NIR Spectrometer Research Articles

Rapid and non-destructive prediction of adulterated chilli powder by employing pocket-sized NIR spectrometer and chemometrics

Chilli pepper is an economically important vegetable. This study aims to develop a non-destructive and rapid technique to detect the adulteration of chilli pepper powder with avocado pear seed and cola nut powder using handheld near-infrared spectroscopy (NIRS) in the range of 740–1070 nm. A clear cluster trend was achieved for chilli pepper and the adulterated samples by using principal component analysis (PCA). Classification models such as random forest (RF), support vector machine (SVM) and partial least square discriminant analysis (PLS-DA) together with various pre-processing methods were used to predict adulteration of chilli pepper samples. The optimum classification rate of 91.25 % was found for chilli pepper adulterated with pear seeds using MC-SVM while MC-PLSDA had 86.25 % for chilli pepper adulterated with cola nuts. The partial least square (PLS) regression model had a coefficient of determinant (R2) and root mean square error of prediction (RMSEP) values of 0.9853 and 5.06 % for pear seed and 0.9724 and 6.91 % for cola nut, respectively. The current study shows that NIR spectroscopy can classify and determine chilli pepper authenticity and adulteration levels of pear seed and cola nuts.

Rapid quantification of phenolic content and antioxidant activity in cookies produced with amazonian palm fruit flour using Micro-NIR spectrometer and PLS regression

There are several reports of the potential benefits of phenolic compound (PC) in food products, due to their antioxidant activities (AC). However, in recent years, new research results have demonstrated that PC has potential health risks due to the reduction in absorption of protein nutrients and cytotoxic effects. The PC and AC quantifications are laborious and time-consuming methods, therefore it is necessary to develop simple, fast and precise method to determine these parameters, not only in the raw materials, but also in food products. Therefore, this study focused on the potential of Micro-NIR spectrometer data modeled with partial least square regression to predict PC and AC in processed food (cookies) prepared with peach palm (PP), that is rich in PC. The cookies were prepared using 12.5 to 100 % of PP flour in substitution to wheat flour (WF). The NIR model for AC, determined by the ferric reducing antioxidant power (FRAP) method, shows R2cv = 0.93 (regression coefficient of cross-validation step); RMSECV = 0.05; R2p = 0.87 (regression coefficient of prediction step); RMSEP = 0.04; RPD = 2.73, and by 2,2-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) radical capture (ABTS) exhibit R2cv = 0.83; RMSECV = 3.72; R2p = 0.70; RMSEP = 4.12; RPD = 1.76, and for PC, determined by Folin-Ciocalteu, shows R2cv = 0.86; RMSECV = 0.44; R2p = 0.80; RMSEP = 0.43; RPD = 2.04. These excellent results, mainly for FRAP and PC, demonstrated that portable NIR spectrometers could be a fast, simple and reliable method to predict PC and AC in cookies prepared with different proportion of PP flour and WF. Similar models can also be developed to predict PC and AC in other food products.

Research on the Effects of Drying Temperature for the Detection of Soil Nitrogen by Near-Infrared Spectroscopy.

Nitrogen nitrates play a significant role in the soil's nutrient cycle, and near-infrared spectroscopy can efficiently and accurately detect the content of nitrate-nitrogen in the soil. Accordingly, it can provide a scientific basis for soil improvement and agricultural productivity by deeply examining the cycle and transformation pattern of nutrients in the soil. To investigate the impact of drying temperature on NIR soil nitrogen detection, soil samples with different N concentrations were dried at temperatures of 50 °C, 65 °C, 80 °C, and 95 °C, respectively. Additionally, soil samples naturally air-dried at room temperature (25 °C) were used as a control group. Different drying times were modified based on the drying temperature to completely eliminate the impact of moisture. Following data collection with an NIR spectrometer, the best preprocessing method was chosen to handle the raw data. Based on the feature bands chosen by the RFFS, CARS, and SPA methods, two linear models, PLSR and SVM, and a nonlinear ANN model were then established for analysis and comparison. It was found that the drying temperature had a great effect on the detection of soil nitrogen by near-infrared spectroscopy. In the meantime, the SPA-ANN model simultaneously yielded the best and most stable accuracy, with Rc2 = 0.998, Rp2 = 0.989, RMSEC = 0.178 g/kg, and RMSEP = 0.257 g/kg. The results showed that NIR spectroscopy had the least effect and the highest accuracy in detecting nitrogen at 80 °C soil drying temperature. This work provides a theoretical foundation for agricultural production in the future.

