Near-infrared Diffuse Reflectance Research Articles

Ba5Gd3(BO3)6F: A new alkaline earth-rare earth fluoroborate with magnetocaloric effect

A new alkaline-earth and Gd3+ borate, Ba5Gd3(BO3)6F, has been successfully grown through a high-temperature solution method employing a BaF2-NaF-B2O3 flux. This compound crystallizes in the monoclinic space group P21/c with lattice parameters of a = 13.679(4) Å, b = 8.694(3) Å, c = 7.664(2) Å, β = 99.116(7)°, V = 899.9(5) Å3, and Z = 1. Its three-dimensional structure consists of the [Ba/GdO] polyhedra and [FBa4Gd2] octahedra layers. Magnetic analysis reveals that Ba5Gd3(BO3)6F exhibits a magnetocaloric effect with a maximum −ΔSm of 29.86 J kg–1 K–1 at 3 K and 9 T. Additionally, the thermal stability, along with infrared (IR) and ultraviolet–visible–near-infrared (UV–Vis–NIR) diffuse reflectance spectra, were thoroughly examined. First-principles calculations further explored the electronic band structure and densities of states.

Temporal evaluation of soil chemical quality using VNIR and XRF spectroscopies

New soil sensing technologies, such as visible and near-infrared diffuse reflectance spectroscopy (VNIR) and X-ray fluorescence spectroscopy (XRF), emerged as alternatives to low-cost, rapid, and environmental-friendly soil analyses. Studies involving the prediction of integrated soil quality indices using individual and combined soil sensing techniques have not been conducted yet on tropical soils, especially regarding the temporal stability of models' performance. This study evaluated tropical soils to give insight into the potential of using individual and combined VNIR and XRF sensors to predict the Soil Chemical Quality Index (SCQI) and its sub-functions (Nutrient Availability, Acidity/Al Toxicity, and Nutrient Storage and Cycling). Furthermore, the temporal stability of these models' performance and a spiking strategy to mitigate performance loss throughout crop seasons were assessed. We conducted this study using 924 soil samples collected over three years (2018, 2019, and 2022) in a field managed with an integrated crop-livestock system (200 ha) in a Brazilian tropical area. We determined the SCQI and its sub-functions for each sample from 15 soil quality indicators. Soil samples were scanned with VNIR (350–2500 nm) and XRF (0–35 kV) spectrometers. VNIR spectroscopy predicted the SCQI, Nutrient Availability, and Nutrient Storage and Cycling with satisfactory accuracy (models classified as good, good, and excellent, respectively), outperforming the XRF technique and the fusion approach. The prediction performance of SCQI and its sub-functions does not show temporal stability. However, the performance loss can be mitigated using the spiking strategy, i.e., using new data from subsequent seasons to recalibrate the global model.

Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage.

The hardness of passion fruit is a critical feature to consider when determining maturity during post-harvest storage. The capacity of near-infrared diffuse reflectance spectroscopy (NIRS) for non-destructive detection of outer and inner hardness of passion fruit epicarp was investigated in this work. The passion fruits' spectra were obtained using a near-infrared spectrometer with a wavelength range of 10,000-4000 cm-1. The hardness of passion fruit's outer epicarp (F1) and inner epicarp (F2) was then measured using a texture analyzer. Moving average (MA) and mean-centering (MC) techniques were used to preprocess the collected spectral data. Competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) were used to pick feature wavelengths. Grid-search-optimized random forest (Grids-RF) models and genetic-algorithm-optimized support vector regression (GA-SVR) models were created as part of the modeling process. After MC preprocessing and CARS selection, MC-CARS-Grids-RF model with 7 feature wavelengths had the greatest prediction ability for F1. The mean square error of prediction set (RMSEP) was 0.166 gN. Similarly, following MA preprocessing, the MA-Grids-RF model displayed the greatest predictive performance for F2, with an RMSEP of 0.101 gN. When compared to models produced using the original spectra, the R2P for models formed after preprocessing and wavelength selection improved. The findings showed that near-infrared spectroscopy may predict the hardness of passion fruit epicarp, which can be used to identify quality during post-harvest storage.

