Application Of Near-infrared Spectroscopy Research Articles

An efficient and thermally stable Cr3+-activated Y2GdSc2Al2GaO12 garnet phosphor for NIR spectroscopy applications.

Near-infrared (NIR) spectroscopy realized by an NIR phosphor-converted light-emitting diode (pc-LED) as a light source has aroused considerable interest due to its numerous merits and widespread application scenarios. Nevertheless, developing NIR emitting phosphors with high performance is still the top priority. Here, we report a new Y2GdSc2Al2GaO12:Cr3+ (YGSAG:Cr3+) garnet phosphor, which demonstrates a broadband emission peaking at 754 nm with a full width at half maximum (FWHM) of around 120 nm in the range of 650-1200 nm. The YGSAG:0.08Cr3+ sample irradiated by blue light exhibits the most intense emission intensity, leading to a high absorption efficiency of 49.54%. In addition, compared with room temperature, the integrated PL intensity of the sample can still be maintained at 90.97% at 423 K. Benefitting from the outstanding optical properties, the as-manufactured NIR pc-LED device driven by a 100 mA current represents a high NIR output power of 35.14 mW and a photoelectric efficiency of 12.51%. These results verify that the as-synthesized YGSAG:Cr3+ phosphor possesses great potential for the applications of NIR spectroscopy.

Espectroscopia no infravermelho próximo (NIRS) em unidade de terapia intensiva neonatal: experiência de um hospital universitário brasileiro

Near-infrared spectroscopy (NIRS) is a non-invasive method to assess regional brain and/or renal oxygenation, and an important tool for a better clinical management. The aim of this study was to describe clinical cases of newborns admitted to the Neonatal Intensive Care Unit of a Brazilian university hospital who were monitored with NIRS, and to evaluate the applicability of NIRS demonstrating its use as a continuous monitoring method for newborns at high risk for brain injury. This study was part of the Conclusion Work of Medical Residency in Neonatology in Neonatology. Case 1: Persistent ductus arteriosus-NIRS guided indication for drug treatment. Case 2: Septic shock-NIRS was helpful identifying the need of inotropes. Case 3: Anemia of prematurity-NIRS showed an increase of cerebral oxygenation after red blood cells transfusions. Case 4: Hypoxic-ischemic encephalopathy-NIRS identified sign of poor neurological prognostic. NIRS is a continuous cerebral and renal perfusion monitoring method that has easy and good applicability and no complications. The cases reflect the importance and potential of NIRS in clinical practice and in helping diagnose for newborns, as well as enabling prognostic assessment. NIRS proved to be a method of good applicability, without short-term complications. Observing brain oxygenation values can help to avoid conditions associated with the development of neurological sequelae.

Employing machine learning to assess the accuracy of near-infrared spectroscopy of spent dialysate fluid in monitoring the blood concentrations of uremic toxins

Hemodialysis (HD) removes nitrogenous waste products from patients? blood through a semipermeable membrane along a concentration gradient. Near-infrared spectroscopy (NIRS) is an underexplored method of monitoring the concentrations of several molecules that reflect the efficacy of the HD process in dialysate samples. In this study, we aimed to evaluate NIRS as a technique for the non-invasive detection of uremic solutes by assessing the correlations between the spectrum of the spent dialysate and the serum levels of urea, creatinine, and uric acid. Blood and dialysate samples were taken from 35 patients on maintenance HD. The absorption spectrum of each dialysate sample was measured three times in the wavelength range of 700-1700 nm, resulting in a dataset with 315 spectra. The artificial neural network (ANN) learning technique was used to assess the correlations between the recorded NIR-absorbance spectra of the spent dialysate and serum levels of selected uremic toxins. Very good correlations between the NIR-absorbance spectra of the spent dialysate fluid with serum urea (R=0.91) and uric acid (R=0.91) and an excellent correlation with serum creatinine (R=0.97) were obtained. These results support the application of NIRS as a non-invasive, safe, accurate, and repetitive technique for online monitoring of uremic toxins to assist clinicians in assessing HD efficiency and individualization of HD treatments.

