Near Infrared Spectroscopy (NIRS) Model-Based Prediction for Protein Content in Cowpea

Cowpea (Vigna unguiculata L. Walp) is a multi-purpose legume with high quality protein for human consumption and livestock. The objective of this work was to develop near-infrared spectroscopy (NIRS) prediction models to estimate protein content in cowpea. A total of 116 cowpea breeding lines with a wide range of protein contents (19.28 % to 32.04%) were selected to build the model using whole seed and ground seed samples. Partial least-squares discriminant analysis (PLS-DA) regression technique with different pre-treatments (derivatives, standard normal variate, and multiplicative scatter correction) were carried out to develop the protein prediction model. Results showed: 1) spectral plots of both the whole seed and ground seed showed higher spectral scatter at higher wavelengths (>1450 nm), 2) data pre-processing affects prediction accuracy for bot whole seed and ground seed samples, 3) prediction using ground seed samples (0.64 R2 0.85) is better than the whole seed (0.33 R2 0.78), and 4) the data pre-processing second derivative with standard normal variate has the best prediction (R2_whole seed = 0.78, R2_ground seed = 0.85). The results will be of interest in cowpea breeding programs aimed at improving total seed protein content.

Comparing simulated and experimental spectral line splitting in visible spectroscopy diagnostics in the HL-2A tokamak

We established the passive-visible spectroscopy diagnostics(P-VSD)and active-VSD(A-VSD)spectral splitting models for the HL-2A tokamak.Spectral splitting due to the influence of electromagnetic fields on the spectra in VSD is studied.Zeeman splitting induced by the magnetic field(B)is used to distinguish reflected light overlap in the divertor for P-VSD.Stark splitting caused by the Lorentz electric field(E_(Lorentz))from the neutral beam injection particle’s interaction with the magnetic field(V_(beam)×B)is used to measure the safety factor q profile for A-VSD.We give a comparison and error analysis by fitting the experimental spectra with the simulation results.The distinguishing of edge(scrape-off layer and divertor)hydrogen/deuterium spectral lines and the q profile derived from the spectra provides a reference for HL-2M VSD.

Study on the secondary structure and hydration effect of human serum albumin under acidic pH and ethanol perturbation with IR/NIR spectroscopy

Human serum albumin(HSA)is the most abundant protein in plasma and plays an essential physiological role in the human body.Ethanol precipitation is the most widely used way to obtain HSA,and pH and ethanol are crucial factors affecting the process.In this study,infrared(IR)spectroscopy and near-infrared(NIR)spectroscopy in combination with chemometrics were used to investigate the changes in the secondary structure and hydration of HSA at acidic pH(5.6-3.2)and isoelectric pH when ethanol concentration was varied from 0%to 40%as a perturbation.IR spectroscopy combined with the two-dimensional correlation spectroscopy(2DCOS)analysis for acid pH system proved that the secondary structure of HSA changed significantly when pH was around 4.5.What's more,the IR spectroscopy and 2DCOS analysis showed different secondary structure forms under different ethanol concentrations at the isoelectric pH.For the hydration effect analysis,NIR spectroscopy combined with the McCabe-Fisher method and aquaphotomics showed that the free hydrogen-bonded water fluctuates dynamically,with ethanol at 0-20%enhancing the hydrogen-bonded water clusters,while weak hydrogen-bonded water clusters were formed when the ethanol concentration increased continuously from 20%to 30%.These measurements provide new insights into the structural changes and changes in the hydration behavior of HSA,revealing the dynamic process of protein purification,and providing a theoretical basis for the selection of HSA alcoholic precipitation process parameters,as well as for further studies of complex biological systems.

Observations of NO_2 and O_3 during Thunderstorm Activity Using Visible Spectroscopy

