Near-infrared Reflectance Research Articles

Functional support vector machine.

Linear and generalized linear scalar-on-function modeling have been commonly used to understand the relationship between a scalar response variable (e.g. continuous, binary outcomes) and functional predictors. Such techniques are sensitive to model misspecification when the relationship between the response variable and the functional predictors is complex. On the other hand, support vector machines (SVMs) are among the most robust prediction models but do not take account of the high correlations between repeated measurements and cannot be used for irregular data. In this work, we propose a novel method to integrate functional principal component analysis with SVM techniques for classification and regression to account for the continuous nature of functional data and the nonlinear relationship between the scalar response variable and the functional predictors. We demonstrate the performance of our method through extensive simulation experiments and two real data applications: the classification of alcoholics using electroencephalography signals and the prediction of glucobrassicin concentration using near-infrared reflectance spectroscopy. Our methods especially have more advantages when the measurement errors in functional predictors are relatively large.

Determination of asbestos cement rooftop surface composition using regression analysis and hyper-spectral reflectance data in the visible and near-infrared ranges

The effects of asbestos on human health have spurred numerous studies examining its risks in urban environments. Recent works have shifted towards less-invasive techniques for remote detection and classification of asbestos-cement. In this context, this study combines visible (VIS) and near-infrared (NIR) reflectance data collected in-situ with reference signals from the USGS spectral library, utilizing optimized regression analysis to determine the surface composition of corrugated asbestos-cement rooftops. An outlier filter was successfully implemented to enhance the accuracy of regression calculations, achieving a high level of agreement with actual field observations. The regression analysis revealed varying proportions of weathered cement, hazardous asbestos fibers (specifically chrysotile and cummingtonite), and biological growth (such as lichens and moss). These results are consistent with previous research on the composition of asbestos-cement rooftops, including a comparable field study and XRD analysis conducted in 2019. This underscores the importance of using regression analysis, preceded by an outlier filtering step, on VIS and NIR reflectance data to ascertain the surface composition of asbestos-cement rooftops. This methodology holds potential for application to larger hyperspectral datasets across more extensive sample surfaces and areas.

GOES-R land surface products at Western Hemisphere eddy covariance tower locations

The terrestrial carbon cycle varies dynamically on hourly to weekly scales, making it difficult to observe. Geostationary (“weather”) satellites like the Geostationary Environmental Operational Satellite - R Series (GOES-R) deliver near-hemispheric imagery at a ten-minute cadence. The Advanced Baseline Imager (ABI) aboard GOES-R measures visible and near-infrared spectral bands that can be used to estimate land surface properties and carbon dioxide flux. However, GOES-R data are designed for real-time dissemination and are difficult to link with eddy covariance time series of land-atmosphere carbon dioxide exchange. We compiled three-year time series of GOES-R land surface attributes including visible and near-infrared reflectances, land surface temperature (LST), and downwelling shortwave radiation (DSR) at 314 ABI fixed grid pixels containing eddy covariance towers. We demonstrate how to best combine satellite and in-situ datasets and show how ABI attributes useful for ecosystem monitoring vary across space and time. By connecting observation networks that infer rapid changes to the carbon cycle, we can gain a richer understanding of the processes that control it.

Ni-doped ZnO nanoparticles derived by the sol–gel method: structural, optical, and magnetic characteristics

