NIR Spectra Research Articles

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.

Combination of near-infrared spectroscopy and key wavelength-based screening algorithm for rapid determination of rice protein content

Rice taste quality is one of the most important factors influencing rice marketing and distribution, and rice with a high taste quality is more popular with consumers and has a higher price. Accurate and rapid determination of rice protein content helps to assess the rice taste quality and aids in marketing. In this study, NIR spectra of 84 rice samples combined with partial least squares regression (PLSR) were used to model protein content, and different selection algorithms based on key wavelengths (competitive adaptive reweighted sampling, CARS; Monte-Carlo uninformative wavelength elimination, MC-UVE; random frog, RF) were used to understand the accuracy of NIR spectra in predicting the rice protein content. Our results showed that the R2P and RPD of the original full-spectrum PLSR model were 0.83 and 1.95, respectively. After the second-order derivative preprocessing, the R2P and RPD of the full spectrum were improved to 0.95 and 4.14. Both CARS and MC-UVE increased the R2P of the PLSR model to 0.97 and the RPD to 5.57 and 5.65, respectively. R2C and R2CV in the PLSR model based on CARS algorithm were 0.93 and 0.91, respectively. The CARS algorithm had excellent results in predicting the rice protein content.

A multivariable signal conversion strategy for quality assessment of Gastrodiae rhizome based on convolutional neural network

Chromatographic fingerprinting is a time-consuming analytical method used to evaluate the overall chemical properties of natural products. While NIR spectroscopy is a rapid analytical tool, it is typically used to predict only a few quality markers. In this study, we proposed a multivariable signal conversion strategy that can rapidly assess the quality of natural products by reproducing HPLC signals from NIR spectra. The strategy converted the NIR spectroscopic fingerprints to chromatographic fingerprints using PLS and CNN, respectively, and evaluated the performances of conversion using Pearson correlation coefficient and nearness index. Hierarchical clustering analysis and correlation analysis were used to evaluate the similarity between the converted and real fingerprints to reflect the goodness of the conversion. The CNN models showed comparable and satisfactory results to PLS models in predicting quality markers and converting fingerprints. Moreover, CNN models did not require spectral pretreatment, which makes them more advantageous than PLS models. The proposed strategy combines the strengths of NIR spectroscopy, such as information-rich and high-speed analysis, and the benefits of chromatographic fingerprinting, which provides a better understanding of overall chemical properties. Therefore, it has great potential for application in the field of natural product analysis.

Photophysical and Photochemical Properties of Perylene–(Cyanine Dye) Dyad in the VIS–NIR Spectrum Region

Photophysical and Photochemical Properties of Perylene–(Cyanine Dye) Dyad in the VIS–NIR Spectrum Region

Photophysical and Photochemical Properties of Perylene–(Cyanine Dye) Dyad in the VIS–NIR Spectrum Region

The photophysical properties of an original covalently-bonded dyad based on a perylene derivative and IR-780 cyanine dye were studied. The dyad has pronounced absorption in the NIR region of the spectrum and a strong fluorescence signal, which is weakly quenched by the influence of the perylene derivative. Upon excitation of the dyad in the absorption region of perylene, a fluorescence signal from IR-780 is detected due to the Förster energy transfer mechanism. It is shown that the dyad does not generate singlet oxygen upon photoexcitation in the NIR region of the spectrum. However, it can generate superoxide anion radicals, indicating the presence of the photoinduced electrons transfer from the dye to the perylene.