The use of visible and near‐infrared spectroscopy for in‐situ characterization of agricultural soil fertility: A proposition of best practice by comparing scanning positions and spectrometers

AbstractThe application of visible and near‐infrared (vis–NIR) spectroscopy to characterize soil samples has gained growing interest as a fast and cost‐effective methodology for soil fertility assessment. In order to profit from the full potential of vis–NIR spectroscopy, the acquisition of soil spectra directly in‐situ would increase the possibility to obtain data rapidly and at a high spatial and temporal resolution. In the present study, we test and propose the best practice to characterize a set of fertility‐related parameters (i.e. texture, organic carbon, pH, cation exchange capacity and major nutrients) of agricultural soils by measuring vis–NIR spectra in the field. To reach this goal, we compare the spectra obtained from different scanning positions with two portable spectrometers, that is, a micro‐electro‐mechanical systems (MEMS)‐based spectrometer and a research‐grade vis–NIR spectrometer. On the basis of 134 soil sampling points, vis–NIR spectra were recorded from: (1) the cutaway side of a soil sample collected with an Edelman auger to a depth of 20 cm, (2) the raw soil surface, as well as (3) the cleaned and smoothed soil surface. Partial least squares regression (PLSR) calibration models were built for the selected soil parameters, scanning positions and different spectral pretreatments for both spectrometers. The model performance was evaluated based on the ratio of performance to interquartile range (RPIQ), the R2, the root mean squared error (RMSE) and Lin's concordance correlation coefficient (CCC). Overall, the following soil parameters were successfully predicted: clay, sand, pH, organic carbon, cation exchange capacity, total nitrogen and exchangeable magnesium. In contrast, total and exchangeable Ca, K and P, as well as total Mg could not be predicted at a satisfactory level for both the spectrometers. The best scanning position for the successfully calibrated models was along the cutaway sides of the Edelman auger. Although the research‐grade spectrometer gave better performance indicators for most of the parameters, the calibrations with the MEMS‐based spectrometer still resulted in satisfactory predictions. Based on these findings, the proposed best practice for obtaining in‐situ soil vis–NIR scans is to scan along the cutaway sides of a soil core using at least five replicate scans.

Optimized variable selection and machine learning models for olive oil quality assessment using portable near infrared spectroscopy

Olive oil is a key component of the Mediterranean diet, rich in antioxidants and beneficial monounsaturated fatty acids. As a result, high-quality olive oil is in great demand, with its price varying depending on its quality. Traditional chemical tests for assessing olive oil quality are expensive and time-consuming. To address these limitations, this study explores the use of near infrared spectroscopy (NIRS) in predicting key quality parameters of olive oil, including acidity, K232, and K270. To this end, a set of 200 olive oil samples was collected from various agricultural regions of Morocco, covering all three quality categories (extra virgin, virgin, and ordinary virgin). The findings of this study have implications for reducing analysis time and costs associated with olive oil quality assessment. To predict olive oil quality parameters, chemical analysis was conducted in accordance with international standards, while the spectra were obtained using a portable NIR spectrometer. Partial least squares regression (PLSR) was employed along with various variable selection algorithms to establish the relationship between wavelengths and chemical data in order to accurately predict the quality parameters. Through this approach, the study aimed to enhance the efficiency and accuracy of olive oil quality assessment. The obtained results show that NIRS combined with machine learning accurately predicted the acidity using iPLS methods for variable selection, it generates a PLSR with coefficients of determination R2 = 0.94, root mean square error RMSE = 0.32 and ratios of standard error of performance to standard deviation RPD = 4.2 for the validation set. Also, the use of variable selection methods improves the quality of the prediction. For K232 and K270 the NIRS shows moderate prediction performance, it gave an R2 between 0.60 and 0.75. Generally, the results showed that it was possible to predict acidity K232, and K270 parameters with excellent to moderate accuracy for the two last parameters. Moreover, it was also possible to distinguish between different quality groups of olive oil using the principal component analysis PCA, and the use of variable selection helps to use the useful wavelength for the prediction olive oil using a portable NIR spectrometer.