Cheese quality assessment by use of near-infrared spectroscopy

Dairy products are worldwide spread and have great commercial importance. Rapid and reliable analysis of cheese would be highly desirable both for the manufacturers and consumers. The results of experiments, related to the application of near-infrared spectroscopy for cheese quality estimation will be presented. Several kinds of Bulgarian white brine cheese - natural from cow milk, imitation products with vegetable oil, and cheese with different water content were investigated. Fatty acids composition of samples was determined by using gas chromatography and moisture content by the oven-dry method. Spectra of all tested samples were obtained with a scanning NIRQuest 512 (Ocean Optics, Inc.) instrument in the range of 900-1700 nm using a reflection fiber-optics probe. PLS models were developed for quantitative determination and SIMCA for classification. The misclassification rate of the SIMCA model for discrimination of natural cheese and imitation products with vegetable oil was 2.9%. Quantitative determination of water content based on NIR spectra showed high accuracy, Models for classification of cheese samples into 3 groups according to water content achieved 5.64% misclassification rate for the independent test set. Results showed the potential of near-infrared spectroscopy as a non-destructive and rapid screening tool for assessing cheese quality and detecting adulteration.

Study on Near-infrared Spectroscopy Non-destructive Testing of Strawberry Quality

Non-destructive testing is a technology that has been developed and applied rapidly in recent years. It is widely used in the quality testing of vegetables and fruits, which solves some problems faced in traditional fruit and vegetable testing. As far as strawberry is concerned, this kind of fruit has high market value, but it is not easy to preserve. It is also necessary to conduct non-destructive testing on strawberry quality. It is a detection technology to improve the testing effect and avoid strawberry damage, which plays a positive role in the development of strawberry planting industry. In the non-destructive testing technology of fruits and vegetables, near-infrared spectroscopy is a relatively ideal testing technology. This paper mainly explores the application of near-infrared spectroscopy in the non-destructive testing of strawberry quality, providing ideas for better understanding the application of this technology in strawberry quality testing.

Detection of Strawberry Quality Based on Near Infrared Spectroscopy

In recent years, the food quality and safety testing technology in China has been developing continuously. Strawberry is a popular fruit. The detection of strawberry quality includes the detection of its maturity, sugar content, appearance and other parameters. Through the application of effective detection technology, it can provide effective model support for the internal quality, maturity and identification of different varieties of strawberries. In this paper, near-infrared spectroscopy detection technology is studied. First, this technology and its advantages and disadvantages are introduced, and the application of near-infrared spectroscopy based strawberry quality detection technology is explored.

Dopant and compositional modulation triggered long-wavelength ultra-broadband and tunable NIR emission in MgO: Cr3+ phosphor for NIR spectroscopy applications

Efficient ultra-broadband near-infrared (NIR) phosphors with long-wavelength ( λ max > 850 nm) and wide full width at half-maximum (FWHM, >200 nm) have sparked tremendous interest, demonstrating their immense potential in NIR spectroscopy technology. Nevertheless, the development of NIR spectroscopy technology suffers from the restricted capability to efficiently emit the ultra-broadband NIR light. Herein, the synergetic enhancement strategy of heterogeneous substitution and compositional modulation was applied to create a novel Cr 3+ doped long-wavelength ultra-broadband MgO: Cr 3+ , Ga 3+ phosphor, which exhibited a long-wavelength ultra-broadband NIR emission ( λ max = 850 nm) covering the range of 650–1300 nm on the electromagnetic spectrum with the FWHM of more than 200 nm under the excitation of 468 nm light. Furthermore, the tunable NIR emission from 818 nm to 862 nm with an optimized quantum efficiency of 30% was accomplished by the Ga 3+ ions substitution and Cr 3+ ions modulation. The phosphor exhibited remarkable thermal stability up to 100 °C, remaining 83% of the integrated emission intensity at room temperature. A prototype of the NIR phosphor-converted LED (pc-LED) demonstrated that the novel MgO: Cr 3+ , Ga 3+ phosphor possessed a relatively strong NIR output power (15.05 mW at 100 mA driven current) with a photoelectric conversion efficiency of 5.53%, which is impressive compared with other Cr 3+ -doped long-wavelength ultra-broadband phosphors. This work not only proposes a novel long-wavelength ultra-broadband NIR phosphors with industrialization and great application prospect in night vision but highlights a synergetic enhancement strategy to effectively boost the performance of long-wavelength ultra-broadband NIR pc-LED light sources.

Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review.

Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.

Super Broadband Near‐Infrared Solid Solution Phosphors with Adjustable Peak Wavelengths from 1165 to 875 nm for NIR Spectroscopy Applications

AbstractCr3+‐activated near‐infrared (NIR) phosphors have attracted wide attention owing to applications in phosphor‐converted light‐emitting diodes (pc‐LED) for spectroscopy. Because a broader spectrum covers more information to detect, the width of the spectrum is an important parameter to consider. Here, the blue LED excitable ultra‐broadband Cr3+‐activated LiInGeO4 phosphors, which exhibit unprecedented long‐wavelength NIR emission with a wide full‐width at half maximum (FWHM) of 305 nm, are developed. The overall emission tuning from 1165 to 875 nm with broadened FWHM from 305 to 508 nm is realized through designing continuous solid solution hosts of LiIn1−zSczGeO4/(LiIn)1−yMg2yGeO4 and finite solid solution hosts of (LiIn)1−yZn2yGeO4:Cr3+. The observation of such long‐wavelength NIR from Cr3+ not Cr4+ is studied and confirmed by electron paramagnetic resonance, X‐ray absorption near‐edge structure, and steady‐/transient‐state photoluminescence. The formation of finite solid solutions induces the change in the local structure of Cr3+ that ultimately leads to the observation of super broadband (FWHM = 508 nm) luminescence. The luminescence mechanism is discussed in this study. Finally, this study demonstrates that the as‐synthesized phosphors with tunable NIR luminescence can be well used in NIR pc‐LEDs for spectroscopy detection.

393 PERIODIC BREATHING: WHAT HAPPENS IN THE MUSCLE?

Abstract Periodic breathing (PB) is a recognized sign of poor prognosis in heart failure (HF). It is defined as a cyclic fluctuation of minute ventilation, and oxygen uptake (V̇O2) and carbon dioxide elimination (V̇CO2) at the lungs. It is unknow whether PB influences O2 availability in the cardiac, respiratory and locomotor muscles. To evaluate whether O2 availability at the muscles was affected by PB, we measured at rest, continuously and simultaneously, ventilation, ventilatory gas exchange, arterial hemoglobin O2 saturation and oxygenated/deoxygenated haemoglobin (O2Hb/HHb) content over the quadriceps by near infra-red spectroscopy (NIRS) in a patient with severe HF due to a dilated cardiomyopathy with severe biventricular dysfunction with PB at rest. NIRS application in clinical medicine started after the observation that biological tissues are quite transparent to light in the near infrared spectrum (i.e.700-1,300 nm), the second critical element that enables the use of NIRS is the oxygenation-dependent light absorbing characteristics of haemoglobin (Hb): by applying different light impulse wavelengths, the relative changes in O2Hb and HHb concentration in skeletal muscle can be monitored. The left panel shows cyclic fluctuation of ventilation, V̇O2, V̇CO2, PetO2 and PetCO2 patterns, which are partially out of phase between each other and respiratory exchange ratio pattern, and haemoglobin O2 saturation shows a cyclic pattern (95-90%). The cycle length of ventilation was 110 ± 6 s with an amplitude of 28.9 ± 5.1 l/min. NIRS (upper right panel) shows a directionally opposite fluctuation of O2Hb and HHb with a cycle length similar to that observed with ventilation. The average fluctuation on 6 consecutive cycles was 2.95% ± 0.27 (p<0.0001) and 2.73% ± 0.27 (p<0.0001) for O2Hb and HHb respectively. Total Hb was not significantly affected by PB in the muscle, 0.22%±0.38 (p=ns). The present study is the first recording of periodic oscillations of O2 saturated and desaturated Hb concentration at the muscular level at rest in a patient with severe HF and PB: we speculate that the cyclic ventilatory pattern is responsible for a cycling of ventilation and perfusion coupling in the lung. This so-called ventilation/perfusion mismatch induces cyclic fluctuation of blood flow to perfused but unventilated or poorly ventilated (shunt and low V̇A/Q) lung zones and to unperfused or poorly perfused (high V̇A/Q and dead space) ventilated lung zones.

