In situ infrared, Raman and X-ray spectroscopy for the mechanistic understanding of hydrogen evolution reaction

Hydrogen production by water reduction reactions has received considerable attention because hydrogen is considered a clean-energy carrier,key for a sustainable energy future.Computational methods have been widely used to study the reaction mechanism of the hydrogen evolution reaction(HER),but the calculation results need to be supported by experimental results and direct evidence to confirm the mechanistic insights.In this review,we discuss the fundamental principles of the in situ spectroscopic strategy and a theoretical model for a mechanistic understanding of the HER.In addition,we investigate recent studies by in situ Fourier transform infrared(FTIR),Raman spectroscopy,and X-ray absorption spectroscopy(XAS) and cover new findings that occur at the catalyst-electrolyte interface during HER.These spectroscopic strategies provide practical ways to elucidate catalyst phase,reaction intermediate,catalyst-electrolyte interface,intermediate binding energy,metal valency state,and coordination environment during HER.

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.

Infrared Spectroscopy-Based Chemometric Analysis for Lard Differentiation in Meat Samples

One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness.The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary,for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis.Fourier Transform Infrared Spectroscopy(FTIR)is used in this work to identify lard adulteration in cow,lamb,and chicken samples.A simplified extraction method was implied to obtain the lipids from pure and adulterated meat.Adulterated samples were obtained by mixing lard with chicken,lamb,and beef with different concentrations(10%–50%v/v).Principal component analysis(PCA)and partial least square(PLS)were used to develop a calibration model at 800–3500 cm^(−1).Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken,lamb,and beef samples.The corresponding FTIR peaks for the lard have been observed at 1159.6,1743.4,2853.1,and 2922.5 cm−1,which differentiate chicken,lamb,and beef samples.The wavenumbers offer the highest determination coefficient R2 value of 0.846 and lowest root mean square error of calibration(RMSEC)and root mean square error prediction(RMSEP)with an accuracy of 84.6%.Even the tiniest fat adulteration up to 10%can be reliably discovered using this methodology.

Differential diagnosis of Crohn’s disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning

BACKGROUND Crohn’s disease(CD)is often misdiagnosed as intestinal tuberculosis(ITB).However,the treatment and prognosis of these two diseases are dramatically different.Therefore,it is important to develop a method to identify CD and ITB with high accuracy,specificity,and speed.AIM To develop a method to identify CD and ITB with high accuracy,specificity,and speed.METHODS A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB.Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.RESULTS The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm^(-1) and 1234 cm^(-1) bands,and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy,specificity,and sensitivity of 91.84%,92.59%,and 90.90%,respectively,for the differential diagnosis of CD and ITB.CONCLUSION Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level,and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.

Process Characterization of the Transesterification of Rapeseed Oil to Biodiesel Using Design of Experiments and Infrared Spectroscopy

For optimization of production processes and product quality,often knowledge of the factors influencing the process outcome is compulsory.Thus,process analytical technology(PAT)that allows deeper insight into the process and results in a mathematical description of the process behavior as a simple function based on the most important process factors can help to achieve higher production efficiency and quality.The present study aims at characterizing a well-known industrial process,the transesterification reaction of rapeseed oil with methanol to produce fatty acid methyl esters(FAME)for usage as biodiesel in a continuous micro reactor set-up.To this end,a design of experiment approach is applied,where the effects of two process factors,the molar ratio and the total flow rate of the reactants,are investigated.The optimized process target response is the FAME mass fraction in the purified nonpolar phase of the product as a measure of reaction yield.The quantification is performed using attenuated total reflection infrared spectroscopy in combination with partial least squares regression.The data retrieved during the conduction of the DoE experimental plan were used for statistical analysis.A non-linear model indicating a synergistic interaction between the studied factors describes the reactor behavior with a high coefficient of determination(R^(2))of 0.9608.Thus,we applied a PAT approach to generate further insight into this established industrial process.

GP Algorithm-Based Fourier Transform Infrared Spectrum Trend Term Removal Model

Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such as highly correlated initial values of the expression parameters,the need to pre-estimate the trend term shape,and poor fitting accuracy at low signal-to-noise ratios.In order to achieve real-time and robust trend term removal,a new trend term removal method using genetic programming(GP)in symbolic regression is constructed in this paper,and the FTIR simulation interference results and experimental measurement data for common volatile organic compounds(VOCs)gases are analyzed.The results show that the genetic programming algorithm can both reduce the initial value requirement and greatly improve the trend term accuracy by 20%-30% in three evaluation indicators,which is suitable for gas FTIR detection in complex scenarios.

