivo and in vivo tissues, as well as tissue-mimicking phantoms. Choosing a specific estimation method for a certain application has to trade-off its advantages and limitations.

Conclusion

Optical absorption and scattering property estimation is an increasingly important and accessible approach for medical diagnosis and health monitoring.

Quantum dot-enabled infrared hyperspectral imaging with single-pixel detection

Near-infrared (NIR) hyperspectral imaging is a powerful technique that enables the capture of three-dimensional (3D) spectra-spatial information within the NIR spectral range, offering a wide array of applications. However, the high cost associated with InGaAs focal plane array (FPA) has impeded the widespread adoption of NIR hyperspectral imaging. Addressing this challenge, in this study, we adopt an alternative approach-single-pixel detection for NIR hyperspectral imaging. Our investigation reveals that single-pixel detection outperforms conventional FPA, delivering a superior signal-to-noise ratio (SNR) for both spectral and imaging reconstruction. To implement this strategy, we leverage self-assembled colloidal quantum dots (CQDs) and a digital micromirror device (DMD) for NIR spectral and spatial information multiplexing, complemented by single-pixel detection for simultaneous spectral and image reconstruction. Our experimental results demonstrate successful NIR hyperspectral imaging with a detection window about 600 nm and an average spectral resolution of 8.6 nm with a pixel resolution of 128 × 128. The resulting spectral and spatial data align well with reference instruments, which validates the effectiveness of our approach. By circumventing the need for expensive and bulky FPA and wavelength selection components, our solution shows promise in advancing affordable and accessible NIR hyperspectral imaging technologies, thereby expanding the range of potential applications.

Multidimensional discriminant analysis of species, strains and culture age of closely related entomopathogenic fungi using reflectance spectroscopy

The diversity of fungal strains is influenced by genetic and environmental factors, growth conditions and mycelium age, and the spectral features of fungal mycelia are associated with their biochemical, physiological, and structural traits. This study investigates whether intraspecific differences can be detected in two closely related entomopathogenic species, namely Cordyceps farinosa and Cordyceps fumosorosea, using ultraviolet A to shortwave infrared (UVA-SWIR) reflectance spectra. Phylogenetic analysis of all strains revealed a high degree of uniformity among the populations of both species. The characteristics resulting from variation in the species, as well as those resulting from the age of the cultures were determined. We cultured fungi on PDA medium and measured the reflectance of mycelia in the 350-2500 nm range after 10 and 17 days. We subjected the measurements to quadratic discriminant analysis (QDA) to identify the minimum number of bands containing meaningful information. We found that when the age of the fungal culture was known, species represented by a group of different strains could be distinguished with no more than 3-4 wavelengths, compared to 7-8 wavelengths when the age of the culture was unknown. At least 6-8 bands were required to distinguish cultures of a known species among different age groups. Distinguishing all strains within a species was more demanding: at least 10 bands were required for C. fumosorosea and 21 bands for C. farinosa. In conclusion, fungal differentiation using point reflectance spectroscopy gives reliable results when intraspecific and age variations are taken into account.

Depressed-cladding thulium-doped fiber for applications below 1800 nm

We present a thulium-doped silica fiber, featuring a depressed cladding, for applications at wavelengths below 1800 nm. The depressed cladding is used as a distributed filter suppressing amplified spontaneous emission at longer wavelengths, which helps promote emission at shorter wavelengths. We describe the fiber design process that was carried out by using a combination of numerical methods. The fiber was prepared in-house by a combination of the standard modified chemical vapor deposition method and nanoparticle doping. We demonstrate the effectiveness and tunability of ASE filtering, which is influenced by fiber bend radius and its variation.

Dynamic diffusive interfacial transport (D-DIT): A novel quantitative swelling technique for developing binary phase diagrams of aqueous surfactant systems

Current methods to develop surfactant phase diagrams are time-intensive and fail to capture the kinetics of phase evolution. Here, the design and performance of a quantitative swelling technique to study the dynamic phase behavior of surfactants are described. The instrument combines cross-polarized optical and short-wave infrared imaging to enable high-resolution, high-throughput, and in situ identification of phases and water compositions. Data across the entire composition spectrum for the dynamics and phase evolution of a binary aqueous non-ionic surfactant solution at two isotherms are presented. This instrument provides pathways to develop non-equilibrium phase diagrams of surfactant systems-critical to predicting the outcomes of formulation and processing. It can be applied to study time-dependent material relationships across a diverse range of materials and processes, including the dissolution of surfactant droplets and the drying of aqueous polymer films.

