Quantitative Spectral Analysis Research Articles

Noninvasive blood glucose detection system based on dynamic spectrum and “M+N″ theory

Noninvasive detection of blood glucose is an urgent problem to be solved in the prevention, treatment and management of diabetes. However, in the system of noninvasive detection of blood glucose content by spectral method, due to individual differences, interference in the measurement system and the fact that the content of blood glucose is three orders of magnitude smaller than the most abundant component in blood, the spectral signal of blood glucose component is weak, and the problem has not been solved so far. Dynamic spectrum (DS) method can theoretically eliminate the influence of individual differences and changes in measurement conditions, and “M + N″ theory comprehensively considers the internal and external factors of spectral quantitative analysis of complex solution components, and provides some strategies to suppress the errors caused by multiple factors in spectral quantitative analysis of complex solutions. Therefore, this paper proposes a system for non-invasive quantitative analysis of human blood glucose based on DS and “M + N″ theory. In this paper, feasible methods are used in each step of the system to effectively reduce the influence of noise and interference factors, so that the model has good prediction performance. The correlation coefficient and root mean square error of calibration set were 0.9539 and 0.3965 (mmol/L), respectively. The correlation coefficient and root mean square error of prediction set were 0.9542 and 0.7305 (mmol/L), respectively. The system of noninvasive quantitative analysis of blood glucose proposed in this paper is effective and feasible, which not only makes great progress in noninvasive detection of human blood glucose, but also has application value for realizing noninvasive real-time detection of human blood components.

The association between sleep microarchitecture and cognitive function in middle-aged and older men: a community-based cohort study.

Sleep microarchitecture parameters determined by quantitative power spectral analysis of electroencephalograms have been proposed as potential brain-specific markers of cognitive dysfunction. However, data from community samples remain limited. This study examined cross-sectional associations between sleep microarchitecture and cognitive dysfunction in community-dwelling men. Florey Adelaide Male Ageing Study participants (n = 477) underwent home-based polysomnography (2010-2011). All-night electroencephalogram recordings were processed using quantitative power spectral analysis following artifact exclusion. Cognitive testing (2007-2010) included the inspection time task, Trail-Making Tests A and B, and Fuld object memory evaluation. Complete case cognition, polysomnography, and covariate data were available in 366 men. Multivariable linear regression models controlling for demographic, biomedical, and behavioral confounders determined cross-sectional associations between sleep microarchitecture and cognitive dysfunction overall and by age-stratified subgroups. In the overall sample, worse Trail-Making Test A performance was associated with higher rapid eye movement (REM) theta and alpha and non-REM theta but lower delta power (all P < .05). In men ≥ 65 years, worse Trail-Making Test A performance was associated with lower non-REM delta but higher non-REM and REM theta and alpha power (all P < .05). Furthermore, in men ≥ 65 years, worse Trail-Making Test B performance was associated with lower REM delta but higher theta and alpha power (all P < .05). Sleep microarchitecture parameters may represent important brain-specific markers of cognitive dysfunction, particularly in older community-dwelling men. Therefore, this study extends the emerging community-based cohort literature on a potentially important link between sleep microarchitecture and cognitive dysfunction. The utility of sleep microarchitecture for predicting prospective cognitive dysfunction and decline warrants further investigation. Parker JL, Appleton SL, Melaku YA, etal. The association between sleep microarchitecture and cognitive function in middle-aged and older men: a community-based cohort study. J Clin Sleep Med. 2022;18(6):1593-1608.

