Application Of Near-infrared Spectroscopy Research Articles

MTiTaO6: Cr3+ (M = Al3+, Ga3+, Sc3+) Phosphors with Ultra‐Broadband Excitation Spectra and Enhanced Near‐Infrared Emission for Solar Cells

AbstractAchieving ultra‐broadband absorption and enhancing the near‐infrared (NIR) emitting properties of NIR phosphors is a key challenge to realize its multiple applications, such as solar spectrum conversion, spectral analysis, and night vision. Here, a novel NIR phosphor system MTiTaO6: Cr3+ (M = Al3+, Ga3+, Sc3+), with an excitation spectrum as broad as 421 nm and enhanced NIR emission is reported. It is the widest excitation spectrum among the known Cr3+‐doped NIR phosphors. Structural and spectroscopic analysis shows that the ultra‐broadband excitation spectrum originates from the overlap of multiple luminescence centers. Additionally, the emission peak is red‐shifted from 816 to 871 nm with increasing M ion radius, and the emission intensity is enhanced, which not only makes the emission spectra more compatible with the response curve of c‐Si solar cells, but also gives it an advantage in NIR spectroscopy applications. Comparison of the excitation spectra with the solar spectrum and spectral analysis of the emission spectra of water and alcohol demonstrate its promising applications.

On-Farm Application of Near-Infrared Spectroscopy for the Determination of Nutrients in Liquid Organic Manures: Challenges and Opportunities

On-Farm Application of Near-Infrared Spectroscopy for the Determination of Nutrients in Liquid Organic Manures: Challenges and Opportunities

Near Infrared Spectroscopy as a Diagnostic Tool for Screening of Intracranial Hematomas; A Systematic Review and Meta-Analysis.

Evidence supports the clinical applicability of near-infrared spectroscopy (NIRS) in intracranial hematoma detection in prehospital settings. This systematic review and meta-analysis aimed to determine the diagnostic yield of NIRS for detecting intracranial hematoma in traumatic brain injury (TBI) patients. A systematic search was performed in July 2024 in Medline, Embase, Scopus, and Web of Science databases. We included studies that evaluated the diagnostic performance of NIRS in detecting intracranial hematoma in both adult and pediatric patients suspected of TBI in prehospital or emergency settings, using brain computed tomography (CT) scan or magnetic resonance imaging as the gold standard. Eighteen studies enrolling 2979 patients were included. NIRS exhibited an Area Under the Curve (AUC) of 0.91 (95% confidence interval [CI]: 0.88, 0.93), with a sensitivity of 0.86 (95% CI: 0.78, 0.91), and specificity of 0.82 (95% CI: 0.72, 0.89) across all age groups. In children, the results demonstrated an AUC of 0.92 (95% CI: 0.89, 0.94), sensitivity of 0.95 (95% CI: 0.21, 1.00), and specificity of 0.81 (95% CI: 0.65, 0.91). Among adults, the AUC was 0.91 (95% CI: 0.88, 0.93), with sensitivity and specificity of 0.86 (95% CI: 0.78, 0.92) and 0.83 (95% CI: 0.70, 0.91), respectively. Performance improved when NIRS was operated by non-physicians (AUC = 0.94 [95% CI: 0.91, 0.96], sensitivity = 0.90 [95% CI: 0.79, 0.95], specificity = 0.85 [95% CI: 0.71, 0.93]) compared to physicians (AUC = 0.90 [95% CI: 0.87, 0.92], sensitivity = 0.88 [95% CI: 0.77, 0.94], specificity = 0.75 [95% CI: 0.59, 0.76]). Patients' age group and operator type were identified as potential sources of heterogeneity. Sensitivity analyses confirmed the robustness of the findings, particularly in mild TBI cases and studies implementing a ΔOD > 0.2 as the threshold for a positive NIRS result. NIRS proves to be an effective diagnostic tool for detecting traumatic intracranial hematoma in both pediatric and adult groups, with high sensitivity and specificity. Its utility in prehospital triage, operated by physicians or paramedics, underscores its potential for broader clinical application.

