Visible Near-infrared Spectroscopy Research Articles

Ultraviolet–Visible-Near InfraRed spectroscopy for assessing metal powder cross-contamination: A multivariate approach for a quantitative analysis

The last few years have seen an increasing use of spherical metals powders to produce bulk parts through metal forming technologies like Additive Manufacturing and Metal Injection Molding. This, coupled with the wide availability of metal powders, leads to a critical issue: contamination across different systems in different process steps. Consequently, it is necessary to find a new, fast, and reliable analysis sensible to tiny traces of contamination. This work evaluates the applicability of Ultraviolet–Visible-Near InfraRed (UV–Vis-NIR) spectroscopy, a technique providing information on powders’ reflectance, for studying contaminated powders. This work focuses on assessing 3 binary systems obtained from the cross-contamination of 3 components (A92618, C10200 and S31603) in a low contamination range (from 0.5 vol% to vol. 6%) and in a high contamination range (25 vol% and vol.50%). After the UV–Vis-NIR analysis, multivariate analysis has been used to obtain quantitative results. Results show that, as the contamination level increases in the binary system, the shape of spectra changes and becomes progressively more similar to the contaminant one. The chemometric analysis allows the detection of the contaminant type and its concentration percentage in the contaminated powder.

Investigation on the exopolysaccharide production from blueberry juice fermented with lactic acid bacteria: Optimization, fermentation characteristics and Vis-NIR spectral model

The exopolysaccharide production from blueberry juice fermented were investigated. The highest exopolysaccharide yield of 2.2 ± 0.1 g/L (increase by 32.5 %) was reached under the conditions of temperature 26.5 °C, pH 5.5, inoculated quantity 5.4 %, and glucose addition 9.1 % using the artificial neural network and genetic algorithm. Under the optimal conditions, the viable cell counts and total acids were increased by 2.0 log CFU/mL and 1.6 times, respectively, while the content of phenolics and anthocyanin was decreased by 9.26 % and 7.86 %, respectively. The changes of these components affected the exopolysaccharide biosynthesis. The absorption bands of –OH and –CH associated with the main functional groups of exopolysaccharide were detected by Visible near-infrared spectroscopy. The prediction model based on spectrum results was constructed. Competitive adaptive reweighted sampling and the random forest were used to enhance the model's prediction performance with the value of RC = 0.936 and RP = 0.835, indicating a good predictability of exopolysaccharides content during fermentation.

Estimating yolk weight of duck eggs using VIS-NIR Spectroscopy and RGB images and whole egg weights

Duck eggs are widely-consumed food and cooking ingredient. The heavier yolk weight (YW) corresponds to a larger size and greater value. However, there is no non-destructive method available to estimate the weight of the yolk. Accurate weight prediction of duck egg yolks must combine both phenotypic and internal information. In this research, we used Visible-Near Infrared (VIS-NIR) spectroscopy to obtain internal information of duck eggs, and a high-definition camera to capture their phenotypic features. YW was predicted by combiningthe reduced spectral and RGB image information with the whole egg weight. We also investigated the impact of color and thickness of the duck egg on spectral transmittance (ST), as these factors can influence the extent of ST. The results showed that the spectral curves of duck eggs produced two peaks and one valley, which may be caused by the dual-frequency absorption of the C-H group and O-H group, and can be used to symbolize the internal information of duck eggs. The ST was somewhat affected by the color and thickness of the duck eggshell. Before modelling, Principal Component Analysis (PCA) was used to significantly reduce the dimensionality of the RGB image with spectral data. A Partial Least Squares Regression (PLSR) model was utilized to fit all the features. The test set yielded a coefficient of determination (R²) of 0.82 and a Root Mean Squared Error (RMSE) of 1.05 g. After removing the eggshell's color and thickness features, the model showed an R² of 0.79 and an RMSE of 1.11 g. This study demonstrated that the yolk weight of duck eggs can be estimated using VIS-NIR spectroscopy, RGB images and whole egg weight. Furthermore, the effects of shell color and thickness can be neglected.