Extended-wavelength diffuse reflectance spectroscopy dataset of animal tissues for bone-related biomedical applications

Diffuse reflectance spectroscopy (DRS) has been extensively studied in both preclinical and clinical settings for multiple applications, notably as a minimally invasive diagnostic tool for tissue identification and disease delineation. In this study, extended-wavelength DRS (EWDRS) measurements of ex vivo tissues ranging from ultraviolet through visible to the short-wave infrared region (355-1919 nm) are presented in two datasets. The first dataset contains labelled EWDRS measurements collected from bone cement samples and ovine specimens including 10 tissue types commonly encountered in orthopedic surgeries for data curation purposes. The other dataset includes labelled EWDRS measurements of primarily bone structures at different depths during stepwise drilling into intact porcine skulls until plunging into the cranial cavity. The raw data with code for pre-processing and calibration is publicly available for reuse on figshare. The datasets can be utilized not only for exploratory purposes in machine learning model construction, but also for knowledge discovery in the orthopedic domain to identify important features for surgical guidance, extract physiological parameters and provide diagnostic insights.

Fertility detection of unincubated chicken eggs by hyperspectral transmission imaging in the Vis-SWNIR region

Detection of infertile eggs prior to incubation can lead to an increase in the hatchability rate and prevent the wastage of billions of non-fertile eggs ended up by failed incubation. In this study, the feasibility of a line-scan hyperspectral imaging system in the visible and short-wavelength near-infrared region was assessed for early detection of non-fertile eggs on day 0 before incubation. A total of 227 white-shell eggs including 131 fertile and 96 infertile eggs were collected from a flock with similar conditions in terms of hen age, feeding, and management. Hyperspectral images of eggs were captured on day 0 before incubation in a transmittance mode of illumination and then the eggs were incubated in a commercial incubator. The edge detection method was used to segment the egg, including both the white and yolk, from the background, and the image spectral information was extracted from the egg region. After applying various pretreatment methods, different classifiers including soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and artificial neural networks (ANN) classifiers were utilized to extract the predictive models. Following the acceptable results of SIMCA analysis accomplished by 1st derivative pretreatment (accuracy of 86.67%), the discrimination power plot was used to select the most informative wavebands. The results showed that by using fewer variables in effective wavebands better performance (precision and accuracy of 92.59% and 93.33%, respectively) could be obtained in comparison with the ANN classifier based on the whole spectral data (precision and accuracy of 89.29% and 91.11%, respectively). This study revealed the potential application of hyperspectral transmittance imaging in the Vis-SWNIR region to discern the fertile and infertile eggs before starting the incubation process.

Prediction of soluble solids content using near-infrared spectra and optical properties of intact apple and pulp applying PLSR and CNN

Soluble solids content (SSC) is one of the most important internal quality attributes of fruit and could be predicted using near-infrared (NIR) spectra and optical properties. Partial least squares regression (PLSR) is a conventional regression method in SSC prediction. In recent years, deep learning methods represented by convolutional neural network (CNN) was suggested to be implied in spectral analysis. However, researchers are inevitably facing problems with regard to the selection of spectral pretreatment methods and the evaluation of the performance of the chosen regression. This study employed PLSR and CNN regression to predict SSC of apple based on the collected diffuse reflectance spectra of intact apple, total reflectance and total transmittance spectra of apple pulp, and the calculated optical property spectra, i.e., absorption coefficient and reduced scattering coefficient spectra of apple pulp. Five different spectral pretreatment methods were exerted on these spectra. Results showed that at a given regression (PLSR or CNN), the built models based on the diffuse reflectance spectra of intact apple had the best SSC prediction, and the built models based on pulp's reduced scattering coefficient spectra had the poorest prediction performance. The best prediction performance was achieved by PLSR models using Savitzky-Golay with multiple scattering correction (Rp = 0.96, RMSEP = 0.54 %) and CNN regressions using Savitzky-Golay with standard normal variational transformation (Rp = 0.95, RMSEP = 0.59 %), respectively. Additionally, when the unknown original spectra were used for modeling, CNN had a better performance compared to PLSR, indicating the outstanding preponderance of CNN in spectral analysis. This study provides an effective reference for the selection of chemometric method based on NIR spectra.

Temperature-Dependent Modulation of Short-Wave-Infrared Light Transparency Based on Associated Structures of a Liquescent Nickel(III) Complex

A liquescent bis(malononitriledithiolato)nickel(III) complex with a bis(methoxyethyl)imidazolium cation, 1[Ni(mnt)2 ], exhibits three-stage thermochromic modulation of transparency/absorption in the short-wave-infrared (SWIR) region (1000-2500 nm), driven by associated structural changes. Upon heating, the electronic spectra of 1[Ni(mnt)2 ] in the SWIR region shift to shorter wavelengths accompanying with the solid-liquid phase transition at 76 °C. Further heating to over 109 °C induces a second transition of the electronic spectra, characterized by a blue-shift of the SWIR absorption in the liquid phase. The results of temperature-dependent electronic spectra and magnetic susceptibility indicated that the thermochromic changes can be attributed to the two-step dissociation of the associated structures of [Ni(mnt)2 ]- , occurring during the solid-liquid phase transition and the shift of dimer-monomer equilibrium in the liquid state. These changes can be visualized using an SWIR imaging camera under appropriate SWIR lights.

Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine

Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.