Comparison of Multiple NIR Instruments for the Quantitative Evaluation of Grape Seed and Other Polyphenolic Extracts with High Chemical Similarities.

Grape seed extract (GSE), one of the world's bestselling dietary supplements, is prone to frequent adulteration with chemically similar compounds. These frauds can go unnoticed within the supply chain due to the use of unspecific standard analytical methods for quality control. This research aims to develop a near-infrared spectroscopy (NIRS) method for the rapid and non-destructive quantitative evaluation of GSE powder in the presence of multiple additives. Samples were prepared by mixing GSE with pine bark extract (PBE) and green tea extract (GTE) on different levels between 0.5 and 13% in singular and dual combinations. Measurements were performed with a desktop and three different handheld devices for performance comparison. Following spectral pretreatment, partial least squares regression (PLSR) and support vector regression (SVR)-based quantitative models were built to predict extract concentrations and various chemical parameters. Cross- and external-validated models could reach a minimum R2p value of 0.99 and maximum RMSEP of 0.27% for the prediction of extract concentrations using benchtop data, while models based on handheld data could reach comparably good results, especially for GTE, caffeic acid and procyanidin content prediction. This research shows the potential applicability of NIRS coupled with chemometrics as an alternate, rapid and accurate quality evaluation tool for GSE-based supplement mixtures.

Applications of Near-Infrared Spectroscopy for Nondestructive Quality Analysis of Fish and Fishery Products.

Fish has become one of the most popular aquatic products for its beneficial effects. The quality of fish and fishery products may be influenced by their geographical origin, transportation, processing, and storage conditions. The availability of rapid and reliable techniques is important for nondestructive determination of their quality. Recently, near-infrared spectroscopy (NIRS) has been widely employed in the nondestructive evaluation of fish and fishery products. However, a comprehensive review on NIRS for this topic remains to be published. Based on this demand, the applications of NIRS in the nondestructive evaluation of fish and fishery products have been discussed in this review. This review firstly introduces the fundamentals of NIRS. Then the application of NIRS for the assessment of species, geographical origin, adulteration, freshness, nutrient components, and texture is summarized. In addition, the application of near-infrared hyperspectral imaging technology in fish and fishery products is also discussed. Finally, the challenges and prospects are outlined. The current review may provide a reference for research on NIRS in this field. In the future, NIRS could be used for online assessment of quality attributes in the fish industry through the development of new instruments and chemometrics.

A novel particle size distribution correction method based on image processing and deep learning for coal quality analysis using NIRS-XRF.

The combined application of near-infrared spectroscopy (NIRS) and X-ray fluorescence spectroscopy (XRF) has achieved remarkable results in coal quality analysis by leveraging NIRS's sensitivity to organic compounds and XRF's reliability for inorganic composition. However, variations in particle size distribution negatively affect the diffuse reflectance of NIRS and the fluorescence signal intensities of XRF, leading to decreased accuracy and repeatability in predictions. To address this issue, this study innovatively proposes a particle size correction method that integrates image processing and deep learning. The method first captures micro-images of the coal sample surface using a microscope camera and employs the Segment Anything Model (SAM) for binarization to represent particle size distribution. Subsequently, a Spatial Transformer Network (STN) is applied for geometric correction, followed by feature extraction using a Convolutional Neural Network (CNN) to establish a correlation model between particle size distribution and ash measurement errors. In experiments involving 56 coal samples, including 48at 0.2mm for the standard ash prediction model and 8 within a 0∼1mm range for correction, the results showed significant improvements: standard deviation (SD), mean absolute error (MAE), and root mean square error of prediction (RMSEP) decreased from 0.321%, 0.317%, and 0.335% to 0.229%, 0.225%, and 0.257%, respectively. Using the accuracy of the 0.2mm particle size validation set as a reference, compared to before correction, the errors in these metrics were reduced by 64.06%, 50%, and 60.80%, respectively. This study demonstrates that integrating deep learning and image analysis significantly enhances the repeatability and accuracy of NIRS-XRF measurements, effectively mitigating sub-millimeter particle size effects on spectral detection results and improving model adaptability. This method, through automated particle size distribution analysis and real-time result correction, holds promise for providing essential technical support for the development of online quality detection technologies for conveyor belt materials.