Simultaneous observations for the total column densities of NO2,O3 and H2O were carried on using the portable Spectrometer (438-450 nm and 400-450 nm) and the visible Spectrometer (544.4-628 nm) during premonsoon thunderstorms and embedded hail storm activity at Pune (18°32'N & 73°51'E),India.These observations confirm the fact that there is an increase in O3 and NO2 column densities during thunderstorms.The increase in O3 was observed following onset of thunderstorm,while the increase in NO2 was observed only after the thunder flashes occur.This implies that the production mechanisms for O3 and NO2 in thunderstorm are different.The observed column density of NO,value (1 to 3×1017molecules cm-2) during thunderstorm activity is 10 to 30 times higher than the value (1×10th molecules cm-2) of a normal day total column density.The spectrometric observations and observations of thunder flashes by electric field meter showed that 6.4×1025molecules/flash of NO2 are produced.The increased to-oil column density of ozone during thunderstorm period is 1.2 times higher than normal (clear) day ozone concentration.The multiple scattering in the clouds is estimated from H2O and O2 absorption bands in the visible spectral region Considering this effect the calculated amount of ozone added in the global atmosphere due to thunderstorm activity is 0.26 to 0 52 DU,and the annual production of ozone due to thunderstorm activity is of the order of 4.02×10 molecules/year The annual NO2 production may be of the order of 2.02×1035molecules/year.

Visible and Near-Infrared Spectroscopy with Multi-Parameters Optimization of Savitzky-Golay Smoothing Applied to Rapid Analysis of Soil Cr Content of Pearl River Delta

Using visible and near-infrared (Vis-NIR) spectroscopy combined with partial least squares (PLS) regression, the rapid reagent-free analysis model for chromium (Cr) content in tideland reclamation soil in the Pearl River Delta, China was established. Based on Savitzky-Golay (SG) smoothing and PLS regression, a multi-parameters optimization platform (SG-PLS) covering 264 modes was constructed to select the appropriately spectral preprocessing mode. The optimal SG-PLS model was determined according to the prediction effect. The selected optimal parameters d, p, m and LV were 2, 6, 23 and 8, respectively. Using the validation samples that were not involved in modeling, the root mean square error (SEPV), relative root mean square error (R-SEPV) and correlation coefficients (RP, V) of prediction were 11.66 mg·kg-1, 10.7% and 0.722, respectively. The results indicated that the feasibility of using Vis-NIR spectroscopy combined with SG-PLS method to analyze soil Cr content. The constructed multi-parameters optimization platform with SG-PLS is expected to be applied to a wider field of analysis. The rapid detection method has important application values to large-scale agricultural production.

Characterization on the Exfoliation Degree of Graphite Oxide into Graphene Oxide by UV-visible Spectroscopy

The exfoliation degree of graphite oxide into graphene oxide plays an important role in the massive production method of reduced graphene oxide. It is significant to find a simple and feasible method to analyze the exfoliation degree of graphite oxide. In the present work, graphite oxide was synthesized by a modified Hummers method, and then graphene oxide colloids were obtained by exfoliation of graphite oxide dispersed in de-ionized water. UV-visible spectroscopy was used to characterize the absorption of the graphene oxide colloids, and the concentration of graphene oxide colloids indicated by absorption area of UV-visible spectra was studied. Results show that there is a relatively stable relationship between them, indicating that UV- visible spectroscopy is a potential method for analyzing the exfoliation degree of graphite oxide into graphene oxide.

Combined spatial frequency spectroscopy analysis with visible resonance Raman for optical biopsy of human brain metastases of lung cancers

The purpose of this study is to examine optical spatial frequency spectroscopy analysis(SFSA)combined with visible resonance Raman(VRR)spectroscopic method,for thefirst time,to discriminate human brain metastases of lung cancers adenocarcinoma(ADC)and squamous cell carcinoma(SCC)from normal tissues.A total of 31 label-free micrographic images of three type of brain tissues were obtained using a confocal micro-Raman spectroscopic system.VRR spectra of the corresponding samples were synchronously collected using excitation wavelength of 532 nm from the same sites of the tissues.Using SFSA method,the difference in the randomness of spatial frequency structures in the micrograph images was analyzed using Gaussian functionfitting.The standard deviations,calculated from the spatial frequencies of the micrograph images were then analyzed using support vector machine(SVM)classifier.The key VRR biomolecularfingerprints of carotenoids,tryptophan,amide II,lipids and proteins(methylene/methyl groups)were also analyzed using SVM classifier.All three types of brain tissues were identified with high accuracy in the two approaches with high correlation.The results show that SFSA–VRR can potentially be a dual-modal method to provide new criteria for identifying the three types of human brain tissues,which are on-site,real-time and label-free and may improve the accuracy of brain biopsy.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Identification of Soy Sauce Adulteration