Near-Infrared Spectroscopy: Assessment of Soil Organic Carbon Stock in a Colombian Oxisol

Soil organic carbon (SOC) is a property known for its influence on the physical, chemical, and biological characteristics of soils, which are essential when assessing their quality. SOC stock (SOCS) monitoring is a key task in climate change mitigation studies. However, the resources necessary to obtain the information required by these studies tend to be high. The objective of this study was to develop a model for estimating the SOCS of a Colombian oxisol using near-infrared (NIR) diffuse reflectance spectroscopy. In a sampling scheme of 70 points distributed over 248 ha, 313 soil samples were collected in five defined depth intervals of 10 cm each, from 0 to 50 cm. SOC was determined through an elemental analyzer, and bulk density (BD) by means of sampling cylinders. A NIRFlex spectrometer was used to acquire spectral signatures in the NIR range from the processed soil samples, and, together with the data measured in the laboratory, a statistical analysis was performed using partial least squares regression (PLSR) in order to calibrate the spectral models. Based on the residual prediction deviation (RPD), the root mean square error (RMSE), and the coefficient of determination (R2) of the validation groups, a highly representative model was achieved for the estimation of SOCS (R2 = 0,93; RMSE = 2,12 tC ha-1; RPD = 3,69), which was also corroborated with geostatistical interpolation surfaces and depth splines. This research showed NIR diffuse reflectance spectroscopy to be a viable technique for SOCS estimation in the study area.

Near-Infrared Spectral Analysis for Assessing Germination Rate of Rapeseed Seeds: An Applied Sciences Approach

Brassica rapa, commonly known as the rapeseed plant, is globally recognized for its nutrient-rich composition and oil-packed seeds, earning its distinction as a substantial oil-seed crop. The seed quality, particularly the germination rate, is instrumental in guaranteeing a high-yield rapeseed crop. Given this, the accurate, quantitative determination and selection of germination rates in seed batches prior to sowing is of paramount importance. However, conventional germination tests, employed to determine the average germination rate of seed batches, are marred by substantial time and cost inefficiencies. This study proposes the use of near-infrared spectral analysis as a proficient, non-invasive approach for assessing germination rates in rapeseed seed batches. The research involved artificial aging of seeds procured from a variety of rapeseed strains, resulting in 228 batches with a broad germination rate spectrum of 15.73% to 99.13%. We recorded near-infrared diffuse reflectance spectra and applied a range of strategies for spectral data preprocessing and feature variable selection. Furthermore, we leveraged support vector regression (SVR) modeling to augment the detection methodology. SVR training and detection were conducted using MATLAB, with selected feature wavelengths undergoing rigorous scrutiny and discussion. The results indicated that employing Savitzky–Golay convolution smoothing for spectral preprocessing, along with Synergy interval Partial Least Squares (SiPLS) in conjunction with Random Frog (RF) for the selection of 50 feature wavelength points, yielded optimal germination rate prediction performance within the SVR model. The coefficients of determination (R2c) for the training set and (R2p) for the testing set were observed to be 0.8559 and 0.8386, respectively, while the Root Mean Square Errors of Calibration (RMSEC) and Prediction (RMSEP) were calculated to be 13.76% and 17.04%. The mechanism of detecting seed vigor through near-infrared spectroscopy was analyzed based on joint variable screening and sensitive variable traceability. Consequently, the SG–SiPLS–RF–SVR model demonstrates its effectiveness in predicting the average germination rate of seed batches, offering a rapid, non-invasive detection method that can be universally applied to various rapeseed strains, thus significantly improving seed production efficiency.