Authentication/discrimination, identification and quantification of cinnamon adulterants using NIR spectroscopy and different chemometric tools: A tutorial to deal with counterfeit samples

Cinnamon is one of the most consumed spices worldwide and its adulteration can pose several risks to human health. This work proposes a method for the authentication/discrimination, identification and quantification of cinnamon adulterants (Cinnamomum cassia known as false cinnamon, clove and black pepper) based on NIR spectroscopy and different chemometric tools namely, DD-SIMCA, MCR-ALS and PLS regression. In addition, this work intends to provide a guide for dealing with adulterated samples. A total of 133 samples were analysed through NIR spectroscopy and several spectral regions and pre-processing techniques were tested to optimize the respective models using only the calibration set. For the authentication/discrimination purpose using DD-SIMCA, all the testing samples were properly classified, with a sensitivity and specificity of 100%. Concerning the identification of all the samples’ constituents using MCR-ALS, the spectral profiles retrieved were very similar to the pure NIR spectra of Cinnamomum verum and each adulterant, with Pearson correlation coefficients of 0.9996, 0.9973, 0.9674 and 0.8797 for Cinnamomum verum, Cinnamomum cassia, black pepper and clove, respectively. In relation to the quantification of cinnamon adulterants, the PLS models showed a R2P, RER and RPD higher than 0.95, 12 and 4, respectively, for all the models which indicate that the developed PLS models can be considered as excellent predictive models. Globally, the obtained results reveal the suitability and accuracy of NIR spectroscopy to authenticate/discriminate, identify the adulterants and quantify their respective concentration in a rapid, robust, low-cost and environment-friendly way.

Influence of the Physical Properties of Samples in the Use of NIRS to Predict the Chemical Composition and Gas Production Kinetic Parameters of Corn and Grass Silages

Livestock production is the main economic engine of the Azores. Given the role nutrition plays in animal performance and the context of limited Averages sharing the same letter are noagricultural area of the archipelago, optimising the quality and availability of the fodder used in feeds takes on an important role in increasing the competitiveness and viability of the industry. This work evaluated the potential of NIR spectroscopy to quantify several chemical and biological parameters used to assess silage quality in the specific agroclimatic context of the Azores. Reference values were determined for 400 grass silage and 400 corn silage samples. The corresponding NIR spectra were extracted, and mathematical models capable of correlating these readings with the various parameters were built. The best results were obtained for crude protein, neutral detergent fibre, acid detergent fibre, and crude ash for both fresh and dry samples. For the biological parameters concerning gas production, no satisfactory results were reached. It was concluded that it was feasible to build models capable of accurately predicting these important quality parameters, which demonstrates that NIRS is a viable technique that can be used in the Azores to perform a close and timely monitoring of forage quality and to facilitate the management and optimisation of diets.

Near-Infrared Spectroscopy with Machine Learning for Classifying and Quantifying Nutmeg Adulteration

Near-infrared spectroscopy (NIRS) provides broadbands, overtones, and combinations of organic-bond vibrations and has been used to characterize agricultural and food products. The adulteration of grated nutmeg with cinnamon is extremely profitable and difficult to detect; to prevent retail fraud, it is vital to differentiate between these materials. This study proposes a model for classifying the adulteration of nutmeg with cinnamon and predicting the level of adulteration. NIR spectra were characterized with six machine learning (ML) algorithms, namely, the principal component-multilayer perceptron (PC-MLP), principal component-linear discriminant analysis (PC-LDA), partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and decision tree (DT) methods. PC-MLP provided 100% accuracy in calibration and prediction in distinguishing nutmeg from cinnamon. In addition, this approach showed excellent performance in predicting the adulteration ratio of nutmeg and cinnamon with a high coefficient of determination of prediction (R 2 pred) value of 0.9969, low root mean square error of prediction (RMSEP) value of 0.5728%, and high ratio of prediction to deviation (RPD) value of 17.9605. Therefore, this study indicates the potential of integrating NIR spectroscopy with PC-MLP to classify and quantify the adulteration of nutmeg.