MUSE crowded field 3D spectroscopy in NGC 300

Aims.A quantitative spectral analysis of BA-type supergiants and bright giants in an inner spiral arm region of the nearby spiral galaxy NGC 300 is presented, based on observations with the Multi Unit Spectroscopic Explorer (MUSE) on the European Southern Obsevatory, Very Large Telescope. The flux-weighted gravity–luminosity relationship (FGLR), a stellar spectroscopic distance determination method for galaxies, is extended towards stars at lower luminosities.Methods.Point spread function fitting 3D spectroscopy was performed with PampelMUSE on the datacube. The 16 stars with the highest signal-to-noise ratios are classified with regard to their spectral type and luminosity class using Galactic templates. They were analysed using hybrid non-local thermodynamic equilibrium model spectra to fit the strongest observed hydrogen, helium, and metal lines in the intermediate-resolution spectra. Supplemented by photometric data, this facilitates fundamental stellar parameters and interstellar reddening which have yet to be determined.Results.Effective temperatures, surface gravities, reddeningE(B−V), bolometric magnitudes and luminosities, as well as radii and masses are presented for the sample stars. The majority of the objects follow the FGLR as established from more luminous BA-type supergiants in NGC 300. An increase in the scatter in the flux-weighted gravity–luminosity plane is observed at these lower luminosities, which is in line with predictions from population synthesis models.

Modified approximate quantitative spectral analysis of rocks

Two main methods of approximate quantitative atomic emission spectral analysis have traditionally been developed in the Central Laboratory of the Karpinsky All-Russian Research Geological Institute (VSEGEI): evaporation from the channel of carbon electrodes and a more efficient spill method. When comparing these two methods for introducing a sample into the arc discharge, two factors should be taken into account: the amount of material entering the discharge and the completeness of evaporation of chemical elements. A sample weighing about 40 mg is almost completely evaporated from the channel of a carbon electrode. The spill-injection method provides a uniform supply of a large powder sample (400 – 500 mg) into the arc discharge. The first method of analysis is well suited for determination of volatile and nonvolatile elements, whereas the second method provides stable conditions for evaporation and excitation during all the time when the sample is supplied into the discharge. The spill-injection method is more sensitive to changes in the bulk composition of the sample and the particle size of the sample material compared to evaporation from the electrode channel. These are the main factors affecting the magnitude of the systematic error in determining the concentrations of semi-volatile elements. The errors that make up the total error of approximate quantitative atomic emission spectral analysis are systematically analyzed in spectral laboratories. Comparison of the results of analysis of the same samples (state standard samples) with the results of chemical methods over a period of several years revealed that the maximum contribution to the total error is attributed to the visual assessment of the content and visual interpolation, as well as to a discrepancy in interpretation of results after a long time, etc. The impact of various errors has been reduced through the use of the MAES analyzer and the wide capabilities of the Atom software, which offer a correct consideration of the background with the option of individual settings, the use of coefficients accounting for the interference effect, and the possibility of analysis by several analytical lines of each chemical element thus allowing the use of constant calibration curves for a wide range of rock compositions.

Quantitative spectral analysis by femtosecond pulse laser-induced breakdown spectroscopy

Quantitative spectral analysis by femtosecond pulse laser-induced breakdown spectroscopy

Quantitative spectral analysis of electromagnetic scattering. II: Evolution semigroups and non-perturbative solutions

We carry out quantitative studies on the Green operator $ \hat{\mathscr G}$ associated with the Born equation, an integral equation that models electromagnetic scattering, building the strong stability of the evolution semigroup $\{\exp(-i\tau\hat{\mathscr G})|\tau\geq0\} $ on polynomial compactness and the Arendt-Batty-Lyubich-V\~u theorem. The strongly-stable evolution semigroup inspires our proposal of a non-perturbative method to solve the light scattering problem and improve the Born approximation.

