Near-infrared Technique Research Articles

Non-Dispersive Near Infrared Gas Flow Cell Design for Oxygenator-Exhaust Capnometry

Non-dispersive near-infrared technique is widely used nowadays for the detection of gases, especially in harsh environments. In this study, an optical gas cell was designed for oxygenator exhaust capnometry. A computer-based simulation was used for the analysis of air flows for model selection. ANSYS Discovery 2020 R2 was used for model simulation. The gas flow cells were tested using a custom-made gas rig to measure the fraction absorbance of carbon dioxide gas at the detector. Two gases were used, nitrogen gas as a reference gas (0%) and 9% carbon dioxide. Three gas cells with the following optical path lengths were tested: 31mm, 36mm, and 40mm. The results showed that all gas flow cells produced laminar flow and small pressure drop across the inlet and outlet of the cell (11~12 Pa). Further, the minimum velocity is obtained in the 40mm gas flow sensor and it is located at the gas outlet path away from the effective optical gas path. The simulation and experimental results indicate that the gas flow cell of 40mm optical path length is more suitable for the intended application as it offers a maximum effective absorption path compared to the stagnation areas, and as a result, it provides the maximum fraction absorbance.

Nutritional quality screening of oat groats by vibrational spectroscopy using field-portable instruments

This study evaluated the performance of low-cost, real-time, and field-deployable spectroscopic instruments operating at near-infrared (NIR) and mid-infrared (MIR) wavelengths for measuring quality traits (β-glucan, starch, protein, and lipid) of oats to support breeding selection. Samples were kindly provided by PepsiCo R&D (n = 150) as oat groats. A handheld FT-NIR sensor (1350–2560 nm) measured spectra of ground and intact oat samples, while a portable FT-IR spectrometer (4000–650 cm −1) measured ground samples only. Several laboratory reference methods were used to measure β-glucan, starch, protein, and lipid composition to develop spectroscopic analysis models based on Partial Least Squares Regression (PLSR). Best model performance was obtained from NIR spectra of ground groats, with standard error of prediction (SEP) for β-glucan, starch, protein, and lipid of 0.2%, 1.0%, 0.6%, and 0.3%, respectively. PLSR models for the MIR spectra exhibited similar predictive accuracy. The performance of these PLSR models either matched or outperformed NIR techniques reported in the literature using portable and benchtop systems. Therefore, novel miniaturized NIR sensors can provide breeders with a rapid method (15 s) to screen for unique traits in the field with equivalent reliability and sensitivity as benchtop systems.

Comparison of near-infrared, mid-infrared, Raman spectroscopy and near-infrared hyperspectral imaging to determine chemical, structural and rheological properties of apple purees

Near-infrared (NIR), mid-infrared (MIR), Raman spectroscopy and hyperspectral imaging (HSI) were comprehensively compared for their capacity to evaluate the composition and texture characteristics of apple purees issued from a large variability (cultivar, fruit thinning, post-harvest mealy texture and processing). NIR, MIR and HSI techniques had a good ability to estimate puree composition such as soluble solids (RPD >2.5), titratable acidity (RPD >2.4) and dry matter (RPD >2.3). Raman spectroscopy was less accurate to determine puree biochemical (RPD <1.8) and textural parameters (RPD <1.4) than the other techniques. MIR was the best tool to identify aforementioned factors (>91.7% of correct classification) and satisfactory predict the puree average particle size (RPD = 2.9), viscosity (RPD ≥2.1) and viscoelasticity (RPD >2.3). Consequently, NIR, MIR and HSI should be prioritized as process analytical technologies to detect apple puree variability, and assess their texture and taste. • MIR provided a better discrimination of puree variability than other techniques. • MIR gave the best prediction of puree textural and rheological properties. • HSI technique had a better ability to assess puree quality and variability than NIR. • Raman spectroscopy could not provide sufficient assessment of puree quality.

Prediction of dry matter, carbon and ash contents and identification of Calycophyllum spruceanum (Benth) organs by Near-Infrared spectrophotometry

Calycophyllum spruceanum (Benth) is a tree of the Amazon region popularly known as ‘mulateiro’, whose wood has multiple uses, from energy to folk medicine. Thus, it is extremely important to evaluate desirable characteristics, through quick methods that meet the principles of green chemistry. The objective of this study was to evaluate the potential of the near-infrared (NIR) spectroscopy technique for the prediction of dry matter, carbon and ash contents and identification of parts of C. spruceanum trees. The Partial Least Squares (PLS) regression model applied within the spectral range of 400–2498 nm proved to be adequate to estimate the dry matter, carbon and ash contents in C. spruceanum sapwood and bark samples, with R2 above 0.90 in both calibration and validation. Hierarchical cluster analysis and principal component analysis techniques when applied to the spectra were able to efficiently separate bark from sapwood. The results show that the NIR technique developed can be used in the determination of dry matter, carbon and ash contents in C. spruceanum, in addition to its discriminatory power in the separation of parts of the plant.