Component Prediction of Antai Pills Based on One-Dimensional Convolutional Neural Network and Near-Infrared Spectroscopy

Convolutional neural networks (CNNs) are widely used for image recognition and text analysis and have been suggested for application on one-dimensional data as a way to reduce the need for preprocessing steps. In this study, the performance of one-dimensional convolutional neural network (1DCNN) machine learning algorithm was investigated for regression analysis of Antai pills spectral data. This algorithm was compared with other chemometric methods, including support vector machine regression (SVR) and partial least-square regression (PLSR) methods. The results showed that the 1DCNN model outperformed the PLSR and SVR models with similar data preprocessing for the three analytes (wogonoside, scutellarin, and ferulic acid) in Antai pills. Taking wogonoside as an example, the indices such as the correction coefficient of determination ( R v 2 ), the root mean-squared error of cross validation (RMSECV) for calibration set, the prediction coefficient of determination ( R p 2 ), and the root mean-squared error of prediction (RMSEP) obtained by PLSR modeling were 0.9340, 0.5568, 0.9491, and 0.5088; the indices obtained by SVR modeling were 0.9520, 0.4816, 0.9667, and 0.4117; and the indices obtained by 1DCNN modeling were 0.9683, 0.3397, 0.9845, and 0.2807, respectively. The evaluation metrics of 1DCNN are better than those of PLSR and SVR, and the prediction effect is the best, proving that 1DCNN has a good generalization ability. Especially with outliers of spectra, PLSR’s R p 2 decreased by 0.0181, SVR’s R v 2 decreased by 0.01, and 1DCNN’s R v 2 increased by 0.0009 and R p 2 decreased by 0.0057. The evaluation indices of 1DCNN have no significant change in comparison with no outliers and can still show good performance, which reflects the inclusiveness of the 1DCNN model for outliers. Simultaneously, the feasibility and robustness of the 1DCNN model in the application of near-infrared spectroscopy was verified, which has a certain application value.

A quick and precise online near-infrared spectroscopy assay for high-throughput screening biomass digestibility in large scale sugarcane germplasm

The near-infrared spectroscopy (NIRS) has been used for efficient characterization and rapid assay of biomass saccharification in other energy plants, but its application in sugarcane is not reported yet. The current study collected a total of 541 sugarcane accessions to take an online NIRS assay. Among these sugarcane collections, we observed large variations in biomass digestibility, particularly for fermentable hexose and total sugar yield from fresh sugarcane stalks, which were detected ranging from 69.88 to 239.86 kg t -1 and 66.56–228.55 kg t -1 respectively. Using the modified partial least squares method, six reliable NIRS models were obtained with a high coefficient of determination ( R 2 ) and the ratio of prediction to deviation (RPD) values during calibration, internal-cross validation and external validation. Notably, the equation for fermentable hexose exhibited the most consistently high R 2 (0.98) and RPD (6.62) values, as well as retaining relatively low root mean square error during calibration (3.74 kg t -1 ) and validation (4.19 kg t -1 ), indicating excellent predictive capacity. All models demonstrated accurate and stable prediction performance in the two-year large-scale germplasm resources evaluation, and the optima accessions with high or low biomass digestibility can be screened out consistently. Therefore, this study provides a precise and consistent NIRS assay for high throughput scanning of biomass digestibility in sugarcane. • Sugarcane is one of the significant bioenergy crops. • Diverse sugarcane germplasm collections allowing for reliable NIRS calibration. • Six excellent equations were generated for biomass digestibility characterization. • Fermentable hexoses and total sugar displayed the best prediction performance. • Model-based NIRS application screened out optima genotypes consistently from large-scale sugarcane germplasm.