Methods to improve the accuracy of spectrophotometer determination of serum creatinine

In order to solve a series of problems that affect the accuracy of spectral quantitative analysis of complex solution components, based on the “M + N” theory, this paper proposes a system for quantitative analysis of serum components by spectroscopy. At the same time, feasible methods are used in each step of the system to effectively reduce the influence of noise and interference factors, and obtain the spectral signal of serum components with high signal-to-noise ratio, so that the model has good prediction performance. In this paper, serum creatinine was taken as the research object. This paper collects multi-location and multi-mode spectral data, and uses the wavelength elimination method to optimize the wavelength to avoid over-fitting, then, based on PLS modeling, the cubic fitting is used to optimize the model, so as to reduce the influence of spectral nonlinearity caused by the scattering characteristics of solution components. After using the above methods, the prediction ability and robustness of the model are improved. Compared with the results without these methods, the correlation coefficient of creatinine concentration of all samples predicted by the model established by using the reasonable methods is increased by 32.43%, and the root mean square error is reduced by 53.61%. The above experiments show that this article combines multi-mode spectrum, the wavelength variable optimization method, the cubic equation optimization model method and other methods to quantitatively analyze the complex solution components based on the spectrum, which can improve the accuracy and robustness of the results.

Mark-Spectra: A convolutional neural network for quantitative spectral analysis overcoming spatial relationships

Spectral analysis is one of the most important and widely used methods for chemometrics in the field of agriculture, and convolutional neural network (CNN) models have achieved excellent performance on spectral analysis. The critical drawback of the CNN approach is that it preserves the spatial relationships among adjacent wavelengths, which contribute to collinearity and redundancies rather than relevant effective information. To confirm this observation, the distribution of characteristic wavelengths extracted by different methods (include F-test, importance weights, and CNN) are visualized in this paper. A convolutional neural network for quantitative spectral analysis, named Mark-Spectra, is presented to overcome spatial relationships and to improve the model performance. A layer (Mark layer) is introduced as part of Mark-Spectra, which is used to overcome spatial relationship of raw spectral data. Mark-Spectra model is compared with three CNN models using three open accessed visible and near infrared spectroscopic datasets (corn, wheat and soil). Mark-Spectra model outperforms the other three convolutional neural network models on two datasets (except dataset of wheat, due to lesser number of features), and it cost much less training time than the others. In addition, this paper compares Mark-Spectra with two classical neural network-based algorithms, principal component analysis – artificial neural network (PCA-ANN) and extreme learning machine (ELM). Mark-Spectra performed best in soil dataset, and ELM performed best in corn and wheat datasets, respectively. These results can illustrate that Mark-Spectra is still limited with the characteristic of raw spectral data (e.g., the number of samples and features), which is a fundamental fact of deep learning-based methods, but it performed better than the other CNN models and reduced the dependence of sample size due to overcoming spatial relationships.

Conduction characteristics of lightning dart leader channel and its influence on the corresponding return stroke discharge intensity

Conductivity characteristics of lightning dart leader channel significantly affect the discharge features of the following return stroke process. Based on spectral quantitative analysis, the electrical conductivity, electron density of the dart leader, and the corresponding subsequent return stroke plasma were investigated, and their changes along the height of the discharge channel were calculated; furthermore, the effects of the conduction characteristics of the dart leader channel on the return stroke discharge intensity were investigated. The results indicated that the electrical conductivity distribution along the height of the return stroke channel was approximately consistent with that of the corresponding dart leader; there was a good linear correlation between the electron density of the dart leader tip as it approaches the ground and the bottom of the return stroke channel. It was inferred that the electron density in the dart leader tip is positively correlated with the discharge intensity of the return stroke.

Electrolytic superconducting niobium coatings for a cryogyroscope rotor: creation and properties

Electrodeposition of niobium coatings on spherical substrates for a cryogyroscope rotor made of carbopyroceram was considered. A special design of the installation for obtaining coatings on spherical samples was created. The coatings were applied at a temperature of 750°C with the cathodic current density of 5·10−3 - 2–10−2 A·cm−2 and the electrolysis time of 8-12 h. It was found that the use of a cathodic current density of 2·10−2 A·cm−2 and higher is impractical, because there is a roughening of the coatings surface. The composition of electrolytic coatings was identified by XRD analysis. The macrostructure of niobium coatings was studied using electron microscopy. The concentration of metallic impurities in the niobium coatings was defined by spectral quantitative analysis. The content of gas impurities was determined by gas chromatography. The roughness, nonsphericity, and superconductive properties of niobium coatings were investigated.