Application of Wavelet Transform in the Prediction of Navel Orange Vitamin C Content by Near-Infrared Spectroscopy

This study was to search for an approach for rapid measurement of orange vitamin C （Vc） content. By using different decomposing levels of Daubechies 3 wavelet transform, the near-infrared spectra signals obtained from intact fruits of 100 navel orange samples were denoised, and the results of the predicted Vc contents for the corresponding samples determined by the reconstructed spectra after denoising were validated by means of PLS-CV （partial least squared-cross validation）. It was shown that the prediction effects verified by PLS-CV analysis varied when different wavelet transform decomposing levels were employed. At the wavelet decomposing level 4, the best prediction effect was obtained, with the correlation coefficient R between the prediction and true values being 0.9574 and the expected variance RMSECV being as low as 3.9 mg 100 g^-1. Furthermore, the 11 different approaches for the pretreatment of the near-infrared spectrum were compared. It was found that the calibration model established by PLS using spectra pretreated by wavelet transform denoising provided the best prediction for Vc content, exhibiting the highest correlation between the prediction and true values by cross validation. In conclusion, the near infrared spectral model denoised by means of wavelet transform can be used for accurate, rapid, and nondestructive quantitative analysis on navel orange Vc content.

Acupuncture enhances brain function in patients with mild cognitive impairment: evidence from a functional-near infrared spectroscopy study

Mild cognitive impairment(MCI)is a precursor to Alzheimer’s disease.It is imperative to develop a proper treatment for this neurological disease in the aging society.This observational study investigated the effects of acupuncture therapy on MCI patients.Eleven healthy individuals and eleven MCI patients were recruited for this study.Oxy-and deoxy-hemoglobin signals in the prefrontal cortex during working-memory tasks were monitored using functional near-infrared spectroscopy.Before acupuncture treatment,working-memory experiments were conducted for healthy control(HC)and MCI groups(MCI-0),followed by 24 sessions of acupuncture for the MCI group.The acupuncture sessions were initially carried out for 6 weeks(two sessions per week),after which experiments were performed again on the MCI group(MCI-1).This was followed by another set of acupuncture sessions that also lasted for 6 weeks,after which the experiments were repeated on the MCI group(MCI-2).Statistical analyses of the signals and classifications based on activation maps as well as temporal features were performed.The highest classification accuracies obtained using binary connectivity maps were 85.7%HC vs.MCI-0,69.5%HC vs.MCI-1,and 61.69%HC vs.MCI-2.The classification accuracies using the temporal features mean from 5 seconds to 28 seconds and maximum(i.e,max(5:28 seconds))values were 60.6%HC vs.MCI-0,56.9%HC vs.MCI-1,and 56.4%HC vs.MCI-2.The results reveal that there was a change in the temporal characteristics of the hemodynamic response of MCI patients due to acupuncture.This was reflected by a reduction in the classification accuracy after the therapy,indicating that the patients’brain responses improved and became comparable to those of healthy subjects.A similar trend was reflected in the classification using the image feature.These results indicate that acupuncture can be used for the treatment of MCI patients.

Structure and Two-dimensional Correlation Infrared Spectroscopy Study of a New One-dimensional Chain Compound: (4,4’-Hbpy)_3[NaMo_8O_(26)](4,4’- bpy)_2(H_2O)_4 (bpy = Bipydine)

