Tuning the Solid Phase Fluorescence Emission from Long Wavelength Visible to Near-Infrared in Oxazol-5-One Derivatives: Structure-Property Relationship, Theoretical and Experimental Studies.

Most of the fluorescent molecules among organic [Formula: see text]-conjugated materials show blue or green emission in the solid phase but few of them emit red-shifted visible and near-infrared light in the material science. To create molecules emitting for this feature, two π-conjugated oxazol-5-one derivatives containing donor (OCH3) and acceptor groups (NO2) were synthesized. Their optical and charge-transport properties were investigated through experimental and theoretical methods including the single crystal X-ray crystallography, Hirshfeld Surface Analysis, photophysical studies and Density Functional Theory (DFT), respectively. In addition, FT-IR, 1H-NMR, 13C-NMR spectroscopy, cyclic voltammetry (CV) measurements were performed. According to our results, both molecules may provide the significant pathway of development of long wavelength visible and red emissive features in solid phase with the aggregation induced enhanced emission (AIEE) properties particularly in the fields of OLEDs, optical communication, defence and bioimaging.

OpenVNT: An Open Platform for VIS-NIR Technology.

Spectrometers measure diffuse reflectance and create a "molecular fingerprint" of the material under investigation. Ruggedized, small scale devices for "in-field" use cases exist. Such devices might for example be used by companies in the food supply chain for inward inspection of goods. However, their application for the industrial Internet of Things workflows or scientific research is limited due to their proprietary nature. We propose an open platform for visible and near-infrared technology (OpenVNT), an open platform for capturing, transmitting, and analysing spectral measurements. It is built for use in the field, as it is battery-powered and transmits data wireless. To achieve high accuracy, the OpenVNT instrument contains two spectrometers covering a wavelength range of 400-1700 nm. We conducted a study on white grapes to compare the performance of the OpenVNT instrument against the Felix Instruments F750, an established commercial instrument. Using a refractometer as ground truth, we built and validated models to estimate the Brix value. As a quality measure, we used coefficient of determination of the cross-validation (R2CV) between the instrument estimation and ground truth. With 0.94 for the OpenVNT and 0.97 for the F750, a comparable R2CV was achieved for both instruments. OpenVNT matches the performance of commercially available instruments at one tenth of the price. We provide an open bill of materials, building instructions, firmware, and analysis software to enable research and industrial IOT solutions without the limitations of walled garden platforms.

Comparison of Depth-Specific Prediction of Soil Properties: MIR vs. Vis-NIR Spectroscopy.

The prediction of soil properties at different depths is an important research topic for promoting the conservation of black soils and the development of precision agriculture. Mid-infrared spectroscopy (MIR, 2500-25000 nm) has shown great potential in predicting soil properties. This study aimed to explore the ability of MIR to predict soil organic matter (OM) and total nitrogen (TN) at five different depths with the calibration from the whole depth (0-100 cm) or the shallow layers (0-40 cm) and compare its performance with visible and near-infrared spectroscopy (vis-NIR, 350-2500 nm). A total of 90 soil samples containing 450 subsamples (0-10 cm, 10-20 cm, 20-40 cm, 40-70 cm, and 70-100 cm depths) and their corresponding MIR and vis-NIR spectra were collected from a field of black soil in Northeast China. Multivariate adaptive regression splines (MARS) were used to build prediction models. The results showed that prediction models based on MIR (OM: RMSEp = 1.07-3.82 g/kg, RPD = 1.10-5.80; TN: RMSEp = 0.11-0.15 g/kg, RPD = 1.70-4.39) outperformed those based on vis-NIR (OM: RMSEp = 1.75-8.95 g/kg, RPD = 0.50-3.61; TN: RMSEp = 0.12-0.27 g/kg; RPD = 1.00-3.11) because of the higher number of characteristic bands. Prediction models based on the whole depth calibration (OM: RMSEp = 1.09-2.97 g/kg, RPD = 2.13-5.80; TN: RMSEp = 0.08-0.19 g/kg, RPD = 1.86-4.39) outperformed those based on the shallow layers (OM: RMSEp = 1.07-8.95 g/kg, RPD = 0.50-3.93; TN: RMSEp = 0.11-0.27 g/kg, RPD = 1.00-2.24) because the soil sample data of the whole depth had a larger and more representative sample size and a wider distribution. However, prediction models based on the whole depth calibration might provide lower accuracy in some shallow layers. Accordingly, it is suggested that the methods pertaining to soil property prediction based on the spectral library should be considered in future studies for an optimal approach to predicting soil properties at specific depths. This study verified the superiority of MIR for soil property prediction at specific depths and confirmed the advantage of modeling with the whole depth calibration, pointing out a possible optimal approach and providing a reference for predicting soil properties at specific depths.

Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features.

The soil organic matter (SOM) content is a key factor affecting the function and health of soil ecosystems. For measurements of land reclamation and soil fertility, SOM monitoring using visible and near-infrared spectroscopy (Vis-NIR) is one approach to quantifying soil quality, and Vis-NIR is important for monitoring the SOM content in a broad and nondestructive manner. To investigate the influence of environmental factors and Vis-NIR spectroscopy in estimating SOM, 249 soil samples were collected from the Werigan-Kuqa oasis in Xinjiang, China, and their spectral reflectance, SOM content and soil salinity were measured. To classify and improve the prediction accuracy, we also take into account the soil salinity content as a variable indicator. Relevant environmental variables were extracted using remote sensing datasets (land-use/land-cover (LULC), digital elevation model (DEM), World Reference Base for Soil Resources (WRB), and soil texture). On the basis of Savitzky-Golay (S-G) smoothing and first derivative (FD) preprocessing of the original spectrum, three clusters were obtained by K-means clustering through the use of Vis-NIR and used as spectral classification variables. Using Vis-NIR as Model 1, Vis-NIR combined with spectral classification as Model 2, environmental variables as Model 3, and the combination of all the above variables (Vis-NIR, spectral classification, environmental variables, and soil salinity) as Model 4, a SOM content estimation model was constructed using partial least squares regression (PLSR). Using the 249 soil samples, the modeling set contained 166 samples and the validation set contained 83 samples. The results showed that Model 2 (validation r2 = 0.78) was better than Model 1 (validation r2 = 0.76). The prediction accuracy for Model 4 (validation r2 = 0.85) was better than Model 2 (validation r2 = 0.78). Among these, Model 3 was the worst (validation r2 = 0.39). Therefore, the combination of environmental variables with Vis-NIR spectroscopy to estimate SOM content is an important method and has important implications for improving the accuracy of SOM predictions in arid regions.

Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils.

In contrast with classic bench-top hyperspectral (multispectral)-sensor-based instruments (spectrophotometers), the portable ones are rugged, relatively inexpensive, and simple to use; therefore, they are suitable for field implementation to more closely examine various soil properties on the spot. The purpose of this study was to evaluate two portable spectrophotometers to predict key soil properties such as texture and soil organic carbon (SOC) in 282 soil samples collected from proportional fields in four Canadian provinces. Of the two instruments, one was the first of its kind (prototype) and was a mid-infrared (mid-IR) spectrophotometer operating between ~5500 and ~11,000 nm. The other instrument was a readily available dual-type spectrophotometer having a spectral range in both visible (vis) and near-infrared (NIR) regions with wavelengths ranging between ~400 and ~2220 nm. A large number of soil samples (n = 282) were used to represent a wide variety of soil textures, from clay loam to sandy soils, with a considerable range of SOC. These samples were subjected to routine laboratory soil analysis before both spectrophotometers were used to collect diffuse reflectance spectroscopy (DRS) measurements. After data collection, the mid-IR and vis-NIR spectra were randomly divided into calibration (70%) and validation (30%) sets. Partial least squares regression (PLSR) was used with leave one out cross-validation techniques to derive the spectral calibrations to predict SOC, sand, and clay content. The performances of the calibration models were reevaluated on the validation set. It was found that sand content can be predicted more accurately using the portable mid-IR spectrophotometer and clay content is better predicted using the readily available dual-type vis-NIR spectrophotometer. The coefficients of determination (R2) and root mean squared error (RMSE) were determined to be most favorable for clay (0.82 and 78 g kg-1) and sand (0.82 and 103 g kg-1), respectively. The ability to predict SOC content precisely was not particularly good for the dataset of soils used in this study with an R2 and RMSE of 0.54 and 4.1 g kg-1. The tested method demonstrated that both portable mid-IR and vis-NIR spectrophotometers were comparable in predicting soil texture on a large soil dataset collected from agricultural fields in four Canadian provinces.

Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics.

Honey is a natural product that is considered globally one of the most widely important foods. Various studies on authenticity detection of honey have been fulfilled using visible and near-infrared (Vis-NIR) spectroscopy techniques. However, there are limited studies on stingless bee honey (SBH) despite the increase of market demand for this food product. The objective of this work was to present the potential of Vis-NIR absorbance spectroscopy for profiling, classifying, and quantifying the adulterated SBH. The SBH sample was mixed with various percentages (10−90%) of adulterants, including distilled water, apple cider vinegar, and high fructose syrup. The results showed that the region at 400−1100 nm that is related to the color and water properties of the samples was effective to discriminate and quantify the adulterated SBH. By applying the principal component analysis (PCA) on adulterants and honey samples, the PCA score plot revealed the classification of the adulterants and adulterated SBHs. A partial least squares regression (PLSR) model was developed to quantify the contamination level in the SBH samples. The general PLSR model with the highest coefficient of determination and lowest root means square error of cross-validation (RCV2=0.96 and RMSECV=5.88 %) was acquired. The aquaphotomics analysis of adulteration in SBH with the three adulterants utilizing the short-wavelength NIR region (800−1100 nm) was presented. The structural changes of SBH due to adulteration were described in terms of the changes in the water molecular matrix, and the aquagrams were used to visualize the results. It was revealed that the integration of NIR spectroscopy with aquaphotomics could be used to detect the water molecular structures in the adulterated SBH.

Rapid Foreign Object Detection System on Seaweed Using VNIR Hyperspectral Imaging.

The consumption of seaweed is increasing year by year worldwide. Therefore, the foreign object inspection of seaweed is becoming increasingly important. Seaweed is mixed with various materials such as laver and sargassum fusiforme. So it has various colors even in the same seaweed. In addition, the surface is uneven and greasy, causing diffuse reflections frequently. For these reasons, it is difficult to detect foreign objects in seaweed, so the accuracy of conventional foreign object detectors used in real manufacturing sites is less than 80%. Supporting real-time inspection should also be considered when inspecting foreign objects. Since seaweed requires mass production, rapid inspection is essential. However, hyperspectral imaging techniques are generally not suitable for high-speed inspection. In this study, we overcome this limitation by using dimensionality reduction and using simplified operations. For accuracy improvement, the proposed algorithm is carried out in 2 stages. Firstly, the subtraction method is used to clearly distinguish seaweed and conveyor belts, and also detect some relatively easy to detect foreign objects. Secondly, a standardization inspection is performed based on the result of the subtraction method. During this process, the proposed scheme adopts simplified and burdenless calculations such as subtraction, division, and one-by-one matching, which achieves both accuracy and low latency performance. In the experiment to evaluate the performance, 60 normal seaweeds and 60 seaweeds containing foreign objects were used, and the accuracy of the proposed algorithm is 95%. Finally, by implementing the proposed algorithm as a foreign object detection platform, it was confirmed that real-time operation in rapid inspection was possible, and the possibility of deployment in real manufacturing sites was confirmed.

Experimental Investigation of the Dielectric Constants of Thin Noble Metallic Films Using a Surface Plasmon Resonance Sensor.