Evaluation of near-infrared spectroscopy as a contactless method for health monitoring of resin-based coating materials applied to concrete surfaces.

The surfaces of concrete structures are often coated with protective materials to minimize corrosion and weathering-based deterioration. Therefore, it is important to monitor the aging of the coating materials and their overall condition to extend the service lifetime of the structure effectively. Near-infrared spectroscopy (NIRS) is a contactless, nondestructive, rapid, and convenient method for material characterization; therefore, it is useful for onsite inspection of coating materials. Hence, in this study, we attempt to determine whether NIRS can be used for simple inspection for health monitoring of organic resin-based coating materials. In addition to identifying different severities of peeling damage, we characterize the ultraviolet-induced deterioration of coating materials with different thicknesses using diffuse reflection spectra acquired in the near-infrared wavelength region. For independent comparison with the NIR spectra, the state of the coating materials on the mortar specimens was analyzed using a combination of Fourier-transform infrared spectroscopy and scanning electron microscopy, while the state of the underlying mortar specimens was analyzed using permeability and salt-water immersion tests. The results confirm that the NIRS could detect the degradation of coating materials at early stages of deterioration before their permeability had been affected. NIRS offers the possibility of intermittent monitoring of coating deterioration. In addition, because the NIR spectrometer is portable, it can help in inspecting high-rise areas and areas that are difficult to reach. Therefore, we believe that NIRS is a simple, safe, and inexpensive method for inspection of surface coating materials.

Miniaturized Vis–NIR handheld spectrometer for non-invasive pigment quantification in agritech applications

Advanced precision agriculture requires the objective measurement of the structural and functional properties of plants. Biochemical profiles in leaves can differ depending on plant growing conditions. By quantitatively detecting these changes, farm production processes can be optimized to achieve high-yield, high-quality, and nutrient dense agricultural products. To enable the rapid and non-destructive detection on site, this study demonstrates the development of a new custom-designed portable handheld Vis–NIR spectrometer that collects leaf reflectance spectra, wirelessly transfers the spectral data through Bluetooth, and provides both raw spectral data and processed information. The spectrometer has two preprogramed methods: anthocyanin and chlorophyll quantification. Anthocyanin content of red and green lettuce estimated with the new spectrometer showed an excellent correlation coefficient of 0.84 with those determined by a destructive gold standard biochemical method. The differences in chlorophyll content were measured using leaf senescence as a case study. Chlorophyll Index calculated with the handheld spectrometer gradually decreased with leaf age as chlorophyll degrades during the process of senescence. The estimated chlorophyll values were highly correlated with those obtained from a commercial fluorescence-based chlorophyll meter with a correlation coefficient of 0.77. The developed portable handheld Vis–NIR spectrometer could be a simple, cost-effective, and easy to operate tool that can be used to non-invasively monitor plant pigment and nutrient content efficiently.

Development of PCA-MLP Model Based on Visible and Shortwave Near Infrared Spectroscopy for Authenticating Arabica Coffee Origins.

Arabica coffee, one of Indonesia's economically important coffee commodities, is commonly subject to fraud due to mislabeling and adulteration. In many studies, spectroscopic techniques combined with chemometric methods have been massively employed in classification issues, such as principal component analysis (PCA) and discriminant analyses, compared to machine learning models. In this study, spectroscopy combined with PCA and a machine learning algorithm (artificial neural network, ANN) were developed to verify the authenticity of Arabica coffee collected from four geographical origins in Indonesia, including Temanggung, Toraja, Gayo, and Kintamani. Spectra from pure green coffee were collected from Vis-NIR and SWNIR spectrometers. Several preprocessing techniques were also applied to attain precise information from spectroscopic data. First, PCA compressed spectroscopic information and generated new variables called PCs scores, which would become inputs for the ANN model. The discrimination of Arabica coffee from different origins was conducted with a multilayer perceptron (MLP)-based ANN model. The accuracy attained ranged from 90% to 100% in the internal cross-validation, training, and testing sets. The error in the classification process did not exceed 10%. The generalization ability of the MLP combined with PCA was superior, suitable, and successful for verifying the origin of Arabica coffee.

