Infrared Spectroscopy-Mid-infrared, Near-infrared, and Far-infrared/Terahertz Spectroscopy

Dual-gas quartz-enhanced photoacoustic spectroscopy sensor exploiting two fiber-combined interband cascade lasers

In this work, a novel indium fluoride glass 2-input-1-output fiber combiner was designed and fabricated to combine two Interband Cascade Laser (ICL) sources emitting in the mid-infrared wavelength range. To test the combiner performance, a dual-gas quartz-enhanced photoacoustic spectroscopy sensor was demonstrated for the detection of carbon dioxide (CO2) and nitric oxide (NO), employing two fiber-coupled ICLs having central emission wavelengths of 4,234 nm and 5,263 nm, respectively. The laser beams were coupled via the fiber combiner and then focused into a commercial acoustic detection module equipped with an input fiber-port, thus resulting in a plug-and-play sensing system. Tens of ppm-level detection limits at 3σ are achieved for both pollutants with a lock-in integration time (τ) of 0.1 s. Finally, an Allan-Werle analysis demonstrated the stability of the sensor, allowing the achievement of detection limit of 13 ppm and 4 ppm at τ = 10 s for CO2 and NO, respectively.

Green and rapid quantitative detection of selenium in selenium-enriched kefir grain based on Fourier transform infrared spectroscopy

Rapid monitoring of total and organic selenium content in kefir grain was essential for microbial screening and selenium-enriched food development. Firstly, spectral information of selenium-enriched kefir grain was obtained using an attenuated total reflection Fourier transform infrared spectrometer. Secondly, the performance of the quantitative prediction models established by the four-variable screening method with three machine learning algorithms, respectively, was compared. For the prediction of total selenium, the competitive adaptive reweighted sampling - least squares support vector machine model performed the best, with prediction set relative coefficient (RP) and relative prediction deviation (RPD) values of 0.97 and 4.36, respectively. For the prediction of organic selenium, the IRF-LSSVM model had a RP and RPD value of 0.95 and 6.44, respectively. The proposed method achieves scientific, rapid (within 1 min) and green detection of total selenium (237.72-2330.82 μg/g) and organic selenium (102.20-1483.59 μg/g) content in selenium-enriched Kefir grain.

Fabrication of lactoferrin-chitosan-etoposide nanoparticles with melatonin via carbodiimide coupling: In-vitro & in-vivo evaluation for colon cancer

This study presents the development of melatonin-coated lactoferrin-chitosan nanoparticles (ETP-CS-LF-MLT-NPs) using ionic gelation and carbodiimide coupling for colorectal cancer treatment. The nanoparticles were characterized by an average size of 208.7 ± 1.25 nm, a zeta potential of 30.77 ± 1.21 mV, and 82.45 % drug encapsulation efficiency. In vitro drug release studies showed sustained, pH-responsive release, with 98.68 ± 4.12 % released at pH 5.5 over 24 h. The nanoparticles exhibited significant cytotoxicity in HCT116 cells (IC50 = 15.32 μg/mL), inducing ROS generation, apoptosis, and G2/M cell cycle arrest, with notable downregulation of BCL2 gene expression. Enhanced cellular uptake due to lactoferrin targeting improved therapeutic efficacy. In In vivo studies, the nanoparticles demonstrated significant tumor reduction and selective colon accumulation in a DMH-induced colorectal cancer rat model, along with improved pharmacokinetics, showing extended plasma circulation and bioavailability compared to free etoposide. Biocompatibility assays, including hemolysis (<1 %), platelet aggregation, and HET-CAM tests, confirmed the safety profiling of the prepared nanoparticles. The nanoparticles also inhibited Proteus mirabilis (ZOI = 1.9 cm) and exhibited promising effects on the gut microbiome of treated animals. Altogether, ETP-CS-LF-MLT-NPs hold great potential for targeted colorectal cancer therapy, improving drug delivery, tumor targeting, bioavailability, and reducing systemic toxicity.

Chemometrics-powered spectroscopic techniques for the measurement of food-derived phenolics and vitamins in foods: A review

Foods are rich in various bioactive compounds, like phenolics, and vitamins, which play important physiological roles in the human body. The analysis of phenolics and vitamins in plant and animal-based foods is a topic of growing interest. Compared with conventional methods, the chemometrics-powered infrared, Fourier transform-near infrared and mid-infrared, ultraviolet-visible, fluorescence, and Raman spectroscopy offer a reliable, low-cost, and nondestructive means to determine phenolics and vitamins. This study briefly presents the physical properties of phenolics and vitamins and their physiological benefits, features of commonly used spectroscopic techniques, sample preparation for spectroscopic data analysis, and the progress of chemometrics methods for model calibration using spectroscopic data and their primary challenges in predicting phenolics and vitamins in real samples for the last five years. The spectral preprocessing method combined feature extraction quantitative chemometric model comparatively showed the best results for simultaneous and single detection. Finally, this study put forward future directions.