The identification of soy sauce adulteration can avoid fraud, and protect the rights and interests of producers and consumers. Based on two measurement models (1 mm, 10 mm), the visible and near-infrared (Vis-NIR) spectroscopy combined with standard normal variate-partial least squares-discriminant analysis (SNV-PLS-DA) was used to establish the discriminant analysis models for adulterated and brewed soy sauces. Chubang soy sauce was selected as an identification brand (negative, 70). The adulteration samples (positive, 72) were prepared by mixing Chubang soy sauce and blended soy sauce with different adulteration rates. Among them, the “blended soy sauce” sample was concocted of salt water (NaCl), monosodium glutamate (C5H10NNaO5) and caramel color (C6H8O3). The rigorous calibration-prediction-validation sample design was adopted. For the case of 1 mm, five waveband models (visible, short-NIR, long-NIR, whole NIR and whole scanning regions) were established respectively;in the case of 10 mm, three waveband models (visible, short-NIR and visible-short-NIR regions) for unsaturated absorption were also established respectively. In independent validation, the models of all wavebands in the cases of 1 mm and 10 mm have achieved good discrimination effects. For the case of 1 mm, the visible model achieved the optimal validation effect, the validation recognition-accuracy rate (RARV) was 99.6%;while in the case of 10 mm, both the visible and visible-short-NIR models achieved the optimal validation effect (RARV = 100%). The detection method does not require reagents and is fast and simple, which is easy to promote the application. The results can provide valuable reference for designing small dedicated spectrometers with different measurement modals and different spectral regions.

Perovskite-Inspired Lead-Free Ag2BiI5 for Self-Powered NIR-Blind Visible Light Photodetection

In recent years,solution-processible semiconductors with perovskite or perovskite-inspired structures have been extensively investigated for optoelectronic applications.In particular,silver-bismuth-halides have been identified as especially promising because of their bulk properties and lack of heavily toxic elements.This study investigates the potential of Ag2BiI5 for near-infrared(NIR)-blind visible light photodetection,which is critical to emerging applications(e.g.,wearable optoelectronics and the Internet of Things).Self-powered photodetectors were realized and provided a near-constant≈100 mA W−1 responsivity through the visible,a NIR rejection ratio of>250,a long-wavelength responsivity onset matching standard colorimetric functions,and a linear photoresponse of>5 orders of magnitude.The optoelectronic characterization of Ag2BiI5 photodetectors additionally revealed consistency with one-center models and the role of the carrier collection distance in self-powered mode.This study provides a positive outlook of Ag2BiI5 toward emerging applications on low-cost and low-power NIR-blind visible light photodetector.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Brand Identification of Wine

High-end wine brand is made through the use of high-quality grape variety and yeast strain, and through a unique process. Not only is it rich in nutrients, but also it has a unique taste and a fragrant scent. Brand identification of wine is difficult and complex because of high similarity. In this paper, visible and near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was used to explore the feasibility of wine brand identification. Chilean Aoyo wine (2016 vintage) was selected as the identification brand (negative, 100 samples), and various other brands of wine were used as interference brands (positive, 373 samples). Samples of each type were randomly divided into the calibration, prediction and validation sets. For comparison, the PLS-DA models were established in three independent and two complex wavebands of visible (400 - 780 nm), short-NIR (780 - 1100 nm), long-NIR (1100 - 2498 nm), whole NIR (780 - 2498 nm) and whole scanning (400 - 2498 nm). In independent validation, the five models all achieved good discriminant effects. Among them, the visible region model achieved the best effect. The recognition-accuracy rates in validation of negative, positive and total samples achieved 100%, 95.6% and 97.5%, respectively. The results indicated the feasibility of wine brand identification with Vis-NIR spectroscopy.

The thermal and storage stability of bovine haemoglobin by ultraviolet-visible and circular dichroism spectroscopies

The effects of temperature,pH and long-term storage on the secondary structure and conformation changes of bovine haemoglobin（bHb） were studied using circular dichroism（CD） and ultraviolet-visible（UV-vis） spectroscopies.Neural network software was used to deconvolute the CD data to obtain the fractional content of the five secondary structures.The storage stability of bHb solutions in pH 6,7 and8 buffers was significantly higher at 4 ℃ than at 23 ℃ for the first 3 days.A complete denaturation of bHb was observed after 40 days irrespective of storage temperature or pH.The bHb solutions were also exposed to heating and cooling cycles between 25 and 65 ℃ and structural changes were followed by UVvis and CD spectroscopies.These experiments demonstrated that α-helix content of bHb decreased steadily with the increasing temperature above 35 ℃ at all pH values.The loss in a-helicity and gain in random coil conformations was pH-dependent and the greatest under alkaline conditions.Furthermore,there was minimal recovery of the secondary structure content upon cooling to 25 ℃.The use of bHb as a model drug is very common and this study elucidates the significance of storage and processing conditions on its stability.