Influence of powder stream density on near infrared measurements upon scale-up of a simulated continuous process

Near-infrared (NIR) spectroscopy is a powerful process analytical tool for monitoring chemical constituents in continuous pharmaceutical processes. However, the density variation introduced when quantitative NIR measurements are performed on powder streams at different flow rates is a potential source of a lack of model robustness. Since different flow rates are often required to meet the production requirements (e.g., during scale-up) of a continuous process, the development of efficient strategies to characterize, understand, and mitigate the impact of powder density on NIR measurements is highly desirable. This study focused on assessing the effect of powder physical variation on NIR by enabling the in-line characterization of powder stream density in a simulated continuous system. The in-line measurements of powder stream density were facilitated through a unique analytical interface to a flowing process. Powder streams delivered at various design levels of flow rate and tube angle were monitored simultaneously by NIR diffuse reflectance spectroscopy, live imaging, and dynamic mass characterization. Statistical analysis and multivariate modeling confirmed powder density as a significant source of spectral variability due to flow rate. Besides providing broader process understanding, results elucidated potential mitigation strategies to facilitate effective continuous process scale-up while ensuring NIR model robustness against density.

Applying DRIFT Spectroscopy for High Temperature Electrochemical Cells-Lessons from SMOX Based Gas Sensors

In operando diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy, the metal oxide sample is illuminated by the infrared beam, and the scattered (rather than transmitted) light is collected with appropriate optics for analysis. In addition to being better suited for operando methods, DRIFT spectroscopy has also been found to be more surface sensitive than other IR techniques suitable for powder samples, such as TIRS, ATR-IRS, and PM-IRRAS.(Roedel et al., 2008) DRIFT spectroscopy has allowed for significant gains in understanding the surface chemistry of semiconducting metal oxide (SMOX) based sensors to be made.Despite the potential usefulness in unravelling different surface mechanisms, ranging from the influence of humidity on the oxygen reduction reaction in fuel cell cathodes to insights about the anodic coking mechanisms when using hydrocarbon fuels, the use of DRIFTs for metal oxide electrochemical cells is still in its infancy, with only a handful of near operando studies being reported. (Cumming et al., 2015; Hauser et al., 2020; Tezel et al., 2022) One critical consideration is the potentially problematic intrinsic IR radiation of samples and the limited stability of cell materials at the high relevant operation temperatures. Nonetheless, using clever cooling systems that prevent the windows from overheating and intricate cell designs to allow for separate oxygen and fuel supply, Cumming et al. and more recently Hauser et al. were able to measure DRIFT spectra at elevated temperatures on cells. (Cumming et al., 2015; Hauser et al., 2020) In both cases, DRIFT spectra were depicted up to 600 ˚C without reports of significant errors due to radiation stemming from the sample. As a result, it is promising that through further optimization, DRIFT spectroscopy could become a useful tool for studying metal oxide based electrochemical systems.For the further optimization, lessons learned during the development of operando cells for SMOX based gases sensors are pertinent. Here a way forward for the optimization will be presented. Starting with practical aspects of DRIFT spectroscopy, e.g. need for precisely controlled room environments in non-evacuated systems or increased signal intensity through window optimization. Common artefacts will be shown, and avoidance strategies will be shown, e.g. ice bands that can be removed through detector evacuation. Additional considerations that are relevant specifically when working with metals oxides will be reviewed and the advantages of selecting the referencing method of Olinger and Griffiths over the traditionally used Kubelka-Munk will be elaborated.(Cumming et al., 2015; Jill M Olinger & Peter R Griffiths, 1988) The usefulness of using isotopically labelled gases to differentiate surface species, e.g. separate metal-oxygen groups from metal-hydroxides, will be discussed. Finally, the potential pitfalls of comparing different samples quantitatively will be highlighted. Overall, this presentation will provide a useful guideline for the future design of an operando DRIFT chamber for oxide based electrochemical cells.BibliographyCumming, D. J., Tumilson, C., Taylor, S. F. R., Chansai, S., Call, A. v., Jacquemin, J., Hardacre, C., & Elder, R. H. (2015). Development of a diffuse reflectance infrared fourier transform spectroscopy (DRIFTS) cell for the in situ analysis of co-electrolysis in a solid oxide cell. Faraday Discussions, 182, 97–111. https://doi.org/10.1039/c5fd00030kHauser, D., Nenning, A., Opitz, A. K., Klötzer, B., & Penner, S. (2020). Spectro-electrochemical setup for in situ and operando mechanistic studies on metal oxide electrode surfaces. Review of Scientific Instruments, 91(8). https://doi.org/10.1063/5.0007435Jill M Olinger, & Peter R Griffiths. (1988). Quantitative effects of an Absorbing Matrix on Near-Infrared Diffuse Reflectance Spectra. Anal. Chem., 60(21), 2427–2435.Roedel, E., Urakawa, A., Kureti, S., & Baiker, A. (2008). On the local sensitivity of different IR techniques: Ba species relevant in NOx storage-reduction. Physical Chemistry Chemical Physics, 10(40), 6190–6198. https://doi.org/10.1039/b808529cTezel, E., Guo, D., Whitten, A., Yarema, G., Freire, M., Denecke, R., McEwen, J.-S., & Nikolla, E. (2022). Elucidating the Role of B-Site Cations toward CO2 Reduction in Perovskite-Based Solid Oxide Electrolysis Cells . Journal of The Electrochemical Society, 169(3), 034532. https://doi.org/10.1149/1945-7111/ac5e9b

Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy.

Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as Escherichia coli, Salmonella enteritidis, Enterococcus faecalis and Listeria monocytogenes. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry.

The potential of near-infrared diffuse reflectance spectroscopy as a tool for characterizing the particle size of dispersion phase in polymer blends

In this study, we investigated the impact of the particle size of dispersion phase in polymer blends on the near-infrared (NIR) diffuse reflectance spectrum. Polypropylene/Polystyrene (PP/PS) blends with different particle sizes of dispersion phase were prepared at the weight ratio of 80/20. When compared to the spectra of the virgin PP, PS, and two-layer sheet of PP-PS, a clear baseline tilt is revealed by the spectrum of the PP/PS blends. By combining the diameter data of the particle size of the PS phase counted by SEM image, the reflectance of the spectrum decreases as the diameter of the particle size of the PS phase increases. In the Monte Carlo simulation results, the number of received scattering photons decreases with an increase in particle size and wavelength respectively, which can well explain the actual spectra variation. The volume mean diameter (Dv) predicted model was built through principal component regression, and the model showed good performance which correlation coefficient (R2) was 0.9532 and root mean square errors of calibration (RMSEC) was 0.2792 μm.

Evaluation of shelf life of egg treated with edible coating by means of NIR spectroscopy and laser induced diffuse reflectance imaging

A coating solution was developed by blending cassava starch at different levels (2%, 3% and 4% w/v) with gelatin and plasticizers to maintain the quality of eggs during 4 weeks of storage (25 ± 1 °C, 60–65% RH). This study suggests the rapid techniques of near-infrared (NIR) spectroscopy and laser-induced diffuse reflectance imaging for determining the internal quality of eggs. The scanning electron microscopy (SEM) micrographs showed that the coatings were effective in protecting eggshells. During storage, coated eggs had lower weight loss and albumen pH, higher Haugh unit and yolk index than control eggs (p < 0.05). These factors are the most common parameters in assessing egg quality. Among the coating formulas, the 4% starch coating had significantly higher effectiveness from the 3rd week of storage. The results indicated that the coating solution containing 4% starch could maintain the egg quality up to 28 days. Furthermore, the NIR spectra were used to develop prediction models for the investigated quality attributes using partial least squares regression (PLSR). Models were created using 3 preprocessing methods of standard normal variate (SNV), first derivative (lD) and second derivative (2D). The PLSR-SNV model achieved the best prediction for weight loss (R2 = 0.883, RPD = 2.920), Haugh unit (R2 = 0.756, RPD = 2.025), and yolk index (R2 = 0.807, RPD = 2.276). Laser scattering was successfully used to detect the egg quality change during storage by linear discriminant analysis (LDA) at wavelengths of 532 nm and 780 nm. The descriptive attributes extracted from intensity profiles showed significant effects of storage time, treatment and their interaction. This research highlighted the promising application of NIR and laser-induced diffuse reflectance imaging for inline control of egg quality at room temperature.

