Strength prediction model for water-bearing sandstone based on nearinfrared spectroscopy

The strength of water-bearing rock cannot be obtained in real time and by nondestructive experiments,which is an issue at cultural relics protection sites such as grotto temples.To solve this problem,we conducted a near-infrared spectrum acquisition experiment in the field and laboratory uniaxial compression strength tests on sandstone that had different water saturation levels.The correlations between the peak height and peak area of the nearinfrared absorption bands of the water-bearing sandstone and uniaxial compressive strength were analyzed.On this basis,a strength prediction model for water-bearing sandstone was established using the long short-term memory full convolutional network(LSTM-FCN)method.Subsequently,a field engineering test was carried out.The results showed that:(1)The sandstone samples had four distinct characteristic absorption peaks at 1400,1900,2200,and 2325 nm.The peak height and peak area of the absorption bands near 1400 nm and 1900 nm had a negative correlation with uniaxial compressive strength.The peak height and peak area of the absorption bands near 2200 nm and 2325 nm had nonlinear positive correlations with uniaxial compressive strength.(2)The LSTM-FCN method was used to establish a strength prediction model for water-bearing sandstone based on near-infrared spectroscopy,and the model achieved an accuracy of up to 97.52%.(3)The prediction model was used to realize non-destructive,quantitative,and real-time determination of uniaxial compressive strength;this represents a new method for the non-destructive testing of grotto rock mass at sites of cultural relics protection.

Determination of Melamine by Infrared Spectroscopy Based on Nonlinear Modeling

[Objective] The aim was to develop a nonlinear model of quantitative analysis of melamine content by infrared spectroscopy and provide theoretical basis for the nondestructive detection of melamine. [Method] According to dynamics,mathematical modeling and optimization theory,linear and nonlinear models were respectively set up by taking an absorption peak of 1 550 cm-1 as characteristic absorption peak. [Result] The correlation coefficient of nonlinear model was 0.922 7 and the recovery was 96%,which showed that the nonlinear model was more accurate than linearity model with correlation coefficient of 0.904 9 and recovery of 557%. [Conclusion] It is feasible to determine melamine content by using the nonlinear model quantitatively.

A New Sample-Selection and Modeling Method Based on Near-Infrared Spectroscopy and Its Industrial Application

Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications,the robustness of the established NIR model is often deteriorated by process condition variations,nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation,a novel method based on principal component analysis( PCA) and artificial neural network( ANN) is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial leastsquares( PLS),principal component regression( PCR) and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported.

Thermal,infrared spectroscopy and molecular modeling characterization of bone:An insight in the apatite-collagen type I interaction

An insight into the interaction of collagen type I with apatite in bone tissue was performed by using differential scanning calorimetry, Fourier transform infrared spectroscopy, and molecular modeling. Scanning electron microscopy shows that bone organic content incinerate gradually through the different temperatures studied. We suggest that the amide regions of the type I collagen molecule (mainly C=O groups of the peptide bonds) will be important in the control of the interactions with the apatite from bone. The amide I infrared bands of the collagen type I change when interacting to apatite, what might confirm our assumption. Bone tissue results in a loss of thermal stability compared to the collagen studied apart, as a consequence of the degradation and further combustion of the collagen in contact with the apatite microcrystals in bone. The thermal behavior of bone is very distinctive. Its main typical combustion temperature is at 360°C with a shoulder at 550°C compared to the thermal behavior of collagen, with the mean combustion peak at ca. 500°C. Our studies with molecular mechanics (MM+ force field) showed different interaction energies of the collagen-like molecule and different models of the apatite crystal planes. We used models of the apatite (100) and (001) planes;additional two planes (001) were explored with phosphate-rich and calcium-rich faces;an energetic preference was found in the latter case. We preliminary conclude that the peptide bond of collagen type I is modified when the molecule interacts with the apatite, producing a decrease in the main peak from ca. 500°C in collagen, up to 350°C in bone. The combustion might be related to collagen type I, as the ΔH energies present only small variations between mineralized and non-mineralized samples. The data obtained here give a molecular perspective into the structural properties of bone and the change in collagen properties caused by the interaction with the apatite. Our study can be useful to understand the biological synthesis of minerals as well as the organic-inorganic interaction and the synthesis of apatite implant materials.