A short review of application of near-infrared spectroscopy (NIRS) for the assessment of microvascular post-occlusive reactive hyperaemia (PORH) in skeletal muscle.

Near-infrared spectroscopy (NIRS) is an optical technique that can be used to non-invasively interrogate haemodynamic changes within skeletal muscle. It can be combined with a short (3-5 min) arterial cuff-occlusion to quantify post-occlusive reactive hyperaemia (PORH). This technique has utility in tracking changes in vascular health in relation to exercise, disease progression or treatment efficacy. However, methods for assessing PORH vary widely and there is little consensus on methodological approaches such as sampling frequency, correction for adipose tissue or the analysis endpoints. The purpose of this review was to: (1) summarise recent advances; (2) compare different methodological approaches and (3) identify current knowledge gaps and future objectives for use of NIRS for vascular assessment. We propose key areas for future work, including optimising occlusion duration and comparing methods of correction for the ischemic stimulus, standardising methods for adjustment of adipose tissue thickness, cross-device comparisons and establishing a standard for minimum sampling rate. Comparisons with alternative methods of capturing PORH or upstream vasodilatory responses would be valuable. Addressing these methodological considerations will aid our understanding of this useful, non-invasive tool for characterising PORH within skeletal muscle and facilitate interpretation of results across studies.

Rapid Detection of Stabilizer Content in Double‐Base Propellant Based on Artificial Neural Network Combined With Near‐Infrared Spectroscopy

ABSTRACTDuring long‐term storage, double‐base propellants are prone to chemical decomposition of internal nitrate esters, leading to decreased burn rate, reduced strength, and degraded ballistic performance. Adding an appropriate amount of Centralite‐II is crucial for ensuring storage safety. This study proposes a novel method combining near‐infrared spectroscopy (NIRS) with artificial intelligence to rapidly and non‐destructively detect the content of Centralite‐II in double‐base propellants. The optimal modeling wavelength ranges of 4000–4600 cm−1 and 5700–6100 cm−1 were identified, and the raw spectral data were preprocessed using standard normal variate (SNV) transformation to improve the signal‐to‐noise ratio. Principal component analysis (PCA) was then applied to reduce data dimensionality, and the first three principal components were used as inputs for a backpropagation (BP‐ANN) neural network. The resulting PCA‐BP‐ANN model showed excellent performance on the training set, with an of 0.9830 and an of 0.0376%. During independent validation, the model demonstrated strong generalization ability, achieving an of 0.9824 and an of 0.3179%, comparative analysis with other models, including BP, PLS, ELM, SVR, and LSTM, indicated that the PCA‐BP‐ANN model exhibited superior prediction accuracy and generalization capability. This method provides a rapid and non‐destructive approach for assessing the stabilizer content in double‐base propellants and expands the application of NIRS and AI techniques in the field of energetic materials.

Integrating progressive screening strategy-based continuous wavelet transform with EfficientNetV2 for enhanced near-infrared spectroscopy