A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy.

Cultivating rice varieties with lower cellulose content in the bran layer has the potential to enhance both the nutritional value and texture of brown rice. This study aims to establish a rapid and accurate method to quantify cellulose content in the bran layer utilizing near-infrared spectroscopy (NIRS), thereby providing a technical foundation for the selection, screening, and breeding of rice germplasm cultivars characterized by a low cellulose content in the bran layer. To ensure the accuracy of the NIR spectroscopic analysis, the potassium dichromate oxidation (PDO) method was improved and then used as a reference method. Using 141 samples of rice bran layer (rice bran without germ), near-infrared diffuse reflectance (NIRdr) spectra, near-infrared diffuse transmittance (NIRdt) spectra, and fusion spectra of NIRdr and NIRdt were used to establish cellulose quantitative analysis models, followed by a comparative evaluation of these models' predictive performance. Results indicate that the optimized PDO method demonstrates superior precision compared to the original PDO method. Upon examining the established models, their predictive capabilities were ranked in the following order: the fusion model outperforms the NIRdt model, which in turn surpasses the NIRdr model. Of all the fusion models developed, the model exhibiting the highest predictive accuracy utilized fusion spectra (NIRdr-NIRdt (1st der)) derived from preprocessed (first derivative) diffuse reflectance and transmittance spectra. This model achieved an external predictive R2p of 0.903 and an RMSEP of 0.213%. Using this specific model, the rice mutant O2 was successfully identified, which displayed a cellulose content in the bran layer of 3.28%, representing a 0.86% decrease compared to the wild type (W7). The utilization of NIRS enables quantitative analysis of the cellulose content within the rice bran layer, thereby providing essential technical support for the selection of rice varieties characterized by lower cellulose content in the bran layer.

Detection of the Adulteration of Dendrobium Huoshanense with Dendrobium Henanense by UV-Vis-Shortwave Near-Infrared Diffuse Reflectance Spectroscopy Combined with Chemometrics.

Dendrobium huoshanense (DHS) is a classic traditional Chinese medicine (TCM) with distinctive medicinal benefits and great economic worth; nevertheless, because of similar tastes and looks, it is simple to adulterate with less expensive substitutes (such as Dendrobium henanense [DHN]). This work aimed to develop a reliable tool to detect and quantify the adulteration of DHS with DHN by using UV-Vis-shortwave near-infrared diffuse reflectance spectroscopy (UV-Vis-SWNIR DRS) combined with chemometrics. Adulterated samples prepared in varying concentrations (0-100%, w/w) were analyzed with UV-Vis-SWNIR DRS methods. Partial least-square-discriminant analysis (PLS-DA) and partial least-squares (PLS) regression techniques were used for the differentiation of adulterated DHN from pure DHS and the prediction of adulteration levels. The PLS-DA classification models successfully differentiated adulterated and nonadulterated DHS with an over 100% correct classification rate. UV-Vis-SWNIR DRS data were also successfully used to predict adulteration levels with a high coefficient of determination for calibration (0.9924) and prediction (0.9906) models and low error values for calibration (3.863%) and prediction (5.067%). UV-Vis-SWNIR DRS, as a fast and environmentally friendly tool, has great potential for both the identification and quantification of adulteration practices involving herbal medicines and foods. UV-Vis-SWNIR DRS combined with chemometrics can be applied to identify and quantify the adulteration of herbal medicines and foods.

Spectral analysis of multiple scattering factors of turbid media for glucose measurement using near-infrared spectroscopy.