Analysis of Protein Content in Panicum miliaceum L. Using Near Infrared Transmittance Spectroscopy

[Objective] To explore a method for the rapid determination of protein con- tent in grains of Panicum miliaceum L. [Method] The near infrared transmittance spec- troscopy （NITS） was used to build the mathematical models for the quantitative analy- sis of protein content in the grains. Four combinations of treatment that first derivative and second derivative were respectively combined with partial least squares （PLS） and modified partial least squares （MPLS） were set to compare their effects on the original transmission spectrum. [Result] The predicting effects of the 4 combinations were similar. The optimal combination was first derivative with MPLS, in which the average determination coefficient of validation （RSQ） was 0.880 6, correlation coeffi- cient of cross validation （1-VR） was 0.857 0, standard error of calibration （SEC） was 0.342 4, standard error of cross validation （SECV） was 0.375 1, and the standard er- ror of prediction （SEP） was 0.454. [Conclusion] The constructed NITS model is a rapid way for the determination of protein content in grains of P. miliaceum.

Determination of Protein and Starch Content in Whole Maize Kernel by Near Infrared Reflectance Spectroscopy

Using 128 bulk-kernel samples of inbred lines and hybrids, a study was conducted toinvestigate the feasibility and method of measuring protein and starch contents inintact seeds of maize by near infrared reflectance spectroscopy (NIRS). The chemometricalgorithms of partial least square (PLS) regression was used. The results indicated thatthe calibration models developed by the spectral data pretreatment of firstderivative+multivariate scattering correction within the spectral region of 10000-4000cm-1, and first derivative + straight line subtraction in 9000-4000cm-1 were thebest for protein and starch, respectively. All these models yielded coefficients ofdetermination of calibration (R2cal) above 0.97, while R2cv and R2val of cross and externalvalidation ranged from 0.92 to 0.95, respectively; however, the root of mean squareerrors of calibration, cross and external validation (RMSEE, RMSECV and RMSEP) werebelow 1(ranged 0.3-0.7),respectively. This study demonstrated that it is feasible touse NIRS as a rapid, accurate, and none-destructive technique to predict protein andstarch contents of whole kernel in the maize quality improvement program.

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.

Discriminant Analysis of Liquor Brands Based on Moving-Window Waveband Screening Using Near-Infrared Spectroscopy

Partial least squares discriminant analysis (PLS-DA) with integrated moving-window (MW) waveband screening was applied to the discriminant analysis of liquor brands with near-infrared (NIR) spectroscopy. Luzhou Laojiao, a popular liquor with strong fragrant flavor, was used as the identified liquor brand (160 samples, negative, 52 vol alcoholicity). Liquors of 10 other brands with strong fragrant flavor were used as the interferential brands (200 samples, positive, 52 vol alcoholicity). The Kennard-Stone algorithm was used for the division of modeling samples to achieve uniformity and representativeness. Based on the MW-PLS-DA, a simplified optimal model set with 157 wavebands was further proposed. This set contained five types of wavebands corresponding to the NIR absorption bands of water, ethanol, and other micronutrients (i.e., acids, aldehydes, phenols, and aromatic compounds) in liquor for practical choice. Using five selected simple models with 4775 - 4239, 7804 - 6569, 6264 - 5844, 9435 - 7896, and 12066 - 10373 cm-1, the validation recognition rates were obtained as 99.3% or higher. Results show good prediction performance and low model complexity, and also provided a valuable reference for designing small dedicated instruments. The proposed method is a promising tool for large-scale inspection of liquor food safety.

Geographic Classification of Chinese Grape Wines by Near-Infrared Reflectance Spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was applied to classify grape wines of different geographical origins (Changli, Huailai, and Yantai, China). Near infrared (NIR) spectra were collected in transmission mode in the wavelength range of 800-2500 nm. Wines (n=90) were randomly split into two sets, calibration set (n=54) and validation set (n=36). Discriminant analysis models were developed using BP neural network and discriminant partial least-squares discriminant analysis (PLS-DA). The prediction performance of calibration models in different wavelength range was also investigated. BP neural network models and PLS-DA models correctly classified 100% of the wines in calibration set. When used to predict wines in validation set, BP neural network models correctly classified 100%, 81.8%, and 90.9% of the wines from Changli, Huailai, and Yantai respectively, and PLS-DA models correctly classified 100% of all samples. The results demonstrated that NIRS could be used to discriminate Chinese grape wines as a rapid and reliable method.

Rapid Detection of Cement Raw Meal Composition Based on Near Infrared Spectroscopy

The composition of cement raw materials was detected by near-infrared spectroscopy.It was found that the BiPLS-SiPLS method selected the NIR spectral band of cement raw materials,and the partial least squares regression algorithm was adopted to establish a quantitative correction model of cement raw materials with good prediction effect.The root-mean-square errors of SiO_(2),Al_(2)O_(3),Fe_(2)O_(3) and CaO calibration were 0.142,0.072,0.034 and 0.188 correspondingly.The results show that the NIR spectroscopy method can detect the composition of cement raw meal rapidly and accurately,which provides a new perspective for the composition detection of cement raw meal.