Quantitative analysis of urea in serum by synchronous modulation and demodulation fluorescence spectroscopy

High-precision spectral data is a necessary prerequisite for quantitative analysis of complex solution components. In order to improve the accuracy of spectral data, this paper proposes a method of synchronous modulation and demodulation. This article also combines the “M + N” theory, cleverly uses the excitation fluorescence of the components in the serum and its self-absorption phenomenon, collects the fluorescence spectrum of the serum sample, and then uses the partial least squares (PLS) method and the cubic optimization model method to establish a model to analyze the urea concentration of serum. At the same time, in order to verify the effectiveness of synchronous modulation and demodulation method, the unmodulated fluorescence spectrum is used to establish the regression model of urea concentration. Compared with the unmodulated fluorescence spectrum modeling results, the fluorescence spectrum modeling results after modulation and demodulation have been significantly improved. In the modeling results of fluorescence spectrum after synchronous modulation and demodulation, the Rc is 0.916753, the RMSEC is 2.05848 mmol/L, the Rp is 0.79663, and the RMSEP is 3.16812 mmol/L, the Rp-all is 0.88879, and the RMSEP-all is 2.32114 mmol/L. The results show that the method of synchronous modulation and demodulation proposed in this paper not only reduces the influence of dark current, ambient light and background noise on the signal-to-noise ratio of the spectral data, but also effectively avoids the error caused by the non-synchronization of the chopper and the spectrometer. Therefore, the method used in this paper not only improves the signal-to-noise ratio and accuracy of spectral data, but also improves the accuracy of spectral quantitative analysis of complex solutions.

EEG power spectral responses to wind farm compared with road traffic noise during sleep: A laboratory study.

Wind turbine noise is dominated by low frequencies for which effects on sleep relative to more common environmental noise sources such as road traffic noise remain unknown. This study examined the effect of wind turbine noise compared with road traffic noise on sleep using quantitative electroencephalogram power spectral analysis. Twenty-three participants were exposed to 3-min samples of wind turbine noise and road traffic noise at three sound pressure levels (33, 38 and 43dBA) in randomised order during established sleep. Acute (0-30s) and more sustained (30-180s) effects of noise presentations during N2 and N3 sleep were examined using spectral analysis of changes in electroencephalogram power frequency ranges across time in 5-s intervals. Both noise types produced time- and sound pressure level-dependent increases in electroencephalogram power, but with significant noise type by sound pressure level interactions in beta, alpha, theta and delta frequency bands (all p<0.05). Wind turbine noise showed significantly lower delta, theta and beta activity immediately following noise onset compared with road traffic noise (all p<0.05). However, alpha activity was higher for wind turbine noise played at lower sound pressure levels (33dBA [p=0.001] and 38dBA [p=0.003]) compared with traffic noise during N2 sleep. These findings support that spectral analyses show subtle effects of noise on sleep and that electroencephalogram changes following wind turbine noise and road traffic noise onset differ depending on sound pressure levels; however, these effects were mostly transient and had little impact on conventionally scored sleep. Further studies are needed to establish if electroencephalogram changes associated with modest environmental noise exposures have significant impacts on sleep quality and next-day functioning.

