Near-infrared Spectrometer Research Articles

Detection of Illicit Conservation Treatments in Sea Bass (Dicentrarchus labrax): Application and Data Integration of NIR Spectrometers

Global fish and seafood consumption is increasing annually, frequently leading to the emergence of food fraud, mainly related to mislabeling and adulteration like, for example, the use of illicit/unauthorized food additives to mask or delay fish spoilage. Among the available diagnostic tools for control purposes, spectroscopic techniques have often been proposed to identify these kinds of illicit practices in fish and seafood products. The presented study aims to test two cheap and portable near infrared (NIR) spectrometers, a handheld MicroNIR and a pocket-sized SCiO, to uncover use of the illicit food additive Cafodos, a mixture of sodium citrate and hydrogen peroxide used to preserve some fish characteristics (like smell, color, na dtexture). The NIR spectroscopy in combination with chemometric approaches, allowed the successfully classification of (81–100%) samples of sea bass (Dicentrarchus labrax) treated with Cafodos. The study highlights the potential of this technique that, by not requiring pre-treatment of samples with further reagents, is cheaper and safer for the environment. In conclusion, the study confirmed the potential of portable devices for rapid NIR spectroscopy analysis to identify food fraud and ensure consumer safety.

Comparing Different Methods for the Diagnosis of Liver Steatosis: What Are the Best Diagnostic Tools?

Due to the ongoing organ shortage, marginal grafts with steatosis are more frequently used in liver transplantation, leading to higher occurrences of graft dysfunction. A histological analysis is the gold standard for the quantification of liver steatosis (LS), but has its drawbacks: it is an invasive method that varies from one pathologist to another and is not available in every hospital at the time of organ procurement. This study aimed to compare non-invasive diagnostic tools to a histological analysis for the quantification of liver steatosis. Male C57BL6J mice were fed with a methioninecholine-deficient (MCD) diet for 14 days or 28 days to induce LS, and were compared to a control group of animals fed with a normal diet. The following non-invasive techniques were performed and compared to the histological quantification of liver steatosis: magnetic resonance spectroscopy (MRS), CARS microscopy, 99mTc MIBI SPECT imaging, and a new near-infrared spectrometer (NIR-SG1). After 28 days on the MCD diet, an evaluation of LS showed ≥30% macrovesicular steatosis. High correlations were found between the NIR-SG1 and the blinded pathologist analysis (R2 = 0.945) (p = 0.001), and between the CARS microscopy (R2 = 0.801 (p < 0.001); MRS, R2 = 0.898 (p < 0.001)) and the blinded pathologist analysis. The ROC curve analysis showed that the area under the curve (AUC) was 1 for both the NIR-SG1 and MRS (p = 0.021 and p < 0.001, respectively), while the AUC = 0.910 for the Oil Red O stain (p < 0.001) and the AUC = 0.865 for the CARS microscopy (p < 0.001). The AUC for the 99mTc MIBI SPECT was 0.640 (p = 0.013), and this was a less discriminating technique for LS quantification. The best-performing non-invasive methods for LS quantification are MRS, CARS microscopy, and the NIR-SG1. The NIR-SG1 is particularly appropriate for clinical practice and needs to be validated by clinical studies on liver grafts.

Rapid on-site multi-indicator detection of Polygonatum cyrtonema Hua by handheld near-infrared spectroscopy with wavelength selection

The rapid, accurate, and on-site quantitative evaluation of the 3 components (total polysaccharide, total flavonoid, and total saponin) of Polygonatum cyrtonema Hua (PCH) is of great significance to ensure the value of its medicine and food, for this purpose, a handheld near-infrared (NIR) spectrometer was used. The raw NIR spectra were preprocessed by seven methods and their combinations, then three kinds of wavelength selection algorithms, the competitive adaptive reweighted sampling (CARS), random frog (RF) sampling, and Monte Carlo uninformative variable elimination (MCUVE) were applied, and the characteristic wavelengths for total polysaccharide, total saponin, and total flavonoid in PCH were obtained, respectively. Subsequently, the partial least squares (PLS) regression models were developed. The results showed that the combination of first derivative and standard normal variate was the optimal pretreatment method, and CARS had the highest performance of the wavelength selection for total polysaccharide and total flavonoid, while RF was the highest for total saponin. With these selected wavelengths, 3 PLS prediction models were built, and their performances were validated with unknown samples, the results showed that the determination coefficient of the prediction set (R2P) of total polysaccharide, total saponin, and total flavonoid were 0.980, 0.948, and 0.942, respectively, and the corresponding root mean square error of prediction set (RMSEP) were 0.162, 0.142, and 0.052. Furthermore, the residual prediction deviations (RPDs) of the three components were all larger than 3, demonstrating the prediction performance of the models was high. Therefore, the three active functional components in PCH could be rapidly determined by a handheld NIR spectrometer, which would be beneficial for ensuring the quality of PCH.