Monitoring real time polymorphic transformation of sulfanilamide by diffuse reflectance visible spectroscopy

This study investigated the development of a novel approach to surface characterization of drug poly- morphism and the extension of the capabilities of this method to perform ＇real time＇ in situ measure- ments. This was achieved using diffuse reflectance visible （DRV） spectroscopy and dye deposition, using the pH sensitive dye, thymol blue （TB）. Two polymorphs, SFN-β and SFN-γ, of the drug substance sulfanilamide （SFN） were examined. The interaction of adsorbed dye with polymorphs showed different behavior, and thus reported different DRV spectra. Consideration of the acid/base properties of the morphological forms of the drug molecule provided a rationalization of the mechanism of differential coloration by indicator dyes. The kinetics of the polymorphic transformation of SFN polymorphs was monitored using treatment with TB dye and DRV spectroscopy. The thermally-induced transformation fitted a first-order solid-state kinetic model （R2=0.992）, giving a rate constant of 2.43 × 10^- 2 s 1.

Capability of Visible-Near Infrared Spectroscopy in Estimating Soils Carbon, Potassium and Phosphorus

The spectroscopy technique has many advantages over conventional analytical methods since it is fast and easy to implement and with no use of chemical extractants. The objective of this study is to quantify soil total Carbon (C), available Phosphorus (P) and exchangeable potassium (K) using VIS-NIR reflectance spectroscopy. A total of 877 soils samples were collected in various agricultural fields in Mali. Multivariate analysis was applied to the recorded soils spectra to estimate the soil chemical properties. Results reveal the over performance of the Principal Component Regression (PCR) compared to the Partial Least Square Regression (PLSR). For coefficient of determination (R2), PLSR accounts for 0.29, 0.42 and 0.57;while the PCR gave 0.17, 0.34 and 0.50, respectively for C, P and K. Nevertheless, this study demonstrates the potential of the VIS-NIR reflectance spectroscopy in analyzing the soils chemical properties.

Online quantitative analysis of soluble solids content in navel oranges using visible-nearinfrared spectroscopy and variable selection methods

Variable selection is applied widely for visible-near infrared(Vis-NIR)spectroscopy analysis of internal quality in fruits.Different spectral variable selection methods were compared for online quantitative analysis of soluble solids content(SSC)in navel oranges.Moving window partial least squares(MW-PLS),Monte Carlo uninformative variables elimination(MC-UVE)and wavelet transform(WT)combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges.The performances of these methods were compared for modeling the Vis NIR data sets of navel orange samples.Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation cofficient(r)of 0.89 and lower root mean square error of prediction(RMSEP)of 0.54 at 5 fruits per second.It concluded that Vis NIR spectroscopy coupled with WT-MC-UVE may be a fast and efective tool for online quantitative analysis of SSC in navel oranges.

Interaction between DNA with Complex of Eu^(3+)-Rutin by UV-Visible Spectroscopy and Electrochemistry

The interaction of Eu^3＋-rutin with CT DNA was studied by UV-visible spectroscopy and electrochemistry. All experimental results indicate that the complex binds to DNA by the electrostatic mode and the groove binding mode. Information such as intrinsic binding costant （ k = 1. 426 × 104 mol·L^-1 ）, and binding numbers （ n = 2）, were determined by cyclic vohammetry and differential pulse vohammetry at glass carbon electrode. Interaction between Eu^3＋ with rutin was also studied by UV-visible spectroscopy.

Determination of the Biodiesel Content in Petrodiesel/Biodiesel Blends: A Method Based on Uv-Visible Spectroscopy and Chemometrics Tools

In this work, we developed an analytical method based on UV-visible spectroscopy to determine the concentration of biodiesel from African palm in blends of petrodiesel. Seventy-five samples with biodiesel concentrations between 0-100 wt% were prepared. The spectral fingerprints that were obtained from the analysis of the samples by UV-visible spectroscopy were used to build predictive model using PLS regression. The predictive ability of the models was evaluated through statistical parameters: the standard error of calibration (SEC), the standard error of validation (SEV), the correlation coefficient of calibration (r Cal) and validation (r Val), the ratio (SEC/SEV), the coefficient of determination R2, the paired data Student’s t-test, cross-validation and external validation. The results indicate that the PLS model predicts the concentration of biodiesel from African palm with high precision in mixtures with petrodiesel. The method developed in this study can be applied to determine the concentration of biodiesel African palm in mixtures of petrodiesel in a more rapid and economical way. Moreover, this method has less analytical errors and is more environmentally friendly than the conventional methods.