A novel compound, （4,4＇-Hbpy）3[NaMo8O26]（4,4＇-bpy）2（H2O）4 1 （bpy=bipydine）, was synthesized by the hydrothermal method. Single-crystal X-ray diffraction shows that compound 1 belongs to the monoclinic system, space group C2/m with a=19.1921（5）, b=18.6931（6）, c=9.3821（3） A° β=104.8020（11）°, V=3254.22（17）A°^3 C50H51Mo8N10NaO30, Mr=2062.52, Z=2, F（000）=2016, μ=1.591 mm^-1 and Dc=2.105 g/cm^3. The final R=0.0283 and wR=0.0912 for 3118 observed reflections （I〉20（I））. Compound 1 contains the β-[Mo8O26]^4-anion, sodium ion, 4,4＇-bpy and lattice crystalline water molecules. The β-[MosO26] units link the sodium ion to form a chain structure. The infinitechains of [Na（Mo8O26）]^3- blocks are surrounded by protonized 4,4＇-bpy cations, 4,4＇-bpy and lattice crystalline water molecules. The 2D-IR correlation spectroscopy study indicates that the stretching vibrations of Mo=O occur more preferentially due to the thermal effect. The TGA analysis shows that compound 1 has high thermal stability.

Rapid Detection of Accelerants in Fire Debris Using a Field Portable Mid-Infrared Quantum Cascade Laser Based Analyzer

Arson presents a challenging crime scene for fire investigators worldwide. Key to the investigation of suspected arson cases is the analysis of fire debris for the presence of accelerants or ignitable liquids. This study has investigated the application and method development of vapor phase mid-Infrared (mid-IR) spectroscopy using a field portable quantum cascade laser (QCL) based system for the detection and identification of accelerant residues such as gasoline, diesel, and ethanol in fire debris. A searchable spectral library of various ignitable fluids and fuel components measured in the vapor phase was constructed that allowed for real-time identification of accelerants present in samples using software developed in-house. Measurement of vapors collected from paper material that had been doused with an accelerant followed by controlled burning and then extinguished with water showed that positive identification could be achieved for gasoline, diesel, and ethanol. This vapor phase mid-IR QCL method is rapid, easy to use, and has the sensitivity and discrimination capability that make it well suited for non-destructive crime scene sample analysis. Sampling and measurement can be performed in minutes with this 7.5 kg instrument. This vibrational spectroscopic method required no time-consuming sample pretreatment or complicated solvent extraction procedure. The results of this initial feasibility study demonstrate that this portable fire debris analyzer would greatly benefit arson investigators performing analysis on-site.

A simple,sensitive and non-destructive technique for characterizing bovine dental enamel erosion:attenuated total reflection Fourier transform infrared spectroscopy

Although many techniques are available to assess enamel erosion in vitro, a simple, non-destructive method with sufficient sensitivity for quantifying dental erosion is required. This study characterized the bovine dental enamel erosion induced by various acidic beverages in vitro using attenuated total reflection Fourier transform infrared （ATR-FTIR） spectroscopy. Deionized water （control） and 10 acidic beverages were selected to study erosion, and the pH and neutralizable acidity were measured. Bovine anterior teeth （110） were polished with up to 1 200-grit silicon carbide paper to produce flat enamel surfaces, which were then immersed in 20 mL of the beverages for 30 min at 37 ℃. The degree of erosion was evaluated using ATR-FTIR spectroscopy and Vickers＇ microhardness measurements. The spectra obtained were interpreted in two ways that focused on the ~1, ~3 phosphate contour： the ratio of the height amplitude of ~3 P04 to that of/11 P04 （Method 1） and the shift of the v3 P04 peak to a higher wavenumber （Method 2）. The percentage changes in microhardness after the erosion treatments were primarily affected by the pH of the immersion media. Regression analyses revealed highly significant correlations between the surface hardness change and the degree of erosion, as detected by ATR-FTIR spectroscopy （P〈0.001）. Method 1 was the most sensitive to these changes, followed by surface hardness change measurements and Method 2. This study suggests that ATR- FTIR spectroscopy is potentially advantageous over the microhardness test as a simple, non-destructive, sensitive technique for the quantification of enamel erosion.