Environmentally friendly molecularly imprinted polymers as an insert for SPE type columns in the gentamicin monitoring process

The quantity and variety of micro-pollutants infiltrating water resources have increased rapidly in recent times. The appearance of many harmful substances in the waters has resulted in so-called chemical cocktails which significantly contribute to the deterioration of water quality. Additionally, the variety of these compounds, often similar to each other in terms of molecular weights, makes their separation and identification very difficult. In this paper we present the possibility of using self-regenerating mechanism of molecularly imprinted polymers to measure the concentration of micropollutants in the aquatic environment. Molecularly imprinted polymers toward gentamicin were prepared by monomer polymerization in aqueous solution at ambient temperature. Results from computer-based molecular modelling demonstrated potential binding sites between gentamicin and functional monomers in water. Various compositions of polymerization mixtures were tested. The ratio of monomers to each other was 1.1:1.4:0.0015 and 1:1:1 for N-isopropylacrylamine:acrylamide:acrylic acid, respectively. For each composition, various amounts of the standard were tested: 0, 3, 5, 7, 10,15 mol% in relation to monomers. The best results were obtained for 5 % gentamicin with an excess of acrylamide in relation to the other monomers. Sorption for this system was 0.783 mg/g at ambient temperature and desorption 0.593 at 4 °C. The synthesized materials, thanks to the incorporation of thermosensitive poly(N-isopropylacrylamide) into their structures, were able to release 89 % of adsorbed gentamicin. This made it possible to use the designed SPE columns repeatably with similar efficiency. The prepared materials were selective in the presence of other antibiotics like amoxicillin and norfloxacin.

C. Calado C. Early Mortality Prediction in Intensive Care Unit Patients Based on Serum Metabolomic Fingerprint

Predicting mortality in intensive care units (ICUs) is essential for timely interventions and efficient resource use, especially during pandemics like COVID-19, where high mortality persisted even after the state of emergency ended. Current mortality prediction methods remain limited, especially for critically ill ICU patients, due to their dynamic metabolic changes and heterogeneous pathophysiological processes. This study evaluated how the serum metabolomic fingerprint, acquired through Fourier-Transform Infrared (FTIR) spectroscopy, could support mortality prediction models in COVID-19 ICU patients. A preliminary univariate analysis of serum FTIR spectra revealed significant spectral differences between 21 discharged and 23 deceased patients; however, the most significant spectral bands did not yield high-performing predictive models. By applying a Fast-Correlation-Based Filter (FCBF) for feature selection of the spectra, a set of spectral bands spanning a broader range of molecular functional groups was identified, which enabled Naïve Bayes models with AUCs of 0.79, 0.97, and 0.98 for the first 48 h of ICU admission, seven days prior, and the day of the outcome, respectively, which are, in turn, defined as either death or discharge from the ICU. These findings suggest FTIR spectroscopy as a rapid, economical, and minimally invasive diagnostic tool, but further validation is needed in larger, more diverse cohorts.

Multimodal In-Sensor Computing System Using Integrated Silicon Photonic Convolutional Processor

Photonic integrated circuits offer miniaturized solutions for multimodal spectroscopic sensory systems by leveraging the simultaneous interaction of light with temperature, chemicals, and biomolecules, among others. The multimodal spectroscopic sensory data is complex and has huge data volume with high redundancy, thus requiring high communication bandwidth associated with high communication power consumption to transfer the sensory data. To circumvent this high communication cost, the photonic sensor and processor are brought into intimacy and propose a photonic multimodal in-sensor computing system using an integrated silicon photonic convolutional processor. A microring resonator crossbar array is used as the photonic processor to implement convolutional operation with 5-bit accuracy, validated through image edge detection tasks. Further integrating the processor with a photonic spectroscopic sensor, the in situ processing of multimodal spectroscopic sensory data is demonstrated, achieving the classification of protein species of different types and concentrations at various temperatures. A classification accuracy of 97.58% across 45 different classes is achieved. The multimodal in-sensor computing system demonstrates the feasibility of integrating photonic processors and photonic sensors to enhance the data processing capability of photonic devices at the edge.

Black Phosphorus for Mid-Infrared Optoelectronics: Photophysics, Scalable Processing, and Device Applications

High efficiency mid-infrared (λ = 3-8 μm) light emitters and photodetectors are pivotal for advancing next-generation optoelectronics. However, narrow-bandgap semiconductors face fundamental challenges such as pronounced nonradiative carrier recombination and thermally generated noise, which impede device performance. Recently, two-dimensional layered black phosphorus (BP) and its alloys have attracted substantial interest for mid-infrared device applications, demonstrating superior performance relative to conventional III-V and II-VI semiconductors with similar bandgaps. In this review, we discuss the optical properties of BP, contrasting these with those of covalent compounds. Owing to its inherently self-terminated surface structure and reduced nonradiative recombination, BP exhibits high performance in light emission and photodetection at room temperature. Furthermore, this review highlights recent advances in the large-area processing of BP thin films, paving the way for practical device applications and integration. Finally, we explore ongoing challenges and emerging opportunities in the utilization of BP for functional mid-infrared devices.