A simple sol–gel route was used for preparation of Ni doped ZnO nanoparticles (Ni:ZnO) with 0, 3, 5, 7 and 9 wt% of Ni to Zn precursor salts. Structural, optical and room temperature magnetic properties were studied. The prevailing structural arrangement that observed in all the examined samples is the wurtzite structure of ZnO. Nevertheless. The orderly development of NiO rock-salt phase on at the surfaces of ZnO nanoparticles upon Ni doping is confirmed by Rietveld refinements. The ZnO wurtzite structure is dominant in all the samples as confirmed by both XRD and Raman results. The average crystallite size showed almost no change upon Ni doping. RTFM behavior is being exhibited for all Ni:ZnO nanoparticles that is demonstrating using of ZnO as the host structure allowed for the efficient formation of DMS. The Near-infrared solar reflectance (RNIR) and the visible solar reflectance (RVis) values reduced with increasing of the Ni content as a result of the rising in density of trap states upon Ni doping of the samples. Kubelka-Munk method was used to determine the energy band gap (Eg) and band Urbach energy (EU) tails from diffused reflectance (DRS) results. The obtained energy gap values were very close in all studied samples with an average value of energy gap is 3.164 eV. The EU values raise from 123 to 173 meV upon increasing of Ni content in the samples in agreement with lower values of RNIR and RVis and their effect on optical performance of the investigated samples in applications.

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.

Investigating the Utility of Near-Infrared Reflectance Imaging for Diabetic Retinopathy Screening.

We investigated the reliability of near-infrared reflectance (NIR) imaging as a method of assessing severity of diabetic retinopathy (DR). One hundred ninety-five NIR images were reviewed by two graders for the number of hyporeflective foci, presence or absence of vascular abnormalities, and presumptive DR stage; these were correlated to fundus photography-defined DR stage. Interrater reliability was confirmed via one-way random effects model of intraclass correlation coefficients. Analysis of variance was used in subgroup analysis, receiver operating characteristic (ROC) curves were created to validate reliability of the model, and logistic regression was used to model foci and vascular abnormalities as predictors for moderate or worse disease. A statistically significant difference in mean number of hyporeflective foci was found between no DR and moderate non-proliferative DR (NPDR; P < 0.0001), no DR and severe NPDR (P < 0.001), no DR and proliferative DR (PDR; P < 0.0001), mild and moderate NPDR (P = 0.008), mild and severe NPDR (P < 0.001), and mild NPDR and PDR (P < 0.001). The area under the ROC curve was 0.849 (CI: 0.792 to 0.905). The threshold for detection of moderate NPDR or worse was 4.75 foci, with a sensitivity of 79.0% and a false positive rate of 20.0%. Multivariate logistic regression model incorporating hyporeflective foci with vascular abnormalities (odds ratio [OR] = 1.592, 95% CI: 1.381 to 1.835; P < 0.001) was able to accurately predict moderate disease or worse, just moderate disease (OR = 1.045, 95% CI: 1.003 to 1.089; P = 0.035), severe disease (OR = 1.050, 95% CI: 1.006 to 1.096; P = 0.027), and proliferative disease (OR = 1.050, 95% CI: 1.008 to 1.095; P = 0.018). NIR imaging may be an adjunct tool in screening for DR. [Ophthalmic Surg Lasers Imaging Retina 2024;55:XX-XX.].

Melt pond fractions on Arctic summer sea ice retrieved from Sentinel-3 satellite data with a constrained physical forward model

Abstract. The presence of melt ponds on Arctic summer sea ice significantly alters its albedo and thereby the surface energy budget and mass balance. Large-scale observations of melt pond coverage and sea ice albedo are crucial to investigate the role of sea ice for Arctic amplification and its representation in global climate models. We present the new Melt Pond Detection 2 (MPD2) algorithm, which retrieves melt pond, sea ice, and open-ocean fractions as well as surface albedo from Sentinel-3 visible and near-infrared reflectances. In contrast to most other algorithms, our method uses neither fixed values for the spectral albedo of the surface constituents nor an artificial neural network. Instead, it aims for a fully physical representation of the reflective properties of the surface constituents based on their optical characteristics. The state vector X, containing the optical properties of melt ponds and sea ice along with the area fractions of melt ponds and open ocean, is optimized in an iterative procedure to match the measured reflectances and describe the surface state. A major problem in unmixing a compound pixel is that a mixture of half open water and half bright ice cannot be distinguished from a homogeneous pixel of darker ice. In order to overcome this, we suggest constraining the retrieval with a priori information. Initial values and constraint of the surface fractions are derived with an empirical retrieval which uses the same spectral reflectances as implemented in the physical retrieval. The snow grain size and optical thickness change with time, and thus the ice surface albedo changes throughout the season. Therefore, field observations of spectral albedo are used to develop a parameterization of the sea ice optical properties as a function of the temperature history of the sea ice. With these a priori data, the iterative optimization is initialized and constrained, resulting in a retrieval uncertainty of below 8 % for melt pond and 9 % for open-ocean fractions compared to the reference dataset. As reference data for evaluation, a 10 m resolution product of melt pond and open-ocean fraction from Sentinel-2 optical imagery is used.