Near-infrared (NIR) diffuse reflectance spectroscopy has been widely used for non-invasive glucose measurement in humans, as glucose can induce a significant and detectable optical signal change in tissue. However, the scattering-dominated glucose spectrum in the range of 1000 to 1700nm is easily confused with many other scattering factors, such as particle density, particle size, and tissue refractive index. Our aim is to identify the subtle distinctions between glucose and these factors through theoretical analysis and experimental verification, in order to employ suitable methods for eliminating these interferences, thus increasing the accuracy of non-invasive glucose measurement. We present a theoretical analysis of the spectra of 1000 to 1700nm for glucose and some scattering factors, which is then verified by an experiment on a 3% Intralipid solution. We found that both the theoretical and experimental results show that the effective attenuation coefficient of glucose has distinct spectral characteristics, which are distinct from the spectra caused by particle density and refractive index, particularly in the range of 1400 to 1700nm. Our findings can offer a theoretical foundation for eliminating these interferences in non-invasive glucose measurement, aiding mathematical methods to model appropriately and enhance the accuracy of glucose prediction.

Non-destructive determination of soluble solids content in intact apples using a self-made portable NIR diffuse reflectance instrument

The soluble solids content (SSC) is an important internal quality parameter of fruits that is monitored on the fruit market to meet different consumer demands. The aim of this study was to develop a self-made portable near-infrared (NIR) diffuse reflectance instrument to evaluate and monitor the SSC of intact apples. The visible and near-infrared (Vis-NIR) spectra of 118 ‘Fuji’ apples were collected using a Vis-NIR diffuse reflectance spectroscopic measurement system in the spectral range of 450–1100 nm. Savitzky-Golay convolution smoothing and first derivative methods were subsequently applied to eliminate noise interference and baseline shift. Successive projections algorithm (SPA) was performed to extract feature wavelengths from the pretreatment spectra, and the back-propagation artificial neural network (BP-ANN) and multivariate nonlinear regression (MNLR) models were employed to evaluate the predictive ability of the extracted and selected feature wavelengths for the SSC of apples. Seven wavelengths (881, 890, 901, 926, 941, 951, 978 nm) were selected as optimal feature wavelengths from the extracted feature wavelengths (18 wavelengths). The MNLR model (R2 = 0.953, RMSE = 0.391 %) exhibited a high prediction accuracy compared to the BP-ANN model (R2 = 0.865, RMSE = 0.754 %). Based on the results, NIR combined with the MNLR model could be applied in the self-made portable instrument to innovatively monitor the SSC of apples. Finally, a self-made portable NIR diffuse reflectance instrument was designed and developed, and the spectral information of 298 ‘Fuji’ apples was collected using the instrument. According to the results, the MNLR model could well predict the SSC of apples (R2 = 0.871, RMSE = 0.687 %). The overall results also revealed that the developed portable NIR instrument is a promising device for rapid non-destructive monitoring of fruit SSC, thereby meeting the practical application requirement of postharvest commercial processing systems. This instrument could also be beneficial to customers, growers, and producers.

Detection of Moisture Content of Pinus massoniana Lamb. Seedling Leaf Based on NIR Spectroscopy with a Multi-Learner Model

The growth quality of Pinus massoniana (Lamb.) seedlings is closely related to the survival rate of afforestation. Moisture content detection is an important indicator in the cultivation of forest seedlings because it can directly reflect the adaptability and growth potential of the seedlings to the soil environment. To improve the accuracy of quantitative analysis of moisture content in P. massoniana seedlings using near-infrared spectroscopy, a total of 100 P. massoniana seedlings were collected, and their near-infrared diffuse reflectance spectra were measured in the range of 2500 to 800 nm (12,000 to 4000 cm−1). An integrated learning framework was introduced, and a quantitative detection model for moisture content in P. massoniana seedlings was established by combining preprocessing and feature wavelength selection methods in chemometrics. Our results showed that the information carried by the spectra after multiple scattering correction (MSC) preprocessing had a good response to the target attribute. The stacking learning model based on the full-band spectrum had a prediction coefficient of determination R2 of 0.8819, and the prediction accuracy of moisture content in P. massoniana seedlings could be significantly improved compared to the single model. After variable selection, the spectrum processed by MSC and feature selection with uninformative variable elimination (UVE) showed good prediction effects in all models. Additionally, the prediction coefficient of determination R2 of the support vector regression (SVR)—adaptive boosting (AdaBoost)—partial least squares regression (PLSR) + AdaBoost model reached 0.9430. This indicates that the quantitative analysis model of moisture content in P. massoniana seedlings established through preprocessing, feature selection, and stacking learning models can achieve high accuracy in predicting moisture content in P. massoniana seedlings. This model can provide a feasible technical reference for the precision cultivation of P. massoniana seedlings.