Visible and infrared lab spectroscopy for soil texture classification: Analysis of entire spectra v/s reduced spectra

Soil, directly or indirectly, impacts seven of the Sustainable Development Goals set by the United Nations. Soil texture is a fundamental physical property that controls other properties ranging from soil stability, erodibility, compaction to water holding capacity, nutrient availability and fertility. While Visible Near-Infrared and Shortwave-Infrared (VNIR/SWIR) laboratory spectroscopy has been already used for soil texture classification, Mid Infrared (MIR) region and unison of the VNIR/SWIR and MIR region warrant quantification. Thus, the objectives of this work were: 1) to investigate the critical spectral regions for soil texture classification, 2) to compare the use of entire spectral bands v/s subset of spectral bands obtained via the feature selection method, and 3) to quantify the degree of misclassification in the neighbouring classes for a given textural class. This study utilized an ICRAF–ISRIC global soil spectral library consisting of 3643 VNIR/SWIR, MIR soil spectra and the USDA soil textural classes.Two classifiers were used for soil texture classification using the three databases (VNIR/SWIR, MIR, VNIR+MIR) with entire spectral bands and three databases with only the Partial Information Correlation (PIC) selected bands (VNIR/SWIR_PIC, MIR_PIC, VNIR+MIR_PIC). The mean confusion matrix, overall accuracy and kappa were calculated to evaluate classifiers' performances over 100 iterations. Two additional measures were proposed for partitioning inaccuracies between neighbouring and far classes: Correct-Neighbouring-Far classes distribution matrix and added neighbourhood accuracy. This work highlighted that: 1) the combined VNIR/SWIR+MIR region provided the best texture classification, followed by MIR and then VNIR/SWIR region, 2) the use of PIC selected bands provided lower classification performances when compared to using all bands but a massive reduction in the number of bands allowing to reduce model complexity, 3) the misclassifications were predominantly in the neighbouring classes rather than far classes, for a given texture class. Finally, the textural classes with poor classification performance had low areal and sample representation. These insights may launch a number of soil texture classification studies worldwide based on global soil spectral databases, further helping map soils and the associated ecosystem services it provides.

Optical response and temperature shielding coatings using tri-layer structure composed of titania/silica/titania

The fabrication of metamaterials with inspiration from nature paved the door for the creation of revolutionary passive and active devices. Butterfly wings are one of them, and their multilayered structure motivated nanotechnologists, physicists, and other optoelectronic and photonic engineers to develop photonic crystals, dielectric reflectors, and Bragg reflectors for use in visible light communication, solar cells, and other photonic and optoelectronic applications. We report the sol–gel spin coating of a dielectric reflector on the glass substrate, an optical passive component consisting of titania and silica thin films for infrared radiation reflection. Individual thin films of titania and silica on glass substrates were studied using an x-ray diffractometer, which indicated anatase of titania and the amorphous nature of silica at a Bragg angle of 25° with a sharp and wide peak, respectively. The multilayer structure of titania/silica/titania was further investigated using Fourier transform infrared spectroscopy, which revealed the presence of Ti–O–Ti and Si–O–Si vibrational bonds at wavenumbers 546 and 973 cm−1, respectively, as well as the presence of Ti–O–Si vibrational bond at 1100 cm−1. The thickness of the multilayer titania/silica/titania was measured using a cross-sectional field emission scanning electron microscope (FESEM) and found to be 160/240/160 nm, respectively. Finally, reflection investigation on the multilayer structure using ultraviolet–visible–near-infrared spectroscopy validated the reflection of the infrared spectrum area by around 70% and showed to be beneficial for temperature shielding applications on glass furnishings.

Estimating Soil Erodible Fraction Using Multivariate Regression and Proximal Sensing Data in Arid Lands, South Egypt

Estimating soil erodible fraction based on basic soil properties in arid lands is a valuable research topic in the field of soil science and land management. The Proximal Sensing (PS) technique offers a non-destructive and efficient method to assess wind erosion potential in arid regions. By using Partial Least Squares Regression (PLSR) and Support Vector Machine (SVM) models and combining soil texture and chemical properties, determined through Visible-Near Infrared (vis-NIR) spectroscopy in 96 soil samples, this study aims to predict soil erodibility, soil organic matter (SOM), and calcium carbonate equivalent (CaCO3) in arid lands located in Elkobaneyya Valley, Aswan Governorate, Egypt. Results showed that the soil erodibility fraction (EF-Factor) had the highest values and possessed a strong relationship between slope and SOM of 0.01% in determining soil erodibility. The PLSR model performed better than SVM for estimating SOM, CaCO3, and EF-Factor. Furthermore, the results showed that the spectral responses of CaCO3 were observed in separate places in the wavelengths of 570, 649, 802, 1161, 1421, 1854, and 2362 nm, and the wavelengths with SOM parameter were 496, 658, 779, 1089, 1417, 1871, and 2423 nm. The EF-factor shows the highest significant correlation with spectral reflectance values at 526, 688, 744, 1418, 1442, 2292, and 2374 nm. The accuracy and performance of the PLSR model in estimating the EF-Factor using spectral reflectance data and the distribution of data points for both the calibration and validation data-sets indicate a good accuracy of the PLSR model, with RMSE values of 0.0921 and 0.0836 Mg h MJ−1 mm−1, coefficient of determination (R2) values of 0.931 and 0.76, and RPD values of 2.168 and 2.147, respectively.