An observation of short-wave near-infrared hyperspectral imaging in tracking of invisible post-traumatic subcutaneous lesions

Post-traumatic soft tissue damage could persist for an extended period, and the non-traumatic side could be affected by indirect consequences. Hyperspectral imaging soft abundance scorer can identify these concealed and asymptomatic lesions.

Frameworks of wavelength selection in diffuse reflectance spectroscopy for tissue differentiation in orthopedic surgery

Significance

Wavelength selection from a large diffuse reflectance spectroscopy (DRS) dataset enables removal of spectral multicollinearity and thus leads to improved understanding of the feature domain. Feature selection (FS) frameworks are essential to discover the optimal wavelengths for tissue differentiation in DRS-based measurements, which can facilitate the development of compact multispectral optical systems with suitable illumination wavelengths for clinical translation.

Aim

The aim was to develop an FS methodology to determine wavelengths with optimal discriminative power for orthopedic applications, while providing the frameworks for adaptation to other clinical scenarios.

Approach

An ensemble framework for FS was developed, validated, and compared with frameworks incorporating conventional algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), and backward interval partial least squares (biPLS).

Results

Via the one-versus-rest binary classification approach, a feature subset of 10 wavelengths was selected from each framework yielding comparable balanced accuracy scores (PCA: 94.8 ± 3.47 % , LDA: 98.2 ± 2.02 % , biPLS: 95.8 ± 3.04 % , and ensemble: 95.8 ± 3.16 % ) to those of using all features (100%) for cortical bone versus the rest class labels. One hundred percent balanced accuracy scores were generated for bone cement versus the rest. Different feature subsets achieving similar outcomes could be identified due to spectral multicollinearity.

Conclusions

Wavelength selection frameworks provide a means to explore domain knowledge and discover important contributors to classification in spectroscopy. The ensemble framework generated a model with improved interpretability and preserved physical interpretation, which serves as the basis to determine illumination wavelengths in optical instrumentation design.

Millimeter-deep micron-resolution vibrational imaging by shortwave infrared photothermal microscopy

Deep-tissue chemical imaging plays a vital role in biological and medical applications. Here, we present a shortwave infrared photothermal (SWIP) microscope for millimeter-deep vibrational imaging with sub-micron lateral resolution and nanoparticle detection sensitivity. By pumping the overtone transition of carbon-hydrogen bonds and probing the subsequent photothermal lens with shortwave infrared light, SWIP can obtain chemical contrast from polymer particles located millimeter-deep in a highly scattering phantom. By fast digitization of the optically probed signal, the amplitude of the photothermal signal is shown to be 63 times larger than that of the photoacoustic signal, thus enabling highly sensitive detection of nanoscale objects. SWIP can resolve the intracellular lipids across an intact tumor spheroid and the layered structure in millimeter-thick liver, skin, brain, and breast tissues. Together, SWIP microscopy fills a gap in vibrational imaging with sub-cellular resolution and millimeter-level penetration, which heralds broad potential for life science and clinical applications.

Optical absorption spectra and corresponding in vivo photoacoustic visualization of exposed peripheral nerves

Significance

Multispectral photoacoustic imaging has the potential to identify lipid-rich, myelinated nerve tissue in an interventional or surgical setting (e.g., to guide intraoperative decisions when exposing a nerve during reconstructive surgery by limiting operations to nerves needing repair, with no impact to healthy or regenerating nerves). Lipids have two optical absorption peaks within the NIR-II and NIR-III windows (i.e., 1000 to 1350 nm and 1550 to 1870 nm wavelength ranges, respectively) which can be exploited to obtain photoacoustic images. However, nerve visualization within the NIR-III window is more desirable due to higher lipid absorption peaks and a corresponding valley in the optical absorption of water.

Aim

We present the first known optical absorption characterizations, photoacoustic spectral demonstrations, and histological validations to support in vivo photoacoustic nerve imaging in the NIR-III window.

Approach

Four in vivo swine peripheral nerves were excised, and the optical absorption spectra of these fresh ex vivo nerves were characterized at wavelengths spanning 800 to 1880 nm, to provide the first known nerve optical absorbance spectra and to enable photoacoustic amplitude spectra characterization with the most optimal wavelength range. Prior to excision, the latter two of the four nerves were surrounded by aqueous, lipid-free, agarose blocks (i.e., 3% w/v agarose) to enhance acoustic coupling during in vivo multispectral photoacoustic imaging using the optimal NIR-III wavelengths (i.e., 1630 to 1850 nm) identified in the ex vivo studies.

Results

There was a verified characteristic lipid absorption peak at 1725 nm for each ex vivo nerve. Results additionally suggest that the 1630 to 1850 nm wavelength range can successfully visualize and differentiate lipid-rich nerves from surrounding water-containing and lipid-deficient tissues and materials.

Conclusions

Photoacoustic imaging using the optimal wavelengths identified and demonstrated for nerves holds promise for detection of myelination in exposed and isolated nerve tissue during a nerve repair surgery, with possible future implications for other surgeries and other optics-based technologies.

Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning

SIGNIFICANCE

Fluorescence-guided surgery (FGS) provides specific real-time visualization of tumors, but intensity-based measurement of fluorescence is prone to errors. Multispectral imaging (MSI) in the short-wave infrared (SWIR) has the potential to improve tumor delineation by enabling machine-learning classification of pixels based on their spectral characteristics.

AIM

Determine whether MSI can be applied to FGS and combined with machine learning to provide a robust method for tumor visualization.

APPROACH

A multispectral SWIR fluorescence imaging device capable of collecting data from six spectral filters was constructed and deployed on neuroblastoma (NB) subcutaneous xenografts ( n = 6 ) after the injection of a NB-specific NIR-I fluorescent probe (Dinutuximab-IRDye800). We constructed image cubes representing fluorescence collected from ∼ 850 to 1450 nm and compared the performance of seven learning-based methods for pixel-by-pixel classification, including linear discriminant analysis, k -nearest neighbor classification, and a neural network.

RESULTS

The spectra of tumor and non-tumor tissue were subtly different and conserved between individuals. In classification, a combine principal component analysis and k -nearest-neighbor approach with area under curve normalization performed best, achieving 97.5% per-pixel classification accuracy (97.1%, 93.5%, and 99.2% for tumor, non-tumor tissue and background, respectively).

CONCLUSIONS

The development of dozens of new imaging agents provides a timely opportunity for multispectral SWIR imaging to revolutionize next-generation FGS.

LED-pumped Er:Cr:YSGG light sources

For, what we believe is, the first time, an Er:Cr:YSGG crystal is pumped by LEDs through a Ce:YAG luminescent concentrator. We demonstrate both laser emission at 2.79 µm and strong spontaneous emission at 1.6 µm. The luminescent concentrator delivers 1.5 ms pulses at 10 Hz in the visible (550-650 nm) to the Er:Cr:YSGG crystal, in a transverse pumping configuration. The Er:Cr:YSGG laser produces up to 6.8 mJ at 2.79 µm in a biconcave cavity. The Er:Cr:YSGG also stands out as a bright broadband incoherent source around 1.6 µm with a unique combination of peak power (351 mW) and brightness (1.4 W/sr/cm2).

Near-Infrared Imaging of Indocyanine Green Identifies Novel Routes of Lymphatic Drainage from Metacarpophalangeal Joints in Healthy Human Hands

Background: Collecting lymphatic vessel (CLV) dysfunction has been implicated in various diseases, including rheumatoid arthritis (RA). RA patients with active hand arthritis exhibit significantly reduced lymphatic clearance of the web spaces adjacent to the metacarpophalangeal (MCP) joints and a reduction in total and basilic-associated CLVs on the dorsal surface of the hand by near-infrared (NIR) imaging of indocyanine green (ICG). In this pilot study, we assessed direct lymphatic drainage from MCP joints and aimed to visualize the total lymphatic anatomy using novel dual-agent relaxation contrast magnetic resonance lymphography (DARC-MRL) in the upper extremity of healthy human subjects. Methods and Results: Two healthy male subjects >18 years old participated in the study. We performed NIR imaging along with conventional- or DARC-MRL following intradermal web space and intra-articular MCP joint injections. ICG (NIR) or gadolinium (Gd) (MRL) was administered to visualize the CLV anatomy of the upper extremity. Web space draining CLVs were associated with the cephalic side of the antecubital fossa, while MCP draining CLVs were localized to the basilic side of the forearm by near-infrared indocyanine green imaging. The DARC-MRL methods used in this study did not adequately nullify the contrast in the blood vessels, and limited Gd-filled CLVs were identified. Conclusion: MCP joints predominantly drain into basilic CLVs in the forearm, which may explain the reduction in basilic-associated CLVs in the hands of RA patients. Current DARC-MRL techniques show limited identification of healthy lymphatic structures, and further refinement in this technique is necessary. Clinical trial registration number: NCT04046146.

Rejuvenating old fluorophores with new chemistry

The field of organic chemistry began with 19th century scientists identifying and then expanding upon synthetic dye molecules for textiles. In the 20th century, dye chemistry continued with the aim of developing photographic sensitizers and laser dyes. Now, in the 21st century, the rapid evolution of biological imaging techniques provides a new driving force for dye chemistry. Of the extant collection of synthetic fluorescent dyes for biological imaging, two classes reign supreme: rhodamines and cyanines. Here, we provide an overview of recent examples where modern chemistry is used to build these old-but-venerable classes of optically responsive molecules. These new synthetic methods access new fluorophores, which then enable sophisticated imaging experiments leading to new biological insights.