A simple spectrogram model for high-accuracy spectral calibration of VIPA spectrometers.

The virtually imaged phased array (VIPA) spectrometer uses the orthogonal dispersion method and has the advantages of compact structure, high spectral resolution, and wide wavelength coverage. It has been widely used in different fields. However, due to the non-linear dispersion of the VIPA etalon and the cross-dispersion structure of the VIPA spectrometer, simple and high-accuracy wavelength calibration remains a challenge. In this paper, a new and simple five-parameter spectrogram model is developed by simplifying the phase-matching equation of the VIPA etalon and considering the angle between the camera and dispersion direction, which can achieve a frequency accuracy better than one pixel. The performance of the model is demonstrated by measuring the CO2 absorption spectrum in the range of 1.42 to 1.45 μm using a self-designed near-infrared VIPA spectrometer . The reported method is simple and easy to solve with high accuracy, which is conducive to promoting the application of VIPA spectrometers in precision measurement.

Advancing CubeSats Capabilities: Ground-Based Calibration of Uvsq-Sat NG Satellite’s NIR Spectrometer and Determination of the Extraterrestrial Solar Spectrum

Uvsq-Sat NG is a French 6U CubeSat (10 × 20 × 30 cm) of the International Satellite Program in Research and Education (INSPIRE) designed primarily for observing greenhouse gases (GHG) such as CO2 and CH4, measuring the Earth’s radiation budget (ERB), and monitoring solar spectral irradiance (SSI) at the top-of-atmosphere (TOA). It epitomizes an advancement in CubeSat technology, showcasing its enhanced capabilities for comprehensive Earth observation. Scheduled for launch in 2025, the satellite carries a compact and miniaturized near-infrared (NIR) spectrometer capable of performing observations in both nadir and solar directions within the wavelength range of 1100 to 2000 nm, with a spectral resolution of 7 nm and a 0.15° field of view. This study outlines the preflight calibration process of the Uvsq-Sat NG NIR spectrometer (UNIS), with a focus on the spectral response function and the absolute calibration of the instrument. The absolute scale of the UNIS spectrometer was accurately calibrated with a quartz-halogen lamp featuring a coiled-coil tungsten filament, certified by the National Institute of Standards and Technology (NIST) as a standard of spectral irradiance. Furthermore, this study details the ground-based measurements of direct SSI through atmospheric NIR windows conducted with the UNIS spectrometer. The measurements were obtained at the Pommier site (45.54°N, 0.83°W) in Charentes–Maritimes (France) on 9 May 2024. The objective of these measurements was to verify the absolute calibration of the UNIS spectrometer conducted in the laboratory and to provide an extraterrestrial solar spectrum using the Langley-plot technique. By extrapolating the data to AirMass Zero (AM0), we obtained high-precision results that show excellent agreement with SOLAR-HRS and TSIS-1 HSRS solar spectra. At 1.6 μm, the SSI was determined to be 238.59 ± 3.39 mW.m−2.nm−1 (k = 2). These results demonstrate the accuracy and reliability of the UNIS spectrometer for both SSI observations and GHG measurements, providing a solid foundation for future orbital data collection and analysis.

TEMPERATURE MONITORING FOR LASER METAL DEPOSITION USING NEAR-INFRARED SPECTROSCOPY

Temperature monitoring during laser metal deposition (LMD) aids process control to ensure high-quality production. However, the fluctuating emissivity of the melt pool limits the accuracy of the existing non-contact temperature measuring devices. Thus, this work explores a new temperature monitoring technique, where the thermal radiation emitted by the processing zone was collected with a near-infrared (NIR) spectrometer and fitted to Planck's law to determine the temperature. This work has established the optimised angle and distance of the fiber probe from the LMD processing area to maximise the spectral signal acquisition. The temperature determined from the technique was cross-validated with a thermocouple, resulted in a small deviation of 2.39%. The applicability of the spectroscopic method for continuous temperature monitoring of the LMD process has been demonstrated. The optimised placement for the fiber probe end was determined at the 45˚ angle relative to the surface of the substrate and positioned 5 cm away from the molten pool. The temperature during the LMD process decreased gradually then stabilised after approximately 17 mm track length, resembling those reports in prior studies. Our findings support the practicality of the proposed temperature monitoring approach in LMD.