Gold and silver have an extremely low refractive index value of about 0.04 in the visible to near infrared (NIR) regions, and this induces a relative error of about 50% in refractive index measurements. This can lead to a large uncertainty in the imaginary part of the dielectric constants. A large difference exists between the experimental results and the classic models. The surface plasmon resonance (SPR) sensors, which use tens of nanometer thick noble metal film as the sensing layer, show ultra-high sensitivity (reaching 10-8 RIU) in this spectral range. As the spectral sensitivity and amplitude of SPR curves depend on the thickness and the dielectric constant of the sensing layer, we obtained high precision optical constants of the noble metal film using a multi-wavelength angle-modulated SPR sensing technology. The dielectric constant inferred from the parameters of the SPR curves, rather than from the refractive index and absorption ratio of noble metals, introduced a relative error within 10% of the resonance angle measurement. The measurement results demonstrate that the dielectric constants of gold and silver nano-films are more consistent with the widely used experimental results than with the classical theoretical model and always fall in the upper half of the imaginary part of the uncertainty range in the spectra of 500-900 nm.

Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions.

Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg-1) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R2), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map.

Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging.

Soil water content is one of the most important physical indicators of landslide hazards. Therefore, quickly and non-destructively classifying soils and determining or predicting water content are essential tasks for the detection of landslide hazards. We investigated hyperspectral information in the visible and near-infrared regions (400-1000 nm) of 162 granite soil samples collected from Seoul (Republic of Korea). First, effective wavelengths were extracted from pre-processed spectral data using the successive projection algorithm to develop a classification model. A gray-level co-occurrence matrix was employed to extract textural variables, and a support vector machine was used to establish calibration models and the prediction model. The results show that an optimal correct classification rate of 89.8% could be achieved by combining data sets of effective wavelengths and texture features for modeling. Using the developed classification model, an artificial neural network (ANN) model for the prediction of soil water content was constructed. The input parameter was composed of Munsell soil color, area of reflectance (near-infrared), and dry unit weight. The accuracy in water content prediction of the developed ANN model was verified by a coefficient of determination and mean absolute percentage error of 0.91 and 10.1%, respectively.

Screening Risk Assessment of Agricultural Areas under a High Level of Anthropopressure Based on Chemical Indexes and VIS-NIR Spectroscopy.

Intensive anthropogenic activity may result in uncontrolled release of various pollutants that ultimately accumulate in soils and may adversely affect ecosystems and human health. Hazard screening, prioritisation and subsequent risk assessment are usually performed on a chemical-by-chemical basis and need expensive and time-consuming methods. Therefore, there is a need to look for fast and reliable methods of risk assessment and contamination prediction in soils. One promising technique in this regard is visible and near infrared (VIS-NIR) spectroscopy. The aim of the study was to evaluate potential environmental risk in soils subjected to high level of anthropopressure using VIS-NIR spectroscopy and to calculate several risk indexes for both individual polycyclic aromatic hydrocarbons (PAHs) and their mixture. Results showed that regarding 16PAH concentration, 78% of soil samples were contaminated. Risk assessment using the most conservative approach based on hazard quotients (HQ) for 10 individual PAHs allowed to conclude that 62% of the study area needs further action. Application of concentration addition or response addition models for 16PAHs mixture gave a more realistic assessment and indicates unacceptable risk in 23% and 55% of soils according to toxic units (TUm) and toxic pressure (TPm) approach. Toxic equivalency quotients (TEQ) were below the safe limit for human health protection in 88% of samples from study region. We present here the first attempt at predicting risk indexes using VIS-NIR spectroscopy. The best results were obtained with binary models. The accuracy of binary model can be ordered as follows: TPm (71.6%) < HI (85.1%) < TUm (87.9%) and TEQ (94.6%). Both chemical indexes and VIS-NIR can be successfully applied for first-tier risk assessment.

Nondestructive Determination of Diastase Activity of Honey Based on Visible and Near-Infrared Spectroscopy.