Photometric and Monte-Carlo modeling unified approach for the calculation of spatially-resolved correction coefficients linking simulated and experimental diffuse reflectance spectra

The estimation of skin optical properties by means of inverse problem solving from spatially resolved diffuse reflectance (SR-DR) spectra is one way to exploit the acquired clinical signals. This method requires the comparison between the experimental spectra collected with a medical device, and spectra generated by the photons transport numerical simulations. This comparison is usually limited to spectral shape due to the absence of intensity standardization of the experimental DR spectra. This study proposes to theoretically (using photometric calculation) and experimentally (from experimental spectra acquired on optical phantom) establish a corrective factor to obtain common intensity unit for experimental and simulated signals.

Relevance and utility of the in-vivo and ex-vivo optical properties of the skin reported in the literature: a review [Invited]

Imaging non-invasively into the human body is currently limited by cost (MRI and CT scan), image resolution (ultrasound), exposure to ionising radiation (CT scan and X-ray), and the requirement for exogenous contrast agents (CT scan and PET scan). Optical imaging has the potential to overcome all these issues but is currently limited by imaging depth due to the scattering and absorption properties of human tissue. Skin is the first barrier encountered by light when imaging non-invasively, and therefore a clear understanding of the way that light interacts with skin is required for progress on optical medical imaging to be made. Here we present a thorough review of the optical properties of human skin measured in-vivo and compare these to the previously collated ex-vivo measurements. Both in-vivo and ex-vivo published data show high inter- and intra-publication variability making definitive answers regarding optical properties at given wavelengths challenging. Overall, variability is highest for ex-vivo absorption measurements with differences of up to 77-fold compared with 9.6-fold for the in-vivo absorption case. The impact of this variation on optical penetration depth and transport mean free path is presented and potential causes of these inconsistencies are discussed. We propose a set of experimental controls and reporting requirements for future measurements. We conclude that a robust in-vivo dataset, measured across a broad spectrum of wavelengths, is required for the development of future technologies that significantly increase the depth of optical imaging.

Label-Free Optical Metabolic Imaging in Cells and Tissues

Over the last half century, the autofluorescence of the metabolic cofactors NADH (reduced nicotinamide adenine dinucleotide) and FAD (flavin adenine dinucleotide) has been quantified in a variety of cell types and disease states. With the spread of nonlinear optical microscopy techniques in biomedical research, NADH and FAD imaging has offered an attractive solution to noninvasively monitor cell and tissue status and elucidate dynamic changes in cell or tissue metabolism. Various tools and methods to measure the temporal, spectral, and spatial properties of NADH and FAD autofluorescence have been developed. Specifically, an optical redox ratio of cofactor fluorescence intensities and NADH fluorescence lifetime parameters have been used in numerous applications, but significant work remains to mature this technology for understanding dynamic changes in metabolism. This article describes the current understanding of our optical sensitivity to different metabolic pathways and highlights current challenges in the field. Recent progress in addressing these challenges and acquiring more quantitative information in faster and more metabolically relevant formats is also discussed.

Noninvasive Monitoring to Detect Dehydration: Are We There Yet?

The need for hydration monitoring is significant, especially for the very young and elderly populations who are more vulnerable to becoming dehydrated and suffering from the effects that dehydration brings. This need has been among the drivers of considerable effort in the academic and commercial sectors to provide a means for monitoring hydration status, with a special interest in doing so outside the hospital or clinical setting. This review of emerging technologies provides an overview of many technology approaches that, on a theoretical basis, have sensitivity to water and are feasible as a routine measurement. We review the evidence of technical validation and of their use in humans. Finally, we highlight the essential need for these technologies to be rigorously evaluated for their diagnostic potential, as a necessary step to meet the need for hydration monitoring outside of the clinical environment.

Measurement of rat and human tissue optical properties for improving the optical detection and visualization of peripheral nerves

Peripheral nerve damage frequently occurs in challenging surgical cases resulting in high costs and morbidity. Various optical techniques have proven effective in detecting and visually enhancing nerves, demonstrating their translational potential for assisting in nerve-sparing medical procedures. However, there is limited data characterizing the optical properties of nerves in comparison to surrounding tissues, thus limiting the optimization of optical nerve detection systems. To address this gap, the absorption and scattering properties of rat and human nerve, muscle, fat, and tendon were determined from 352-2500 nm. The optical properties highlighted an ideal region in the shortwave infrared for detecting embedded nerves, which remains a significant challenge for optical approaches. A 1000-1700 nm hyperspectral diffuse reflectance imaging system was used to confirm these results and identify optimal wavelengths for nerve imaging contrast in an in vivo rat model. Optimal nerve visualization contrast was achieved using 1190/1100 nm ratiometric imaging and was sustained for nerves embedded under ≥600 µm of fat and muscle. Overall, the results provide valuable insights for optimizing the optical contrast of nerves, including those embedded in tissue, which could lead to improved surgical guidance and nerve-sparing outcomes.