Fungal fermentation of Fuzhuan brick tea: A comprehensive evaluation of sensory properties using chemometrics, visible near-infrared spectroscopy, and electronic nose

Fuzhuan brick tea (FBT) fungal fermentation is a key factor in achieving its unique dark color, aroma, and taste. Therefore, it is essential to develop a rapid and reliable method that could assess its quality during FBT fermentation process. This study focused on using electronic nose (e-nose) and spectroscopy combination with sensory evaluations and physicochemical measurements for building machine learning (ML) models of FBT. The results showed that the fused data achieved 100 % accuracy in classifying the FBT fermentation process. The SPA-MLR method was the best prediction model for FBT quality (R2 = 0.95, RMSEP = 0.07, RPD = 4.23), and the fermentation process was visualized. Where, it was effectively detecting the degree of fermentation relationship with the quality characteristics. In conclusion, the current study’s novelty comes from the established real-time method that could sensitively detect the unique post-fermentation quality components based on the integration of spectral, and e-nose and ML approaches.

Determination of Soil Organic Matter and Total Nitrogen from Visible Near-Infrared Spectroscopy by Multivariate Models and Variable Selection Techniques

Determination of Soil Organic Matter and Total Nitrogen from Visible Near-Infrared Spectroscopy by Multivariate Models and Variable Selection Techniques

Non-destructive determination of water content in fruits using Vis-NIR spectroscopy

Visible near-infrared (Vis-NIR) spectroscopy is less accurate for detecting -OH molecules, therefore, the use of Vis-NIR spectroscopy is challenging for samples containing high water content, such as fruits. However, as Vis-NIR spectroscopy is a portable and economical instrument, it can be used in the field by small-scale farmers. This study aimed to evaluate Vis-NIR spectra to measure water content in fruits. Four fruits were used in this study, including dragon fruit, guava, sapodilla and banana (100 pieces each). All fruits were randomly divided into a calibration set (two-thirds of the samples) and a prediction set (one-third of the samples). Water content was predicted using partial least square regression (PLSR) analysis. The PLSR calibration model had a coefficient of determination (R²) of 0.29 for dragon fruit, 0.63 for guava, 0.62 for sapodilla and 0.80 for banana. The prediction model had an R² of 0.11 for dragon fruit, 0.63 for guava, 0.52 for sapodilla and 0.75 for banana. These results show that Vis-NIR spectroscopy has the potential to predict water content in relatively low water-content fruits, such as bananas.

Vis-NIR spectroscopy based rapid and non-destructive method to quantitate microplastics: An emerging contaminant in farm soil

Microplastics (MPs) is the second most important environmental issue and can potentially enter into food chain through farmland contamination and other means. There are no standardized extraction methods for quantification of MPs in soil. The embedded errors and biases generated serious problems regarding the comparability of different studies and leading to erroneous estimation. To address this gap, present study was formulated to develop an efficient method for MPs analysis suitable for a wide range of soil and organic matrices. A method based on Vis-NIR (Visible-Near Infra Red) spectroscopy is developed for four different soil belonging to Alfisol, Inceptisol, Mollisol and Vertisol and two organic matter matrices (FYM and Sludge). The developed method was found as rapid, reproducible, non-destructive and accurate method for estimation of all three-density groups of MPs (Low, Medium and High) with a prediction accuracy ranging from 1.9 g MPs/kg soil (Vertisol) to 3.7 g MPs/kg soil (Alfisol). Two different regression models [Partial Least Square Regression (PLSR) and Principal Component Regression (PCR)] were assessed and PLSR was found to provide better information in terms of prediction accuracy and minimum quantification limit (MQL). However, PCR performed better for organic matter matrices than PLSR. The method avoids any complicated sample preparation steps except drying and sieving thus saving time and acquisition of reflectance spectrum for single sample is possible within 18 s. Owing to have the minimum quantification limit ranging from 1.9–3.7 g/kg soil, the vis-NIR based method is perfectly suitable for estimation of MPs in soil samples collected from plastic pollution hotspots like landfill sites, regular based sludge amended farm soils. Additionally, the method can be adapted by small scale compost industries for assessing MPs load in product like city compost which are applied at agricultural fields and will be helpful in quantifying possible MPs at the sources itself.

