Journal of Spectroscopy
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Acceptance rate46%
Submission to final decision85 days
Acceptance to publication23 days
CiteScore3.500
Journal Citation Indicator0.490
Impact Factor1.750

GC-MS Phytochemical Profiling, Antidiabetic, and Antioxidant Activities of Khaya senegalensis Stem Bark and Azadirachta indica Leaves Extracts in Rats

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 Journal profile

Journal of Spectroscopy publishes research into the theory and application of spectroscopy across all disciplines, including biology, chemistry, engineering, earth sciences, medicine, materials science, physics, and space science.

 Editor spotlight

Chief Editor Dr Daniel Cozzolino is based at the University of Queensland, Australia. His research focuses on the developments of chemometric and spectroscopic methods for use in agriculture and food applications.

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Research Article

Clinical, Biochemical, and ATR-FTIR Spectroscopic Parameters Associated with Death or Survival in Patients with Severe COVID-19

The wide range of symptoms of the coronavirus disease 2019 (COVID-19) makes it challenging to predict the disease evolution using a single parameter. Therefore, to describe the pathophysiological response to SARS-CoV-2 infection in hospitalized patients with severe COVID-19, we compared according to survival or death, the sociodemographic and clinical characteristics, the biochemical and immunological attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectra from saliva samples and their correlation with chemometric findings. Herein, we demonstrate that ATR-FTIR spectroscopy allows the description of the events related to cell damage, such as lipids biogenesis and the secondary structure of proteins associated with lactate dehydrogenase and albumin levels. Moreover, humoral (IgM) and cellular (IFN-γ, TNF-α, IL-10, and IL-6) responses were also increased in patients who died from COVID-19.

Research Article

Considerations in Raman Spectroscopy for Real-Time API Concentration Measurement at Tablet Press Feed Frame

Raman spectroscopy is one of the important process analytical technology tools available for implementation in the continuous manufacturing of oral solid dosages. The aim of this study was to investigate several practical considerations in generating real-time measurements using Raman spectrometer at a tablet press feed frame, including the effects of fluorescence interference, photobleaching, feed-frame rpm, and material particle size. Fluorescence, in particular, is a significant drawback of Raman spectroscopy, compared to the use of near-infrared spectroscopy. Potential material sparing strategies were also investigated, including using stationary powders for calibration and isolation of feed-frame materials. Acetaminophen was used as the main active pharmaceutical ingredient (API), and microcrystalline cellulose (MCC) and lactose were used as excipients. The fluorescent behavior of MCC at 785 nm laser wavelength was reported and discussed. Raman spectra of a blend of MCC and acetaminophen and lactose and acetaminophen were collected at the feed frame of the tablet press. A series of preprocessing steps applied to remove the fluorescence interference was found to be effective, including the use of standard normal variate, subtraction of spectra of fluorescent material, baseline correction, and smoothing. Three different PLS models were prepared for different scenarios and their performances were compared. The models were able to predict the concentration of acetaminophen with root mean squared error prediction (RMSEP) of 0.29% w/w when there was no fluorescence interference and 0.57% w/w when there was fluorescence interference in background spectra. The study demonstrated the feasibility of using Raman spectroscopy for API concentration prediction even in the case of fluorescent interference and showed that Raman measurements were robust; that is, they were not much affected by feed-frame rpm and excipient particle size.

Research Article

Nonlinear Correction Methods of Temperature-Caused Peak Shift for a NaI(Tl) Gamma-Ray Spectrometer

NaI(Tl) detectors are frequently operated under unstable temperature conditions when used in an open environment. Temperature changes would result in a peak shift and spectral distortion during measurement. Two easy-to-implement methodologies are proposed to stabilize the measured spectrum without the necessity of adjusting the gain, which are a correction algorithm for temperature-caused peak-shift based on multiple characteristic peak area weighting factors and an interpolation correction algorithm based on multicharacteristic peak sequence. Both of them can be used when the relative channel displacement of characteristic peaks in the spectrum due to temperature changes is not constant. Experimental data obtained under controlled temperature conditions in the laboratory were adopted to correct a spectrum, with joint consideration of some known characteristic peaks, such as 40K, U (214Bi), or Th (208Tl) peaks. Through constructing a reversible temperature coefficient matrix, one can easily obtain the coefficients of the n-th polynomial describing the influence of temperature on peak position, which presents their nonlinear mathematical relationship. Then, corrections of these two effects can also be easily calculated. Comparing the experimental results, peak positions before and after correction, it is proved that the interpolation correction algorithm based on multicharacteristic peak sequence has better correction accuracy, but the temperature-caused peak shift correction algorithm based on the multicharacteristic peak area weighting factor has a shorter calibration time.