Fast and robust NIRS-based characterization of raw organic waste: Using non-linear methods to handle water effects

Fast characterization of organic waste using near infrared spectroscopy (NIRS) has been successfully developed in the last decade. However, up to now, an on-site use of this technology has been hindered by necessary sample preparation steps (freeze-drying and grinding) to avoid important water effects on NIRS. Recent research studies have shown that these effects are highly non-linear and relate both to the biochemical and physical properties of samples. To account for these complex effects, the current study compares the use of many different types of non-linear methods such as partial least squares regression (PLSR) based methods (global, clustered and local versions of PLSR), machine learning methods (support vector machines, regression trees and ensemble methods) and deep learning methods (artificial and convolutional neural networks). On an independent test data set, non-linear methods showed errors 28% lower than linear methods. The standard errors of prediction obtained for the prediction of total solids content (TS%), chemical oxygen demand (COD) and biochemical methane potential (BMP) were respectively 8%, 160 mg(O2).gTS−1 and 92 mL(CH4).gTS−1. These latter errors are similar to successful NIRS applications developed on freeze-dried samples. These findings hold great promises regarding the development of at-site and online NIRS solutions in anaerobic digestion plants.

Rapid and Nondestructive Detection of Proline in Serum Using Near-Infrared Spectroscopy and Partial Least Squares.

Proline is an important amino acid that widely affects life activities. It plays an important role in the occurrence and development of diseases. It is of great significance to monitor the metabolism of the machine. With the great advantages of deep learning in feature extraction, near-infrared analysis technology has great potential and has been widely used in various fields. This study explored the potential application of near-infrared spectroscopy in the detection of serum proline. We collected blood samples from clinical sources, separated the serum, established a quantitative model, and determined the changes in proline. Four algorithms of SMLR, PLS, iPLS, and SA were used to model proline in serum. The root mean square errors of prediction were 0.00111, 0.00150, 0.000770, and 0.000449, and the correlation coefficients (Rp) were 0.84, 0.67, 0.91, and 0.97, respectively. The experimental results show that the model is relatively robust and has certain guiding significance for the clinical monitoring of proline. This method is expected to replace the current mainstream but time-consuming HPLC, or it can be applied to rapid online monitoring at the bedside.

Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review.

The qualitative and quantitative evaluation of agricultural products has often been carried out using traditional, i.e., destructive, techniques. Due to their inherent disadvantages, non-destructive methods that use near-infrared spectroscopy (NIRS) coupled with chemometrics could be useful for evaluating various agricultural products. Advancements in computational power, machine learning, regression models, artificial neural networks (ANN), and other predictive tools have made their way into NIRS, improving its potential to be a feasible alternative to destructive measurements. Moreover, the incorporation of suitable preprocessing techniques and wavelength selection methods has arguably proven its practical feasibility. This review focuses on the various computation methods used for processing the spectral data collected and discusses the potential applications of NIRS for evaluating the quality and safety of agricultural products. The challenges associated with this technology are also discussed, as well as potential future perspectives. We conclude that NIRS is a potentially useful tool for the rapid assessment of the quality and safety of agricultural products.

Highly distorted Cr3+-doped fluoroantimonate with high absorption efficiency for multifunctional near-infrared spectroscopy applications

The growing near-infrared (NIR) spectroscopic technology has significantly prompted extensive exploration of broadband NIR-emitting phosphors. Cr3+-doped fluorides have recently emerged as a new prototype NIR emitter, whereas they endure low external quantum efficiency (EQE) limited by low absorption efficiency (AE < 30%). Here, an extremely high AE ∼56.2% is firstly realized in a highly distorted fluoroantimonate NaSbF4 and newly developed in multifunctional NIR spectroscopy applications. Formation energy and distortion index calculation demonstrate that Cr3+ occupies an inherently highly distorted [SbF6E] octahedron in a low local symmetry monoclinic structure and achieves a large parity-forbidden transition (4T2→4A2) probability of Cr3+. Moreover, NaSbF4:Cr3+ exhibits excellent stability against UV radiation, thermal impact, and extended device operation. The NIR phosphor-converted lighting-emitting diode (pc-LED) produces a photoelectric conversion efficiency of 10.48% at 20 mA and realizes non-destructive precision inspections of circuit boards, veins imaging, and night vision. Further combination with white pc-LED produces a full-VIS-NIR spectrum device applied to plant growth lighting. These findings open a new matrix model with aberrant crystal structures for high AE and advance the multifunctional NIR pc-LED applications covering industrial detection, bio-imaging, and modern agriculture.