Impact of Sm3+ ions on structural, thermal, optical and photoluminescence properties of ZnO–Na2O–PbO–B2O3 glasses for optoelectronics device applications

This work describes the impact of trivalent samarium (Sm3+) ions concentrations on the thermal, structural, physical, optical and photoluminescence (PL) characteristics in Zinc Sodium Lead Borate (ZnNaPbB) glasses with configuration (70-x) B2O3–10PbO–10ZnO–10Na2CO3-xSm2O3, (where x = 0.1–2.0 mol%). The glass samples were synthesized using a traditional melt-quench technique and characterized by various spectroscopic methods such as X-ray diffraction (XRD), Raman spectroscopy, Fourier Transform Infrared (FT-IR), UV–Vis–NIR spectra, Differential scanning calorimetry (DSC) and PL emission-excitation spectra to illuminate their photonic applications. The broad band perceived in the XRD diffractogram confirmed the amorphous nature of an un-doped ZnNaPbB glass. The physical properties including thickness, density, molar volume, dielectric constant, rare earth (RE) ion concentration, field strength and molecular electronic polarizability, etc. were calculated from the standard formulae. The FT-IR spectra identify the presence of stretching vibration in BO3 and BO4 units with some additional structural and functional groups with their bond nature. The observed DSC data was used to estimate the glass transition temperature (Tg). The absorption spectra presented all the probable transitions of Sm3+ ion-doped ZnNaPbB glass and the optical band gaps were computed using Tauc's method. All the glass samples depicted band gap values over 3 eV, proving that the material is insulating. The absorption spectra were utilized to evaluate Judd-Ofelt (J-O) parameters which were further used in different radiative parameters calculations to find the luminous levels of Sm3+ ions doped ZnNaPbB glasses. The PL emission spectra of the present ZnNaPbBSm glasses parade four bands parallel to the transitions 4G5/2 to 6H5/2 (563 nm), 6H7/2 (599 nm), 6H9/2 (646 nm) and 6H11/2 (706 nm), respectively. Additionally, the evaluated values of the emission cross-section and branching ratios describe the suitability of the present ZnNaPbBSm glasses for optoelectronic applications. Further, the colorimetric parameters such as CIE coordinates (x, y) and correlated color temperature (CCT) were evaluated using PL emission data and the measured CIE coordinates are found in the reddish-orange region for the present glasses. The time decay profiles of as-prepared glasses were used to evaluate the experimental lifetime, energy transfer parameter and type of donor-acceptor interaction. The Dexter theory and Inokuti-Hirayama (I–H) fitting are used to recognize the type of interaction in non-radiative energy transfer which is responsible for the concentration quenching. The result demonstrates that the as-prepared ZnNaPbBSm glasses are applicable for optoelectronics device applications.

Rapid Determination of the Total Petroleum Hydrocarbon Content of Soils by Handheld Fourier Transform Near-Infrared Spectroscopy.

For successful soil remediation and hydrocarbon exploration operations, determining the total petroleum hydrocarbon (TPH) content of soils is an indispensable process step. This paper reports on the performance of a handheld Fourier transform near-infrared (FT-NIR) spectrometer for rapid and quantitative determination of TPH content of soils from two different sites by diffuse reflection measurements. For rapid decisions for exploration work or environmental site assessment projects, a quick─preferably on-site─determination of TPH content is valuable. Diffuse reflection NIR spectra were recorded from soil samples of two different sites with TPH reference values ranging from 350 to 30,000 ppm, as determined by capillary gas chromatography and flame ionization detection with hydrocarbon fingerprinting C1-C44. However, this paper not only addresses the development of site-specific partial-least squares (PLS) calibrations but also demonstrates the locally-weighted PLS (LW-PLS) technique, which can be used to develop global, site-independent PLS calibrations without significant penalty in calibration performance. As a first step, the diffuse reflection spectra were used to develop conservative, site-specific PLS calibration models with root-mean-square calibration/cross-validation errors (RMSEC/RMSECV) of 1043/1106 and 741/785 ppm TPH, respectively, and the average absolute prediction errors for samples not contained in the calibration set were 451 and 293 ppm for the two sites, respectively. In a further step, significant degradation of the RMSE values of a conservative PLS model based on the NIR spectra of both sites was then compared to the application of the LW-PLS method, with only a slight loss of the prediction accuracy relative to the site-independent models. This study confirms the ability of next-generation portable FT-NIR spectrometers to predict low TPH levels in various soil types through both─soil-specific and site-independent─calibrations, giving these spectrometers the potential to become rapid screening tools in the field.