Determination of Chemical Content of Porang Flour (Amorphophallus muelleri blume) by Near Infrared Spectroscopy

Porang is well known with its high glucomannan content. Determination of the chemical content of porang flour is commonly carried out by chemical methods in the laboratory, which are known to take a long time and expensive. This study aims to determine the chemical content of porang flour using near infrared spectroscopy (NIRS). The reflectance of porang flour was measured using an FT NIR spectrometer at a wavelength of 1000 to 2500 nm. The chemical content (water and glucomannan content) of porang flour was determined by oven and spectrophotometry as the reference method. The reflectance was transformed to absorbance and several spectral pre-treatments such as normalisation, detrending, standard normal variate (SNV), orthogonal signal correction (OSC), and multiplicative scatter correction (MSC) with various PLS and PCR factors number were applied to obtain the best chemical content prediction of porang flour with NIRS. PLS and PCR were used for calibration of NIR spectra and chemical content of porang flour. The best calibration for determining the moisture content of porang flour was obtained using NIR spectra with normalisation pre-treatment and 8 PLS factor (r=0.92; SEC=0.15%; CV=2.13%; RPD=2.68). The best calibration for determining the glucomannan content of porang flour was NIR spectra with detrending pre-treatment at PLS factor 7 (r=0.86; SEC=0.54%; CV=0.90%; RPD=1.62). A lower accuracy was obtained for predicting water and glucomannan content using PCR calibration method. For water content, the best calibration was obtained by NIR spectra with normalisation pre-treatment and 12 PCR factors (r=0.91; SEC=0.16%; CV=2.21%; RPD=2.59). For glucomannan content, the best calibration was NIR spectra using detrending pre-treatment at 9 PCR factors (r=0.81; SEC=0.62%; CV=0.94%; RPD=1.55). This study shows that NIRS can be used to determine the chemical content of porang flour.

Monitoring S-ovalbumin content in eggs during storage using portable NIR spectrometer and multivariate analysis

The study investigated the feasibility of using portable NIR spectrometer coupled with multivariate analysis to monitor the S-ovalbumin content in eggs during storage. The NIR spectra of egg samples were obtained in the spectral range of 900–1700 nm. Savitzky-Golay (SG) smoothing, wavelet threshold denoising (WTD), multiplicative scatter correction (MSC), standard normal variate (SNV) and their combinations were used to pretreat the raw spectral data. In addition, successive projection algorithm (SPA) and iteratively retains informative variables (IRIV) were employed to extract feature related wavelengths from the full spectrum. Finally, the developed partial least squares regression (PLSR) model based on 14 IRIV feature wavelengths preprocessed by WTD-MSC had the best performance (R2P = 0.86, RMSEP = 9.33%). The result indicates that portable NIR device can be used with caution for nondestructive monitoring of S-ovalbumin content in eggs and has the potential to be applied in the supply chain.

Near-infrared spectroscopy for structural bone assessment

Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds. A hand-held NIR spectrometer was used to scan bone samples from 20 patients and predict: bone volume fraction (BV/TV); and trabecular (Tb) and cortical (Ct) thickness (Th), porosity (Po), and spacing (Sp). NIRS scans on both the inner (trabecular) surface or outer (cortical) surface accurately identified variations in bone collagen, water, mineral, and fat content, which then accurately predicted bone volume fraction (BV/TV, inner R2 = 0.91, outer R2 = 0.83), thickness (Tb.Th, inner R2 = 0.9, outer R2 = 0.79), and cortical thickness (Ct.Th, inner and outer both R2 = 0.90). NIRS scans also had 100% classification accuracy in grading the quartile of bone thickness and quality. We believe this is a fundamental step forward in creating an instrument capable of intraoperative real-time use.