Spectral analysis of multiple scattering factors of turbid media for glucose measurement using near-infrared spectroscopy

Significance

Near-infrared (NIR) diffuse reflectance spectroscopy has been widely used for non-invasive glucose measurement in humans, as glucose can induce a significant and detectable optical signal change in tissue. However, the scattering-dominated glucose spectrum in the range of 1000 to 1700 nm is easily confused with many other scattering factors, such as particle density, particle size, and tissue refractive index.

Aim

Our aim is to identify the subtle distinctions between glucose and these factors through theoretical analysis and experimental verification, in order to employ suitable methods for eliminating these interferences, thus increasing the accuracy of non-invasive glucose measurement.

Approach

We present a theoretical analysis of the spectra of 1000 to 1700 nm for glucose and some scattering factors, which is then verified by an experiment on a 3% Intralipid solution.

Results

We found that both the theoretical and experimental results show that the effective attenuation coefficient of glucose has distinct spectral characteristics, which are distinct from the spectra caused by particle density and refractive index, particularly in the range of 1400 to 1700 nm.

Conclusions

Our findings can offer a theoretical foundation for eliminating these interferences in non-invasive glucose measurement, aiding mathematical methods to model appropriately and enhance the accuracy of glucose prediction.

Azimuth mapping of fibrous tissue in linear dichroism-sensitive photoacoustic microscopy

Photoacoustic imaging (PAI) has emerged as a molecular-selective imaging technology based on optical absorption contrast. Dichroism-sensitive photoacoustic (DS-PA) imaging has been reported, where the absorption coefficient has a vector characteristic, featuring dimensions of contrast in polarization and wavelength. Herein, we present a DS-PA microscopy (DS-PAM) system that implements optical anisotropy contrast and molecular selectivity. Moreover, we propose mathematical solutions to fully derive dichroic properties. A wavelength for the PAI of collagenous tissue was used, and the proposed algorithms were validated using linear dichroic materials. We successfully mapped dichroic information in fibrous tissue imaging based on the degree of anisotropy and axis orientation, and also deduced mechanical assessment from the tissue arrangement. The proposed DS-PAM system and algorithms have great potential in various diagnostic fields using polarimetry, such as musculoskeletal and cardiovascular systems.

1.8-µm laser operation based on femtosecond-laser direct written Tm:YVO4 cladding waveguides

In this work, we have demonstrated tunable 1.8-µm laser operation based on a Tm:YVO₄ cladding waveguide fabricated by means of femtosecond laser direct writing. Benefiting from the good optical confinement of the fabricated waveguide, efficient thulium laser operation, with a maximum slope efficiency of 36%, a minimum lasing threshold of 176.8 mW, and a tunable output wavelength from 1804 to 1830nm, has been achieved in a compact package via adjusting and optimizing the pump and resonant conditions of the waveguide laser design. The lasing performance using output couplers with different reflectivity has been well studied in detail. In particular, due to the good optical confinement and relatively high optical gain of the waveguide design, efficient lasing can be obtained even without using any cavity mirrors, thereby opening up new possibilities for compact and integrated mid-infrared laser sources.

Development of a compounded propofol nanoemulsion using multiple non-invasive process analytical technologies

Propofol is the preferred anaesthetic for induction and maintenance of sedation in critically ill mechanically ventilated COVID-19 patients. However, during the outbreak of the COVID-19 pandemic, regular supply chains could not keep up with the sudden increase in global demand, causing drug shortages. Propofol is formulated as an oil-in-water emulsion which is administered intravenously. This study explores the extemporaneous preparation of a propofol emulsion without specialized manufacturing equipment to temporally alleviate such shortages. A commercially available lipid emulsion (IVLE, SMOFlipid 20%), intended for parenteral nutrition, was used to create a propofol loaded nanoemulsion via addition of liquid propofol drug substance and subsequent mixing. Critical quality attributes such as mean droplet size and the volume-weighted percentage of large-diameter (>5µm) droplets were studied. The evolution of droplet size and propofol distribution was monitored in situ and non-destructively, maintaining sterility, using Spatially Resolved Dynamic Light Scattering and Near Infrared Spectroscopy, respectively. Using response surface methodology, an optimum was found for a 4% w/v propofol formulation with a ∼15 minute mixing time in a flask shaker at a 40° shaking angle. This study shows that extemporaneous compounding is a viable option for emergency supply of propofol drug product during global drug shortages.

Hyperspectral imaging of lipids in biological tissues using near-infrared and shortwave infrared transmission mode: A pilot study

Label-free hyperspectral imaging (HSI) of lipids was demonstrated in the near-infrared (NIR) and shortwave infrared (SWIR) regions (950-1800 nm) using porcine tissue. HSI was performed in the transmission light-pass configuration, using a NIR-SWIR camera coupled with a liquid crystal tunable filter. The transmittance spectra of the regions of interest (ROIs), which correspond to the lipid and muscle areas in the specimen, were utilized for the spectrum unmixing. The transmittance spectra in ROIs were compared with those recorded by a spectrophotometer using samples of adipose and muscle. The lipid optical absorption bands at 1210 and 1730 nm were first used for the unmixing and mapping. Then, we performed the continuous multiband unmixing over the entire available spectral range, thereby, considering a combination of characteristic absorption bands of lipids, proteins, and water. The enhanced protocol demonstrates the ability to visualize small adipose inclusions of 1-10 μm size.