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.

Comparison of near and mid-infrared reflectance spectroscopy for the estimation of soil organic carbon fractions in Madagascar agricultural soils

Assessing the different pools of soil organic carbon (SOC) improves our understanding of how and at what rate the different forms of carbon (C) are being formed or lost in soils. Physical fractionation of soil organic matter (SOM) has often been used to separate and quantify SOC pools, but this approach is very tedious and can rarely be performed on large sample sets. Infrared spectroscopy has proven useful for time- and cost-effective quantification of total SOC, which prompted us to study its ability to characterise the distribution of SOC in physical fractions. This study aimed to compare the potential of near- and mid-infrared reflectance spectroscopy (NIRS and MIRS, respectively) to predict the distribution of SOC in particle-size and particle-density fractions, using spectra of unfractionated soils. A set of 134 sieved (< 2 mm) soil samples originating from seven sites in contrasting pedoclimatic regions of Madagascar was studied. For each sample, five SOM fractions were separated: the particulate organic matter fraction (POM) and the particle-size fractions >200 μm, 50–200-μm, 20–50-μm and < 20 μm. The mass (g fraction 100 g−1 soil), SOC concentration (gC kg−1 fraction) and SOC amount (gC fraction kg−1 soil) were determined for each fraction in the laboratory. The NIR and MIR spectra were acquired on finely ground (< 0.2 mm) aliquots of unfractionated soil. Then, spectra were used for the prediction of each variable (i.e., the mass, SOC concentration and amount of each fraction, and SOC content of unfractionated soil), which was achieved with locally weighted partial least squares regression (LW-PLSR) on NIRS and MIRS data separately. For both spectral ranges, the same samples were used for calibration (n = 109, selected for spectral representativeness) and validation (n = 25).Models based on NIRS and MIRS yielded excellent predictions for SOC content in unfractionated soil (R2 = 0.98 and ratio of performance to interquartile range RPIQ ≥13) and accurate predictions (R2 ≥ 0.75 and RPIQ ≥2) for the mass, SOC concentration and SOC amount in most fractions. The predictions of SOC concentrations and SOC amounts were better in the fractions <200 μm (R2 ≥ 0.85 and RPIQ >3), especially in the fraction <20 μm (R2 > 0.9 and RPIQ >4.5). In most cases, NIRS slightly outperformed MIRS. This result contradicts most previous studies performed on soils from temperate regions but confirms those performed on soils from tropical regions.Infrared spectroscopy allowed accurate prediction of SOC distribution in particle-size fractions, which paves the way to high-throughput characterization of SOM.

Quantifying soil properties relevant to soil organic carbon biogeochemical cycles by infrared spectroscopy: The importance of compositional data analysis

