Journal of Spectroscopy

Composite materials have increasingly become a high proportion of the structural weight of aircraft due to their excellent performances. Different types of damages may occur in the aircraft service period, which will bring potential safety risks to aircrafts. To investigate the defect damage detection and its spectral characteristics and imaging of carbon-fiber-reinforced polymer composite laminates, defects from the low-velocity impact damage in composites were measured by the THz time-domain reflection imaging system. Results show that there exists obvious THz spectral differences between the impact damaged defects and nondefect. The effective detection frequency band for the low-speed impact damaged defect is 0.12–2.0 THz. In the time domain, there are attenuations and delays in the spectra of defects relative to those of nondefects. In the frequency domain, with the increase of frequency, the power spectral density of the defect first increases and then decreases, and the absorption coefficient increases slowly. In general, the imaging results in time-domain imaging are better than those from the frequency-domain imaging, which not only is suitable for the qualitative detection of defects but also has great potential and application prospects in quantitative detection. This work shows an important guide for the application of THz technology to detect the composite material defects in civil aircraft.

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Low-field NMR theory was employed to study the pore structure of the upper cretaceous oil shale, on the basis of fourteen core samples collected from Qingshankou (UCQ) and Nenjiang (UCN) formations in the Songliao basin. Results indicated that the T₂ spectra from NMR measurements for collected samples contain a dominant peak at T₂ = 1∼10 ms and are able to be categorized as three types—unimodal, bimodal, and trimodal distributions. The various morphologies of T₂ spectra indicate the different pore type and variable connection relationship among pores in shale. By contrast, UCN shale has more single pore type and adsorption pores than UCQ shale. Besides, NMR-based measurements provide reliable characterization on shale porosity, which is verified by the gravimetric approach. Porosities in both UCN and UCQ shales have a wide range (2.3%∼12.5%) and suggest the strong heterogeneity, which partly makes the challenge in selection of the favorable area for shale oil exploration in the Songliao basin. In addition, the pore size of the collected sample has two distribution types, namely, peaked at ∼10 nm and peaked at ∼100 nm. Similarly, two distribution patterns emerge to the specific surface area of the study shale—peaked at ∼2 nm⁻¹ and peaked at ∼20 nm⁻¹. Here, more investigations are needed to clarify this polarization phenomenon. Basically, this study not only exhibits a preliminary understanding on the pore structure of the upper cretaceous oil shale, but also shows the reliability and pertinency of the low-field NMR technique in the petrophysical characterization of the shale oil reservoir. It is expected that this work is helpful to guide the investigation on the pore structure of oil shale from the Songliao basin in theory.

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A kinetic model based on visible/near-infrared spectroscopy of the peel brittleness of “Xintian-125” Cucumis melons, the research object, stored under room temperature, was established in order to realize real-time monitoring of the peel brittleness of Cucumis melons and for prediction of storage time. The NIR and peel brittleness of melons stored for 1, 4, and 7 days were collected and measured. SG was confirmed to be the best pretreatment by comparing the PLS models established with 4 pretreatment methods, and the differences of the prediction set determination coefficient and root-mean-square were 0.818 and 23.755, respectively. CARS and SPA were adopted to extract the feature wavelengths and establish the peel brittleness of PLS prediction model. The model’s prediction accuracy was 0.919, and the prediction root-mean-square was 25.413, indicating that NIR is able to realize the prediction of the peel brittleness of Cucumis melons. As a result, a NIR-based peel brittleness kinetic model was created. The value of the regression model was less than 0.001, and the model’s correlation coefficient was 0.8503, showing that the model is of extreme significance and high precision. The zero-order reaction equation was overfitted according to the variation tendency of the average peel brittleness of stored melons. The model’s correlation coefficient was 0.981, the standard error was 4.624, and the linear relation between the stored period and NIR was established based on it. The research proves that the NIR-based technology is able to realize quick and loss-free inspection of melons’ peel brittleness and prediction of the stored period.

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Fourier-transform infrared (FTIR) spectroscopy is a rapid and nondestructive technology for monitoring atmospheric quality. The identification of each component from the FTIR spectra is a prerequisite for the accurate quantitative analysis of gaseous pollutants. Due to the overlap of different gas absorption peaks and the interference of water vapor in the actual measurement, the existing identification methods of gas spectra have drawbacks of low identification rate and the inability to carry out real-time online analysis in atmospheric quality monitoring. In this work, independent component analysis (ICA) is applied to the spectral separation of heavily overlapped spectra of gaseous pollutants. The proposed method is validated by the analysis of mixture spectra obtained in laboratory and actual atmospheric spectra collected from stationary source. The average time consumption of separation process is less than 0.2 seconds, and the identification rate of experimental gases is up to 100%, as shown by the results of peak searching and the analysis of the correction coefficient between the separated spectra and the standard spectra database. The identification results of actual atmospheric spectra demonstrated that the proposed method can effectively identify the gaseous pollutants whose concentration changes in the measured spectra, and it is a promising qualitative spectral analysis tool that can shorten the identification time, as well as increase the identification rate. Therefore, this method can be a useful alternative to traditional qualitative identification methods for real-time online atmospheric pollutant detection.

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Pedicle screw (PS) implantation is an ideal method for the treatment of severe multilevel vertebral instability. The key problem is the accuracy of PS fixation. In this paper, the spectrum of different tissues along the fixation trajectory of PS is studied to tackle the accuracy problem. Fresh porcine vertebrae, bovine vertebrae, and ovine vertebrae were measured by using the near-infrared spectrum (NIRs) device to obtain the reflected spectrum from these vertebrae. Along the fixation trajectory of PS, the classification method based on the sparse representation-based classifier (SRC) was applied to different vertebral tissues (cortical bones and cancellous bones). Considering the large amount of spectral data, sparse preserving projection (SPP) was applied to improve the performance of SRC. The proposed method based on the SPP method for dimensionality reduction and the SRC method for tissue recognition was first used in vertebrae classification and showed superior performance compared with other classification methods, such as SVM and 1NN. The results gained from this project are vital significant to the development of hi-tech medical instruments with independent intellectual property rights.

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Surface-enhanced Raman spectroscopy (SERS) was used to simply and sensitively detect the artificial sweetener aspartame added to purified water. In this paper, a cavity formed spontaneously by silver ion droplets, and liquid polydimethylsiloxane (PDMS) is used as an SERS substrate to integrate plasma nanoparticles into optical devices. Firstly, Raman spectral characteristics of aspartame powder and aspartame aqueous solution were analyzed. Secondly, the effect of aspartame content in purified water on SERS intensity was investigated by using the prepared PDMS plasma cavity to test the samples. Thirdly, the SERS calibration curve was established by using the characteristic peak intensity of aspartame, and a good linearity relationship between the concentration of aspartame added in purified water and the characteristic peak intensity of 1588(±5) cm-1 was obtained. The linear regression equation and correlation coefficient (r) were y = 11412.73874 x + 107.36722 and 0.99593, respectively. The average recovery of aspartame in purified water was 101–106%, and the relative standard deviation (RSD) was 0.121–0.496%. The experimental results show that using this method can detect aspartame in purified water correctly, which is expected to be used in the identification and detection of sweeteners in purified water.

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