Physiological quality in maize seeds using conventional methods and NIR spectroscopy

Maize is one of the most important cereals, and stands out for its wide genetic diversity, adaptation to the most diverse climate conditions, and being, both directly and indirectly, the most basic food source of man. Globally, production is dependent on seed quality. Known methodologies for evaluating seed quality require improvement in developing technologies capable of achieving, in less time, results for the targets under evaluation. The aim of this study, therefore, was to compare conventional vigour testing with the ability of NIR technology to classify the physiological quality of maize seeds, cv. DKB-290-HS. For the experiment, which was conducted in a completely randomised design (CRD), six Batches of 200 seeds were used, with four replications. Conventional methods of evaluating seed vigour were used in the study. For the evaluation using the near-infrared (NIR) method, the seeds were placed with the embryo facing the source of electromagnetic waves. The resulting spectra were analysed by the Pirouette software using the PLS-DA method. The conventional methods for vigour, and the NIR technique both efficiently detected the levels of physiological quality. NIR also revealed the differences between Batches, classifying the physiological quality of maize seeds, cv. DKB-290-HS, into different levels quickly and efficiently.

Near-infrared techniques for fraud detection in dairy products: A review.

The dairy products sector is an important part of the food industry, and their consumption is expected to grow in the next 10 years. Therefore, the authentication of these products in a faster and precise way is required for the sake of public health. This review proposes the use of near-infrared techniques for the detection of food fraud in dairy products as they are faster, nondestructive, environmentally friendly, do not require sample preparation, and allow multiconstituent analysis. First, we have described frequent forms of food fraud in dairy products and the application of traditional techniques for their detection, highlighting gaps and counterproductive characteristics for the actual global food chain, as longer sample preparation time and use of reagents. Then, the application of near-infrared spectroscopy and hyperspectral imaging for the detection of food fraud mainly in cheese, butter, and yogurt are described. As these techniques depend on model development, the coverage of different dairy products by the literature will promote the identification of food fraud in a faster and reliable way.

Deep-Tissue Fluorescence Imaging Study of Reactive Oxygen Species in a Tumor Microenvironment.

Tumor microenvironment (TME) is the survival environment for tumor cells to proliferate and metastasize in deep tissue. TME contains tumor cells, immune cells, stromal cells and a variety of active molecules including reactive oxygen species (ROS). Inside the TME, ROS regulate the oxidation-reduction (redox) homeostasis and promote oxidative stress. Due to the rapid proliferation ability and specific metabolic patterns of the TME, ROS pervade virtually all complex physiological processes and play irreplaceable roles in protein modification, signal transduction, metabolism, and energy production in various tumors. Therefore, measurements of the dynamically, multicomponent simultaneous changes of ROS in the TME are of great significance to reveal the detailed proliferation and metastasis mechanisms of the tumor. Near-infrared (NIR) and two-photon (TP) fluorescence imaging techniques possess real-time, dynamic, highly sensitive, and highly signal-to-noise ratios with deep tissue penetration abilities. With the rationally designed probes, the NIR and TP fluorescence imaging techniques have been widely used to reveal the mechanisms of how ROS regulates and constructs complex signals and metabolic networks in TME. Therefore, we summarize the design principles and performances of NIR and TP fluorescence imaging of ROS in the TME in the last four years, as well as discuss the advantages and potentials of these works. This Review can provide guidance and prospects for future research work on TME and facilitate the development of antitumor drugs.

Research on Chemically Deuterated Cellulose Macroperformance and Fast Identification.