A NIRS-based recognition of coal and rock using convolution-multiview broad learning system

Achieving high production in the top coal caving process from thick coal seams is crucial. Thus, the timely decision of when to stop caving poses an urgent challenge to impact the mining loss rate and cost recovery. To address this issue, an innovative recognition system has been developed using Near-Infrared Spectroscopy (NIRS) technology. It stands out for its on-site usability, it enables rapid data collection and local recognition at the longwall face. Furthermore, to overcome the limitations of existing methods in adapting to variations in spectral data quality during on-site collection and the lack of integration of spectral data across different feature processing stages, a coal-rock recognition method has been developed which can ignore the influence of acquisition factors(granularity, light source angle, and detection sensor angle). This method incorporates the features of convolution and multi-view into the BLS model, the designed model structure exhibits a remarkable recognition accuracy of 99.78 %. The model was deployed into the recognition system, and experimental tests were conducted on the working face. The results showed that the recognition system can effectively identify the entire coal-caving process and achieve a recognition accuracy of 92.3 %. This capability is crucial for determining the optimal point to stop roof caving.

Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges

Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.

Patch-Based Far-Infrared Radiation (FIR) Therapy Does Not Impact Cell Tracking or Motility of Human Melanoma Cells In Vitro

Far-Infrared Radiation (FIR) is emerging as a novel non-invasive tool for mitigating inflammation and oxidative stress, offering potential benefits for certain medical conditions such as cardiovascular disease and chronic inflammatory disorders. We previously demonstrated that the application of patch-based FIR therapy on human umbilical vein endothelial cells (HUVECs) reduced the expression of inflammatory biomarkers and the levels of reactive oxygen species (ROS). Several in vitro studies have shown the inhibitory effects of FIR therapy on cell growth in different cancer cells (including murine melanoma cells), mainly using the wound healing assay, without direct cell motility or tracking analysis. The main objective of the present study was to conduct an in-depth analysis of single-cell motility and tracking during the wound healing assay, using an innovative high-throughput technique in the human melanoma cell line M14/C2. This technique evaluates various motility descriptors, such as average velocity, average curvature, average turning angle, and diffusion coefficient. Our results demonstrated that patch-based FIR therapy did not impact cell proliferation and viability or the activation of mitogen-activated protein kinases (MAPKs) in the human melanoma cell line M14/C2. Moreover, no significant differences in cell motility and tracking were observed between control cells and patch-treated cells. Altogether, these findings confirm the beneficial effects of the in vitro application of patch-based FIR therapy in human melanoma cell lines, although such effects need to be confirmed in future in vivo studies.

Far-infrared radiation and its therapeutic parameters: A superior alternative for future regenerative medicine?

In future regenerative medicine, far-infrared radiation (FIR) may be an essential component of optical therapy. Many studies have confirmed or validated the efficacy and safety of FIR in various diseases, benefiting from new insights into FIR mechanisms and the excellent performance of many applications. However, the lack of consensus on the biological effects and therapeutic parameters of FIR limits its practical applications in the clinic. In this review, the definition, characteristics, and underlying principles of the FIR are systematically illustrated. We outline the therapeutic parameters of FIR, including the wavelength range, power density, irradiation time, and distance. In addition, the biological effects, potential molecular mechanisms, and preclinical and clinical applications of FIR are discussed. Furthermore, the future development and applications of FIR are described in this review. By applying optimal therapeutic parameters, FIR can influence various cells, animal models, and patients, eliciting diverse underlying mechanisms and offering therapeutic potential for many diseases. FIR could represent a superior alternative with broad prospects for application in future regenerative medicine.

Imaging-based terahertz pixelated metamaterials for molecular fingerprint sensing

With the rapid development of terahertz-enabled devices, the study of miniaturized and integrated systems has attracted significant attention. We experimentally demonstrate an imaging-based pixelated metamaterial for detecting terahertz molecular fingerprints related to intermolecular vibrations and large-amplitude intramolecular modes, including chemical identification and compositional analysis. The compact THz sensor consists of a 4 × 4 pixelated filter-detector array with transmission resonances tuned to discrete frequencies. The absorption spectra of analytes are computationally reconstructed from different spectral responses of meta-pixels, and the resulting information is characterized via near-field imaging. Due to the spectrometer-less operation principle, such imaging-based approaches provide an alternative method for developing sensitive, versatile, and miniaturized THz biosensors, especially for practical field deployment applications.