Near-infrared (NIR) spectroscopy has gained wide acceptance across various fields as a result of advances in portable equipment that can record spectra on site or at production lines. Continuous wavelet transform (CWT) can transform traditional one-dimensional (1D) NIR spectra into more informative two-dimensional (2D) spectrograms, thus enhancing the analysis and interpretation of spectral information. This study introduces a high-efficiency 2D CWT-EfficientNetV2 regression model to optimize NIR spectroscopy applications. A novel progressive screening strategy is employed to select the optimal wavelet functions and scales for CWT, which are then used to transform the features into wavelet coefficient matrices. Direct digital mapping (DDM) with Gray colormap generates 2D spectrograms from matrices, significantly preserving the representation of wavelet coefficients. The 2D CWT-EfficientNetV2 model was used to predict the content of five polyphenols in tobacco leaf samples with superior performance compared to partial least squares regression (PLSR) and other high-efficiency models. Moreover, to further validate the robustness and reliability of the proposed method, two additional public NIR spectral datasets were included in this study. The model achieves lower root mean square error of prediction (RMSEP), as well as higher coefficient of determination of prediction (RP2) and the ratio of the standard error of prediction to the standard deviation of the reference values (RPD) on the test datasets. These results demonstrate that the 2D CWT-EfficientNetV2 model is a robust and efficient approach for the accurate quantification of various target compounds utilizing NIR spectroscopy.

NIR-Guided Coating Optimization of Omega-3 Fatty Acid Mini Soft Capsules with Pitavastatin and Ezetimibe.

This study aimed to optimize the coating process of Omega-3 fatty acid (OM3-FA) mini soft capsules containing the active pharmaceutical ingredients (APIs) pitavastatin and ezetimibe using near-infrared (NIR) spectroscopy for in-process monitoring. Cardiovascular disease treatments benefit from combining OM3-FA with lipid-lowering agents, but formulating such combinations in mini soft capsules presents challenges in maintaining stability and mechanical integrity. The coating process was developed using a pan coater and real-time NIR monitoring to ensure uniformity and quality. NIR spectroscopy enabled precise control of coating thickness, ensuring consistent drug distribution across the capsule surface. The optimized process minimized OM3-FA oxidation and preserved the mechanical integrity of the capsules, as confirmed by texture analysis and in-vitro dissolution testing. This integration of NIR spectroscopy as a process analytical technology (PAT) significantly improved coating quality control, resulting in a stable and effective combination therapy for pitavastatin and ezetimibe in a mini soft capsule form. This approach offers an efficient solution for enhancing patient adherence in cardiovascular disease management. The application of NIR spectroscopy for real-time monitoring highlights its broader significance in pharmaceutical manufacturing, where it can serve as a versatile tool for ensuring product quality and optimizing production efficiency in diverse formulation processes. By incorporating NIR-based PAT, manufacturers can not only achieve product-specific improvements but also establish a foundation for continuous manufacturing and automated quality assurance systems, ultimately contributing to a more streamlined and robust production environment.

Near-infrared luminescence in Ca3ZnMGe3O12:Cr3+ (M = Zr, Hf, Sn, Ti) garnet family for light-emitting diode applications

Near-infrared (NIR) phosphors as crucial components of NIR light sources have multifunctional and smart spectroscopy applications, prompting an increasing research interest in developing efficient and stable NIR materials. Focusing on this issue, the present study explored the luminescent properties of Cr3+-doped Ca3ZnMGe3O12 (M = Zr, Hf, Sn and Ti) garnet family. These materials exhibit an emission peak falls within the range of 785–805 nm with a full width at half maximum (FWHM) varying from 114 to 123 nm. Among them, the Ca3ZnZrGe3O12:Cr3+ phosphor shows the highest photoluminescence quantum yield (PLQY = 60.3 %) and an excellent thermal stability (I423K/I298K = 77.51 %). Moreover, the performance of a prototype Ca3ZnZrGe3O12:Cr3+-containing NIR pc-LED device was estimated, which achieves an NIR output of 23.46 mW and a photonic efficiency of 14.09 % under 100 mA driving current. Non-destructive examination can be realized using this light source, demonstrating the good potential of the material in NIR spectroscopy applications.