Towards the non-invasive assessment of staling in bovine hides with hyperspectral imaging

Microbial spoilage or staling of bovine hides during storage leads to poor leather quality and increased chemical consumption during processing. Conventional microbiological examinations of hide samples which require time-consuming microbe culture cannot be employed as a practical staling detection approach for leather production. Hyperspectral imaging (HSI), featuring fast data acquisition and implementation flexibility has been considered ideal for in-line detection of microbial contamination in Agri- food products. In this study, a linescan hyperspectral imaging system working in a spectral range of 550 nm to 1700 nm was utilized as a rapid and non-destructive technique for predicting the aerobic plate counts (APC) on raw hide samples during storage. Fresh bovine hide samples were stored at 4 °C and 20 °C for 3 days. Every day, hyperspectral images were acquired on both sides for each sample. The APCs were determined simultaneously by conventional microbiological plating method. Leather quality was evaluated by microscopic inspection of grain surfaces, which indicate the acceptable threshold of microbe load on hide samples for leather processing. Partial least squares regression (PLSR) was applied to fit the spectral information extracted from the samples to the logarithmic values of APC to develop microbe load prediction models. All models showed good prediction accuracy, yielding a R_cv² in the range of 0.74-0.92 and standard error of cross validation (SECV) in the range of 0.61-0.76 %. The prediction capability of the HSI was explored using the model developed with SNV + smoothened pre-processing to spatially predict plate count in the samples. Models established in this study successfully predicted the staling states characterised by bacterial loads on hide samples with low prediction errors. Models, visually, showed the differences in microbial load across the storage time and temperatures. Results illustrate that HSI can be potentially implemented as a non-invasive tool to predict microbe loads in bovine hides before leather processing, so that real-time grading of hides based on staling states can be achieved. This will reduce the cost of leather production and waste management and pave the way for allocating material supply for different production purposes.

Face presentation attack detection: Research opportunities and perspectives

The rapid development of biometric methods and their implementation in practice has led to the widespread attacks called spoofing, which are purely biometric vulnerabilities, but are not used in conjunction with other IT security solutions. Although biometric recognition as a branch of computer science dates back to the 1960s, attacks on biometric systems have become more sophisticated since the 2010s due to great advances in pattern recognition. It should be noted that face recognition is the most attractive topic for deceiving recognition systems. Popular presentation attacks, such as print, replay and mask attacks, have demonstrated a high security risk for SOTA face recognition systems. Many Presentation Attack Detection (PAD) methods (also known as face anti-spoofing methods or countermeasures) have been proposed that can automatically detect and mitigate such targeted attacks. The article presents a systematic survey in face anti-spoofing with prognostic trends in this research area. A brief description of 16 outstanding previous surveys on the face PAD field is mentioned, from which it is possible to trace how this scientific topic has developed. SOTA in PAD provides an analysis of a wide range of the PAD methods, which are categorized into two unbalanced groups: digital (feature-based) and physical (sensor-based) methods. Generalization of deep learning methods as a recent trend aimed at improving recognition results requires special attention. This survey presents five types of generalization such as transfer learning, anomaly detection, few-shot and zero-shot learning, auxiliary supervision, and multi-spectral methods. A summary of over than 40 existing 2D/3D face spoofing databases is a guideline for those who want to select databases for experiments. One can also find a description of performance evaluation metrics and testing protocols. In addition, we discuss trends and perspectives in the emerging field of facial biometrics.

Across countries implementation of handheld near-infrared spectrometer for the on-line prediction of beef marbling in slaughterhouse

Only few studies have used Near-Infrared (NIR) spectroscopy to assess meat quality traits directly in the chiller. The aim of this study was therefore to investigate the ability of a handheld NIR spectrometer to predict marbling scores on intact meat muscles in the chiller. A total of 829 animals from 2 slaughterhouses in France and Italy were involved. Marbling was assessed according to the 3G (Global Grading Guaranteed) protocol using 2 different scores. NIR measurements were collected by performing 5 scans at different points of the Longissimus thoracis. An average MSA marbling score of 330-340 was obtained in the two countries. The prediction models provided a R² in external validation between 0.46 and 0.59 and a standard error of prediction between 83.1 and 105.5. Results did provide a moderate prediction of the marbling scores but can be useful in the European industry context to predict classes of MSA marbling.