Comparing laser-induced breakdown spectroscopy and visible near-infrared spectroscopy for predicting soil properties: A pan-European study

Soil organic carbon (SOC), texture, clay/organic carbon (OC) ratio, and extractable phosphorus are among the key soil descriptors representing the physical, chemical, and biological properties. However, analyzing these soil properties using conventional methods of analysis is time-consuming and often involves the use of hazardous chemicals. Therefore, scaling these methods to analyze more samples covering large geographical distributions within a reasonable turn-around time has become a challenge. In this study, two rapid techniques, namely laser-induced breakdown spectroscopy (LIBS) and visible and near-infrared spectroscopy (vis-NIRS), were compared for the prediction of SOC, texture, extractable phosphorus, and clay/OC ratio. A total of 896 samples collected from 23 European countries from the Land Use/Cover Area frame Survey (LUCAS) database were used to represent a wide distribution in terms of soil texture and geographical coverage. The data was manually split into calibration and validation samples while ensuring a representative geographical distribution in the calibration and validation set for the selected samples. Partial least square regression (PLSR) models were developed for both LIBS and vis-NIRS and compared based on prediction accuracy. LIBS outperformed vis-NIRS in predicting SOC, sand content, and clay/OC ratio, with root mean square error of prediction (RMSEP) values of 1.79 %, 12 %, and 7.69, respectively. Conversely, vis-NIRS yielded slightly higher RMSEP values for SOC, sand content, and clay/OC ratio predictions at 1.88 %, 15 %, and 8.13, respectively. Both LIBS and vis-NIRS performed similarly in predicting clay content, with an RMSEP of 6 %. However, there was a low predictive ability for silt and phosphorus concentration, as shown by the weak correlations (R2 < 0.5) between the predicted and reference soil properties. There was no significant difference between LIBS and vis-NIRS predictions for clay, silt, and SOC content (p > 0.05). However, there was a significant difference between the predicted sand content (p = 0.011) and clay/OC ratio (p < 0.001). The overall better performance of LIBS highlights its potential for application in a wide range of soils, especially for soils covering large geographical distributions and varying soil properties. Further, both techniques present opportunities for rapid determination of soil properties important for evaluating soil health on continental scales.

Assessment of Persian export pomegranate quality: A reliable non-destructive method based on spectroscopy and chemometrics

The present study investigated the feasibility of visible near-infrared (Vis-NIR) spectroscopy method as a low-cost, quick and reliable method for the prediction of qualitative characteristics (fruit juice percentage, soluble solids, acidity, taste index and vitamin C) of an export variety of Persian pomegranate. For this purpose, pomegranate samples were first analyzed by Vis-NIR spectroscopy, followed by destructive reference methods for quality determination. Subsequently, the data from Vis-NIR were evaluated with different pre-processing methods, among which the combination of the Savitzky–Golay (SG) smoothing model and standard normal variation (SNV) normalization model, generates the best outcomes in predicting six desired qualitative characteristics by partial least square regression (PLSR) model. Fruit juice percentage and pH presented the best prediction models with a correlation coefficient (rP) of 0.98 for both, and root mean square errors of prediction (RMSEP) equal to 0.036 and 0.041, respectively. The values of rp for all desired parameters in this study were higher than 0.95, which pointed to the excellent accuracy of the PLSR models in the prediction of pomegranate qualitative features. It can be concluded from the present study that, Vis-NIR spectroscopy using the PLSR model as a non-destructive functional approach is capable of verifying the quality of Persian export variety pomegranate as an online technique with the aim of grading products before the export phase in order to augment the product marketability.