Near-infrared luminescence shift in Ni2+-doped double perovskite phosphors

For applications such as near-infrared (NIR) spectroscopy requiring energy-efficient and compact broadband NIR light sources, a variety of Ni2+-doped phosphors emitting in the 1100–2000 nm range has been developed. However, the commercialization of Ni2+-doped NIR phosphors has not yet been achieved, and material exploration is ongoing. To gain insight into the luminescence properties of Ni2+, including emission wavelength and efficiency influenced by compositional and structural variations, Ni2+-doped double-perovskite AELaMgTaO6:Ni2+ (AE = Ba, Sr, or Ca) ceramic samples were synthesized, and their optical properties were examined. Because the energy of the Ni2+ 3d-3d transition is sensitive to structural variations in the local environment around the Ni2+ ions, we investigated the variation in optical properties by indirect ligand field engineering of NiO6 octahedra following the substitution of alkaline earth ions AE2+. The prepared AELaMgTaO6:Ni2+ samples were Ba-Sr or Ba-Ca complete solid solution systems with cubic-to-monoclinic phase transitions. The broad NIR luminescence band at ∼1350–1700 nm was blue-shifted by ∼100 nm, accompanied by Sr2+ or Ca2+ substitution. The local symmetry of the NiO6 octahedra was reduced from Oh to Ci owing to the phase transition, resulting in an enhancement of the NIR luminescence intensity and an increase in the quenching temperature. This study demonstrated that Ni2+ phosphors with the desired optical properties for NIR spectroscopy applications can be designed by controlling the composition ratio of the host compounds and the resultant variation in the local environment of the solid solution compounds.

Classification of Histamine Content in Fish Using Near-Infrared Spectroscopy and Machine Learning Techniques

Near-infrared (NIR) spectroscopy has emerged as a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on histamine content has not been extensively explored. This study investigates the use of NIR spectroscopy in combination with machine learning (ML) techniques to classify fish samples into two safety classes, Safe and Unsafe, based on their histamine content. A comprehensive NIR dataset comprising 11,360 spectra collected at eight distinct positions within the fish body was obtained from 284 fish samples of mackerel, tuna, and pompano species. ML experiments were conducted to classify fish samples based on whether their histamine content exceeded the permissible limit of 100 ppm. To address class imbalance and optimize ML models, various data pre-processing and feature extraction techniques as well as ML algorithms were explored. The results demonstrated that utilizing NIR data specifically obtained from the tail’s flesh, a specific location within the fish, yielded superior models for fish safety classification. A feature extraction method employing pre-processed NIR spectra and their second derivatives, combined with an optimized convolutional neural network architecture, outperformed traditional ML classifiers with an accuracy of approximately 93%.

Comparative Analysis of Machine Learning and Deep Learning Algorithms for Assessing Agricultural Product Quality Using NIRS.

The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing the interrelationships between adjacent spectral variables, and the analysis results are easily affected by spectral noise, which dramatically limits the breadth and depth of applications of NIRS. Deep learning (DL) methods, with their capacity to discern intricate features from limited samples, have been progressively integrated into NIRS. In this paper, two discriminant analysis problems, including wheat kernels and Yali pears as examples, and several representative calibration models were used to research the robustness and effectiveness of the model. Additionally, this article proposed a near-infrared calibration model, which was based on the Gramian angular difference field method and coordinate attention convolutional neural networks (G-CACNNs). The research results show that, compared with SL, spectral preprocessing has a smaller impact on the analysis accuracy of consensus learning (CL) and DL, and the latter has the highest analysis accuracy in the modeling results using the original spectrum. The accuracy of G-CACNNs in two discrimination tasks was 98.48% and 99.39%. Finally, this research compared the performance of various models under noise to evaluate the robustness and noise resistance of the proposed method.