NIR spectroscopy combined with 1D-convolutional neural network for breast cancerization analysis and diagnosis

Near-infrared (NIR) spectroscopy with deep penetration can characterize the composition of biological tissue based on the vibration of the X-H group in a rapid and high-specificity way. Deep learning is proven helpful for rapid and automatic identification of tissue cancerization. In this study, NIR spectroscopic detection equipped with the lab-made NIR probe was performed to in situ explore the change of molecular compositions in breast cancerization, where the diffused NIR spectra were efficiently collected at different locations of cancerous and paracancerous areas. The breast cancerous-paracancerous discriminant model was established based on one-dimensional convolutional neural network (1D-CNN). By optimizing the structure of the neural network, the high classification accuracy (94.67%), recall/sensitivity (95.33%), specificity (94.00%), precision (94.08%) and F1 score (0.9470) were achieved, showing the better discrimination ability and reliability than the K-Nearest Neighbor (KNN, 88.34%, 98.21%, 76.11%, 83.59%, 0.9031) and Fisher Discriminant Analysis (FDA, 90.00%, 96.43%, 81.82%, 87.10%, 0.9153) methods. The experimental results indicate that the application of 1D-CNN can discriminate the cancerous and paracancerous breast tissues, and provide an intelligent method for clinical locating, diagnosis and treatment of breast cancer.

Development and comparison of classification models on VIS-NIR hyperspectral imaging spectra for qualitative detection of the Staphylococcus aureus in fresh chicken breast

Chicken is at risk of contaminated foodborne pathogens in the production process. Timely and nondestructive detection of foodborne pathogens in chicken is essential for food security. The study aims to explore the feasibility of developing efficient classification models for qualitative detection of Staphylococcus aureus in chicken breast using the hyperspectral imaging technique. Principal component analysis was used to process the full spectral information and three wavelength selection methods (competitive adaptive reweighted sampling, genetic algorithm, and successive projections algorithm) were applied to extract effective wavelengths. These methods were combined with the support vector machine algorithm to develop conventional classification models, respectively. In addition, a convolutional neural network model based on deep learning was designed and trained for comparison. The performance of the convolutional neural network model was significantly better than that of conventional classification models. The overall accuracy for chicken sample classifications was improved from 83.88% to 91.38%. The results demonstrated that deep learning can effectively extract spectral features and promote the application of hyperspectral imaging in foodborne pathogens detection of chicken products.

A new label-free optical imaging method for the lymphatic system enhanced by deep learning

Our understanding of the lymphatic vascular system lags far behind that of the blood vascular system, limited by available imaging technologies. We present a label-free optical imaging method that visualizes the lymphatic system with high contrast. We developed an orthogonal polarization imaging (OPI) in the shortwave infrared range (SWIR) and imaged both lymph nodes and lymphatic vessels of mice and rats in vivo through intact skin, as well as human mesenteric lymph nodes in colectomy specimens. By integrating SWIR-OPI with U-Net, a deep learning image segmentation algorithm, we automated the lymph node size measurement process. Changes in lymph nodes in response to cancer progression were monitored in two separate mouse cancer models, through which we obtained insights into pre-metastatic niches and correlation between lymph node masses and many important biomarkers. In a human pilot study, we demonstrated the effectiveness of SWIR-OPI to detect human lymph nodes in real time with clinical colectomy specimens.

One Sentence Summary

We develop a real-time high contrast optical technique for imaging the lymphatic system, and apply it to anatomical pathology gross examination in a clinical setting, as well as real-time monitoring of tumor microenvironment in animal studies.

Spatiotemporal Control of Biology: Synthetic Photochemistry Toolbox with Far-Red and Near-Infrared Light

The complex network of naturally occurring biological pathways motivates the development of new synthetic molecules to perturb and/or detect these processes for fundamental research and clinical applications. In this context, photochemical tools have emerged as an approach to control the activity of drug or probe molecules at high temporal and spatial resolutions. Traditional photochemical tools, particularly photolabile protecting groups (photocages) and photoswitches, rely on high-energy UV light that is only applicable to cells or transparent model animals. More recently, such designs have evolved into the visible and near-infrared regions with deeper tissue penetration, enabling photocontrol to study biology in tissue and model animal contexts. This Review highlights recent developments in synthetic far-red and near-infrared photocages and photoswitches and their current and potential applications at the interface of chemistry and biology.

Detection of DLBCL by pixel purity index and iterative linearly constrained minimum variance into hyperspectral imaging analysis

It is unclear whether a hyperspectral imaging-based approach can facilitate the diagnosis of diffuse large B-cell lymphoma (DLBCL), and further investigation is required. In this study, the pixel purity index (PPI) coupled with iterative linearly constrained minimum variance (ILCMV) was used to bridge this gap. We retrospectively reviewed 22 pathological DLBCL specimens. Ten normal lymph node specimens were used as controls. PPI endmember extraction was performed to identify seed-training samples. ILCMV was then used to classify cell regions. The 3D receiver operating characteristic (ROC) showed that the spectral information divergence possessed superior ability to distinguish between normal and abnormal lymphoid cells owing to its stronger background suppression compared with the spectral angle mapper and mean square error methods. An automated cell hyperspectral image classification approach that combined the PPI and ILCMV was used to improve DLBCL diagnosis. This strategy intelligently resolved critical problems arising in unsupervised classification.