Far-infrared therapy promotes exercise capacity and glucose metabolism in mice by modulating microbiota homeostasis and activating AMPK

The benefits of physical exercise on human health make it desirable to identify new approaches that would mimic or potentiate the effects of exercise to treat metabolic diseases. However, whether far-infrared (FIR) hyperthermia therapy could be used as exercise mimetic to realize wide-ranging metabolic regulation, and its underling mechanisms remain unclear. Here, a specific far-infrared (FIR) rays generated from graphene-based hyperthermia devices might promote exercise capacity and metabolisms. The material characterization showed that the graphene synthesized by chemical vapour deposition (CVD) was different from carbon fiber, with single-layer structure and high electrothermal transform efficiency. The emission spectra generated by graphene-FIR device would maximize matching those adsorbed by tissues. Graphene-FIR enhanced both core and epidermal temperatures, leading to increased blood flow in the femoral muscle and the abdominal region. The combination of microbiomic and metabolomic analysis revealed that graphene-FIR modulates the metabolism of the gut-muscle axis. This modulation was characterized by an increased abundance of short-chain fatty acids (SCFA)-producing bacteria and AMP, while lactic acid levels decreased. Furthermore, the principal routes involved in glucose metabolism, such as glycolysis and gluconeogenesis, were found to be altered. Graphene-FIR managed to stimulate AMPK activity by activating GPR43, thus enhancing muscle glucose uptake. Furthermore, a microbiota disorder model also demonstrated that the graphene-FIR effectively restore the exercise endurance with enhanced p-AMPK and GLUT4. Our results provided convincing evidence that graphene-based FIR therapy promoted exercise capacity and glucose metabolism via AMPK in gut-muscle axis. These novel findings regarding the therapeutic effects of graphene-FIR suggested its potential utility as a mimetic agent in clinical management of metabolic disorders.

Fluorescence in depth: integration of spectroscopy and imaging with Raman, IR, and CD for advanced research

Fluorescence spectroscopy serves as a vital technique for studying the interaction between light and fluorescent molecules. It encompasses a range of methods, each presenting unique advantages and applications. This technique finds utility in various chemical studies. This review discusses Fluorescence spectroscopy, its branches such as Time-Resolved Fluorescence Spectroscopy (TRFS) and Fluorescence Lifetime Imaging Microscopy (FLIM), and their integration with other spectroscopic methods, including Raman, Infrared (IR), and Circular Dichroism (CD) spectroscopies. By delving into these methods, we aim to provide a comprehensive understanding of the capabilities and significance of fluorescence spectroscopy in scientific research, highlighting its diverse applications and the enhanced understanding it brings when combined with other spectroscopic methods. This review looks at each technique's unique features and applications. It discusses the prospects of their combined use in advancing scientific understanding and applications across various domains.

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.

Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging

Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed's nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed species widely cultivated in Northwest China as the subjects of our study. Firstly, we gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan, and utilized the SPXY algorithm to classify the sample set. Subsequently, the spectral data underwent seven distinct preprocessing methods, revealing that the PLSR model exhibited superior performance after being processed with the SG smoothing method. Feature wavelength extraction was carried out using the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS). Finally, four quantitative analysis models, namely Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Principal Component Regression (PCR), were individually established. Experimental results demonstrated that among all the models for predicting protein content, the SG-CARS-MLR model predicted the best, with and of 0.9563 and 0.9336, with the corresponding Root Mean Square Error Correction (RMSEC) and Root Mean Square Error Prediction (RMSEP) of 0.4892 and 0.5616, respectively. In the optimal prediction models for oil content, linoleic acid and lignan, the R p 2 was 0.8565, 0.8028, 0.9343, and the RMSEP was 0.8682, 0.5404, 0.5384, respectively. The study results show that hyperspectral imaging technology has excellent potential for application in the detection of quality characteristics of flaxseed and provides a new option for the future non-destructive testing of the nutritional quality of flaxseed.

Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix

Accurate assessment of isoflavone and starch content in Puerariae Thomsonii Radix (PTR) is crucial for ensuring its quality. However, conventional measurement methods often suffer from time-consuming and labor-intensive procedures. In this study, we propose an innovative and efficient approach that harnesses hyperspectral imaging (HSI) technology and deep learning (DL) to predict the content of isoflavones (puerarin, puerarin apioside, daidzin, daidzein) and starch in PTR. Specifically, we develop a one-dimensional convolutional neural network (1DCNN) model and compare its predictive performance with traditional methods, including partial least squares regression (PLSR), support vector regression (SVR), and CatBoost. To optimize the prediction process, we employ various spectral preprocessing techniques and wavelength selection algorithms. Experimental results unequivocally demonstrate the superior performance of the DL model, achieving exceptional performance with mean coefficient of determination (R2) values surpassing 0.9 for all components. This research underscores the potential of integrating HSI technology with DL methods, thereby establishing the feasibility of HSI as an efficient and non-destructive tool for predicting the content of isoflavones and starch in PTR. Moreover, this methodology holds great promise for enhancing efficiency in quality control within the food industry.

Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review

The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.