The activities of enzymes are the basis of evaluating the quality of honey. Beekeepers usually use concentrators to process natural honey into concentrated honey by concentrating it under high temperatures. Active enzymes are very sensitive to high temperatures and will lose their activity when they exceed a certain temperature. The objective of this work is to study the kinetic mechanism of the temperature effect on diastase activity and to develop a nondestructive approach for quick determination of the diastase activity of honey through a heating process based on visible and near-infrared (Vis/NIR) spectroscopy. A total of 110 samples, including three species of botanical origin, were used for this study. To explore the kinetic mechanism of diastase activity under high temperatures, the honey of three kinds of botanical origins were processed with thermal treatment to obtain a variety of diastase activity. Diastase activity represented with diastase number (DN) was measured according to the national standard method. The results showed that the diastase activity decreased with the increase of temperature and heating time, and the sensitivity of acacia and longan to temperature was higher than linen. The optimum temperature for production and processing is 60 °C. Unsupervised clustering analysis was adopted to detect spectral characteristics of these honeys, indicating that different botanical origins of honeys can be distinguished in principal component spaces. Partial least squares (PLS) and least squares-support vector machine (LS-SVM) algorithms were applied to develop quantitative relationships between Vis/NIR spectroscopy and diastase activity. The best result was obtained through Gaussian filter smoothing-standard normal variate (GF-SNV) pretreatment and the LS-SVM model, known as GF-SNV-LS-SVM, with a determination coefficient (R²) of prediction of 0.8872, and root mean square error (RMSE) of prediction of 0.2129. The overall results of this paper showed that the diastase activity of honey can be determined quickly and non-destructively with Vis/NIR spectral methods, which can be used to detect DN in the process of honey production and processing, and to maximize the nutrient content of honey.

Vis-NIR spectroscopy with moving-window PLS method applied to rapid analysis of whole blood viscosity.

A rapid analytical method of human whole blood viscosity with low, medium, and high shear rates [WBV(L), WBV(M), and WBV(H), respectively] was developed using visible and near-infrared (Vis-NIR) spectroscopy combined with a moving-window partial least squares (MW-PLS) method. Two groups of peripheral blood samples were collected for modeling and validation. Separate analytical models were established for male and female groups to avoid interference in different gender groups and improve the homogeneity and prediction accuracy. Modeling was performed for multiple divisions of calibration and prediction sets to avoid over-fitting and achieve parameter stability. The joint analysis models for three indicators were selected through comprehensive evaluation of MW-PLS. The selected joint analysis models were 812-1278 nm for males and 670-1146 nm for females. The root-mean-square errors (SEP) and the correlation coefficients of prediction (RP) for all validation samples were 0.54 mPa•s and 0.91 for WBV(L), 0.25 mPa•s and 0.92 for WBV(M), and 0.22 mPa•s and 0.90 for WBV(H). Results indicated high prediction accuracy, with prediction values similar to the clinically measured values. Overall, the findings confirmed the feasibility of whole blood viscosity quantification based on Vis-NIR spectroscopy with MW-PLS. The proposed rapid and simple technique is a promising tool for surveillance, control, and treatment of cardio-cerebrovascular diseases in large populations. Graphical Abstract The caption/legend of the online abstract figure: The selected wavebands and the prediction effects for the three indicators of whole blood viscosity.

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils.

This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF), artificial neural network (ANN), radial basis function- and linear function- based support vector machine (RBF- and LF-SVM) were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs). The statistical significance of the difference between models was evaluated by using McNemar's test (Z value). The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%), ANN (OA = 89%), RBF- (OA = 89%) and LF-SVM (OA = 87%) had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05). These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies.

Development of an Optical Detector of Thrombus Formation on the Pivot Bearing of a Rotary Blood Pump.