Qualitative discrimination and quantitative prediction of salt in aqueous solution based on near-infrared spectroscopy

Freshwater resources have been gradually salinized in recent years, dramatically impacting the ecosystem and human health. Therefore, it is necessary to detect the salinity of freshwater resources. However, traditional detection methods make it difficult to check the type and concentration of salt quickly and accurately in solution. This paper uses a portable near-infrared spectrometer to qualitatively discriminate and quantitatively predict the salt in the solution. The study was carried out by adding ten salts of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 to 2 milliliters of deionized water to prepare a single salt solution (0.02% - 1.00%) totaling 100 sets. It was found that the Support vector machine (SVM) model was only effective in discriminating the class of salt anions in the solution. The Partial least squares-discriminant analysis (PLS-DA) model, on the other hand, can effectively discriminate the classes of salt in solution, and the accuracies of the optimal model prediction set and the interactive validation set are 98.86% and 99.66%, respectively. Furthermore, the Partial least squares regression (PLSR) models can accurately predict the concentration of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 salt solutions. The coefficients of determination R2 of their model interactive validation sets were 0.99, 0.99, 0.99, 0.97, 0.99, 0.99, 0.98, 0.99, 0.98, and 0.98, respectively. This study shows that NIRS can achieve rapid and accurate qualitative and quantitative detection of salts in solution, which provides technical support for the utilization of safe water resources.

Transfer of near-infrared analysis models for gasoline RON based on ensemble learning

The research octane number (RON) has guiding significance for evaluating the quality of gasoline, while near-infrared (NIR) spectroscopy analysis technology provides an important means for the detection of RON non-destructively and rapidly. When using a near-infrared spectrometer to obtain the RON of gasoline, if the analysis model can be shared among different instruments, it will greatly reduce the cost of re-modeling or model maintenance. Aiming to achieve the sharing of a NIR spectroscopy analysis model for RON between two portable near-infrared spectrometers of the same model, two ensemble learning algorithms, random forest (RF) and extreme gradient boosting (XGBoost), were employed for investigation, as well as two other machine learning algorithms, support vector regression (SVR) and decision tree (DT). Based on the RON of 120 gasoline samples and their NIR spectroscopy collected on the two spectrometers, hybrid and pure models were established to evaluate their sharing performance among SVR, DT, RF and XGBoost. In order to further simplify the model and improve its robustness and prediction accuracy, the characteristic wavelength selection strategies, including elimination of uninformative variables (UVE), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS), were also adopted to optimize the model. The results showed that the hybrid model based on the CARS-RF method yielded the best prediction performance, with the coefficient of determination (R2) of 0.96, 0.86, and 0.94 for the single prediction sets of instrument A, instrument B, and the hybrid prediction set of the two instruments, respectively. Therefore, the hybrid modeling method based on ensemble learning algorithms combined with an appropriate wavelength selection strategy can effectively improve the robustness and universality of the model, and achieve the sharing of gasoline RON models on two near-infrared spectrometers of the same model.

Quality in beans: tracking and tracing coffee through automation and machine learning

PurposeCoffee is one of the most consumed beverages in the world and the global coffee industry is worth over $100bn. However, the industry faces significant sustainability challenges. Developing a quality traceability system to select the coffee beans and to ensure their authentication would result in economic advantages, because it allows for fraud to be avoided and increases consumer confidence.Design/methodology/approachTraceability is one of the key elements of sustainability in the coffee sector. The literature reveals that near-infrared (NIR) approaches have a huge potential for gaining rapid information about the origin and properties of coffee beans, without invasive procedures. This study demonstrates the scalability potential of automated methods of manipulation and image acquisition of coffee beans, from experimental scale to industrial lines.FindingsA solution based on the interaction of a manipulation system, a NIR spectrometer acquisition station integrated with a machine learning infrastructure and a compressed air classifier allows for the automatic separation of coffee beans into different classes of origin.Originality/valueApart from traceability, the wide industrialization of this system offers further advantages, including reduced workforce, decreased subjectivity in the evaluation and the acquisition of real-time data for labeling.