Evaluation of near-infrared spectroscopy as a contactless method for health monitoring of resin-based coating materials applied to concrete surfaces

The surfaces of concrete structures are often coated with protective materials to minimize corrosion and weathering-based deterioration. Therefore, it is important to monitor the aging of the coating materials and their overall condition to extend the service lifetime of the structure effectively. Near-infrared spectroscopy (NIRS) is a contactless, nondestructive, rapid, and convenient method for material characterization; therefore, it is useful for onsite inspection of coating materials. Hence, in this study, we attempt to determine whether NIRS can be used for simple inspection for health monitoring of organic resin-based coating materials. In addition to identifying different severities of peeling damage, we characterize the ultraviolet-induced deterioration of coating materials with different thicknesses using diffuse reflection spectra acquired in the near-infrared wavelength region. For independent comparison with the NIR spectra, the state of the coating materials on the mortar specimens was analyzed using a combination of Fourier-transform infrared spectroscopy and scanning electron microscopy, while the state of the underlying mortar specimens was analyzed using permeability and salt-water immersion tests. The results confirm that the NIRS could detect the degradation of coating materials at early stages of deterioration before their permeability had been affected. NIRS offers the possibility of intermittent monitoring of coating deterioration. In addition, because the NIR spectrometer is portable, it can help in inspecting high-rise areas and areas that are difficult to reach. Therefore, we believe that NIRS is a simple, safe, and inexpensive method for inspection of surface coating materials.

Applications of Hyperspectral Imaging Technology Combined with Machine Learning in Quality Control of Traditional Chinese Medicine from the Perspective of Artificial Intelligence: A Review

Traditional Chinese medicine (TCM) is the treasure of China, and the quality control of TCM is of crucial importance. In recent years, with the quick rise of artificial intelligence (AI) and the rapid development of hyperspectral imaging (HSI) technology, the combination of the two has been widely used in the quality evaluation of TCM. Machine learning (ML) is the core wisdom of AI, and its progress in rapid analysis and higher accuracy improves the potential of applying HSI to the field of TCM. This article reviewed five aspects of ML applied to hyperspectral data analysis of TCM: partition of data set, data preprocessing, data dimension reduction, qualitative or quantitative models, and model performance measurement. The different algorithms proposed by researchers for quality assessment of TCM were also compared. Finally, the challenges in the analysis of hyperspectral images for TCM were summarized, and the future works were prospected.

Quantitative Analysis of Mixed Minerals with Finite Phase Using Thermal Infrared Hyperspectral Technology

It is crucial but challenging to detect intermediate or end products promptly. Traditional chemical detection methods are time-consuming and cannot detect mineral phase content. Thermal infrared hyperspectral (TIH) technology is an effective means of real-time imaging and can precisely capture the emissivity characteristics of objects. This study introduces TIH to estimate the content of potassium salts, with a model based on Competitive Adaptive Reweighted Sampling (CARS) and Partial Least Squares Regression (PLSR). The model takes the emissivity spectrum of potassium salt into account and accurately predicts the content of Mixing Potassium (MP), a mineral mixture produced in Lop Nur, Xinjiang. The main mineral content in MP was measured by Mineral Liberation Analyzer (MLA), mainly including picromerite, potassium chloride, magnesium sulfate, and less sodium chloride. 129 configured MP samples were divided into calibration (97 samples) and prediction (32 samples) sets. The CARS-PLSR method achieved good prediction results for MP mineral content (picromerite: correlation coefficient of correction set (Rp2) = 0.943, predicted root mean square error (RMSEP) = 2.72%, relative predictive deviation (RPD) = 4.24; potassium chloride: Rp2 = 0.948, RMSEP = 2.86%, RPD = 4.42). Experimental results convey that TIH technology can effectively identify the emissivity characteristics of MP minerals, facilitating quantitative detection of MP mineral content.

Organic Pollutants Associated with Plastic Debris in Marine Environment: A Systematic Review of Analytical Methods, Occurrence, and Characteristics

Plastic pollution has become one of the most serious environmental problems, and microplastics (MPs, particles < 5 mm size) may behave as a vehicle of organic pollutants, causing detrimental effects to the environment. Studies on MP-sorbed organic pollutants lack methodological standardization, resulting in a low comparability and replicability. In this work, we reviewed 40 field studies of MP-sorbed organic contaminants using PRISMA guidelines for acquiring information on sampling and analytical protocols. The papers were also scored for their reliability on the basis of 7 criteria, from 0 (minimum) to 21 (maximum). Our results showed a great heterogeneity of the methods used for the sample collection, MPs extraction, and instruments for chemicals' identification. Measures for cross-contamination control during MPs analysis were strictly applied only in 13% of the studies, indicating a need for quality control in MPs-related research. The most frequently detected MP-sorbed chemicals were polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and organochlorine pesticides (OCPs). Most of the studies showed a good reliability (>75% of the total score), with 32 papers scoring 16 or higher. On the basis of the collected information, a standardizable protocol for the detection of MPs and MP-sorbed chemicals has been suggested for improving the reliability of MPs monitoring studies.