Chemically deuterated cellulose fiber was expected to provide novel applications due to its spectral, biological, and kinetic isotope effect. In this research, the performance of the chemically deuterated cotton fibers, including their mechanical property, enzymatic degradation performance, effect on bacterial treatment, and fast identification (near-infrared modeling) was investigated. The breaking tenacity of the deuterated cotton fibers was slightly lower, which might be attributed to the structural damage during the chemical deuteration. The glucose yield by enzymatic hydrolysis was less than that of the protonic cotton fibers, implying the deuterated fibers are less sensitive to enzymatic degradation. Furthermore, the deuterated fibers could promote the growth of bacteria such as Escherichia. coli, which was associated with the released low-level deuterium content. At last, the near-infrared technique combined with partial least squares regression successfully achieved a fast identification of the protiated and deuterated cotton fibers, which significantly promoted the potential application of deuterated cellulose as anticounterfeiting materials (e.g., special paper).

Development of Attenuated Total Reflectance Mid-Infrared (ATR-MIR) and Near-Infrared (NIR) Spectroscopy for the Determination of Resistant Starch Content in Wheat Grains.

The chemical method for the determination of the resistant starch (RS) content in grains is time-consuming and labor intensive. Near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy are rapid and nondestructive analytical techniques for determining grain quality. This study was the first report to establish and compare these two spectroscopic techniques for determining the RS content in wheat grains. Calibration models with four preprocessing techniques based on the partial least squares (PLS) algorithm were built. In the NIR technique, the mean normalization + Savitzky–Golay smoothing (MN + SGS) preprocessing technique had a higher coefficient of determination (Rc2 = 0.672; Rp2 = 0.552) and a relative lower root mean square error value (RMSEC = 0.385; RMSEP = 0.459). In the ATR-MIR technique, the baseline preprocessing method exhibited a better performance regarding to the values of coefficient of determination (Rc2 = 0.927; Rp2 = 0.828) and mean square error value (RMSEC = 0.153; RMSEP = 0.284). The validation of the developed best NIR and ATR-MIR calibration models showed that the ATR-MIR best calibration model has a better RS prediction ability than the NIR best calibration model. Two high grain RS content wheat mutants were screened out by the ATR-MIR best calibration model from the wheat mutant library. There was no significant difference between the predicted values and chemical measured values in the two high RS content mutants. It proved that the ATR-MIR model can be a perfect substitute in RS measuring. All the results indicated that the ATR-MIR spectroscopy with improved screening efficiency can be used as a fast, rapid, and nondestructive method in high grain RS content wheat breeding.

Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions

Prostate Cancer (PCa) is one of the common cancers among men in the world. About 16.67% of men will be affected by PCa in their life. Due to the integration of magnetic resonance imaging in the current clinical procedure for detecting prostate cancer and the apparent success of imaging techniques in the estimation of PCa volume in the gland, we provide a more detailed review of methodologies that use specific parameters for prostate tissue representation. After collecting over 200 researches on image-based systems for diagnosing prostate cancer, in this paper, we provide a detailed review of existing computer-aided diagnosis (CAD) methods and approaches to identify prostate cancer from images generated using Near-Infrared (NIR), Mid-Infrared (MIR), and Magnetic Resonance Imaging (MRI) techniques. Furthermore, we introduce two research methodologies to build intelligent CAD systems. The first methodology applies a fuzzy integral method to maintain the diversity and capacity of different classifiers aggregation to detect PCa tumor from NIR and MIR images. The second methodology investigates a typical workflow for developing an automated prostate cancer diagnosis using MRI images. Essentially, CAD development remains a helpful tool of radiology for diagnosing prostate cancer disease. Nonetheless, a complete implementation of effective and intelligent methods is still required for the PCa-diagnostic system. While some CAD applications work well, some limitations need to be solved for automated clinical PCa diagnostic. It is anticipated that more advances should be made in computational image analysis and computer-assisted approaches to satisfy clinical needs shortly in the coming years.

Fast determination of lipid and protein content in green coffee beans from different origins using NIR spectroscopy and chemometrics

The chemical compounds including lipid and protein in green coffee beans are important indicators of the final quality of the coffee products, which are usually determined by time-consuming and destructive chemical methods. Therefore, a fast and reliable method was attempted to exploit by near-infrared (NIR) spectroscopy combined with partial least squares (PLS) regression for the determination of lipid and protein in green coffee beans from different origins. Orthogonal signal correction (OSC) and several traditional spectral pretreatment methods were compared during the PLS regression model building process. Important variables selection was further achieved based on the regression coefficients (β). The results showed that the 1st and 2nd derivative reduced the model quality, while OSC, MSC, and SNV pretreatment enhanced the model quality. The quality of PLS models was significantly improved after important variable selection. Especially, OSC-PLS models were the most robust for protein and lipid prediction with the best performance indicators (R2p>0.982, RPD > 7.55, RMSEcv <0.101, RMSEP < 0.106). The excellent performance showed that the NIR technique together with PLS regression could be applied as a substitute way to determine the protein and lipid content in green coffee beans.