Analysis of a large buried impact crater and vertical mineral composition at the Chang'E-4 landing site by multi-source remote sensing data

Exploring the concealed subsurface structures and materials beneath the lunar surface can reveal significant insights into geological history. This study offers a comprehensive analysis of the stratigraphic interpretation and subsurface material composition at the Chang'E-4 landing site, integrating both in-situ and orbital radar with multispectral datasets. We report the identification of a subsurface structure, which resembles a buried impact crater (∼420 m in diameter) under the Yutu-2 rover's path. This crater could degrade over a period of 0.42 to 0.53 Ga, with an initial diameter of 293 to 323 m and an initial depth of 45.9 to 51.4 m. Surface material above the buried crater, evaluated by the in-situ visible and near-infrared imaging spectrometer (VNIS) detector, shows a higher abundance of clinopyroxene compared to surrounding areas, where a near-equal mix of clinopyroxene and orthopyroxene is observed. Assessment of crater diameters in proximity to the Chang'E-4 landing site, along with the mineral compositions at their epicenters, reveals a decrease in the abundance of clinopyroxene and plagioclase with depth. Conversely, the quantities of orthopyroxene and olivine increase, implying that orthopyroxene-rich Finsen ejecta significantly influenced the Chang'E-4 landing site's geological composition. Two potential stratigraphic boundary depths are identified at 13.5 and 22 m, based on pronounced variations in mineral abundance, offering fresh insights into subsurface delineation beyond radar data. Considering the VNIS and vertical mineral composition, we propose the buried crater's formation resulted from Finsen crater's ejecta. Also, we identify eight potential historical impacts by comparing subsurface relief variations with mineral composition ratios between clinopyroxene and orthopyroxene. The integration of subsurface structure, along with surface and subsurface mineral composition, enables a more robust stratigraphic interpretation, facilitates shallow material source analysis, and allows for historical impact tracing.

Smart classification of organic and inorganic pineapple juice using dual NIR spectrometers combined with chemometric techniques

The global demand for organic foods, driven by health benefits and consumer preferences, necessitates reliable methods for distinguishing organic products from their inorganic counterparts. This study investigates the application of dual handheld near infrared (NIR) spectroscopy devices, SCiO and Tellspec, combined with chemometric techniques for the nondestructive differentiation of organic and inorganic pineapple juices. The objective was to establish a rapid and robust method to differentiate organic pineapple juice from inorganic juice using unique spectral data from the two devices. Eighty-four pineapple juice samples were analyzed with preprocessing techniques, including mean centering, multiplicative scatter correction, standard normal variate, first derivative, and second derivative applied to the spectral data. Partial least squares discriminant analysis (PLS-DA) was employed for classification, and variable importance in projection (VIP) was used for optimal wavelength selection. The results demonstrated that the Tellspec scanner, particularly with second derivative preprocessing, achieved high accuracy in differentiating organic from inorganic pineapple juice. The fusion of data from both SCiO (740–1070 nm) and Tellspec (900–1700 nm) scanners, without preprocessing, coupled with the PLS-DA model, achieved perfect classification accuracy, sensitivity, and specificity (100 %) in both training and testing sets. This study highlights the potential of integrating dual handheld NIR spectroscopy with chemometrics to effectively and accurately classify organic and inorganic pineapple juices. The findings support using these advanced techniques for quality assurance and authentication in the food industry.

Machine Learning-Based Classification of Soil Parent Materials Using Elemental Concentration and Vis-NIR Data.