Continuous optical monitoring of thrombus formation in extracorporeal mechanical circulatory support (EMCS) devices will contribute to safe, long-term EMCS. A clinically applicable optical detector must be able to distinguish among the optical characteristics of oxygen saturation (SaO2 ), hematocrit (Hct), and thrombus formation. In vitro studies of spectral changes at wavelengths from 400 to 900 nm associated with SaO2 , Hct, and thrombus formed around the top pivot bearing of a Gyro C1E3 pump were conducted. Fresh porcine blood anticoagulated with sodium citrate was circulated in a mock circuit using the pump. The SaO2 , Hct, and anticoagulation activity were altered using an oxygenator, autologous plasma, and calcium chlorite injection, respectively. Light from a xenon lamp was guided by an incident fiber perpendicularly fixed on the top bearing. This light was scattered by blood pooled between the male and female pivots. The detection fiber was perpendicularly fixed against the incident fiber, and the side-scattered light was detected and guided to a spectrophotometer. As a result, light at two different wavelengths, 420 and 810 nm, was identified as suitable for thrombus detection because it was negligibly influenced by SaO2 and was able to detect the optical characteristics of fibrin. The light at these two wavelengths responded more quickly to thrombus formation than the inlet or outlet pressure, and flow rate change. The optical changes showed the changes in Hct around the top pivot bearing, which is caused by the reduction in density of fibrin-trapped red blood cells (RBCs) due to the RBCs being swept away by the surrounding blood flow. The proposed method was also able to detect fibrin production by extracting subtle differences in the optical characteristics between the Hct and thrombus formation.

Leaf Chlorophyll Content Estimation of Winter Wheat Based on Visible and Near-Infrared Sensors.

The leaf chlorophyll content is one of the most important factors for the growth of winter wheat. Visual and near-infrared sensors are a quick and non-destructive testing technology for the estimation of crop leaf chlorophyll content. In this paper, a new approach is developed for leaf chlorophyll content estimation of winter wheat based on visible and near-infrared sensors. First, the sliding window smoothing (SWS) was integrated with the multiplicative scatter correction (MSC) or the standard normal variable transformation (SNV) to preprocess the reflectance spectra images of wheat leaves. Then, a model for the relationship between the leaf relative chlorophyll content and the reflectance spectra was developed using the partial least squares (PLS) and the back propagation neural network. A total of 300 samples from areas surrounding Yangling, China, were used for the experimental studies. The samples of visible and near-infrared spectroscopy at the wavelength of 450,900 nm were preprocessed using SWS, MSC and SNV. The experimental results indicate that the preprocessing using SWS and SNV and then modeling using PLS can achieve the most accurate estimation, with the correlation coefficient at 0.8492 and the root mean square error at 1.7216. Thus, the proposed approach can be widely used for winter wheat chlorophyll content analysis.

Visible and near-infrared bulk optical properties of raw milk.

The implementation of optical sensor technology to monitor the milk quality on dairy farms and milk processing plants would support the early detection of altering production processes. Basic visible and near-infrared spectroscopy is already widely used to measure the composition of agricultural and food products. However, to obtain maximal performance, the design of such optical sensors should be optimized with regard to the optical properties of the samples to be measured. Therefore, the aim of this study was to determine the visible and near-infrared bulk absorption coefficient, bulk scattering coefficient, and scattering anisotropy spectra for a diverse set of raw milk samples originating from individual cow milkings, representing the milk variability present on dairy farms. Accordingly, this database of bulk optical properties can be used in future simulation studies to efficiently optimize and validate the design of an optical milk quality sensor. In a next step of the current study, the relation between the obtained bulk optical properties and milk quality properties was analyzed in detail. The bulk absorption coefficient spectra were found to mainly contain information on the water, fat, and casein content, whereas the bulk scattering coefficient spectra were found to be primarily influenced by the quantity and the size of the fat globules. Moreover, a strong positive correlation (r ≥ 0.975) was found between the fat content in raw milk and the measured bulk scattering coefficients in the 1,300 to 1,400 nm wavelength range. Relative to the bulk scattering coefficient, the variability on the scattering anisotropy factor was found to be limited. This is because the milk scattering anisotropy is nearly independent of the fat globule and casein micelle quantity, while it is mainly determined by the size of the fat globules. As this study shows high correlations between the sample's bulk optical properties and the milk composition and fat globule size, a sensor that allows for robust separation between the absorption and scattering properties would enable accurate prediction of the raw milk quality parameters.

Design and Fabrication of a Real-Time Measurement System for the Capsaicinoid Content of Korean Red Pepper (Capsicum annuum L.) Powder by Visible and Near-Infrared Spectroscopy.