Research Article

Surface Soil Moisture Estimation Using a Neural Network Model in Bare Land and Vegetated Areas

Most of the approaches to retrieve surface soil moisture (SSM) by optical and thermal infrared (TIR) spectroscopies are purposed to calculate various characteristic bands/indices and then to establish the regression relationship between them in combination with the measurement data. However, due to the combined impact of many factors, the regression relationship often shows nonlinearity. Moreover, the relationship between the single temporal image and the measured data are not transplantable in time and space, which makes it difficult to construct a more general model for the remote sensing (RS) estimation of SSM. In order to solve this problem, the back propagation (BP) neural network (NN) with an excellent nonlinear mapping ability is introduced to determine the relationship between the characteristic band/index and the measurement data. In the BPNN model, the optical and TIR RS data in different periods were taken as the input parameters, and the in situ soil moisture data were treated as the output parameter. There are 12 schemes designed to retrieve SSM. The key findings of study were as follows: (1) the BPNN model could retrieve SSM with a high accuracy that indicates the correlation coefficient between the estimated and measured soil moisture as 0.9001 and (2) the SSM retrieval model based on the BPNN can be applied to estimate the SSM with different spatial resolution values.

Research Article

High Zoom Ratio Foveated Snapshot Hyperspectral Imaging for Fruit Pest Monitoring

Snapshot hyperspectral imaging technology is increasingly used in agricultural product monitoring. In this study, we present a 9× local zoom snapshot hyperspectral imaging system. Using commercial spectral sensors with spectrally resolved detector arrays, we achieved snapshot hyperspectral imaging with 14 wavelength bands and a spectral bandwidth of 10–15 nm. An experimental demonstration was performed by acquiring spatial and spectral information about the fruit and Drosophila. The results show that the system can identify Drosophila and distinguish well between different types of fruits. The results of this study have great potential for online fruit classification and pest identification.

Research Article

Component Prediction of Antai Pills Based on One-Dimensional Convolutional Neural Network and Near-Infrared Spectroscopy

Convolutional neural networks (CNNs) are widely used for image recognition and text analysis and have been suggested for application on one-dimensional data as a way to reduce the need for preprocessing steps. In this study, the performance of one-dimensional convolutional neural network (1DCNN) machine learning algorithm was investigated for regression analysis of Antai pills spectral data. This algorithm was compared with other chemometric methods, including support vector machine regression (SVR) and partial least-square regression (PLSR) methods. The results showed that the 1DCNN model outperformed the PLSR and SVR models with similar data preprocessing for the three analytes (wogonoside, scutellarin, and ferulic acid) in Antai pills. Taking wogonoside as an example, the indices such as the correction coefficient of determination (), the root mean-squared error of cross validation (RMSECV) for calibration set, the prediction coefficient of determination (), and the root mean-squared error of prediction (RMSEP) obtained by PLSR modeling were 0.9340, 0.5568, 0.9491, and 0.5088; the indices obtained by SVR modeling were 0.9520, 0.4816, 0.9667, and 0.4117; and the indices obtained by 1DCNN modeling were 0.9683, 0.3397, 0.9845, and 0.2807, respectively. The evaluation metrics of 1DCNN are better than those of PLSR and SVR, and the prediction effect is the best, proving that 1DCNN has a good generalization ability. Especially with outliers of spectra, PLSR’s decreased by 0.0181, SVR’s decreased by 0.01, and 1DCNN’s increased by 0.0009 and decreased by 0.0057. The evaluation indices of 1DCNN have no significant change in comparison with no outliers and can still show good performance, which reflects the inclusiveness of the 1DCNN model for outliers. Simultaneously, the feasibility and robustness of the 1DCNN model in the application of near-infrared spectroscopy was verified, which has a certain application value.

Journal of Spectroscopy
 Journal metrics
See full report
Acceptance rate46%
Submission to final decision85 days
Acceptance to publication23 days
CiteScore3.500
Journal Citation Indicator0.490
Impact Factor1.750
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