Near-Infrared Spectroscopic Monitoring Technique of Physical Blowing Agent Concentration in Foam Injection Molding Processes

Near-Infrared Spectroscopic Monitoring Technique of Physical Blowing Agent Concentration in Foam Injection Molding Processes

Characterising corn grain using infrared imaging and spectroscopic techniques: a review

Corn is the largest cultivated grain crop in the world and the need to characterize corn grains based on various quality parameters is becoming essential as the demand for corn is increasing continuously in various end product applications. The conventional methods like visual inspection and analytical techniques involves sample destruction and are either subjective or time-consuming resulting in a growing requirement for the development of non-destructive techniques for a rapid and accurate determination of corn grain constituents and contaminants. This paper reviews the potential of infrared (IR) based imaging and spectroscopic techniques to determine quality parameters of corn grains, and their opportunities to incorporate in the supply chain of corn-based agro-industries. The variety and hardness of corn grains could be efficiently identified through the near infrared (NIR) hyperspectral imaging with accuracies ranging from 80 to 95% and 60 to 85%, respectively. IR imaging techniques determined the oil content of corn grains with standard error ranging between 0.7 and 1.3%. The detection of fungal infestations and mycotoxins in corn grains were widely studied using NIR, short-wave infrared (SWIR) and fluorescence hyperspectral imaging techniques with accuracies ranging from 75 to 98% and 70 to 97%, respectively. These techniques showed a better accuracy for infestations and variety classification (85–90%) and kernel hardness (60–65%) when used in the reflectance operational mode and proved effective for both single and bulk kernel analysis.

Nondestructive Testing for Wheat Quality with Sensor Technology Based on Big Data.

Nondestructive testing with sensor technology is one of the fastest growing and most promising wheat quality information analysis technologies. Nondestructive testing with sensor technology benefits from the latest achievement of many disciplines such as computer, optics, mathematics, chemistry, and chemometrics. It has the advantages of simplicity, speed, low cost, no pollution, and no contact. It is widely used in wheat quality analysis and testing research. This article summarizes nondestructive testing with sensor technology for wheat quality, including the mechanical model, hyperspectral technology, Raman spectroscopy, and near-infrared techniques for wheat mechanical properties, storage properties, and physical and chemical properties (such as moisture, ash, protein, and starch) in the past decade. Based on the current research progress, big data technology needs a lot of research in spectral data mining, modeling algorithm optimization, model robustness, etc. to provide more data support and method reference for the research and application of wheat quality.

Feasibility study for the use of colorimetric sensor arrays, NIR and FT-IR spectroscopy in the quantitative analysis of volatile components in honey

In this work, the feasibility of colorimetric sensor array (CSA), near-infrared (NIR), and Fourier transform infrared (FT-IR) spectroscopy (both individually and in combination) coupled with chemometrics for the quantitative analysis of volatile compounds (VCs) in honey were evaluated. The compounds were extracted using solid-phase microextraction and determined by gas chromatography-mass spectrometry. Nine calibration models were established with the intention of the quantitative analysis of the abundant VCs in honey samples. CSA showed the best predictive models for 2-furanmethanol (Rp = 0.873), benzyl alcohol (Rp = 0.959), phenyl ethyl alcohol (Rp = 0.959), furfural (Rp = 0.979), benzaldehyde (Rp = 0.906), 5-methyl furfural (Rp = 0.877), phenol, 2-methoxy- (Rp = 0.751) and 4-ketoisophorone (Rp = 0.986), while NIR showed the best model for 2-heptanone (Rp = 0.936). The results achieved with the NIR technique are superior to the ones obtained with FT-IR spectroscopy. Overall, the results demonstrated that combining CSA with NIR or FT-IR was feasible and could improve the prediction accuracy of VCs in honey.

Light at the museum – A near impossible result

The monitoring and quantification of the illegal harvest of protected animal products is very vital for the conservation and protection of endangered species. Most of the methods and techniques used in the trade of these products are recognised to be incredibly time consuming and labour intensive requiring significant analyst expertise. In this study, we have demonstrated the potential of near-infrared spectroscopy combined with either principal component analysis or partial least square discriminant analysis regression as a rapid and non-invasive tool to classify horn and ivory samples stored in the Australian Museum, Sydney. This study has also demonstrated the attractiveness of the near-infrared technique as a screening tool that could revolutionise the tracking and identification of contraband materials produced from horn and ivory biomaterials.