AKTeO2(CO3) (A = Li, Na): The First Carbonatotellurites Featuring a Zero-Dimensional [Te2C2O10]4– Cluster and a Wide Band Gap

The first carbonatotellurites, AKTeO2(CO3) (A = Li, Na), have been successfully synthesized by using boric acid as the mineralizer. AKTeO2(CO3) (A = Li, Na) crystallize in the monoclinic space group P21/n (no. 14), and their structures exhibit the novel zero-dimensional (0D) [Te2C2O10]4- clusters, in which two [TeO4]4- groups form a [Te2O6]4- dimer via edge-sharing, with each side of the dimer attached by a [CO3]2- group via a Te-O-C bridge. The alkali metal cations occupy the voids between the 0D clusters and maintain the charge balance. The ultraviolet-visible-near-infrared diffuse reflectance spectra show that the short absorption cut-off edges of LiKTeO2(CO3) (LKTC) and NaKTeO2(CO3) (NKTC) are 248 and 240 nm, respectively, and LKTC exhibits the largest experimental band gap (4.58 eV) among all of the tellurites containing the π-conjugated anionic groups reported. Theoretical calculations revealed that they exhibit moderate birefringences of 0.029 and 0.040@1064 nm, respectively.

Intracoronary Imaging of Coronary Atherosclerotic Plaque: From Assessment of Pathophysiological Mechanisms to Therapeutic Implication

Atherosclerotic cardiovascular disease is the leading cause of morbidity and mortality worldwide. Several cardiovascular risk factors are implicated in atherosclerotic plaque promotion and progression and are responsible for the clinical manifestations of coronary artery disease (CAD), ranging from chronic to acute coronary syndromes and sudden coronary death. The advent of intravascular imaging (IVI), including intravascular ultrasound, optical coherence tomography and near-infrared diffuse reflectance spectroscopy has significantly improved the comprehension of CAD pathophysiology and has strengthened the prognostic relevance of coronary plaque morphology assessment. Indeed, several atherosclerotic plaque phenotype and mechanisms of plaque destabilization have been recognized with different natural history and prognosis. Finally, IVI demonstrated benefits of secondary prevention therapies, such as lipid-lowering and anti-inflammatory agents. The purpose of this review is to shed light on the principles and properties of available IVI modalities along with their prognostic significance.

A strategy to reduce the effect of organic matter on fluorescence intensity for improving the detection accuracy of polycyclic aromatic hydrocarbons in soil

Fluorescence spectroscopy has been used for rapid detection of PAHs in soil, but soil organic matter (SOM) produces strong interference to the fluorescence intensity of PAHs, which restricts the application of fluorescence spectroscopy for rapid detection of PAHs in soil. A correction method of reducing the interference of SOM on PAHs fluorescence intensity was proposed combining fluorescence and near-infrared (NIR) spectroscopy. Six soil samples with different concentrations of humic acid (HA) at a given phenanthrene concentration (5 mg/g) were prepared and scanned for obtaining the fluorescence and NIR diffuse reflectance spectra. The spectral data showed that the fluorescence intensity and NIR diffuse reflectance had an approximate trend with the change of HA concentration. It was found that the NIR diffuse reflection at 4672 cm−1 as a calibration factor could effectively reduce the interference of HA on the fluorescence intensity of phenanthrene. Subsequently, a standard curve for the quantitative analysis of phenanthrene in soil was established based on the fluorescence intensity before and after calibration. For the unknown samples, the predicted average relative errors of the standard curves before and after calibration were 27.46 % and 9.00 %, respectively. The results showed that the proposed correction method could reduce the interference of HA on the quantitative analysis of PAHs, and provide a reference for eliminating the interference constraint of fluorescence spectroscopy technique for rapid real-time detection of PAHs in soil.