Estimating macronutrient contents in Thai paddy soils using near-infrared (NIR) spectroscopy and locally weighted partial least square regression analysis

ABSTRACT Soil analysis is a crucial component of agricultural production, as it provides information about the nutrient content in soil to recommend fertilizer guidelines and make informed decisions to optimize crop yields and promote sustainable agriculture. Near-infrared (NIR) reflectance spectroscopy holds great promise as an analytical tool with several advantages, such as rapid, nondestructive, and without the need for chemicals, making it a promising alternative to traditional laboratory methods to determine nutrient contents in soil. This study used two multivariate techniques partial least square regression (PLSR) and locally weighted PLSR (LWPLSR) to construct calibration models to relate NIR spectra to the total nitrogen (total N), available phosphorus (available P), extractable potassium (extractable K) and extractable ammonium (NH4 +) contents in soil samples. The results showed that the LWPLSR model outperformed the PLSR model. The best predictions were obtained using LWPLSR model for total N (R2 = 0.87 and RMSEP = 0.133), available P (R2 = 0.68 and RMSEP = 9.013), extractable K (R2 = 0.84 and RMSEP = 15.436) and extractable NH4 + (R2 = 0.73 and RMSEP = 6.789). This work demonstrated the potential of LWPLSR coupled with NIR spectroscopy for efficient soil analysis, providing the geographical neighbors of the test sample present in the calibration database.

A new green thermal insulating ceramic pigment with high near-infrared reflectance: Mn2+ doped BaAl2O4

In this paper, a series of Ba1-xMnxAl2O4 (x = 0.00 0.02 0.05 0.10 0.15 0.20) green high NIR reflective pigments were prepared by solution gel method and applied to ceramic glaze for thermal insulation evaluation. The experimental results show that Mn2+ ions successfully replace Ba2+ ions without changing the morphology of the phase structure of the materials. The Ba1-xMnxAl2O4 morphology shows nanowire-like shape as detected by SEM. The absorption of Mn2+ ions in the visible region changes the color of the materials from white to green, and with the a* values in the range of −20.28 to −58.17, the materials have excellent coloring properties. The near-infrared reflectivity of the material in the range of 700–2500 nm is 47.35%–68.07%, which is significantly higher than other materials of the same color. In the heat insulation application experiment, the inner surface temperature of ceramic glazes with Ba1-xMnxAl2O4 materials was reduced by 16.17%–23.87% compared to ordinary ceramic samples.

Near Infrared Technology Coupled with Different Spectra Correction Approaches for Fast and Non-Destructive Prediction of Chlorogenic Acid on Intact Coffee Beans

Abstract The primary objective of this research was to utilise near-infrared reflectance spectroscopy as a swift, non-destructive method for identifying chlorogenic acid in whole coffee beans. Additionally, this investigation explored the efficacy of different spectral improvement techniques alongside partial least square regression to construct predictive models. NIR spectral data was gleaned from whole coffee beans spanning a wavelength range of 1000–2500 nm, while the chlorogenic acid content was ascertained via high-performance liquid chromatography procedures. Our findings revealed that the highest coefficient of determination reached for chlorogenic acid was 0.97, and the root mean square error for calibration was 0.31% when using the multiplicative scatter correction method. Furthermore, upon testing the model using an external validation dataset, a determination coefficient of 0.91 and a ratio error to range index of 11.56 with a root mean square prediction error at 0.51% was attained. From these results, it can be inferred that the near-infrared technology, coupled with an effective spectral enhancement process, can facilitate quick, non-invasive determination of chlorogenic acid in whole coffee beans.