Insight into the Partitioning and Clustering Mechanism of Rare-Earth Cations in Alkali Aluminoborosilicate Glasses

Rare-earth (RE) containing alkali aluminoborosilicate glasses find increasingly broad technological applications, with their further development only impeded by yet-poor understanding of coordination environment and structural role of RE ions in glasses. In this work we combine free induction decay (FID)-detected electron paramagnetic resonance (EPR), electron spin echo envelope modulation (ESEEM), and MAS NMR spectroscopies, to examine the coordination environment and the clustering tendencies of RE3+ in a series of peralkaline aluminoborosilicate glasses co-doped with Nd2O3 (0.001-0.1 mol%) and 5 mol% La2O3. Quantitative EPR spectral analysis reveals three different Nd3+ forms coexisting in the glasses: isolated Nd3+ centers, dipole-coupled Nd clusters (Nd-O-X-O-Nd, where X = Si/B/Al), and spin-exchange-coupled Nd clusters, (Nd-O-Nd) and (Nd-O-La-O-Nd). Extensive RE clustering is observed at high RE2O3 concentrations, with more than 90% REs converting to dipole- and exchange-coupled Nd clusters already at [RE2O3] = 0.01 mol%. ESEEM analysis of the EPR-detectable Nd centers indicates a Na/Si-rich environment (four Na+ per Nd3+) for the isolated Nd3+ centers and the Na/Si/B-rich environment (2-3 Na+ and 1-2 boron per each Nd3+) for the dipole-coupled Nd clusters, while the EPR-undetectable exchanged-coupled RE clusters are predicted to exist in a Na/B-rich environment. The RE clustering induces nano-scale glass phase separation, while the Na/B-rich environment of the RE clusters implies a depletion of the same elements from the remaining host glass. Based on our results, we develop a mechanistic model that explains the high tendency of RE3+ to form clusters in alkali aluminoborosilicate glasses.

P109 The association between sleep microarchitecture and cognitive function in middle-aged and older men: A community-based study

Abstract Introduction Sleep microarchitecture metrics determined by quantitative power spectral analysis (PSA) of the electroencephalogram (EEG) have been proposed as potential biomarkers of cognitive function. However, there remain no data from community-based samples. This study examined cross-sectional associations between sleep microarchitecture metrics determined by PSA and cognitive function outcomes in community-dwelling men. Methods Men, Androgen, Inflammation, Lifestyle, Environment, and Stress (MAILES) study participants (n=477) underwent home-based polysomnography between 2010–2011. All-night EEG recordings were processed using PSA following exclusion of artefacts. MAILES participants also completed the inspection time task, Fuld object memory evaluation, and trail-making test A (TMT-A) and B (TMT-B). Multivariable linear regression models were used to determine the associations of sleep microarchitecture (relative spectral power) with cognitive function in the complete and age-stratified samples. Results Power spectral densities in theta-alpha ranges during NREM and REM sleep were associated with worse TMT-A performance, whereas higher delta density was associated with better TMT-A performance in the complete sample and men ≥65 years (all p&amp;lt;0.05). Similar associations were observed with TMT-B performance in men ≥65 years. Furthermore, in men &amp;lt;65 years, higher sigma density during NREM sleep was associated with faster inspection time (B= -3.14, 95% CI [-6.00, -0.27], p=0.032), whereas in men ≥65 years, higher theta density during NREM sleep was associated with faster inspection time (B = -3.33, 95% CI [-6.65, -0.02], p=0.049). Discussion PSA markers of sleep microarchitecture are independently associated with cognitive function. Longitudinal studies are needed to determine whether sleep microarchitecture metrics predict future cognitive dysfunction and decline.

Sifting weakly-coordinated solvents within solvation sheath through an electrolyte filter for high-voltage lithium-metal batteries