Application of near-infrared spectroscopy as at line method for the evaluation of histamine in tuna fish (Thunnus albacares)

The assessment of histamine levels in fishery products has emerged as a paramount issue in the context of global food safety, given its profound implications for human health and the consequential impact on food quality and trade. Histamine intoxication, stemming from the ingestion of foods containing heightened histamine levels, results from the bacterial decarboxylation of histidine under conditions of improper handling, processing, or storage. This study endeavors to provide a thorough examination of histamine contamination in frozen-thawed tuna (Thunnus albacares) samples, employing an integrated approach that combines near-infrared spectroscopy (NIRS) with advanced machine learning techniques. One hundred and one samples were considered, and a systematic fortification process was applied to obtain samples with 4 histamine concentrations (0; 50; 150; 250 mg/kg); the fortification levels were confirmed by the LC-MS/MS analysis. Subsequently, NIRS spectra were collected and chemometric analyses, including modified partial least squares regression (MPLS) and support vector machine (SVM), were employed for quantitative and qualitative evaluation, respectively. Histamine quantification through MPLS utilizing the full spectrum exhibited good predictive performance in cross-validation and in hold-out validation (R2CV = 0.88; R2P = 0.74, respectively), confirming the potential of NIRS for estimating histamine levels in tuna. SVM classification models, both binary (presence/absence) and multiclass (four levels), demonstrated high accuracy (100% and 93%, respectively). The study highlights the effectiveness of NIRS combined with machine learning for rapid and accurate histamine detection in frozen-thawed tuna, offering a non-destructive, environmentally friendly alternative to traditional methods. This approach holds significant promise for food business operators and regulatory authorities, enhancing product safety, quality control, and decision-making processes related to histamine contamination in the seafood industry.

Near-Infrared Spectroscopic Determination of Pentacyclic Triterpenoid Concentrations in Additives for Animal Food.

The nutritional composition of food for animal production can be enhanced using olive tree and plant by-products due to their high content of bioactive compounds such as pentacyclic triterpenes. Here, we present a novel application of near-infrared spectroscopy (NIRS) for the prediction of the total or individual [maslinic acid (MA), oleanolic acid (OA), and uvaol (UO)] pentacyclic triterpene concentrations in a feed additive obtained from a plant mixture. The oxygen radical absorbance capacity of these types of samples demonstrated the existence of a high antioxidant capacity. The conventional determination methods of pentacyclic triterpene concentration are costly, labor-intensive, and not practical for analyzing several lines within a limited timeframe at the factory level. The optimal regression model developed in our work demonstrated high correlation values for the calibration and validation sets, along with a high residual prediction deviation value. We used 63 samples for the development of the model. The NIRS method can be applied directly to dried powder and makes extraction and high-performance liquid chromatography (HPLC) analysis unnecessary. Our results also demonstrate that NIRS can accurately quantify pentacyclic triterpenes even at low concentrations in food additives. It can be used at the factory level to directly determine the pentacyclic triterpene concentrations in the additive powder at the same time that the powder is produced.

Application of Near Infrared Spectroscopy to Enhance Safety and Individualize Distraction of Severely Contracted Joints in Far-Advanced Dupuytren's Disease.

Background: Slow distraction of contracted joints is a well-established treatment in far-advanced stages of Dupuytren's disease (DD). To assess finger perfusion and avoid malperfusion, we studied near infrared spectroscopy (NIRS) to evaluate the maximum extent of distraction that would not harm microcirculation to the finger. This technique also allows an optimized treatment in accordance with sufficient blood perfusion during distraction. Methods: Eligible patients with stage IV finger contractures who needed treatment for Dupuytren's contracture were included and prospectively investigated. The operation was performed with local anaesthesia. First, the Dupuytren strand of the treated finger was dissected in the palm to allow distraction. Under X-ray control, the distraction device was applied. Then, slow distraction of the treated joint was performed to evaluate the finger perfusion. To assess perfusion of the treated finger, NIRS was used to measure tissue oxygen saturation. If impaired finger perfusion was detected, traction was reduced until sufficient oxygen levels and perfusion patterns were reestablished. Results: NIRS was performed after application of the distraction device in seven cases. We treated six male and one female patient (mean age 70 years, range 51-80 years). Rapid distraction resulted in malperfusion of the treated fingers. Using NIRS proved to render reliable and reproducible information on finger perfusion and oxygenation in all seven patients. Conclusions: Application of NIRS enhances safety in the treatment of far-advanced DD finger contractures with an external skeletal distraction device. It is non-invasive, reproducible, easy to use and allows for an individualized adapted distraction velocity.