Analytical chemistry toward on-site diagnostics

Analytical Chemistry, through quantitative and/or qualitative analysis (identification), is a discipline that involves the development of methodologies and the exploration of new principles to obtain answers to given problems. In situ analysis techniques have attracted attention for its ability to elucidate phenomena occurring and to evaluate amount of a certain component in substances at real time and biological samples as applications of such analysis technology. Lots of techniques have been performed to understand the fundamental phenomena in varied fields such as X-ray, vibrational, and electrochemical impedance spectroscopies and also analytical reagents that enable to semi-quantitative analysis just observation. In fact, applying various in situ techniques in analytical chemistry expands to the medical diagnosis, which leads to be able to detect early diseases. Here, we describe some of previous researches in many fields such as electrochemical device for energy storage, biology, environment, and pathology and briefly introduce our recent challenges to analytical chemistry toward the on-site diagnosis.

The Convergence of FTIR and EVs: Emergence Strategy for Non-Invasive Cancer Markers Discovery

In conjunction with imaging analysis, pathology-based assessments of biopsied tissue are the gold standard for diagnosing solid tumors. However, the disadvantages of tissue biopsies, such as being invasive, time-consuming, and labor-intensive, have urged the development of an alternate method, liquid biopsy, that involves sampling and clinical assessment of various bodily fluids for cancer diagnosis. Meanwhile, extracellular vesicles (EVs) are circulating biomarkers that carry molecular profiles of their cell or tissue origins and have emerged as one of the most promising biomarkers for cancer. Owing to the biological information that can be obtained through EVs' membrane surface markers and their cargo loaded with biomolecules such as nucleic acids, proteins, and lipids, EVs have become useful in cancer diagnosis and therapeutic applications. Fourier-transform infrared spectroscopy (FTIR) allows rapid, non-destructive, label-free molecular profiling of EVs with minimal sample preparation. Since the heterogeneity of EV subpopulations may result in complicated FTIR spectra that are highly diverse, computational-assisted FTIR spectroscopy is employed in many studies to provide fingerprint spectra of malignant and non-malignant samples, allowing classification with high accuracy, specificity, and sensitivity. In view of this, FTIR-EV approach carries a great potential in cancer detection. The progression of FTIR-based biomarker identification in EV research, the rationale of the integration of a computationally assisted approach, along with the challenges of clinical translation are the focus of this review.

A Review of Machine Learning for Near-Infrared Spectroscopy

The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.

Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives

Herbal medicines (HMs) have been utilized to prevent and treat human ailments for thousands of years. Especially, HMs have recently played a crucial role in the treatment of COVID-19 in China. However, HMs are susceptible to various factors during harvesting, processing, and marketing, affecting their clinical efficacy. Therefore, it is necessary to conclude a rapid and effective method to study HMs so that they can be used in the clinical setting with maximum medicinal value. Non-targeted analytical technology is a reliable analytical method for studying HMs because of its unique advantages in analyzing unknown components. Based on the extensive literature, the paper summarizes the benefits, limitations, and applicability of non-targeted analytical technology. Moreover, the article describes the application of non-targeted analytical technology in HMs from four aspects: structure analysis, authentication, real-time monitoring, and quality assessment. Finally, the review has prospected the development trend and challenges of non-targeted analytical technology. It can assist HMs industry researchers and engineers select non-targeted analytical technology to analyze HMs' quality and authenticity.

Research Progress on Nutritional Value, Preservation and Processing of Fish-A Review

The global population has rapidly expanded in the last few decades and is continuing to increase at a rapid pace. To meet this growing food demand fish is considered a balanced food source due to their high nutritious value and low cost. Fish are rich in well-balanced nutrients, a good source of polyunsaturated fatty acids and impose various health benefits. Furthermore, the most commonly used preservation technologies including cooling, freezing, super-chilling and chemical preservatives are discussed, which could prolong the shelf life. Non-thermal technologies such as pulsed electric field (PEF), fluorescence spectroscopy, hyperspectral imaging technique (HSI) and high-pressure processing (HPP) are used over thermal techniques in marine food industries for processing of most economical fish products in such a way as to meet consumer demands with minimal quality damage. Many by-products are produced as a result of processing techniques, which have caused serious environmental pollution. Therefore, highly advanced technologies to utilize these by-products for high-value-added product preparation for various applications are required. This review provides updated information on the nutritional value of fish, focusing on their preservation technologies to inhibit spoilage, improve shelf life, retard microbial and oxidative degradation while extending the new applications of non-thermal technologies, as well as reconsidering the values of by-products to obtain bioactive compounds that can be used as functional ingredients in pharmaceutical, cosmetics and food processing industries.