The compulsive lithium dendrite growth and inevitable electrolyte decomposition induced by immediate contact between electrodes and free/weakly coordinated solvent molecules within the solvation sheaths of solvated lithium-ions are long-lasting tricky problems that existed in lithium-metal batteries (LMBs). Salt-concentrated strategy can effectively suppress those detrimental issues, the apparently increased production costs, the increased electrolyte viscosities, or even catastrophic battery explosions caused by adding extra salts, however, greatly restrained their successes. Here, by employing a metal-organic framework (MOF) with narrow channel sizes of 4.2 Å (ZIF-71) as a unique electrolyte solvation sheath filter to isolate free or weakly-coordinated solvents from contact with electrodes, we have obtained a special electrolyte composed of merely strongly coordinated solvent molecules. Drawn from the quantitative spectral analysis (FT-IR, Raman), the number of electrolyte solvent molecules in each Li+ solvation sheath was calculated to be 0.54 (the lowest value achieved among carbonate electrolytes), indicated almost completely de-solvation towards the solvated lithium-ions. This ZIF-71 filtered electrolyte displayed remarkably expanded electrochemical stability windows (extended to 5.2 V vs. Li/Li+). When coupled it with two typical high-voltage cathodes (4.4 V-level NCM-811 and 4.9 V-level LNMO), the high-voltage lithium-metal batteries demonstrated remarkably improved electrochemical performances in terms of capacities and lifespans. The cycled high-voltage cathodes and lithium metals demonstrate greatly improved electrode/electrolyte interfaces with negligible cathode electrolyte interface (CEI) and solid electrolyte interface (SEI). This special electrolyte filtering strategy is expected to push the development of high-energy-density LMBs.

Research on Infrared Spectral Quantitative Analysis of Hydrocarbon Gases Based on Adaptive Boosting Classifier and PLS

The traditional gas logging using hydrogen flame gas chromatography is gradually difficult to meet the requirements of the current complex oil and gas reservoir exploration because of its long detection cycle, many additional equipment, complex use, maintenance process and other defects. Infrared spectroscopy has obvious advantages in detection speed and convenience. But when it comes to homologues with similar molecular structure, it is difficult to quantitatively analyze the component concentration because of the serious overlap of characteristic absorption peaks. To solve this problem, this paper uses the self-developed experimental platform to carry out a large number of infrared spectral experimental studies on the single substance and six component mixtures. Based on a large number of experimental data, a method combining the AdaBoost classifier with optimization window partial least squares (PLS) modeling is proposed. Through the PLS local modeling for the spectra of different components and different concentration ranges, the boosting algorithm is used to classify the original spectral data, and then the component concentration is calculated. This method solves the problems of difficult identification and inaccurate quantitative analysis of alkane mixed gas components in traditional methods, and effectively improves the detection accuracy of infrared spectrum quantitative analysis of mixed components, which provides the core theoretical support for infrared spectroscopy to replace hydrogen flame gas chromatography which is widely used in gas logging.

Modeling with Multiple Correlated Spectral Data Based on Approximating the Nonlinear Spectrum Induced by Scattering.

In the spectral quantitative analysis of scattering solution, the improvement of accuracy is seriously restricted by the nonlinearity caused by scattering, and even the measurement will fail due to the influence of scattering. The important reasons are that the modeling variables are greatly affected by nonlinearity, and the information contained in the modeling data cannot represent the scattering characteristics. In this paper, a method is proposed, in which the spectral data of several optical pathlengths with equal space are combined as the modeling data set of a sample. These highly correlated spectral data contain relatively nonlinear information. The addition of the spectral data provides more options for the selection of principal components in modeling with PLS method. By giving lower weight to the corresponding wavelength which is greatly affected by scattering, the model is insensitive to scattering and the prediction accuracy is improved. Through the spectral quantitative analysis experiment on strong scattering material, the prediction accuracy of the model was 61.7% higher than that of the traditional method and was 58.5% higher than that of the variable sorting for normalization method. The feasibility of the method is verified.