Rapid Identification of the Geographical Origin of Black Pepper in Vietnam Using Near-Infrared Spectroscopy and Chemometrics

The aim of this study was to develop a rapid method for determining geographical origin of black pepper based on combination of near-infrared spectroscopy and chemometric algorithms. Near-infrared spectroscopy was used to determine the geographical origin of 111 black pepper samples from three regions in Vietnam: North Central Region, Central Highlands and Southeast Region. A portable NIR spectrometer was used to collect the spectra of intact black pepper. Support vector machine (SVM), linear discriminant analysis (LDA) and K-nearest neighbors (KNN) were established and the identification effect of these models with different pre-processing methods were optimized and compared. By using support vector machine (SVM) classification and standard normal variate (SNV) spectral pre-processing, classification accuracy was 98.7% on the calibration and 100% on the validation sets for the determination of origin of black pepper. This study has demonstrated that portable spectrometers can be effective tools to use in the classification of black pepper samples according to their geographical origin.

Miniaturized NIR Spectrometers in a Nutshell: Shining Light over Sources of Variance

The increasing portability and accessibility of miniaturized NIR spectrometers are promoting the spread of in-field and online applications. Alongside the successful outcomes, there are also several problems related to the acquisition strategies for each instrument and to experimental factors that can influence the collected signals. An insightful investigation of such factors is necessary and could lead to advancements in experimental set-up and data modelling. This work aimed to identify variation sources when using miniaturized NIR sensors and to propose a methodology to investigate such sources based on a multivariate method (ANOVA—Simultaneous Component Analysis) that considers the effects and interactions between them. Five different spectrometers were chosen for their different spectroscopic range and technical characteristics, and samples of worldwide interest were chosen as the case study. Comparing various portable sensors is interesting since results could significantly vary in the same application, justifying the idea that this kind of spectrometer is not to be treated as a general class of instruments.

A simple but effective evaluation criterion for parameters optimization of EPO and its application to moisture insensitive prediction of soil organic matter

Field measurement using portable NIR spectrometer is becoming a popular way to provide in situ, rapid estimation of soil organic matter content. However, in situ NIR spectra is altered by external environmental factors, such as soil moisture. The external parameter orthogonalization (EPO) has been recognized as one of the most effective method to minimize effect of soil moisture on spectra. In general, the root mean squared error of cross validation (RMSECV) was mainly used as an evaluation criterion to optimize the parameters involved in EPO algorithm, which ensures that the wet soil spectra corrected by EPO could have a good quantitative performance, but ignores the spectral similarity between the dry soil spectra and the wet soil spectra corrected by EPO. In this study, we present a simple but effective evaluation criterion for optimization of EPO, which we refer to as the mean of spectral correlation coefficient (MSCC). Compared with the commonly used RMSECV criterion, the spectra corrected by EPO (MSCC) are more similar to the corresponding dry soil spectra, and MSCC is more robust to different spectral pretreatment methods. In addition, the quantitative results of MSCC are equivalent to those of RMSECV, and sometimes slightly better than those of RMSECV.

Investigation of structural, morphological and magnetic properties of nanostructured strontium hexaferrite through co-precipitation technique: Impacts of annealing temperature and Fe/Sr ratio

M-type strontium hexaferrite (SrM) were successfully synthesized from Sr2+ and Fe3+ precursor salt through co-precipitation technique. Different higher sintering temperatures (800–1000 °C) were used to get the desired SrM with variation of Fe3+/Sr2+ mole ratio as well. The characterization of SrM and its properties were investigated using modern instrumental techniques viz. X-ray diffraction (XRD), Fourier Transform Infrared Spectrometer, Scanning Electron Microscopy, Vibrating Sample Magnetometer, UV–Visible NIR Spectrometer, Impedance Analyzer and Thermal Conductivity Meter. The phase of the synthesized SrM were confirmed by comparing the XRD patterns with the standard ICDD data and Reitvelt Refinement for the SrM having Fe3+/Sr2+ ratio 10 and SrM with distinct annealing temperature were performed. The structural parameters, particle size (75 nm–318 nm) and shape of the as prepared samples were changed with calcination temperature as well as mole ratio. The saturation magnetization (73.77–24.27 emu/g), coercivity (3732.28–642.10 Oe) and remanant magnetization (39.15–8.86 emu/g) were varied with calcination temperature and composition. The dielectric properties, optical properties and thermophysical properties were measured for the SrM keeping Fe3+/Sr2+ ratio 10 calcined at 1000 °C. The synthesized SrM can be applied in magnetic recording media and as photocatalyst due to its low coercivity (2764.48 Oe), high saturation magnetization (73.77 emu/g) and low band gap energy (Eg-2.04 eV) respectively.