This research aims to design and fabricate a system to measure the capsaicinoid content of red pepper powder in a non-destructive and rapid method using visible and near infrared spectroscopy (VNIR). The developed system scans a well-leveled powder surface continuously to minimize the influence of the placenta distribution, thus acquiring stable and representative reflectance spectra. The system incorporates flat belts driven by a sample input hopper and stepping motor, a powder surface leveler, charge-coupled device (CCD) image sensor-embedded VNIR spectrometer, fiber optic probe, and tungsten halogen lamp, and an automated reference measuring unit with a reference panel to measure the standard spectrum. The operation program includes device interface, standard reflectivity measurement, and a graphical user interface to measure the capsaicinoid content. A partial least square regression (PLSR) model was developed to predict the capsaicinoid content; 44 red pepper powder samples whose measured capsaicinoid content ranged 13.45-159.48 mg/100 g by per high-performance liquid chromatography (HPLC) and 1242 VNIR absorbance spectra acquired by the pungency measurement system were used. The determination coefficient of validation (RV2) and standard error of prediction (SEP) for the model with the first-order derivative pretreatment method for Korean red pepper powder were 0.8484 and ±13.6388 mg/100 g, respectively.

Comparing visible and near infrared 'point' spectroscopy and hyperspectral imaging techniques to visualize the variability of apple firmness.

In this work, visible and near-infrared 'point' (Vis-NIR) spectroscopy and hyperspectral imaging (Vis-NIR-HSI) techniques were applied on three different apple cultivars to compare their firmness prediction performances based on a large intra-variability of individual fruit, and develop rapid and simple models to visualize the variability of apple firmness on three apple cultivars. Apples with high degree of intra-variability can strongly affect the prediction model performances. The apple firmness prediction accuracy can be improved based on the large intra-variability samples with the coefficient variation (CV) values over 10%. The least squares-support vector machine (LS-SVM) models based on Vis-NIR-HSI spectra had better performances for firmness prediction than that of Vis-NIR spectroscopy, with the with the Rc2 over 0.84. Finally, The Vis-NIR-HSI technique combined with least squares-support vector machine (LS-SVM) models were successfully applied to visualize the spatial the variability of apple firmness.

Hygroscopic Mg-Al LDHs composite microspheres for highly efficient hyperspectral camouflage in the VIS and NIR wavebands.

In order to enhance the hyperspectral camouflage efficacy of stealth coatings against a natural vegetative backdrop, LiCl, known for its significant hygroscopic properties, was incorporated into green Mg-Al layered double hydroxide (Mg-Al LDHs) material. Micron-sized composite microspheres were subsequently synthesized via the spray-drying granulation technique. The structure, morphology, and chemical composition of these microspheres were thoroughly characterized by X-ray diffraction, scanning electron microscopy, laser particle size analysis, nitrogen adsorption-desorption isotherms, and Fourier-transform infrared spectroscopy. The effect of LiCl content on the moisture absorption capacity and near-infrared reflectance spectra of the microspheres was systematically evaluated. We found that incorporating an optimal amount of LiCl into the internal pores of the Mg-Al LDHs microspheres did not compromise their smooth surface morphology and uniform particulate distribution. Notably, when the LiCl content was 10%, the maximum saturation moisture uptake ratio of the coating increased to 0.75 g/g. This hygroscopicity significantly enhanced the absorption and scattering of near-infrared radiation by the coating while concurrently improving its ability to modulate the shape and reflectance of both the visible and near-infrared spectral curves. Spectral congruence between the synthetic coating and natural green foliage was quantified at 97.41%. Moreover, this performance was maintained over 10 cycles of programmed drying and re-humidification, and the coating consistently demonstrated stable hygroscopic properties and sustained over 95% spectral congruence. These optimized artificial coatings were found to effectively confuse hyperspectral classification algorithms, thus blending seamlessly into a natural foliage backdrop. This study provides a new method for regulating VIS and NIR spectral (visible-near infrared spectrum) features, which will be critical for applications in advanced hyperspectral camouflage materials.