Deep belief network-based drug identification using near infrared spectroscopy

Near infrared spectroscopy(NIRS)analysis technology,combined with chemometrics,can be effectively used in quick and nondestructive analysis of quality and category.In this paper,an effective drug identification method by using deep belief network(DBN)with dropout mecha-nism(dropout-DBN)to model NIRS is introduced,in which dropout is employed to overcome the overfitting problem coming from the small sample.This paper tests proposed method under datasets of different sizes with the example of near infrared diffuse refectance spectroscopy of erythromycin ethylsuccinate drugs and other drugs,aluminum and nonaluminum packaged.Meanwhile,it gives experiments to compare the proposed method's performance with back propagation(BP)neural network,support vector machines(SVMs)and sparse denoising auto-encoder(SDAE).The results show that for both binary classification and multi-classification,dropout mechanism can improve the classification accuracy,and dropout-DBN can achieve best classification accuracy in almost all cases.SDAE is similar to dropout-DBN in the aspects of classification accuracy and algorithm stability,which are higher than that of BP neural network and SVM methods.In terms of training time,dropout-DBN model is superior to SDAE model,but inferior to BP neural network and SVM methods.Therefore,dropout-DBN can be used as a modeling tool with effective binary and multi-class classification performance on a spectrum sample set of small size.

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.

Study on Detection of Pesticide Residues on Winter Jujube Surface by Near-infrared Spectroscopy Combined with PLS and SPA

With fresh winter jujube from the southern region of Xinjiang as the object of study, the method for detecting pesticide residues on winter jujube surface by near-infrared spectroscopy （NIR） combined with successive projections algorithm （SPA） and partial least squares （PLS） was investigated. The absorbance information of winter jujube sample surface was obtained through NIR technology, for the building of a full-wave band PLS model of fresh winter jujube sample sprayed with different concentrations of pesticide （taking chlorpyrifos as an example） as well as an SPA-PLS model which was built with the characteristic wavelengths extracted with SPA as the input variables for PLS, and the prediction precision of the two kinds of models was compared. The model built with the five characteristic waveslengths extected by SPA method only used the variables 0.32% of all the variables in the full wave band, but its accuracy and precision were better than the model built with the full wave band. It is feasible to build a model for different concentrations of chlorpyrifos on fresh winter jujube surfarce by NIR technology combined with SPA and PLS, and SPA method could simplify the complexity of the model and improve the precision and stablity of the model.

Rapid Non-destructive Detection for Molds Colony of Paddy Rice Based on Near Infrared Spectroscopy

Near infrared spectrometer technology under a wavelength range of 918-1045 nm was used to rapidly detect paddy rice that was stored at 5℃, 15℃ and 25℃. A total of 121 paddy rice samples were collected from artificial infection with moulds to build the calibration models to calculate the total number colony of moulds based on the principal component regression method and multiple linear regression method. The results of statistical analysis indicated that multiple linear regression method was applicable to the detection of the total number colony of moulds. The correlation of calibration data set was 0.943. The correlation of prediction data set was 0.897. Therefore, the result showed that near infrared spectroscopy could be a useful instrumental method for determining the total number colony of moulds in paddy rice. The near infrared spectroscopy methodology could be applied for monitoring mould contamination in postharvest paddy rice during storage and might become a powerful tool for monitoring the safety of the grain.

Synchrotron-based infrared microspectroscopy under high pressure:An introduction

Synchrotron sources with high photon flux,small source size,and broad energy range have revolutionized ultrafine characterization of condensed matter.With the addition of the pressure dimension realized by the use of diamond anvil cells,enormous progress has been achieved throughout high-pressure science.This is particularly so for synchrotron-based infrared microspectroscopy(SIRMS)with its very high signal-tonoise ratio,high spatial resolution,and extended measurement conditions.SIRMS has high sensitivity,providing a platform for the investigations of the very small amounts of material that need to be used in high-pressure research.This review summarizes developments in SIRMS,focusing on instrumentation and high-pressure measurements.Applications to measurements of infrared reflectance and absorption are presented,illustrating how SIRMS results play a crucial role in advancing understanding of the crystalline phase transitions,electronic transitions,metallization,lattice dynamics,superconductivity,and novel functional behavior.New insights into spectroscopic properties,together with some cutting edge issues and open problems,are also briefly discussed.

Application of near-infrared spectroscopy to predict sweetpotato starch thermal properties and noodle quality

Sweetpotato starch thermal properties and its noodle quality were analyzed using a rapid predictive method based on near-infrared spectroscopy (NIRS). This method was established based on a total of 93 sweetpotato genotypes with diverse genetic background. Starch samples were scanned by NIRS and analyzed for quality properties by reference methods. Results of statistical modelling indicated that NIRS was reasonably accurate in predicting gelatinization onset temperature (To) (standard error of prediction SEP=2.014 °C, coefficient of determination RSQ=0.85), gelatinization peak temperature (Tp) (SEP=1.371 °C, RSQ=0.89), gelatinization temperature range (Tr) (SEP=2.234 °C, RSQ=0.86), and cooling resistance (CR) (SEP=0.528, RSQ=0.89). Gelatinization completion temperature (Tc), enthalpy of gelatinization (?H), cooling loss (CL) and swelling degree (SWD), were modelled less well with RSQ between 0.63 and 0.84. The present results suggested that the NIRS based method was sufficiently accurate and practical for routine analysis of sweetpotato starch and its noodle quality.