Rapid Survey of Total Protein Content in Mycelia of Oyster Mushroom Based on Near Infrared Spectroscopy

Using the total protein content in mycelia of oyster mushroom cultured in plate medium as the index, the spectral information in 1 000-1 799 nm region was collected to establish a quantitative prediction model for the parameters of strains through partial least squares regression combined with chemometrics. The results showed that the optimal spectral pretreatment method was the combination of Savitzky-Golay smoothing＋Savitzky-Golay derivative＋MSC＋Mean-Centefing. Parameters of the quantitative model including RC, SEC, RP, SEP, MF, SEP /SEC were all in the reasonable regions. The correlation coefficient of the real value and predictive value of the model was 0.672 63. The prediction model had better reliability, robustness and predictive effects, so it could be used for protein content detection in mycelia.

Rapid, Non-Destructive, Textile Classification Using SIMCA on Diffuse Near-Infrared Reflectance Spectra

Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the time at the 95% confidence level (p = 0.05 significance level). In the present study, cotton and silk had a 62% and 24% chance, respectively, of being classified with their own group and also with rayon. SIMCA correctly identified a counterfeit “silk” sample as polyester. When coupled with diffuse NIR reflectance spectroscopy and a large sample library, SIMCA shows considerable promise as a quick, non-destructive, multivariate method for fiber identification. A major advantage is simplicity. No sample pretreatment of any kind was required, and no adjust-ments were made for fiber origin, manufacturing process residues, topical finishes, weave pattern, or dye content. Increasing the sample library should make the models more robust and improve identification rates over those reported in this paper.

Wavelength Selection for Near-Infrared Spectroscopic Analysis of Teicoplanin

Teicoplanin (TCP) is a multiple drug-resistant lipoglycopeptide antibiotic produced by fermenting Actinoplanes teichomyceticus. In this study, a mixture of TCP with the Tris-HCl buffer (TCP-Tris- HCl) was used to simulate TCP fermentation broth. The reagent-free, rapid and simultaneous quantitative analysis models for TCP and Tris in the TCP-Tris-HCl mixtures were established by near-infrared (NIR) spectroscopy. The equidistant combination partial least squares (EC-PLS) method and the equivalent model sets were proposed, the simplest equivalent model with the smallest number of wavelengths were further selected. The initial wavelength, number of wavelengths, number of wavelength gaps, number of PLS factors were 1520 nm, 28, 5, 5 for TCP and 1084 nm, 13, 6, 4 for Tris, respectively. Compared with the optimal EC-PLS models, the simplest equivalent models adopted fewer wavelengths. Thus, the redundant wavelengths were removed, the models were further simplified. The root-mean-square errors (SEP) and correlation coefficients (RP) for prediction were 0.043 mg·mL-1 and 0.9998 for TCP, and 0.222 mg·mL-1 and 0.9989 for Tris, respectively. The results indicate that NIR method can be applied to highly accurate quantitative analysis for TCP and provide valuable references for further application to TCP fermentation broth.

Molecular structure characterization of middle-high rank coal via^(13)C NMR,XPS,and FTIR spectroscopy

Elemental analysis,nuclear magnetic resonance carbon spectroscopy(^(13)C-NMR),X-ray photoelectron spectroscopy(XPS)and Fourier transform infrared spectroscopy(FTIR)experiments were carried out to determine the existence of aromatic structure,heteroatom structure and fat structure in coal.MS(materials studio)software was used to optimize and construct a 3D molecular structure model of coal.A method for establishing a coal molecular structure model was formed,which was“determination of key structures in coal,construction of planar molecular structure model,and optimization of three-dimensional molecular structure model”.The structural differences were compared and analyzed.The results show that with the increase of coal rank,the dehydrogenation of cycloalkanes in coal is continuously enhanced,and the content of heteroatoms in the aromatic ring decreases.The heteroatoms and branch chains in the coal are reduced,and the structure is more orderly and tight.The stability of the structure is determined by theπ-πinteraction between the aromatic rings in the nonbonding energy EN.Key Stretching Energy The size of EB determines how tight the structure is.The research results provide a method and reference for the study of the molecular structure of medium and high coal ranks.

A new strategy to iteratively update scalable universal quantitative models for the testing of azithromycin by near infrared spectroscopy

The training set of a universal near infrared (NIR) model for quantitative analysis of a drug should cover as many samples of this drug in the market as possible. Inevitably the model may fail for new products that have different excipients and production processes. In such circumstances the model should be updated. We here propose a new strategy to iteratively update a universal NIR quantitative model for azithromycin. We prove that universal quantitative models generated from this new strategy are comparably effective for azithromycin injection powders and azithromycin tablets, compared to the strategy using hierarchical clustering method which we reported previously. Furthermore, we establish the correlation coefficient r between a new sample and the training set samples can be used to decide whether or not the model should be updated.