Effect of spectrum measurement position on detection of Klason lignin content of snow pears by a portable NIR spectrometer

AbstractSnow pears are an important and widespread agricultural product that can relieve respiratory symptoms, constipation, and alcoholism. Lignin content (LC) has a direct and negative role on the fruit texture and taste of snow pears. Here, we studied the effect on the near‐infrared (NIR) spectroscopy determination of the LC in snow pears due to the position at which spectral measurements were obtained. NIR diffuse reflection spectra were collected from nine measurement positions on each sample by a portable NIR spectrometer. Partial least squares regression (PLSR) was used to develop spectrum compensation models of the LC for three local spectrum models, an average spectrum model, and a global spectrum model. The results indicated that the prediction accuracy of the LC was affected by the spectral measurement position. Compared with the local spectrum models and the global spectrum model, the average spectrum model had good prediction results. Next, synergy interval partial least squares, bootstrapping soft shrinkage, competitive adaptive reweighted sampling, genetic algorithm, and an improved variable stability and frequency analysis algorithm (VSFAA) method were used to select the most effective variables to build the PLSR model. The average spectrum calibration model established using the 10 effective variables selected by VSFAA reduced the influence of the variation of the spectral measurement position for LC prediction and achieved more promising results, with the correlation coefficient of calibration and prediction of 0.842 and 0.824, respectively. The root mean square error of cross‐validation and prediction were 0.736 and 0.694, respectively. The overall results showed that the average spectrum model based on the nine spectral measurement positions reduced the sensitivity to the variation of spectral measurement position for predicting the LC and combined the VSFAA variable selection algorithm to improve the accuracy and provide a robust model for prediction of LC in snow pears. Compared with the local spectrum position models and the global spectrum position model, the average spectrum position model combining the nine measurement positions (three stem‐calyx longitudes intersected three latitudes (stem, equator, calyx)) produced good prediction results and reduced the sensitivity to the variation of the spectral measurement position. The effective wavelengths (SNV‐VSFAA‐PLSR)‐average spectrum position model achieved good results, reducing the influence of the variation of the spectral measurement position for LC prediction, and the effective wavelengths selected from the average spectrum position model were helpful for offsetting the influence of the variation of spectral measurement position on the PLSR models based on the spectrum from the equatorial positions alone.

Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN.

Internally mildewed sunflower seeds, which cannot be recognized and discarded based on their appearance, pose a serious risk to human health. Thus, there is a need for a rapid non-destructive mildew grade discrimination method. Currently, few reports are available regarding this process. In this study, a method based on the combination of the near-infrared diffuse reflectance and near-infrared diffuse transmission (NIRr-NIRt) fusion spectra and a one-dimension convolutional neural network (1D-CNN) is proposed. The NIRr-NIRt fusion spectra can provide more complementary and comprehensive information, and therefore better discrimination accuracy, than a single spectrum. The first derivative (FD) preprocessing method could further improve the discrimination effect. By comparison against three conventional machine learning algorithms (artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN)), the 1D-CNN model based on the fusion spectra was found to perform the best. The mean prediction accuracy was 2.01%, 5.97%, and 10.55% higher than that of the ANN, SVM, and KNN models, respectively. These results indicate that the CNN model was able to precisely classify the mildew grades with a prediction accuracy of 97.60% and 94.04% for the training and test set, respectively. Thus, this study provides a non-destructive and rapid method for classifying the mildew grade of sunflower seeds with the potential to be applied in the quality control of sunflower seeds.