The evolving role of Fourier-transform midinfrared spectroscopy in genetic improvement of dairy cattle

Over the last 100 years,significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs.Technological progress has enabled a shift from labour intensive,on-farm collection and processing of samples that assess yield and fat levels in milk,to large-scale processing of samples through centralised laboratories,with the scope extended to include quantification of other traits.Fourier-transform midinfrared(FT-MIR)spectroscopy has had a significant role in the transformation of milk composition phenotyping,with spectral-based predictions of major milk components already being widely used in milk payment and animal evaluation systems globally.Increasingly,there is interest in analysing the individual FT-MIR wavenumbers,and in utilising the FT-MIR data to predict other novel traits of importance to breeding programs.This includes traits related to the nutritional value of milk,the processability of milk into products such as cheese,and traits relevant to animal health and the environment.The ability to successfully incorporate these traits into breeding programs is dependent on the heritability of the FT-MIR predicted traits,and the genetic correlations between the FT-MIR predicted and actual trait values.Linking FT-MIR predicted traits to the underlying mutations responsible for their variation can be difficult because the phenotypic expression of these traits are a function of a diverse range of molecular and biological mechanisms that can obscure their genetic basis.The individual FT-MIR wavenumbers give insights into the chemical composition of milk and provide an additional layer of granularity that may assist with establishing causal links between the genome and observed phenotypes.Additionally,there are other molecular phenotypes such as those related to the metabolome,chromatin accessibility,and RNA editing that could improve our understanding of the underlying biological systems controlling traits of interest.Here we review topics of importance to phenotyping and genetic applications of FT-MIR spectra datasets,and discuss opportunities for consolidating FT-MIR datasets with other genomic and molecular data sources to improve future dairy cattle breeding programs.

Discrimination of Wild-Grown and Cultivated <i>Ganoderma lucidum</i>by Fourier Transform Infrared Spectroscopy and Chemometric Methods

Wild-grown Ganoderma lucidum (G. lucidum), a traditional Chinese herbal medicine, is highly cherished and expensive for its medicinal efficiency. This study targets the development of an accurate and effective analytical method to distinguish wild-grown G. lucidum from cultivated ones, which are of essential importance for the quality assurance and estimation of its medicinal value. Furthermore, different parts of G. lucidum have been studied to examine the differences between wild-grown and cultivated ones. Fourier transform infrared (FTIR) diffuse reflectance spectroscopy combined with the appropriate chemometric method has been proven to be a rapid and powerful tool for discrimination of wild-grown and cultivated G. lucidum with classification accuracy of 98%. The informative spectral absorption bands for discrimination emphasized by the linear diagnostic rule have provided quantitative interpretations of the chemical constituents of wild-grown G. lucidum regarding its anticancer effects.

Highly-sensitive NO,NO2,and NH3 measurements with an open-multipass cell based on mid-infrared wavelength modulation spectroscopy

A compact prototype based on mid-infrared wavelength modulation spectroscopy（WMS）is developed for the simul-taneous monitoring of NO,NO2,and NH3 in the urban area.Three quantum cascade lasers（QCLs）with central frequencies around 1900.0 cm^-1,1600.0 cm^-1,and 1103.4 cm^-1are used for NO,NO2,and NH3detections,respectively,by timedivision multiplex.An open-path multi-pass cell of 60-m optical path length is applied to the instrument for high sensitivity and reducing the response time to less than 1 s.The prototype achieves a sub-ppb detection limit for all the three target gases with an average time of about 100 s.The instrument is installed in the Jiangsu environmental monitoring center to conduct performance tests on ambient air.Continuous 24-hour measurements show good agreement with the results of a reference instrument based on the chemiluminescence technique.