In soil science, the allocation of soil samples to their respective origins holds paramount significance, as it serves as a crucial investigative tool. In recent times, with the increasing use of proximal sensing and advancements in machine-learning techniques, new approaches have accompanied these developments, enhancing the effectiveness of soil utilization in soil science. This study investigates soil classification based on four parent materials. For this purpose, a total of 59 soil samples were collected from 12 profiles and the vicinity of each profile at a depth of 0-30 cm. Surface soil samples were analyzed for elemental concentrations using X-Ray fluorescence (XRF) and inductively coupled plasma-optical emission spectrometry (ICP-OES) and soil spectra using a visible near-infrared (Vis-NIR) spectrometer. Soil samples collected from soil profiles (12 soil samples) and surface (47 soil samples) were used to classify parent materials using machine learning-based algorithms such as Support Vector Machine (SVM), Ensemble Subspace k-Near Neighbor (ESKNN), and Ensemble Bagged Trees (EBTs). Additionally, as a validation of the classification techniques, the dataset was subjected to five-fold cross-validation and independent sample set splitting (80% calibration and 20% validation). Evaluation metrics such as accuracy, F score, and G mean were used to evaluate prediction performance. Depending on the dataset and algorithm used, the classification success rates varied between 70% and 100%. Overall, the ESKNN (99%) produced better results than other classification methods. Additionally, Relief algorithms were employed to identify key variables for each dataset (ICP-OES: CaO, Fe2O3, Al2O3, MgO, and MnO; XRF: SiO2, CaO, Fe2O3, Al2O, and MnO; Vis-NIR: 567, 571, 572, 573, and 574 nm). Subsequent soil reclassification using these reduced variables revealed reduced accuracies using Vis-NIR data, with ESKNN still yielding the best results.

Euclid. III. The NISP instrument

The Near-Infrared Spectrometer and Photometer (NISP) on board the satellite provides multiband photometry and $R slitless grism spectroscopy in the 950--2020\,nm wavelength range. In this reference article, we illuminate the background of NISP's functional and calibration requirements, describe the instrument's integral components, and provide all its key properties. We also sketch the processes needed to understand how NISP operates and is calibrated as well as its technical potentials and limitations. Links to articles providing more details and the technical background are included. The NISP's 16 H2RG detectors with a plate scale of $ deliver a field of view of 0.57\,deg$^2$. In photometric mode, NISP reaches a limiting magnitude of sim \,24.5\,AB\,mag in three photometric exposures of about 100\,s in exposure time for point sources and with a S/N of five. For spectroscopy, NISP's point-source sensitivity is a SNR = 3.5 detection of an emission line with flux sim \,$2 $ integrated over two resolution elements of 13.4\ in 3times 560\,s grism exposures at 1.6\ (redshifted Halpha ). Our calibration includes on-ground and in-flight characterisation and monitoring of the pixel-based detector baseline, dark current, non-linearity, and sensitivity to guarantee a relative photometric accuracy better than 1.5&lt;!PCT!&gt; and a relative spectrophotometry better than 0.7&lt;!PCT!&gt;. The wavelength calibration must be accurate to 5\ or better. The NISP is the state-of-the-art instrument in the near-infrared for all science beyond small areas available from HST and JWST -- and it represents an enormous advance from any existing instrumentation due to its combination of field size and high throughput of telescope and instrument. During six-year survey covering 14\,000\,deg$^2$ of extragalactic sky, NISP will be the backbone in determining distances of more than a billion galaxies. Its near-infrared data will become a rich reference imaging and spectroscopy data set for the coming decades.

Rapid prediction of the chemical composition of pet food using a benchtop and handheld near-infrared spectrometer

Quality of pet foods can be affected by many factors such as raw materials, formulations, and processing techniques. The pet food manufacturers require fast analyses to control the nutritional quality of their products. Herein, near-infrared spectroscopy (NIR) was evaluated to quantify the chemical composition of pet food, and the performances of two NIR spectrometers were investigated and compared: a benchtop instrument (1000–2500 nm) and a low-cost handheld instrument (900–1700 nm). Seventy cat food and thirty-six dog samples were characterized using reference methods for crude protein, crude fat, crude fibre, crude ash, moisture, calcium (Ca), and phosphorus (P). Principal component regression (PCR) and partial least squares regression (PLSR) were used to establish the models that involved the cat food and mixed model. The characteristic wavelengths were selected using a competitive adaptive reweighted-sampling (CARS) algorithm. The Optimal models obtained from the benchtop instrument for crude protein, crude fat, and moisture were classified as “Good” or “Very good” (Residual prediction variation (RPD) > 3), for crude fibre were classified as “Poor” (RPD>2), and for crude ash, Ca and P (RPD<2) were classified as “Very poor”. The Optimal calibrations obtained from the handheld instrument for crude protein, crude fat, and moisture were classified as “Good” or “Very good” (RPD>3), for crude fibre, crude ash, Ca, and P were classified as “Very poor” (RPD<2). Generally, the the performance of benchtop and handheld instrument was close, and the cat food model outperformed the mixed model. Results from the current study revealed the potential to monitor the chemical compositions in pet food on a large scale.