Single-Layered Biosynthesized Copper Oxide (CuO) Nanocoatings as Solar-Selective Absorber

Herein, spectrally selective single-layered CuO nanocoatings were successfully demonstrated via green synthesis and deposited on stainless steel (SS) substrates using a spin coater at 700, 800, 900, and 1000 rpm. The morphological, structural, and compositional analyses of the obtained nanocoatings were studied using SEM, XRD, EDX, and Raman spectroscopy. The SEM images show nanorod-like structure surfaces with dense surface morphology. The XRD patterns confirmed the presence of peaks indexed to a monoclinic structural phase of CuO. The EDX spectra clearly revealed the presence of Cu and O elements, and XPS spectra showed peaks of Cu2p and O1s core levels, which are typical characteristics of Cu (II) and O(II), respectively, in CuO. The Raman spectra showed peaks at 305, 344, and 642 cm−1 attributed to Raman active (Ag+2Bg) modes for Cu-O stretching. Rutherford backscattering spectrometry (RBS) determined the content of the elements and the changes in the thicknesses of the coatings with the rotational speed (RS) of the spin coater. The elemental content of Cu and O atoms were, respectively, 54 and 46%. The thicknesses were calculated to be 1.406 × 1018 atoms/cm2 (296.3 nm), 1.286 × 1018 atoms/cm2 (271.0 nm), 1.138 × 1018 atoms/cm2 (239.8 nm), and 0.985 × 1015 atoms/cm2 (207.5 nm) at 700, 800, 900 and 1000 rpm, respectively. The optical properties of the CuO nanocoatings were characterized using UV–Vis–NIR and FTIR spectrometers; its vital solar selectivity parameters of solar absorptance (α) and emissivity (ε) were evaluated in the ranges of 0.3–2.5 and 2.5–20 µm wavelengths, respectively. The obtained coatings exhibited solar parameters (α = 0.90, and ε = 0.31) associated with 700 rpm due to an intrinsic and interference-induced absorption as well as higher attenuation of light.

Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms.

Rapid and nondestructive measurement of moisture content in crude palm oil is essential for promoting the shelf-stability and quality. In this research, micro NIR spectrometer coupled with a multivariate calibration model was used to collect and analyse fingerprinted information from palm oil samples at different moisture contents. Several preprocessing methods such as standard normal variant (SNV), multiplicative scatter correction (MSC), Savitzky-Golay first derivative (SGD1), Savitzky-Golay second derivative (SGD2) together with partial least square (PLS) regression techniques, full PLS, interval PLS (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS (GAPLS), and successive projection algorithm PLS (SPA-PLS) were comparatively employed to construct an optimum quantitative prediction model for moisture content in crude palm oil. The models were evaluated according to the coefficient of determination and root mean square error in calibration (Rc and RMSEC) and prediction (Rp and RMSEC) set, respectively. The model SGD1 + SiPLS was the optimal novel algorithm obtained among the others with the performance of Rc = 0.968 and RMSEC = 0.468 in the calibration set and Rp = 0.956 and RMSEP = 0.361 in the prediction set. The results showed that rapid and nondestructive determination of moisture content in palm oil is feasible and this would go a long way to facilitating quality control of crude palm oil.