Analysis of genotype-by-environment effects on starch content in 281 Tartary buckwheat varieties and evaluation of the physicochemical properties of two elite varieties

Tartary buckwheat (TB) starch is a low-glycaemic-index food source. Analysis of genotype-by-environment effects on starch content is needed to improve high-quality TB production. However, an accurate and high-throughput evaluation method for starch content is a bottleneck for the analysis of genotype-by-environment effects on starch content. In the current study, a rapid and non-destructive method for detecting starch was developed based on near-infrared reflectance (NIR) spectroscopy. Overall, 281 varieties were planted in three environments for 2 years and measured. The coefficient of correlation between the laboratory-determined and NIR model-predicted starch content values was 0.9577. The average starch content of TB varieties had higher variants among the three environments. The genotype-by-environment interaction analysis showed the significant impact of environmental factors on TB starch content. Of them, two varieties (Ft155 and Ft184) with stable and relatively high starch contents (733.43 and 689.16 mg/g) were identified. The amylose content, structure character, thermal properties and pasting character revealed that these physicochemical properties of TB starch were distinct from those of potato and rice. Moreover, Ft155 was more difficult to gelatinise, and Ft184 has desired high-starch-quality cooking properties. These assessments provide robust nutritional quality evaluation approaches and food processing materials.

Estimation of Relative Chlorophyll Content in Lettuce (Lactuca sativa L.) Leaves under Cadmium Stress Using Visible—Near-Infrared Reflectance and Machine-Learning Models

Chlorophyll content is a crucial assessment parameter in the growth monitoring of lettuce, particularly in cases when it is affected by disease. Accurate estimation of chlorophyll content is beneficial for early detection and prevention of diseases and holds significant importance in practical production. To construct a model for estimating the chlorophyll content in lettuce leaves under cadmium stress, this study utilized lettuce as the experimental material. The visible–near-infrared reflectance spectra of lettuce leaves, as well as the relative chlorophyll content of the leaves, were detected and analyzed under different concentrations of cadmium stress. Subsequently, an inversion model for estimating the relative chlorophyll content in lettuce leaves was established. First, to determine the optimal spectral preprocessing method, eight techniques are utilized: Savitzky–Golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variable transformation (SNV), mean normalization (MN), baseline offset (B), detrending (D), gap derivatives—first derivative (FD), and gap derivatives—second derivative (SD). These methods are used to preprocess the spectra and establish a partial least squares regression (PLSR) monitoring model. The optimal spectral preprocessing method is then selected. Next, the feature bands are extracted from the preprocessed spectral data using the correlation coefficient method. Finally, the selected feature bands will be combined with support vector regression (SVR) to establish a chlorophyll content estimation model using a training-to-testing set ratio of 4:1. The results showed that the PLSR model established after preprocessing with detrending (D) had the highest accuracy, with the coefficient of determination (Rv2) and root mean squared error (RMSEv) values of 0.87 and 1.16, respectively. The feature bands selected by the correlation coefficient method were used to establish SVR models for estimating the chlorophyll content of lettuce leaves under cadmium stress, with the highest accuracy being achieved by the genetic algorithm (GA)–SVR model. It can be seen that near-infrared spectroscopy technology provides a scientific basis for rapid, nondestructive, and accurate detection of lettuce diseases and stress.

Harvest Timing of Standing Corn Using Near-Infrared Reflectance Spectroscopy.