Monitoring Compositional Changes in Black Soldier Fly Larvae (BSFL) Sourced from Different Waste Stream Diets Using Attenuated Total Reflectance Mid Infrared Spectroscopy and Chemometrics

Black soldier fly (Hermetia illucens, L.) larvae are characterized by their ability to convert a variety of organic matter from food waste into a sustainable source of food (e.g., protein). This study aimed to evaluate the use of attenuated total reflectance (ATR) mid-infrared (MIR) spectroscopy to monitor changes in the composition as well as to classify black soldier fly larvae (BSFL) samples collected from two growth stages (fifth and sixth instar) and two waste stream diets (bread and vegetables, soy waste). The BSFL samples were fed on either a soy or bread-vegetable mix waste in a control environment (temperature 25 °C, and humidity 70%). The frass and BSFL samples harvested as fifth and sixth instar samples were analyzed using an ATR-MIR instrument where frequencies at specific wavenumbers were compared and evaluated using different chemometric techniques. The PLS regression models yield a coefficient of determination in cross-validation (R2) > 0.80 for the prediction of the type of waste used as diet. The results of this study also indicated that the ratio between the absorbances corresponding to the amide group (1635 cm−1) and lipids (2921 + 2849 cm−1) region was higher in diets containing a high proportion of carbohydrates (e.g., bread-vegetable mix) compared with the soy waste diet. This study demonstrated the ability of MIR spectroscopy to classify BSFL instar samples according to the type of waste stream used as a diet. Overall, ATR-MIR spectroscopy has shown potential to be used as tool to evaluate and monitor the development and growth of BSFL. The utilization of MIR spectroscopy will allow for the development of traceability systems for BSFL. These tools will aid in risk evaluation and the identification of hazards associated with the process, thereby assisting in improving the safety and quality of BSFL intended to be used by the animal feed industry.

Investigating the Impact of Synchrotron THz Radiation on the Corneal Hydration Using Synchrotron THz-Far Infrared Beamline

Due to increasing interest in imaging, industrial, and the development of wireless communication operating at THz frequencies, it is crucial to ascertain possible health impacts arising from exposure to THz radiation. This paper reports on the pilot study of transmittance and absorbance spectra of the porcine cornea following THz frequency irradiation at a synchrotron THz/Far-IR beamline. The exposure period was 4 hours. One cornea was exposed to the radiation, with a second cornea acting as a control. An Attenuated Total Reflection (ATR) apparatus was used, and the frequency range of 2.4 to 8 THz was selected to evaluate any changes. It was found that the synchrotron THz radiation intensities are too low to produce induced corneal injury, but may lead to subtle changes in the state of water. Our results suggest that THz spectroscopy is a promising modality for corneal tissue hydration sensing.

Authenticity and Typicity of Traditional Cheeses: A Review on Geographical Origin Authentication Methods

Food fraud, corresponding to any intentional action to deceive purchasers and gain an undue economical advantage, is estimated to result in a 10 to 65 billion US dollars/year economical cost worldwide. Dairy products, such as cheese, in particular cheeses with protected land- and tradition-related labels, have been listed as among the most impacted as consumers are ready to pay a premium price for traditional and typical products. In this context, efficient food authentication methods are needed to counteract current and emerging frauds. This review reports the available authentication methods, either chemical, physical, or DNA-based methods, currently used for origin authentication, highlighting their principle, reported application to cheese geographical origin authentication, performance, and respective advantages and limits. Isotope and elemental fingerprinting showed consistent accuracy in origin authentication. Other chemical and physical methods, such as near-infrared spectroscopy and nuclear magnetic resonance, require more studies and larger sampling to assess their discriminative power. Emerging DNA-based methods, such as metabarcoding, showed good potential for origin authentication. However, metagenomics, providing a more in-depth view of the cheese microbiota (up to the strain level), but also the combination of methods relying on different targets, can be of interest for this field.

Review of Recent Modern Analytical Technology Combined with Chemometrics Approach Researches on Mushroom Discrimination and Evaluation

Mushroom is a macrofungus with precious fruiting body, as a food, a tonic, and a medicine, human have discovered and used mushrooms for thousands of years. Nowadays, mushroom is also a "super food" recommended by the World Health Organization (WHO) and Food and Agriculture Organization (FAO), and favored by consumers. Discrimination of mushroom including species, geographic origin, storage time, etc., is an important prerequisite to ensure their edible safety and commodity quality. Moreover, the effective evaluation of its chemical composition can help us better understand the nutritional properties of mushrooms. Modern analytical technologies such as chromatography, spectroscopy and mass spectrometry, etc., are widely used in the discrimination and evaluation researches of mushrooms, and chemometrics is an effective means of scientifically processing the multidimensional information hidden in these analytical technologies. This review will outline the latest applications of modern analytical technology combined with chemometrics in qualitative and quantitative analysis and quality control of mushrooms in recent years. Briefly describe the basic principles of these technologies, and the analytical processes of common chemometrics in mushroom researches will be summarized. Finally, the limitations and application prospects of chromatography, spectroscopy and mass spectrometry technology are discussed in mushroom quality control and evaluation.