Near‐infrared spectroscopy and deep neural networks for early common root rot detection in wheat from multi‐season trials

AbstractIn Australia, the soil‐borne disease common root rot (Bipolaris sorokiniana) (CRR) in wheat (Triticum aestivum L.) leads to substantial yield losses, yet has limited visible aboveground symptoms, making detection and identification labor intensive. Near‐infrared (NIR) spectroscopy offers an early potential identification solution for CRR in wheat and has previously been reported with success for crop disease detection. This study investigated the ability of nondestructive NIR spectroscopy in combination with deep neural networks (DNN), logistic regression (LR), and principal component analysis combined with support vector machines (PCA‐SVM) for early‐stage CRR detection in wheat. NIR spectra of five different wheat varieties with varying resistance to CRR were collected in two seasons of glasshouse and three seasons of field trials using a portable spectrometer. Results demonstrated that DNN outperformed LR and PCA‐SVM, achieving 66%–91% average classification accuracy in glasshouse trials and an average accuracy of 73% with up to 87% in field trials, effectively distinguishing inoculated and non‐inoculated wheat plants from seedling to anthesis stages. Validation with a third season of field data achieved an average of 77% accuracy for the most susceptible variety during the stem elongation stage. NIR reflectance within 1600–1700 nm was identified as most important for estimating CRR presence, with initial detection occurring 35 days after sowing (DAS) in the glasshouse and 46 DAS in the field. In conclusion, a NIR spectrometer with a DNN model successfully performed disease classification, with the potential as a portable early disease detection tool to assist farm management decisions.

Early or delayed cord clamping during transition of term newborns: does it make any difference in cerebral tissue oxygenation?

BackgroundAccording to the World Health Organization’s recommendation, delayed cord clamping in term newborns can have various benefits. Cochrane metaanalyses reported no differences for mortality and early neonatal morbidity although a limited number of studies investigated long-term neurodevelopmental outcomes. The aim of our study is to compare the postnatal cerebral tissue oxygenation values in babies with early versus delayed cord clamping born after elective cesarean section.MethodsIn this study, a total of 80 term newborns delivered by elective cesarean section were included. Infants were randomly grouped as early (clamped within 15 s, n:40) and delayed cord clamping (at the 60th second, n:40) groups. Peripheral arterial oxygen saturation (SpO2) and heart rate were measured by pulse oximetry while regional oxygen saturation of the brain (rSO2) was measured with near-infrared spectrometer. Fractional tissue oxygen extraction (FTOE) was calculated for every minute between the 3rd and 15th minute after birth. (FTOE = pulse oximetry value-rSO2/pulse oximetry value). The measurements were compared for both groups.ResultsThe demographical characteristics, SpO2 levels (except postnatal 6th, 8th, and 14th minutes favoring DCC p < 0.05), heart rates and umbilical cord blood gas values were not significantly different between the groups (p > 0.05). rSO2 values were significantly higher while FTOE values were significantly lower for every minute between the 3rd and 15th minutes after birth in the delayed cord clamping group (p < 0.05).ConclusionOur study revealed a significant increase in cerebral rsO2 values and a decrease in FTOE values in the delayed cord clamping (DCC) group, indicating a positive impact on cerebral oxygenation and hemodynamics. Furthermore, the DCC group exhibited a higher proportion of infants with cerebral rSO2 levels above the 90th percentile. This higher proportion, along with a lower of those with such parameter below the 10th percentile, suggest that DCC may lead to the targeted/optimal cerebral oxygenetaion of these babies. As a result, we recommend measuring cerebral oxygenation, in addition to peripheral SpO2, for infants experiencing perinatal hypoxia and receiving supplemental oxygen.

Determination of oil quality during crispy pork rind frying: Near infrared spectra and color values as predictors

This study provides a comprehensive analysis of oil degradation during the frying of various semi-finished pork rinds, including high-fat (HF), non-fat (NF), streaky (SK), and thin (TN) types. By quantitatively assessing parameters such as peroxide, acid, anisidine, and Totox values, we sought to elucidate the complex interactions and effects on oil quality during the frying process. The oil samples underwent evaluation utilizing a near-infrared (NIR) spectrometer and the color variations in the frying oil during the process. The resulting data highlighted significant correlations between color parameters and oil quality. The NIR models exhibited an impressive and robust predictive proficiency with high correlation coefficients values (R > 0.9 for all measured parameters) and low root mean square errors of cross-validation (RMSECVs). They promise rapid, non-destructive, and accurate evaluation of oil quality during frying processes. Additionally, the study identified oil color as a viable, low-cost alternative for predicting oil quality parameters where sophisticated spectroscopic equipment is unavailable.