Harvesting corn at the proper maturity is important for managing its nutritive value as livestock feed. Standing whole-plant moisture content is commonly utilized as a surrogate for corn maturity. However, sampling whole plants is time consuming and requires equipment not commonly found on farms. This study evaluated three methods of estimating standing moisture content. The most convenient and accurate approach involved predicting ear moisture using handheld near-infrared reflectance spectrometers and applying a previously established relationship to estimate whole-plant moisture from the ear moisture. The ear moisture model was developed using a partial least squares regression model in the 2021 growing season utilizing reference data from 610 corn plants. Ear moisture contents ranged from 26 to 80 %w.b., corresponding to a whole-plant moisture range of 55 to 81 %w.b. The model was evaluated with a validation dataset of 330 plants collected in a subsequent growing year. The model could predict whole-plant moisture in 2022 plants with a standard error of prediction of 2.7 and an R2P of 0.88. Additionally, the transfer of calibrations between three spectrometers was evaluated. This revealed significant spectrometer-to-spectrometer differences that could be mitigated by including more than one spectrometer in the calibration dataset. While this result shows promise for the method, further work should be conducted to establish calibration stability in a larger geographical region.

Improving retrieval of leaf chlorophyll content from Sentinel-2 and Landsat-7/8 imagery by correcting for canopy structural effects

Accurately estimating leaf chlorophyll content (Chl) at large spatial scales is crucial for monitoring agricultural production and plant photosynthesis. Sentinel-2 and Landsat-7/8 offer the potential to assess Chl with high spatial resolutions using various physically-based, empirical, and hybrid methods, but they still present challenges due to the confounding effects of canopy structure and soil background. The near-infrared reflectance of vegetation (NIRV) has been proposed to characterize canopy structural and soil background effects in sun-induced chlorophyll fluorescence. Therefore, in this study, we developed a novel strategy that incorporates NIRV to minimize canopy structural and soil background effects on Chl retrieval, using bidirectional reflectance factor (BRF) from Sentinel-2 and Landsat-7/8 imagery. We compared Chl estimation using empirical random forest (RF), the physically-based PROSAIL-5B inversion, and a hybrid method using RF to train simulated datasets from PROSAIL-5B. We validated Chl retrieval performance with measured datasets from the National Ecological Observatory Network, covering seven vegetation types. We also compared spatiotemporal Chl variations from Sentinel-2, Landsat-7/8, and MODIS. The results showed that NIRV effectively characterized the canopy structural effect on Chl estimation, associated with leaf area index, average leaf angle, and canopy fraction of senescent leaves. Compared to BRF, correcting BRF by NIRV improved Chl retrieval with RMSE values reduced by 12.2%–27.0% for Sentinel-2, 7.4%–16.4% for Landsat-7, and 6.9%–13.7% for Landsat-8, respectively. This also resulted in a significant increase in R2 values for Sentinel-2 (0.13–0.46), Landsat-7 (0.08–0.19), and Landsat-8 (0.12–0.2), respectively. Sentinel-2 outperformed Landsat-7/8 in Chl estimation, due to the contribution of red edge bands and a higher spatial resolution. Furthermore, the hybrid method performed best for Chl estimation from Sentinel-2 and Landsat-7/8 with relative RMSE values of 17.33%, 20.22%, and 19.23%, respectively. Our hybrid method demonstrated lower model uncertainty with smaller variations of RMSE values across varying biochemical and structural traits and solar and viewing angles, and exhibited more consistent spatiotemporal variations of Chl from Sentinel-2 with phenology compared to Landsat-7/8 and the published MODIS Chl data. Our hybrid method demonstrates significant potential in mitigating canopy structural effects, enhancing Chl retrieval, and facilitating the development of Chl data from various satellite imagery sources.