Oxyhydroxides, soil texture and soil organic carbon (SOC) fractions are key parameters determining organic carbon cycling in soils. Standard laboratory methods to determine these soil properties are, however, time–consuming and expensive. Visible near infrared (Vis–NIR) and mid infrared (MIR) spectroscopy have been recognized as a promising alternative, but previous studies have not explicitly considered the above–mentioned soil properties as compositional data. The fractional components in compositional data are interrelated but their sum should be unity – these features should be represented in the spectral modeling process to minimize the prediction bias. In this study, two unique datasets were used to test these premises. The first one consisted of 655 samples collected from agricultural terraces and lynchets across Europe, which were scanned to acquire MIR spectra, while in the second one 4516 samples from private gardens across Flanders, Belgium were used to acquire Vis–NIR spectra. Memory–based learning models were optimized using both raw data (conventional method) and transformed data of soil properties by additive log–ratio (alr), centered log–ratio (clr), and isometric log–ratio (ilr) transformation methods. Results showed that the log–ratio transformation methods produced predictions as accurate as the conventional method, whilst also added two significant benefits: (1) they ensured the predicted fractions added up to 100% and (2) they reduced the number of samples with extreme prediction errors. We found that for 11 out of 18 investigated soil properties, the three log–ratio transformation methods provided similar model performance, whilst ilr outperformed clr for the prediction of silt and sand content of garden soils, for coarse particulate SOC (>250 µm) and microaggregate–associated SOC (250–53 µm) of terrace soils. For the remaining three properties (Al oxyhydroxides) alr outperformed ilr. Fair to excellent predictive models (RPD from 1.4 to 4.3; R2 from 0.50 to 0.95) were achieved for soil oxyhydroxides (Fe, Al, Mn) and soil texture from MIR spectra. Our approach also enabled accurate predictions of silt and sand content of garden soils using Vis–NIR spectra (RPD = 1.9; R2 = 0.72), although accuracy for clay was lower (RPD = 1.3; R2 = 0.49). This study demonstrates that combining soil infrared spectroscopy with a compositional data analysis is a promising technique that enables cost-effective and reliable quantification of soil properties relevant to SOC stability, thus offering a practical opportunity to assess the role of SOC in global C cycling.

A method for measuring dielectric relaxation of water by NIR spectroscopy: Applicability and application to measurement of water diffusion coefficient

AbstractDeterioration of biomaterial is subject to the interaction between biomolecules and water, which is often of special interest to the realization of long‐term preservation. In previous study, we reported the relationship between dielectric rotational relaxation time and the ν2 + ν3 NIR combination band of water as well as the theory behind it using saccharide solutions as examples and proposed a method accordingly which takes advantages of the infrared microscope to measure the dielectric relaxation time of water in biomaterials. In this work, we examined protein, polypeptide, sugar alcohol and some shelf‐stable vegetables to further discuss its applicability and limit. We also proved that this method can be used for measuring water diffusion coefficient and showed example of how the diffusion coefficient can be used to predict water distribution during drying process at a significantly enhanced spatial resolution relative to other techniques.Practical applicationsMolecular mobility of water determines many properties of biomaterials and can be characterized by different spectroscopies. This paper examined the applicability of a method of calculating the dielectric relaxation time of water using NIR spectrum on different solutions and proved that this method may also be used for calculating the diffusion coefficient of water, a parameter subject to molecular mobility and crucial in the drying process. This paper also provided example of the application of this method in the calculation of water distribution during drying process. The high spatial resolution of infrared microscope relative to other techniques may enable the measurement of water dynamics in biomaterials in a much quicker way and smaller scale.

Analysis of Total Soil Nutrient Content with X-ray Fluorescence Spectroscopy (XRF): Assessing Different Predictive Modeling Strategies and Auxiliary Variables

The difference in the matrix present in soil samples from different areas limits the performance of nutrient analysis via XRF sensors, and only a few strategies to mitigate this effect to ensure an accurate analysis have been proposed so far. In this context, this research aimed to compare the performance of different predictive models, including a simple linear regression (RS), multiple linear regression (MLR), partial least-squares regression (PLS), and random forest (RF) models for the prediction of Ca and K in agricultural soils. RS models were evaluated on XRF data without (RS1) and with (RS2) Compton normalization. In addition, it was assessed whether using soil texture information and/or vis–NIR spectra as auxiliary variables would optimize the predictive performance of the models. The results showed that all strategies allowed the mitigation of the matrix effect to some degree, enabling the determination of their Ca and K contents with excellent predictive performance (R2 ≥ 0.84). The best performance was obtained using RS2 for the Ca prediction (R2 = 0.92, RSME = 48.25 mg kg−1 and relative improvement (RI) of 52.3% compared to RS1) and using an RF for the K prediction (R2 = 0.84, RSME = 17.43 mg kg−1 and RI of 24.3% compared to RS1). The results indicated that sophisticated models did not always perform better than linear models. Furthermore, using texture data and vis–NIR spectra as auxiliary data was promising only for the K prediction, which showed an error reduction in the order of 10%, contrasting with the Ca prediction, which did not reduce the prediction error by more than 1%. The best modeling approach in our study proved to be attribute-specific. These results give further insight into the development of intelligence modeling strategies for sensor-based soil analysis.