Stabilization Effects Induced by Trehalose on Creatine Aqueous Solutions Investigated by Infrared Spectroscopy

Creatine is a very popular amino acid widely utilized in the sports world due to its functions mainly related to muscle building and increasing performance. The present work investigates the behavior of creatine aqueous solutions and of creatine aqueous in the presence of trehalose as a function of time changes by means of Infrared spectroscopy. Infrared spectra have been gathered and studied over time for both the full spectrum and the intramolecular OH-stretching region for the two mixtures. This latter region was studied more specifically using a cutting-edge technique called Spectral Distance (SD). From this analysis of the spectral features of the investigated samples, it emerges that trehalose has a significant stabilizing effect on creatine aqueous solutions.

A Study of C=O…HO and OH…OH (Dimer, Trimer, and Oligomer) Hydrogen Bonding in a Poly(4-vinylphenol) 30%/Poly(methyl methacrylate) 70% Blend and its Thermal Behavior Using Near-Infrared Spectroscopy and Infrared Spectroscopy

Inter- and intramolecular hydrogen bonding and their temperature-dependent changes in a poly(4-vinylphenol)/poly(methyl methacrylate)(PVPh 30%/PMMA 70%) blend were investigated using near-infrared (NIR) and infrared (IR) spectroscopy. Band assignments of the fundamentals and first overtones of the OH stretching mode of a free OH group and OH groups in C=O···HO and OH···OH (dimer, trimer, and oligomer) hydrogen bonding of PVPh 30%/PMMA 70% were carried out by comparison between its NIR and IR spectra and comparison with NIR and IR spectra of phenol. The comparison of the NIR spectra of the PVPh 30%/PMMA 70% blend (hereafter, we denote it as PVPh30%) with the corresponding IR spectra reveals that to observe bands arising from the free OH and OH···OH dimer, which is a weaker hydrogen bonding, NIR is better while to investigate bands originating from OH groups in the OH···O=C and OH···OH (oligomer) hydrogen bonds, which are stronger hydrogen bonding, IR is better. Thus, a combination of IR and NIR spectroscopy has provided convincing results for the hydrogen bonding of PVPh30%. The relative intensity of the two bands at 7058 and 6921 cm^-1 (I₇₀₅₈/I₆₉₂₁) due to the first overtones of the OH stretching modes of the free OH group and the OH group in the dimer, respectively, increases significantly above 90 °C, which is close to Tg of PVPh. In concomitance with the intensity increase in the relative intensity of the free OH band, the intensity of a band at 1706 cm^-1 due to the C=O stretching mode of the C=O···HO hydrogen bond of PVPh30% decreases above 90°C. These results suggest that above the Tg of PVPh the C=O···HO hydrogen bond is broken gradually and that the free OH increases. Of note is that below Tg the intensities of NIR bands due to the OH first overtones of free OH group and OH groups in the OH···OH dimer gain intensity in parallel with temperature increase, and above Tg the intensity of the band derived from the OH···OH group increases linearly much slower than that of the band due to the free OH. Moreover, a band due to an OH···OH oligomer decreases linearly. Hence, it is very likely that the OH···OH oligomers dissociate into free OH groups. Anharmonicity of O-H bonds, which is sensitive to a hydrogen bond, was estimated for the free OH and OH bonds in the C=O···HO and OH···OH (dimer, trimer, and oligomer) hydrogen bonding by comparison between the NIR and IR spectra in the OH stretching band regions.

Attenuated Total Reflection Far-Ultraviolet (ATR-FUV) Spectroscopy is a Sensitive Tool for Investigation of Protein Adsorption

Attenuated total reflection far-ultraviolet (ATR-FUV) spectra in the 145-250 nm region were studied for four kinds of proteins (two α-helix-rich proteins: bovine serum albumin (BSA) and lysozyme and two β-sheet rich proteins: concanavalin A and γ-globulin) in different solutions (pure water and phosphate buffered saline, or PBS) with different concentrations. All the spectra show a band at 191 nm due to the π-π^* transition of amide bonds of the proteins. The wavelength of the band does not change with their second structures, suggesting that the corresponding electronic transition mode is localized and polarized in the direction that is not affected by the difference in the peptide folding. The intensity of the 191 nm band differs with the concentration of salt in the solution, suggesting that the band intensity reflects the adsorption density of a protein on the internal reflection element (IRE) made of a sapphire glass prism. According to the intensity changes of the band at 191 nm, it is revealed that the properties in adsorption are different from one protein to another. It is assumed that there are two types of forces on the protein adsorption: one is that among the molecules and the other is that between a molecule and a substrate. The origin of force includes localized electrostatic polarity and affinity to water. The ions in the solvent give a marked effect on these forces, resulting in the difference in the response to adsorption density against the salt concentration in the solvent.