New methods for the identification of malachite pigments with varying particle sizes used in ancient Chinese murals by spectroscopic techniques

This study attempts to propose a simple, low-interventive and higher accuracy identification method for malachite pigments with varying particle sizes based on attenuated total reflection-Fourier transform infrared (ATR-FTIR), and evaluate the accuracy of ATR-FTIR and ultraviolet–visible near-infrared (UV-VIS-NIR) spectroscopy for identifying particle sizes. The micro-morphology, particle size, colour difference, and spectral characteristics of the malachite samples were determined using scanning electron microscopy (SEM), laser particle size analysis, colourimetry, UV-VIS-NIR and ATR-FTIR spectrometry. The principal component analysis (PCA) and mathematical models based on nonlinear curve fitting using UV-VIS-NIR and FTIR spectral data were developed and evaluated for the particle size identification of various malachite samples (pure malachite samples, fresh and aged malachite-rabbit glue samples). SEM morphological results showed that the malachite pigments with smaller particle sizes appear to have irregular flakes with a few loose agglomerated granules attached to the surface. The weight-specific surface area also increased gradually as the pigment particle size decreased. The colour difference results revealed that smaller malachite particle size gradually shifted toward the red–blue regions in CIE L*a*b* colour space. In addition, more intense reflectance and IR spectral absorbance peaks were observed in the case of the smaller particle size. The above proposed methods of particle size identification proved to be highly feasible and effective in predicting pigment particle size, which were confirmed by the PCA identification results and the mathematical model. Also, ATR-FTIR spectroscopy could provide more accurate predictions than UV-VIS-NIR spectroscopy, by keeping prediction errors below 5.5 μm.

Estimation of Cadmium Content in Lactuca sativa L. Leaves Using Visible–Near-Infrared Spectroscopy Technology

In order to monitor cadmium contamination in lettuce quickly, non-invasively, and accurately, and to understand the growth status of lettuce under cadmium pollution, lettuce was used as the test material to detect and analyze the visible–near-infrared reflectance spectra and leaf cadmium content under different concentrations of cadmium stress. A model for estimating lettuce leaf cadmium content was established. For model establishment, firstly, the original spectra were preprocessed using smoothing (Savitzky–Golay, SG), SG combined with multiplicative scatter correction (MSC), SG combined with standard normal variable transformation (SNV), SG combined with mean normalization (MN), SG combined with the first derivative (FD), SG combined with the second derivative (SD), SG combined with the baseline offset (B), and SG combined with de-trending (D). Then, the principal component analysis (PCA) was applied to perform dimensionality reduction on the data. Finally, the reduced dataset was divided into training and testing sets in a 2:1 ratio, and separate models for estimating the lettuce leaf cadmium content were built using partial least squares regression (PLSR), the backpropagation neural network (BP-NN), and support vector regression (SVR) in combination. The results showed that the accumulated cadmium content in lettuce leaves increased with an increase in the soil cadmium concentration. In the visible light range, the spectral reflectance of lettuce leaves increased with an increase in the cadmium concentration. In the near-infrared range, the spectral reflectance of the lettuce leaves under 10 mg/kg and 20 mg/kg cadmium stress was lower than that of the control group. The PLSR models established using the SG + MSC and SG + SNV preprocessing methods exhibited the strongest estimation capability for lettuce leaf cadmium content, with Rp2 and RMSEp values of 0.92 and 1.53 mg/kg, respectively, for the testing dataset. This study demonstrated that visible–near-infrared spectroscopy has great potential in monitoring cadmium contamination in lettuce.

Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy

Abstract The moisture content of porang powder (PP) is an inherent quality parameter. Therefore, several analytical methods, such as oven drying and Karl–Fischer titration, were applied to determine the content. However, these techniques are noted to have various disadvantages, such as being time-consuming, requiring sample preparation, being labor-intensive, and producing chemical waste. This study aims to investigate the potential of visible near-infrared (Vis-NIR) spectroscopy as a nondestructive and sustainable analytical technology to predict moisture content in PP. In this study, we developed a traditional machine learning algorithm, a partial least squares regression (PLSR), in tandem with two spectral bands, which are Vis-NIR (400–1,000 nm) and NIR (954–1,700 nm). To upgrade the performance of PLSR, we applied seven preprocessing techniques: mean normalization, maximum normalization, range normalization, multiplicative scatter correction, standard normal variate (SNV), and Savitzky–Golay first and second derivatives. We found that PLSR using NIR spectral bands was more effective; the preprocessed mean normalization exhibited the best results with a coefficient of determination ( R p 2 ) \left({R}_{p}^{2}) of 0.96 and a standard error prediction (SEP) of 0.56 using five latent variables. Furthermore, we also extracted 39 optimum wavelengths using variable importance in projection and achieved better performance ( R p 2 {R}_{p}^{2} = 0.95, SEP = 0.56%wb, and 5 LVs) via SNV preprocessed NIR spectra.