The size effect on the optical-electrical properties of Cu2S/CdS thin film towards the performance on Ag/p-Cu2S/n-CdS/ATO heterojunction diode

Here we report, room temperature deposition of Cu2S/CdS thin film on antimony doped tin oxide(ATO) substrate through spray pyrolysis and dip coating technique. The effect of size on the deposited Cu2S/CdS thin films and its heterojunction parameters are extensively investigated. The sizes of the thin film are estimated through field-emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) images. The optical properties of the Cu2S/CdS thin film are studied using UV–Vis–NIR spectrometer. It is found that increase in the size, the transmittance decreases, consequently the band-gap energy decreases from 2.92eV to 2.86eV. The refractive index and the dielectric constant are calculated from the optical measurements. In addition, the dispersion of the refractive index is discussed in terms of the Wemple-DiDomenico single-oscillator model. The oscillator parameters such as optical dispersion energies, dielectric constant, the average value of oscillator strength, wavelength of single oscillator, plasma frequency and lattice energy are evaluated. To examine the size-dependent electrical parameters of Cu2S/CdS thin film, high-quality vacuum evaporated silver (Ag) is used as front contact. The current-voltage (I–V) curves of the Ag/p-Cu2S/n-CdS/ATO/glass heterojunction device are measured under dark mode at standard testing conditions (STC). The dark measurements of the fabricated device are investigated through the conventional I–V method, Norde method, and Cheung method. It is observed that the potential height of the heterojunction device decreases from 0.89 to 0.76 with an increment of the size of Cu2S/CdS thin films. It is observed that the size strongly influenced the diode parameters viz. ideality factor, saturation current, barrier height, series, and shunt resistance performance.

Synthesis of extracellular silica nanoparticles through bioleaching of sand by Fusarium oxysporum and nematicidal evaluation

ABSTRACT Meloidogyne javanica, a destructive soil-borne parasite that may infect practically all cultivated plants worldwide. Utilizing efficient bio-safe management techniques, crop damage caused by nematodes that feed on plants is reduced. Therefore, the aim of this study is to biosynthesize extracellular silica nanoparticles (SiO2-NPs) through bioleaching of sand by Fusarium oxysporum and characterizes SiO2-NPs and evaluates their nematicidal potential against M. javanica in vitro. During in vitro experiments, second stage juveniles (J2s) and egg masses of M. javanica were treated with various concentrations (50, 100, 150, 200 and 250 ppm of SiO2-NPs). All concentrations applied caused J2s mortality and inhibited egg hatching to a varying degree. The SiO2-NPs applied at same concentrations in addition predict preventing J2s from penetrating tomato roots. The characterizations of synthesized SiO2-NPs were done by XRD, FTIR, SEM-EDX, TEM-SAED, and UV Visible NIR Spectrometer. XRD technique analyzed amorphous phase of SiO2-NPs exhibiting a broad peak. The FTIR spectrum identified a range of efficient functional groups, capping and stability agents. TEM images of SiO2-NPs showed porous texture and had an average size of 22.5 nm. To our knowledge, this is first account of synthetic SiO2-NPs acting as natural nematicides through bioleaching of sand by F. oxysporum.

Avaliação não destrutiva de atributos de qualidade em maçãs e peras usando espectroscopia no infravermelho próximo e quimiometria

Abstract The objective of this study was to evaluate the performance of a handheld NIR spectrometer for non-destructive quality analysis of apples and pears produced in the Brazilian Semi-arid region. NIR spectra were acquired with a portable spectrometer in the wavelength range of 750–1065 nm and reference analyses of dry matter content (DMC) and soluble solids content (SSC) were measured weekly during 10 weeks of storage at 0.5 °C. Spectra were pre-processed with standard normal variate and used to develop DMC and SSC models using partial least squares regression with full cross-validation. The models were validated using data not included in the calibration. Satisfactory prediction results were obtained for SSC in apples (R² = 0.58) and pears (R² = 0.55), and for DMC in apples (R² = 0.55) and pears (R² = 0.65). All prediction models showed a relative root mean square error of prediction lower than 8%. These findings indicate that the NIR spectrometer is a promising tool to be used for a rapid and non-destructive determination of internal quality traits in apples and pears.