Quantitative analysis of aflatoxin B1 in moldy peanuts based on near-infrared spectra with two-dimensional convolutional neural network

A two-dimensional convolutional neural network (2D-CNN) model based on Gramian angular summation field (GASF) image coding was proposed to detect aflatoxin B1 (AFB1) in moldy peanuts with high precision. Fourier transform near-infrared spectrometer was employed to acquire near-infrared spectra of moldy peanuts. The GASF was used to encode the NIR spectral features after principal component analysis. A convolutional neural network (CNN) framework was designed and the GASF-2D-CNN model was developed to realize the quantitative detection of AFB1 in peanuts. The results obtained showed that compared with the traditional chemometrics models and one-dimensional CNN (1D-CNN) model, the performance of GASF-2D-CNN model was significantly better than the previous two types of detection models. The root mean square error of prediction (RMSEP), coefficient of determination (RP2), relative percent deviation (RPD), and ratio of performance to interquartile range (RPIQ) of the best GASF-2D-CNN model were 2.0 μg·kg−1, 0.99, 8.3, and 9.3, respectively. The study reveals that GASF image coding technique can effectively mine the potential feature information in NIR spectra, and this technique will have a promising application prospect in the multivariable calibration of spectroscopy.

Synthesis, physico-chemical characterization and quantum chemical studies of 2, 3-dimethyl quinoxalinium-5-sulphosalicylate crystal

2,3-dimethylquinoxalinum-5-sulphosalicylate (MQSA) was synthesized and grown into single crystals by slowly evaporating the solvent in methanol at room temperature. According to the lattice parameters found by the SCXRD analysis method (a = 8.8031 (17) Å, b = 17.768(3) Å, c = 10.7979(16) and α = γ = 90°, β = 90.611 (7) °), the crystal belongs to monoclinic crystal system with the P21/n space group. Moreover, powder X-ray diffraction investigation supported the crystalline nature. The compound's UV–Vis spectra exhibited a CT-band with a wavelength maximum of 372 nm, as well as its NIR spectra revealed good optical transmittance in the visible region with a low cut-off wavelength of 376 nm and an optical band gap of 5.12 eV. Dual emission bands at 418 and 484 nm are observed in the PL emission spectra. The synthesized crystal was examined using FT-IR, FT-Raman, and NMR spectral methods. The crystal is thermally stable up to 274 °C, furthermore, TG-DTA and DSC methods were employed to study the degradation process. The second and third-order NLO efficiencies of the crystal was analyzed by Kurtz-Perry powder and Z-scan methods, respectively. The single crystal of MQSA is further studied through Hirshfeld surface analysis, fingerprint plots, and crystal voids analysis to understand the intermoleculiar interactions and individual atomic arrangements inside the crystal. The H…H and O…H/H…O interactions are greatly aided by crystal structure. Additionally, the quantum computational methods were used to assess several molecular level electronic properties, including molecular orbitals, molecular electrostatic potentials maps, linear polarizability and static third-order nonlinear optical (NLO) polarizability, which is a molecular-level NLO response property. The isotropic linear polarizability and NLO polarizability of MQSA are 29.99 × 10−24 esu and 28.45 × 10−36 esu, respectively, in the M06–2X/6–311G* method, which demonstrates the decent potential of the synthesized crystal for its use as NLO materials. Furthermore, the frontier molecular orbitals, molecular electrostatic potentials, and UV–Visible spectrum were calculated and discussed.