A black pigment that shows strong near-infrared reflectivity for solar heat control

Solar thermal energy regulation is important for sustainable development, comfortable housing, and living. Black compounds are usually considered as an absorber of solar heat. This work reports a series of black near-infrared reflective pigments, Ca2-yMgyMn(1)1-xMn(2)xO4 (x = 0–1, y = 0–0.5), which show strong near-infrared (NIR) reflectivity. Comprehensive analyses were carried out to characterize the obtained pigments including X-ray diffraction, scanning electron microscopy, Raman spectroscopy, and UV–Vis–NIR spectrophotometry. Through the substitution of MnO2 with Mn2O3, NIR solar reflectance of the black pigment can be enhanced by 13%, which might be mainly attributed to differences in particle size and lattice parameters. Meanwhile, through Mg doping, the obtained pigment Ca1.8Mg0.2MnO4 shows excellent color performances (L* = 43, a* = 0.36, b* = −0.37) and NIR solar reflectance (R*, ∼60%). EnergyPlus simulation results show that for a warm city (Haikou), using the Ca1.8Mg0.2MnO4 coating (L* = 25, R* = 51%) can reduce annual cooling energy consumption by 21%.

Environmentally friendly YIn0.9Fe0.1O3-ZnO yellow pigment displaying near infra red (NIR) reflectance and photocatalytic activity

In this research, we produced ochre/yellow pigments of YIn0.9Fe0.1O3, combined with ZnO pigments, through a citrate-assisted sol-gel approach. We investigated the impact of varying ZnO addition methods (incorporated into the sol-gel bath and physically mixed with YIn0.9Fe0.1O3 pigments) on their color properties, near-infrared (NIR) reflectance and photocatalytic activity. The effect of the different ZnO addition methods was investigated on pprecursors and final calcined pigments through X-ray diffraction (XRD), Vis-NIR reflectance, Fourier transform infrared spectroscopy (FT-IR) Scanning Electron Microscope (SEM) and thermal analyses such as thermogravimetry (TGA), and differential scanning calorimetry (DSC). The synthesized nanocomposites were evaluated for their photocatalytic characteristics by examining the degradation of 4-Nitrophenol (4-NP) when exposed to UV radiation. The potential environmental applications of the pigments were investigated not only for direct pollutant degradation but also in terms of energetic sustainability. In fact, the developed pigments can be employed as passive cooling materials to mitigate the temperature of buildings, thus dramatically reducing energy consumption. For this reason, the pigments were embedded into a polymeric binder and coated onto roofing aluminum sheets. The obtained coatings were investigated in terms of NIR reflectance and heat adsorption. The photocatalytic ability of the coatings was assessed by the degradation of 4-NP and methylene blue (MB) under xenon arc lamp. The results obtained indicate the feasibility of producing a pale yellow pigment with both NIR reflectance and photocatalytic capabilities.

Response of Vegetation Productivity to Greening and Drought in the Loess Plateau Based on VIs and SIF

In the context of global warming, the frequent occurrence of drought has become one of the main reasons affecting the loss of gross primary productivity (GPP) of terrestrial ecosystems. Under the influence of human activities, the vegetation greening trend of the Loess Plateau increased significantly. Therefore, it is of great significance to study the response of GPP to drought in the Loess Plateau under the greening trend. Here, we comprehensively assessed the ability of vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) to capture GPP changes at different seasonal scales and during drought. Specifically, we utilized three vegetation indices: normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRV), and kernel NDVI index (kNDVI), and determined the drought period of the Loess Plateau in 2001 based on the standardized precipitation evapotranspiration index (SPEI) and the standardized soil moisture index (SSMI). Moreover, the anomalies of VIs and SIF during the drought period and the relationship with GPP anomalies were compared. The results showed that both SIF and VIs were able to capture changes during the drought period as well as in normal years. Overall, SIF captured drought changes better due to water and heat stress as well as GPP changes compared to VIs. Across different time scales, SIF showed the strongest relationship with GPP (meanR2 = 0.85), followed by NIRV (meanR2 = 0.84), NDVI (meanR2 = 0.76), and kNDVI (meanR2 = 0.74), suggesting that SIF is more sensitive to physiological changes in vegetation. Notably, kNDVI performed best in sparse vegetation (meanR2 = 0.85). In capture during drought, NIRV and kNDVI performed better in less productive land classes; SIF showed superior capture as land use class productivity increased. In addition, GPP anomalies correlated better with kNDVI anomalies (meanR2 = 0.50) than with other index anomalies. In the future, efforts to integrate the respective strengths of SIF, NIRV, and kNDVI will improve our understanding of GPP changes.