A detection method of Saposhnikovia divaricata seed vigor based on near-infrared spectral feature extraction

Saposhnikovia divaricata is an important Chinese herbal medicine, the rapid detection of seed vigor is of great significance and practical value for the breeding selection and cultivation production of Saposhnikovia divaricata varieties. It was difficult to solve the problem of rapid testing technology of Saposhnikovia divaricate seed of the shortcomings of traditional chemical testing methods, such as cumbersome operation, time-consuming, easy to cause seed damage, and so on. In this paper, a rapid method for detecting seed vigor was processed for the Saposhnikovia divaricata based on near-infrared spectral feature extraction. First, 612 sets of near-infrared spectral data were obtained using a Fourier transform near-infrared spectrometer, and randomly divided into a calibration set and prediction set according to the ratio of 3:1. Then, the spectral data were preprocessed by the multivariate scatter correction method (MSC), the successive projection algorithm (SPA) was used to extract the feature wave numbers of spectral data. A total of 5 feature wave numbers were selected from the original 1845 wave numbers, with a streamlining rate of 99.73 %. Finally, a detection method of seed vigor was constructed based on the radial basis function neural network (RBF) with 5 feature wave numbers as input data. A network structure of the model MSC-SPA-RBF is of type 5-48-3, the accuracy of seed vigor detection was 99.0 %, and the detection time was 0.0033 s. The result indicates that the established model is a fast, non-destructive, and accurate method for detecting seed vigor of Saposhnikovia divaricata, which provides a theoretical basis and technical support for the rapid detection of Saposhnikovia divaricata seed vigor.

Estimation of botanical composition of forage crops using laboratory-based hyperspectral imaging and near-infrared spectrometer measurements

Harvested forage is the main raw feed for ruminant animals in Sweden, and is commonly cultivated in mixed stands of legume and grass species. The fraction of legume on a dry matter basis, known as botanical composition (BC) is a very important indicator of forage quality. In this study, hyperspectral imaging and near-infrared spectrometer (NIRS) based methods were used to estimate BC, to overcome the shortcomings of hand separation, which is time and resource consuming. Timothy and red clover mix samples were collected from different harvests in 2017–2019 from multiple sites in Northern Sweden and hand separated. The samples were synthetically mixed to 11 different BC levels, i.e., 0–100 % clover content. Two different instruments (Specim shortwave infrared (SWIR) hyperspectral imaging system and Foss 6500 spectrometer) were used to collect spectral data of samples milled to two levels of coarseness. Three different regression analyses: partial least squares regression (PLSR), support vector regression (SVR) and random forest regression (RFR), were used to build BC estimation models. The effects of the milling particle sizes and the different instruments on the performances of the models were compared. The data from second harvest in 2019 were used for independent validation as evaluation, and the rest of data were randomly split for model calibration (75 %) and validation (25 %). The models were iteratively run 1000 times with different splits, to check the effect from the splitting of calibration and validation datasets. Among different regression analyses, PLSR performed best, with mean Nash-Sutcliffe efficiency (NSE) for model evaluation from 0.76 to 0.87, varying for different instruments and milling sizes. Finer milling made the model accuracies slightly higher. This study developed quick and robust methods to determine the BC of timothy grass and red clover mixtures, which can provide useful information for farmers or researchers.

Quantitative determination of zearalenone in wheat by the CSA-NIR technique combined with chemometrics algorithms

In the current study, a colorimetric sensor array combined with near-infrared (NIR) spectroscopy was used to quantitatively analyze zearalenone in wheat. The portable NIR spectrometer was used to scan the porphyrin reaction points of the wheat colorimetric sensor and collect spectral data. Subsequently, based on all the NIR spectral data, the two models and three feature selection algorithms are compared, and the best performance model and the best feature variable input are selected. Concurrently, the Kernel-based Extreme Learning Machine (KELM) model optimized by the two parameter optimization algorithms was compared, and the best parameter optimization algorithm was selected. Among all evaluation models, the KELM model optimized by the Competitive Adaptive Reweighted Sampling algorithm combined with the rime optimization algorithm has the best prediction effect. The predicted RP2 is 0.9900, and the root mean square error of prediction (RMSEP) is 18.4610 μg∙kg-1.