Near Infrared (NIR) Fluorescence is Not a Substitute for Lymphoscintigraphy and Gamma Probe for Melanoma Sentinel Node Detection: Results from a Prospective Trial.

Sentinel lymph node (SLN) biopsy is the standard care for early detection and staging of lymph node metastasis in melanomas. Radiocolloids (RC) and blue dyes are used for SLN detection. Recently, near infrared (NIR) fluorescence tracing using indocyanine green has been developed as an alternative method for SLN detection. The relatively high tissue penetration depth of several millimeters and the ability to detect low concentrations of tracer both suggest that NIR may have significant advantages over RC and the blue dye methods. The objective of this study was to prospectively compare the performance of all three SLN detection techniques using them sequentially to evaluate the same group of patients. One hundred twenty-one primary cutaneous melanoma patients with an indication for SLN biopsy were assigned to the procedure following NIR, blue dye, and RC detection techniques. No adverse event was reported. SLN was not detected in only 4.1% of cases. In 90.9%, an SLN was identified with NIR, but without any auxiliary technique in only 70.2% of cases. RC detected the SLN in 92.6% of cases. Patent blue was found in the sentinel node in 76.9%. The combination of all three techniques detected an SLN in 95.9% of cases. Metastases were present in 26.7%. The false-negative rate was 8.8%, with a negative predictive value of 91.2%. RC was the only technique with high SLN detection. Both the blue dye and NIR methods added sensitivity to the detection rate but should not be a substitute for RC.

Rapid identification of plant- and chemical-dyed cotton fabrics using the near-infrared technique

Plant dye is a promising dyestuff to be used in textiles due to its unique environmental compatibility. However, currently there is no effective method for the identification of plant-dyed and chemical-dyed textiles. In this study, near-infrared (NIR) spectroscopy combined with three kinds of pattern recognition methods, namely soft independent modeling of class analogy (SIMCA), partial least squares (PLS) regression and principal component regression (PCR), were applied to identify cotton fabrics dyed with plant and chemical dyes. A total of 336 plant dye and chemical dye dyed cotton fabrics were prepared and the NIR spectra were collected; 267 samples were used as the calibration set, while the remaining 69 samples were used as the validation set. After pretreatment with the Savitzky–Golay first derivative, the calibration model was constructed. In the SIMCA model, the correct recognition rate values of the calibration and prediction sets were 100% and 98.55%, respectively. The PLS model showed that the number of principal components (PCs) and the correlation coefficient ( R2) were 8 and 0.9978, respectively, and the results of PCR were PC = 10, R2 = 0.9937. Both methods were satisfactory for the predicted results. The overall results indicated that NIR spectroscopy could be used for rapid and nondestructive identification of plant-dyed cotton fabrics and chemical-dyed cotton fabrics.

Online monitoring of horseradish peroxidase structural changes by Near Infrared (NIR) Spectroscopy

The development of online monitoring techniques is of great relevance for understanding the structural changes of proteins under different conditions in order to maximize their catalytic activity. This study aimed to evaluate the potential of the NIR (near-infrared spectroscopy) technique for the monitoring of alterations of secondary and tertiary structures of Horseradish peroxidase (HRP), an oxidoreductase that has several applications in the industrial environment, food industry and bioremediation. The NIR associated to the multivariate calibration, through the PLS (partial least square) method allowed the construction of a robust model for the prediction of the analysis. The values of the correlation coefficient (R²), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and root mean square error of cross validation (RMSECV) for secondary structure analysis using circular dichroism (CD) data as reference (actual values) were 0.9681, 0.647 (mdeg), 0.945 (mdeg), and 1.12 (mdeg), respectively. For tertiary structure analysis, fluorescence spectroscopy (FL) data were used as reference. R2, RMSEC, RMSEP and RMSECV were, respectively 0.9999, 1.95 (a.u.), 2.09 (a.u.); and 2.19 (a.u.). NIR combined multivariate calibration showed promising results for sctructural changes monitoring of HRP.

A Pilot Study on the Use of Indocyanine Green Near-Infrared Technique for Sentinel Lymph Node Biopsy in Early Endometrial Cancers in Singapore

Objective: We report our initial experience with indocyanine green (ICG) with near-infrared (NIR) fluorescence imaging for sentinel lymph node (SLN) mapping in clinical stage I endometrial cancer. ...