Cycle life prediction and match detection in retired electric vehicle batteries

The lifespan models of commercial 18650-type lithium ion batteries （nominal capacity of 1150 mA-h） were presented. The lifespan was extrapolated based on this model. The results indicate that the relationship of capacity retention and cycle number can be expressed by Gaussian function. The selecting function and optimal precision were verified through actual match detection and a range of alternating current impedance testing. The cycle life model with high precision （〉99%） is beneficial to shortening the orediction time and cutting the prediction cost.

基于波长选择方法Modeling Power的黑木耳产地判别研究

应用近红外光谱技术结合波长选择方法(modeling power,MP)实现了黑木耳产地的快速准确判别。共收集4个产地240个黑木耳样本,通过光谱扫描,建立了最优的偏最小二乘(PLS)判别模型。同时应用MP选择对黑木耳产地判别的有效波长,作为输入变量,建立最小二乘-支持向量机(MP-LS-SVM)模型。比较了3种MP选择波长的阈值方法,分别为MP值大于0.95,0.90和(0.90+Peak),并建立了相应的MP-LS-SVM模型。以预测集样本的准确判别率作为模型评价标准,分别设定预测的残差绝对值标准0.1,0.2和0.5。预测结果表明,MP-LS-SVM(0.90+Peak)模型在残差标准为0.1,0.2和0.5时的判别效果均为最优,正确判别率分别为98.3%,100%和100%。说明ModelingPower是一种非常有效的波长选择方法,应用近红外光谱技术结合MP-LS-SVM进行黑木耳产地判别是可行的,并获得了满意的判别精度。

The establishment and evaluation of near infrared universal model to determinate the effective ingredient content in pesticide rapidly

A near infrared universal quantitative analysis model was established to determinate the effective ingredient content in pesticide EC （hikemalisation） by the PLS （partial least squares） algorithm, the model predictive ability was evaluated by the external inspection method. The model was established among samples containing the same active ingredient from five different companies, and the model determination coefficient R2 and RMSECV （root mean square error of cross validation） were 0.9997 and 0.0223, respectively, the relative error between predicted value and chemical value of the testing set samples was between -2.71% and 3.36%, which indicated that the method to determinate the effective ingredient content in pesticide EC by the established universal model can meet the need of pesticide market monitoring.

Comparison and rapid prediction of lignocellulose and organic elements of a wide variety of rice straw based on near infrared spectroscopy

Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy.Rapid prediction of the lignocellulose(cellulose,hemicellulose,and lignin)and organic elements(carbon,hydrogen,nitrogen,and sulfur)of rice straw would help to decipher its growth mechanisms and thereby improve its sustainable usages.In this study,364 rice straw samples featuring different rice subspecies(japonica and indica),growing seasons(early-,middle-,and late-season),and growing environments(irrigated and rainfed)were collected,the differences among which were examined by multivariate analysis of variance.Statistic results showed that the cellulose content exhibited significant differences among different growing seasons at a significant level(p<0.01),and the contents of cellulose and nitrogen had significant differences between different growing environments(p<0.01).Near infrared reflectance spectroscopy(NIRS)models for predicting the lignocellulosic and organic elements were developed based on two algorithms including partial least squares(PLS)and competitive adaptive reweighted sampling-partial least squares(CARS-PLS).Modeling results showed that most CARS-PLS models are of higher accuracy than the PLS models,possibly because the CARS-PLS models selected optimal combinations of wavenumbers,which might have enhanced the signal of chemical bonds and thereby improved the predictive efficiency.As a major contributor to the applications of rice straw,the nitrogen content was predicted precisely by the CARS-PLS model.Generally,the CARS-PLS models efficiently quantified the lignocellulose and organic elements of a wide variety of rice straw.The acceptable accuracy of the models allowed their practical applications.

Titanium dioxide as an adsorbent to enhance the detection ability of near-infrared diffuse reflectance spectroscopy

A method for quantitative determination of fish sperm deoxyribonucleic acid(fsDNA)was developed by using titanium dioxide(TiO2)as an adsorbent and near-infrared diffuse reflectance spectroscopy(NIRDRS).The selective enrichment of fsDNA was proved by comparing the adsorption efficiency of bovine serum albumin,tyrosine and tryptophan,and the low adsorption background of TiO2 was illustrated by comparing the spectra of four commonly-used inorganic adsorbents(alkaline aluminium oxide,neutral aluminium oxide,nano-hydroxyapatite and silica).The spectral feature of fsDNA can be clearly observed in the spectrum of the sample.Partial least squares(PLS)model was built for quantitative determination of fsDNA using 28 solutions,and 13 solutions with interferences were used for validation of the model.The results showed that the correlation coefficient(R)between the predicted and the reference concentration is 0.9727 and the recoveries of the validation samples are in the range of 98.2%-100.7%.