Optimizing setup of scan number in FTIR spectroscopy using the moment distance index and PLS regression: application to soil spectroscopy

Enhancing spatial resolution in Fourier transform infrared spectral image via machine learning algorithms

Owing to the intrinsic signal noise in the characterization of chemical structures through Fourier transform infrared (FT-IR) spectroscopy, the determination of the signal-to-noise ratio (SNR) depends on the level of the concentration of the chemical structures. In situations characterized by limited concentrations of chemical structures, the traditional approach involves mitigating the resulting low SNR by superimposing repetitive measurements. In this study, we achieved comparable high-quality results to data scanned 64 times and superimposed by employing machine learning algorithms such as the principal component analysis and non-negative matrix factorization, which perform the dimensionality reduction, on FT-IR spectral image data that was only scanned once. Furthermore, the spatial resolution of the mapping images correlated to each chemical structure was enhanced by applying both the machine learning algorithms and the Gaussian fitting simultaneously. Significantly, our investigation demonstrated that the spatial resolution of the mapping images acquired through relative intensity is further improved by employing dimensionality reduction techniques. Collectively, our findings imply that by optimizing research data through noise reduction enhancing spatial resolution using the machine learning algorithms, research processes can be more efficient, for instance by reducing redundant physical measurements.

Non-destructive recognition of copy paper based on advanced spectral fusion and feature optimization

In order to provide more clues for ongoing investigations and case handling, as well as achieve fast, non-destructive, and accurate identification of copy paper found at crime scenes, this study aims to utilize advanced spectral fusion technology to characterize and identify the three-dimensional features of the "origin-manufacturer-brand" of copy paper. Confocal Raman Microscopic and Fourier transform infrared spectroscopy were employed to collect spectral data from 200 samples from four regions (Shandong, Henan, Shaanxi, Jiangsu). The effects of different preprocessing methods, such as Hilbert transformation and deconvolution, on the model's ability to distinguish were compared. Feature variables were extracted using principal component analysis, and Bayesian discriminant classification models were constructed based on single infrared spectroscopy, Raman spectroscopy, and three types of spectral fusion datasets. By comparing the classification accuracy of different models, the primary fusion based on the full spectrum dataset was selected as the optimal model for the three-dimensional feature classification of copy paper. The accuracy achieved for origin (96%), manufacturer (100%), and brand (100%) was satisfactory, and the classification results were highly accurate. This study provides valuable insights and serves as a reference for its application in forensic science research.

Soil Particle Size Thresholds in Soil Spectroscopy and Its Effect on the Multivariate Models for the Analysis of Soil Properties

This study focused on one of the few but critical sample preparations required in soil spectroscopy (i.e., grinding), as well as the effect of soil particle size on the FTIR spectral database and the partial least squares regression models for the prediction of eight soil properties (viz., TC, TN, OC, sand, silt, clay, Olsen P, and CEC). Fifty soil samples from three Moroccan region were used. The soil samples underwent three preparations (drying, grinding, sieving) to obtain, at the end of the sample preparation step, three ranges of particle size, samples with sizes < 500 µm, samples with sizes < 250 µm, and a third range with particles < 125 µm. The multivariate models (PLSR) were set up based on the FTIR spectra recorded on the different obtained samples. The correlation coefficient (R2) and the root mean squared error of cross validation (RMSECV) were chosen as figures of merit to assess the quality of the prediction models. The results showed a general trend in improving the R2 as the finer particles were used (from <500 µm to 125 µm), which was clearly observed for TC, TN, P2O5, and CEC, whereas the cross-validation errors (RMSECV) showed an opposite trend. This confirmed that fine soil grinding improved the accuracy of predictive models for soil properties diagnosis in soil spectroscopy.

Nanoscale insight into biochemical changes in cervical cancer cells exposed to adaptogenic drug

This paper describes for the first time the application of atomic force microscopy-based infrared spectroscopy (AFM-IR) to evaluate cellular response to adaptogen, based on an in vitro model of cervical cancer. HeLa cervical cells were exposed to different concentrations of withaferin A, a very promising anti-cancer adaptogenic substance. AFM-IR approach was used to image single cells post-adaptogen treatment and to track subtle biochemical changes in cells at the nanoscale level. Partial least squares (PLS) regression was applied to build predictive models that allowed for the identification of spectral markers of adaptogen-induced alterations Spectroscopic studies were enriched with fluorescence staining to determine whether the adaptogen affects cell morphology. The results showed that with the increase in the concentration of adaptogen, changes in the cell nucleus and the actin cytoskeleton become more and more significant. It has been demonstrated that the AFM-IR technique can successfully study the cellular response to the anti-cancer agent at the single-cell level with nanoscale spatial resolution. On the basis of the promising findings presented in this paper, it is possible to conclude that withaferin A has great potential in inhibiting the proliferation of cervical cancer cells in a dose-dependent manner. It has been found that both the increase in the concentration of withaferin A and the increase in incubation time with the adaptogen resulted in a decrease in the intensity of the bands assigned to nucleic acids. This may be due to DNA condensation, internuclear cleavage, or degradation during apoptosis. The findings also suggest changes in the secondary structure of proteins that may be a consequence of disruption of the actin cytoskeleton, progressive apoptosis, or significant biochemical changes. Furthermore, noticeable changes were also observed in the bands originating from lipids vibrations, and an increased share of the band near 2920 cm^-1, considered an important marker of apoptosis, was noted. The metabolism of carbohydrates in cells also changes under the influence of the adaptogen. AFM-IR provides nanoscale insight into the structural and morphological properties of cells after drug treatment and is an indisputable milestone in the development of new anti-cancer approaches.