Statistical analysis of ENDOR spectra

Electron-nuclear double resonance (ENDOR) measures the hyperfine interaction of magnetic nuclei with paramagnetic centers and is hence a powerful tool for spectroscopic investigations extending from biophysics to material science. Progress in microwave technology and the recent availability of commercial electron paramagnetic resonance (EPR) spectrometers up to an electron Larmor frequency of 263 GHz now open the opportunity for a more quantitative spectral analysis. Using representative spectra of a prototype amino acid radical in a biologically relevant enzyme, the [Formula: see text] in Escherichia coli ribonucleotide reductase, we developed a statistical model for ENDOR data and conducted statistical inference on the spectra including uncertainty estimation and hypothesis testing. Our approach in conjunction with 1H/2H isotopic labeling of [Formula: see text] in the protein unambiguously established new unexpected spectral contributions. Density functional theory (DFT) calculations and ENDOR spectral simulations indicated that these features result from the beta-methylene hyperfine coupling and are caused by a distribution of molecular conformations, likely important for the biological function of this essential radical. The results demonstrate that model-based statistical analysis in combination with state-of-the-art spectroscopy accesses information hitherto beyond standard approaches.

Advanced Hyperspectral Analysis of Sediment Core Samples from the Chew Bahir Basin, Ethiopian Rift, in the Spectral Range from 0.25 to 17 µm: Support for Climate Proxy Interpretation

Establishing robust environmental proxies at newly investigated terrestrial sedimentary archives is a challenge, because straightforward climate reconstructions can be hampered by the complex relationship between climate parameters and sediment composition, proxy preservation or (in)sufficient sample material. We present a minimally invasive hyperspectral bidirectional reflectance analysis on discrete samples in the wavelength range from 0.25 to 17 µm on 35 lacustrine sediment core samples from the Chew Bahir Basin, southern Ethiopia for climate proxy studies. We identified and used absorption bands at 2.2 μm (Al–OH), at 2.3 μm (Mg–OH), at 1.16 μm (analcime), and at 3.98 μm (calcite) for quantitative spectral analysis. The band depth ratios at 2.3/2.2 μm in the spectra correlate with variations in the potassium content of the sediment samples, which also reflect periods of increased Al-to-Mg substitution in clay minerals during drier climatic episodes. During these episodes of drier conditions, absorption bands diagnostic of the presence of analcime and calcite support this interpretation, with analcime indicating the driest conditions. These results could be compared to qualitative analysis of other characteristic spectral properties in the spectral range between 0.25 and 17 µm. The results of the hyperspectral measurements complement previous sedimentological and geochemical analyses, allowing us in particular to resolve more finely the processes of weathering in the catchment and low-temperature authigenic processes in the sediment. This enables us to better understand environmental changes in the habitat of early humans.

M3GPSpectra: A novel approach integrating variable selection/construction and MLR modeling for quantitative spectral analysis

Quantitative analysis of the physical or chemical properties of various materials by using spectral analysis technology combined with chemometrics has become an important method in the field of analytical chemistry. This method aims to build a model relationship (called prediction model) between feature variables acquired by spectral sensors and components to be measured. Feature selection or transformation should be conducted to reduce the interference of irrelevant information on the prediction model because original spectral feature variables contain redundant information and massive noise. Most existing feature selection and transformation methods are single linear or nonlinear operations, which easily lead to the loss of feature information and affect the accuracy of subsequent prediction models. This research proposes a novel spectroscopic technology-oriented, quantitative analysis model construction strategy named M3GPSpectra. This tool uses genetic programming algorithm to select and reconstruct the original feature variables, evaluates the performance of selected and reconstructed variables by using multivariate regression model (MLR), and obtains the best feature combination and the final parameters of MLR through iterative learning. M3GPSpectra integrates feature selection, linear/nonlinear feature transformation, and subsequent model construction into a unified framework and thus easily realizes end-to-end parameter learning to significantly improve the accuracy of the prediction model. When applied to six types of datasets, M3GPSpectra obtains 19 prediction models, which are compared with those obtained by seven linear or non-linear popular methods. Experimental results show that M3GPSpectra obtains the best performance among the eight methods tested. Further investigation verifies that the proposed method is not sensitive to the size of the training samples. Hence, M3GPSpectra is a promising spectral quantitative analytical tool.