Advancing grape chemical analysis through machine learning and multi-sensor spectroscopy

Chemical testing of grapes is essential for wine-making, allowing informed decisions about grape composition and potential wine characteristics. However, current invasive laboratory methods to identify the key components as total soluble solid contents and acidity, present challenges in terms of time and cost. To address these issues, non-invasive techniques using Visible-Near-Infrared (Vis-NIR) and Raman spectroscopy are explored for rapid and accurate grape chemical analysis. Various machine learning methods are deployed in this study to address the challenges of grape chemical analysis and enhance estimation accuracy. The contributions of this research include pioneering a direct comparison between Vis-NIR and Raman spectroscopy, establishing a regression model benchmark, and providing an open-source dataset for grape composition analysis. We identify Gaussian Process Regression (GPR) and Support Vector Machine Regression (SVMR) as the most effective regression models, with GPR achieving an RMSE of 0.977 °Birx for sugar content estimation in French Colombard grapes and SVMR achieving 0.780 °Brix for Cabernet grapes. For pH estimations, GPR and SVMR also perform exceptionally well.

Preparation study and simulation of high transmittance W–Sn–Zr Co-doped VO2 thin films prepared by sol-gel method for energy-saving smart windows

The thermochromic properties of vanadium dioxide (VO2) hold great promise for applications in smart glass windows. VO2 films exhibit a high phase transition temperature (Tc), low visible transmission (Tlum), and low solar modulating ability (ΔTsol), making them inadequate for the requirements of smart glass windows. While some improvement in VO2 film performance can be achieved through single-element doping, these limited enhancements still fall short of ideal results. Therefore, there is a pressing need for the improvement of VO2 films. In this study, we utilized the sol-gel method to prepare VO2 samples simultaneously doped with three elements: W, Sn, and Zr. Microscopic characterization confirmed the successful incorporation of these elements. Optical measurements, including ultraviolet–visible–near-infrared spectroscopy, were conducted to assess the optical properties of the films. Ultimately, outstanding Tlum (69%) and ΔTsol (14%) were achieved, along with an appropriate Tc (27.6 °C). Energy consumption simulations were performed using the EnergyPlus model with experimental data, revealing that compared to conventional glass, VO2 glass reduces total cooling energy consumption by 9.8%. In comparison to previous work, the multi-element-doped films push the performance limits of VO2 films, offering a viable approach for fabricating superior VO2 smart glass windows.

Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy.

Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming. To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models. The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix). This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.

One-pot synthesis of near-infrared absorbing silver nanoprisms via a simple photochemical method using an ultraviolet-C light source

This study describes a simple one-pot photochemical method to synthesize triangular silver nanoprisms (T-AgNPs), for which a mixture of silver nitrate (AgNO3) and citrate was irradiated with an ultraviolet-C (UVC) lamp reactor. We successfully synthesized T-AgNPs with edge lengths ranging from 50 to 150 nm, showing a significant localized surface plasmon resonance (LSPR) band over a wavelength of 1200 nm. This photochemical reaction appears to be unrestricted by plasmon resonance conditions, allowing for the formation of colloidal T-AgNPs with an LSPR band in the near-IR region. In addition, this new method does not require any highly reactive reducing agents or initially prepared silver seed solution. The reduction of Ag+ ions by citrate is induced by UVC light, and the nascent Ag first undergoes nucleation to form spherical or quasi-spherical silver nanoparticles (S-AgNPs). Subsequently, continuous irradiation with UVC light facilitates the transformation of these S-AgNPs into T-AgNPs. The growth kinetics of the nanoparticles with different reaction conditions were examined using ultraviolet–visible–near-infrared spectroscopy and transmission electron microscopy. This new method offers a simple, efficient, and cost-effective means to synthesize T-AgNPs with near-IR LSPR, contributing to wider applications in the fields of optoelectronics, spectroscopics, photovoltaics, etc.

Optimizing soil carbon content prediction performance by multi-band feature fusion based on visible near-infrared spectroscopy

Optimizing soil carbon content prediction performance by multi-band feature fusion based on visible near-infrared spectroscopy