Nondestructive Quantitative Analysis of Cofrel Medicines by Double ANN-NIR Spectroscopy

In this paper, a double artificial neural network （DANN） algorithm was used to parse near infrared （NIR） reflectance spectrum of Cofrel medicines. The contents of benproperine phosphate, which is the effective ingredient in Cofrel medicines, were accurately nondestructive quantitatively predicted. Compared the results with those of HPLC, the relative errors （RE %） were less than 0.18%. The analytical results could be applied to qualitative control of Cofrel medicines.

Sensing of Moisture Content of In-Shell Peanuts by NIR Reflectance Spectroscopy

It was found earlier that moisture content (MC) of intact kernels of grain and nuts could be determined by Near Infra Red (NIR) reflectance spectrometry. However, if the MC values can be determined while the nuts are in their shells, it would save lot of labor and money spent in shelling and cleaning the nuts. Grain and nuts absorb low levels of NIR, and when NIR radiation is incident on them, a substantial portion of the radiation is reflected back. Thus, studying the NIR reflectance spectra emanating from in-shell peanuts, an attempt is made for the first time to determine the MC of in-shell peanuts. In-shell peanuts of two different market types, Virginia and Valencia, were conditioned to different moisture levels between 6% and 26% (wet basis), and separated into calibration and validation groups. NIR absorption spectral data from 1000 nm to 2500 nm in 1 nm intervals were collected from both groups. Measurements were obtained on 30 replicates within each moisture level. Reference MC values for each moisture level in these groups were obtained using standard air-oven method. Partial Least Square (PLS) analysis was performed on the calibration data, and prediction models were developed. The Standard Error of Calibration (SEC), and R2 of the calibration models were computed to select the best calibration model. The selected models were used to predict the moisture content of peanuts in the validation sets. Predicted MC values of the validation samples were compared with their standard air-oven moisture values. Goodness of fit was determined based on the lowest Standard Error of Prediction (SEP) and highest R2 value obtained for the prediction models. The model, with reflectance plus normalization spectral data with an SEP of 0.74 for Valencia and 1.57 for Virginia type in-shell peanuts was selected as the best model. The corresponding R2 values were 0.98 for both peanut types. This work establishes the possibility of sensing MC of intact in-shell peanuts by NIR reflectance method, and would be useful for the peanut and allied industries.

NIRS-XRF联用的煤炭发热量高稳定检测

实时获知煤炭发热量对于及时调整电站锅炉风粉配比和提高煤炭燃烧效率具有重要意义,为了实现电力生产中发热量的稳定快速检测,提出了一种近红外光谱(Near Infrared Spectroscopy, NIRS)与X射线荧光光谱(X-ray Fluorescence, XRF)联用的煤炭发热量高稳定检测方法,它结合了NIRS能高稳定检测煤中与发热量正相关的有机基团的优势与XRF能高稳定检测与发热量负相关的成灰元素的特点,大大提高了对煤炭发热量的测量重复性。在光谱预处理中,先将两套光谱融合作为偏最小二乘回归的输入变量进行全谱初步建模,依据回归系数选择NIRS光谱中的有效波段,再将它与XRF光谱中的成灰元素谱线一并融合进行归一化处理。建模时将预处理后的融合光谱数据作为输入变量,利用偏最小二乘回归对煤炭发热量进行建模。实验结果表明,NIRS-XRF联用方法对定标集煤样发热量预测的线性相关度系数(R^(2))为0.995,对验证集煤样发热量预测的最小均方根误差、平均相对误差和标准偏差分别为0.24 MJ/kg,0.61%和0.05 MJ/kg,测量重复性满足小于0.12 MJ/kg的国家标准。NIRS-XRF联用的煤炭发热量高稳定检测方法有望推广应用于火力发电、煤化工、冶金、水泥和焦化等“高碳”行业,助力我国按期实现碳中和目标。