Mineralizer and charge-compensator synergistically enhanced novel (Li,Cr)–ZrSiO4 jade green pigment and its near-infrared reflection performance

A novel (Li,Cr)–ZrSiO4 jade green ceramic pigment was prepared using LiF as the mineralizer and synergistically enhanced charge-compensator. The effects of Cr/Si mole ratio on the synthesis and coloration of the pigment and the coloring mechanism were investigated via XRD, SEM, TEM, XPS, EPR, NMR, UV–Vis–NIR and CIELAB colorimetry. The results demonstrate that the charge-compensator promotes the synthesis of ZrSiO4, and more Cr enters into the zircon lattice. When the n(Cr)/n(Si) is 0.2, the pigments exhibit a jade green hue (L* = 75.74, a* = -7.37, b* = 5.18). The band of the (Li,Cr)–ZrSiO4 pigments in the visible light region shifts towards positive wavelength (535 nm), which is closer to the center of the green light region. The novel pigments have excellent coloring performance stability while in a base glaze synthesized at 1200 °C and near-infrared reflectance in the range of 700–2500 nm. The co-doping of lithium balances bonding, thereby improving near-infrared performance, which shows broad development prospects in the field of ceramic applications and energy saving.

Detection of counterfeiting rice bran using near-infrared reflectance spectroscopy (NIRS)

This study aims to compare rice bran quality from different origins and to differentiate rice bran from rice husk based on the spectrum produced by Near Infrared Reflectance Spectroscopy (NIRS) technology. The rice bran used in this study comes from mills and poultry shops in West Java and Sumatra. This study used husks as an adulteration mixing material in rice bran with mixing levels of 25% rice bran: 75% husk, 50% rice bran: 50% husk, and 75% rice bran: 25% husk. Proximate compositions (water content, ash content, ether extract (EE), crude protein (CP), and crude fiber (CF)) were observed. NIRS spectra were collected using the Buchi NIRCal 5.2 application. The results showed an interaction between provider and origin on rice bran EE, CP, and CF contents. Rice bran provided by the miller had better quality with higher CP (17.3% vs. 12.2%) and lower CF (12.0% vs. 16.6%) content in the West Java origin case and higher EE (16.5% vs. 11.1%) in West Sumatera case. The broad spectra distribution showed the variety of the rice bran quality from the poultry shop. The artificial adulteration spectra confirm the impure rice bran quality from the poultry shop.

Transformative shifts in dough and bread structure with pumpkin seed protein concentrate enrichment

Pumpkin seed protein concentrate, a promising raw material with high biological value, holds potential for incorporation into bread technology. Protein structural changes occur during the bread-making process. This study aimed to investigate how pumpkin seed protein concentrate incorporated at different concentrations (5, 10, and 20%) affected the structural, mechanical properties, and conformational changes in protein substances within dough and bread made from wheat flour, utilizing near-infrared reflection spectroscopy. The gas- and shape-holding capacities of dough enriched and unenriched by pumpkin seed protein concentrate resulted similar. This aspect is important for the impact of the final product as bread on the consumers. The near-infrared reflection spectroscopy analysis revealed that the enrichment of bread with pumpkin seed protein concentrate not only introduced higher protein and unsaturated fat content in bread but also reduced the level of lignin, enhancing the digestibility of the final product. A deeper investigation into the nutraceutical and nutritional value of this enriched bread as well as of physio-chemical features investigated in the present work could be useful for the introduction of a new functional food in the market. Moreover, the enriched bread was acceptable in terms of sensory quality analysis, especially using 10% pumpkin seed protein concentrate. Therefore, to better understand the market potentiality of the enriched bread proposed in this study, future research should investigate how consumer acceptability could increase with a higher concentration of nutritional components perceived as beneficial for a lifestyle by specific consumer segments.