Malignant versus normal breast tissue: Optical differentiation exploiting hyperspectral imaging system

Breast malignancy is a critical problem that severely affects women’s health globally with a high-frequency rate, necessitating fast, effective, and early diagnostic methods. The present study aims to measure the breast tissue’s optical properties by capturing the spectral signatures from malignant and normal breast tissue for therapeutic and diagnostic applications. The optical imaging system incorporates a hyperspectral (HS) camera to capture the spectral signatures for both the malignant and normal breast tissues within 400 ~ 1000 nm. The system was subdivided into two exploratory (reflection/transmission) to measure the tissue’s diffuse reflectance (Rd) and light transmission (Tr), respectively. The study involved 30 breast tissue (normal/tumor) samples from 30 females in the age range of 46 ~ 72 years, who were optically inspected in the visible and near-infrared (VIS-NIR) spectra. Then, the inverse adding doubling (IAD) method for breast tissue characterization and descriptive analysis (T-test) was exploited to verify the significant difference between the various types of breast tissues and select the optimum wavelength. Finally, comparing the study outcome with the histopathological examination to evaluate the system’s effectiveness by calculation (sensitivity, specificity, and accuracy). The average outcome values demonstrated that the optimal spectral bands distinguishing between the normal and the tumor tissues regarding the reflectance approach were 600 ~ 680 nm and 750 ~ 960 nm at the VIS and NIR spectrum, respectively. Then, for the transmission technique, the optimal spectral bands were 560 ~ 590 nm and 760 ~ 810 nm at the VIS and NIR spectra, respectively. Later, the T-test and the IAD verified that the highest Rd values for discrimination were 600 ~ 640 nm and 800 ~ 840 nm at the VIS and NIR spectra, respectively. On the other side, the highest Tr values were 600 ~ 640 nm and 760 ~ 800 nm at the VIS and NIR spectra, respectively. The investigation’s average reading accuracy, sensitivity, and specificity were 85%, 81.88%, and 88.8%, respectively. The experimental trials revealed that the system could identify the optimal wavelength for therapeutic and diagnostic applications through the light interaction behavior of the breast tissue’s optical properties.

Biogeographic Variability in Kernel Oil and Press Cake Content of Beauty Leaf Tree (Calophyllum inophyllum L.), as Determined by Chemical and Near-Infrared Spectroscopy Analysis

The aim of this study was to evaluate biogeographic variability in the fruit, kernel, kernel-oil and press-cake contents of 50 accessions of the beauty leaf tree (C. inophyllum L.) collected from 19 locations spanning 4000 km along the eastern and northern coasts of Australia (Northern Territory and Queensland). Mature fallen fruits of C. inophyllum were collected from individual trees and stored in a shed for over a year. The fruits were cracked open to extract the kernels, and the kernels were crushed to 5–10 mm. NIR spectra of crushed kernels were collected using FT–NIR. Results of this study showed large variation between individual trees and the provenances for oil, resin and cake contents. Most of the C. inophyllum genotypes were separated based on their NIR fingerprint using PCA and PLS-DA. It was concluded that NIR spectroscopy not only aids in the screening of large numbers of genotypes, but it also allows the preservation of the tested seeds for further propagation. This feature will have the greatest advantage in plant breeding and commercial cultivation, as only the seeds that contain high oil content could be sown to help establish plantations with high oil-production capacity. Overall, it was concluded that the differences between provenances for oil, resin and cake contents can be predicted using NIR spectra. Furthermore, NIR spectroscopy can be used as a tool to define provenance variations in the kernel oil content of the beauty leaf tree.

An investigation of the correlation between NIR spectrum of the northern sky and atmospheric parameters from April to December in Toyama, Japan by compact spectrometer system

An investigation of the correlation between NIR spectrum of the northern sky and atmospheric parameters from April to December in Toyama, Japan by compact spectrometer system