Development of NIR-Based ANN Models for On-Line Monitoring of Glycerol Concentration during Biodiesel Production in a Microreactor.

During the production process, a whole range of analytical methods must be developed to monitor the quality of production and the desired product(s). Most of those methods belong to the group of off-line monitoring methods and are usually recognized as costly and long-term. In contrast, on-line monitoring methods are fast, reliable, simple, and repeatable. The main objective of this study was to compare different methods for monitoring total glycerol concentration as one of the indicators of process efficiency during biodiesel production in a batch reactor and in a microreactor. During the biodiesel production process, the glycerol concentration was measured off-line using standard methods based on UV-VIS spectrophotometry and gas chromatography. Neither method provided satisfactory results, namely, both analyses showed significant deviations from the theoretical value of glycerol concentration. Therefore, near infrared spectroscopy (NIR) analysis was performed as an alternative analytical method. The analysis using NIR spectroscopy was performed in two ways: off-line, using a sample collected during the transesterification process, and on-line by the continuous measurement of glycerol concentration in a rector. Obtained results showed a great NIR application potential not only for off-line but also for on-line monitoring of the biodiesel production process.

Review of Applications of Near-Infrared Spectroscopy in Two Rare Disorders with Executive and Neurological Dysfunction: UCD and PKU.

Studying rare diseases, particularly those with neurological dysfunction, is a challenge to researchers and healthcare professionals due to their complexity and small population with geographical dispersion. Universal and standardized biomarkers generated by tools such as functional neuroimaging have been forged to collect baseline data as well as treatment effects. However, the cost and heavily infrastructural requirement of those technologies have substantially limited their availability. Thus, developing non-invasive, portable, and inexpensive modalities has become a major focus for both researchers and clinicians. When considering neurological disorders and diseases with executive dysfunction, EEG is the most convenient tool to obtain biomarkers which can correlate the objective severity and clinical observation of these conditions. However, studies have also shown that EEG biomarkers and clinical observations alone are not sensitive enough since not all the patients present classical phenotypical features or EEG evidence of dysfunction. This article reviews disorders, including two rare disorders with neurological dysfunction and the usefulness of functional near-infrared spectroscopy (fNIRS) as a non-invasive optical modality to obtain hemodynamic biomarkers of diseases and for screening and monitoring the disease.

Application of Near-Infrared Spectroscopy to Measure the Water Content in Liquid Dielectrics

The article discusses the methods used to measure the water content in liquid dielectrics. The pros and cons of currently used methods are presented. The main aim of the research was to check the possibility of using near-infrared spectroscopy to measure the water content in liquid dielectrics. On the basis of the conducted research, the absorbance bands are indicated that can be used to determine the water content in synthetic and natural esters. These bands are centered at 1901 and 1904 nm for natural and synthetic esters, respectively. The determined dependence of the absorbance on the water content in liquid dielectrics confirmed the linear nature of this relationship. Moreover, the influence of liquid aging degree on the result of absorbance measurements was checked. The verification of the method allowed for confirming the possibility of using near-infrared spectroscopy for the reliable quantitative analysis of water content in synthetic and natural esters.

Determination of ash content in silicon dioxide filled epoxy-phenolic prepreg using near infrared spectroscopy

This paper presents an application of near infrared spectroscopy associated with partial least squares regression calibration, for ash content analysis of prepreg in the manufacturing progress of copper clad laminate for printed circuited boards. The performance of the model was assessed by cross validation and external validation. The correlation coefficient and the root mean squared error of calibration and validation were 0.99, 0.25% and 0.98, 0.34%, and the measurement process was accomplished in less than 2 min compared to over 60 min for traditional thermo gravimetric analyzsis. The paired t-test results revealed that there was no significant difference between the two methods.