NIR-based models for the evaluation of basic density, moisture and higher heating value of industrial wood chips

ABSTRACT The quick and efficient classification of wood is crucial for optimizing processes in forestry industries, which highlights the use of near infrared (NIR) spectroscopy. This study investigated the application of NIR spectroscopy for estimating the basic density, moisture content and calorific value of wood chips of Eucalyptus urophylla × E. grandis hybrids directly in the company yard. Using two lots of industrial chips collected in the company’s yard (Lots A and B), the NIR signatures were correlated with the results obtained by standard methods via Partial Least Squares Regression (PLS-R). The predictive models presented coefficients of determination (R²) of 0.83 for basic density (Lot A), 0.81 to 0.90 for moisture content (Lots A and B) and 0.74 for calorific value (Lot A). Variations in the moisture content of wood chips impacted the information quality of spectral signatures and thus the model accuracy, highlighting the difficulty of applying this technology in field conditions. However, the NIR models can be used as a solution for fast estimation of wood chips quality in the company yard, optimizing industrial processes.

Broadband near-infrared luminescence in spinel Zn4InSbO8:Cr3+ phosphor for light-emitting diodes application

Near-infrared (NIR) phosphor-converted light-emitting diode (pc-LED) has been considered as a smart light source for NIR spectroscopy applications, leading to a growing need of developing broadband NIR phosphors. Herein, a new composition Zn4InSbO8:Cr3+ phosphor with classical spinel-type structure was synthesized, which emits a broadband NIR light covering a region of 700–1200 nm with a peak centered at 860 nm under 460 nm excitation. The sample with an optimized Cr3+ concentration possesses a full width at half maximum (FWHM) as broad as 180 nm mainly ascribing to multi crystallographic sites occupation. Moreover, the absorption efficiency (AE), internal quantum efficiency (IQE) and thermal stability were investigated to estimate the practicality. The relatively long-wavelength NIR emission produced by Cr3+ in this spinel make it suitable for multi-functional applications. Finally, the asprepared phosphor combining with blue LED chip is used to fabricate a prototype NIR pc-LED, giving a NIR output power of 4.8 mW under 100 mA forward bias current with a photoelectric efficiency of 1.63%, showcasing the potential use of this material as a luminescent converter in the application of NIR pc-LEDs.

Rapid Measurement of Antioxidant Properties of Dendrobium officinale Using Near-Infrared Spectroscopy and Chemometrics.

Dendrobium officinale (D. officinale), often used as a dual-use plant with herbal medicine and food applications, has attracted considerable attention for health-benefiting components and wide economic value. The antioxidant ability of D. officinale is of great significance to ensure its health care value and safeguard consumers' interests. However, the common analytical methods for evaluating the antioxidant ability of D. officinale are time-consuming, laborious, and costly. In this study, near-infrared (NIR) spectroscopy and chemometrics were employed to establish a rapid and accurate method for the determination of 2,2'-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) scavenging capacity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity, and ferric reducing antioxidant power (FRAP) in D. officinale. The quantitative models were developed based on the partial least squares (PLS) algorithm. Two wavelength selection methods, namely the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) method, were used for model optimization. The CARS-PLS models exhibited superior predictive performance compared to other PLS models. The root mean square errors of cross-validation (RMSECVs) for ABTS, FRAP, and DPPH were 0.44%, 2.64 μmol/L, and 2.06%, respectively. The results demonstrated the potential application of NIR spectroscopy combined with the CARS-PLS model for the rapid prediction of antioxidant activity in D. officinale. This method can serve as an alternative to conventional analytical methods for efficiently quantifying the antioxidant properties in D. officinale.