Applied Spectroscopy

As a label-free, nondestructive, and in situ detection method, Raman spectroscopy analysis of single cells has potential application value in biomedical fields such as cancer diagnosis. In this study, the Raman spectral characteristics of nucleophosmin (NPM1)-mutant acute myeloid leukemia (AML) cells and nonmutated AML cells were investigated, and the reasons for the differences in spectral peaks were explained in combination with transcriptomic analysis. Raman spectra of two AML cell lines without NPM1 mutation (THP-1 and HL-60) and the OCI-AML3 cell line carrying the NPM1 mutant gene were cultured and collected experimentally. It was found that the average Raman spectra of NPM1 mutant and nonmutated cells had intensity differences in multiple peaks corresponding to chondroitin sulfate (CS), nucleic acid, protein, and other molecules. The differentially expressed genes were identified by quantitative analysis of the gene expression matrix of the two types of cells, and their roles in the regulation of CS proteoglycan and protein synthesis were analyzed. The results showed that the differences between the two types of cells expressed by the single-cell Raman spectral information were consistent with the differences in transcriptional profiles. This research could advance the application of Raman spectroscopy in cancer cell typing.

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Raman spectroscopy is a useful tool for obtaining biochemical information from biological samples. However, interpretation of Raman spectroscopy data in order to draw meaningful conclusions related to the biochemical make up of cells and tissues is often difficult and could be misleading if care is not taken in the deconstruction of the spectral data. Our group has previously demonstrated the implementation of a group- and basis-restricted non-negative matrix factorization (GBR-NMF) framework as an alternative to more widely used dimensionality reduction techniques such as principal component analysis (PCA) for the deconstruction of Raman spectroscopy data as related to radiation response monitoring in both cellular and tissue data. While this method provides better biological interpretability of the Raman spectroscopy data, there are some important factors which must be considered in order to provide the most robust GBR-NMF model. We here evaluate and compare the accuracy of a GBR-NMF model in the reconstruction of three mixture solutions of known concentrations. The factors assessed include the effect of solid versus solutions bases spectra, the number of unconstrained components used in the model, the tolerance of different signal to noise thresholds, and how different groups of biochemicals compare to each other. The robustness of the model was assessed by how well the relative concentration of each individual biochemical in the solution mixture is reflected in the GBR-NMF scores obtained. We also evaluated how well the model can reconstruct original data, both with and without the inclusion of an unconstrained component. Overall, we found that solid bases spectra were generally comparable to solution bases spectra in the GBR-NMF model for all groups of biochemicals. The model was found to be relatively tolerant of high levels of noise in the mixture solutions using solid bases spectra. Additionally, the inclusion of an unconstrained component did not have a significant effect on the deconstruction, on the condition that all biochemicals in the mixture were included as bases chemicals in the model. We also report that some groups of biochemicals achieve a more accurate deconstruction using GBR-NMF than others, likely due to similarity in the individual bases spectra.

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Germination rate is important for seed selection and planting and quality. In this study, hyperspectral image technology integrated with germination tests was applied for feature association analysis and germination performance prediction of sugarbeet seeds. In this study, we proposed a nondestructive prediction method for sugarbeet seed germination. Sugarbeet seed was studied, and hyperspectral imaging (HIS) performed by binarization, morphology, and contour extraction was applied as a nondestructive and accurate technique to achieve single seed image segmentation. Comparative analysis of nine spectral pretreatment methods, SNV + 1D was used to process the average spectrum of sugarbeet seeds. Fourteen characteristic wavelengths were obtained by the Kullback–Leibler (KL) divergence, as the spectral characteristics of sugarbeet seeds. Principal component analysis (PCA) and material properties verified the validity of the extracted characteristic wavelengths. It was extracted of six image features of the hyperspectral image of a single seed obtained based on the gray-level co-occurrence matrix (GLCM). The spectral features, image features, and fusion features were used to establish partial least squares discriminant analysis (PLS-DA), CatBoost, and support vector machine radial-basis function (SVM-RBF) models respectively to predict the germination. The results showed that the prediction effect of fusion features was better than spectral features and image features. By comparing other models, the prediction results of the CatBoost model accuracy were up to 93.52%. The results indicated that, based on HSI and fusion features, the prediction of germinating sugarbeet seeds was more accurate and nondestructive.

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Obesity is strongly linked with increased risk and poorer prognosis of endometrial cancer (EC). Cancer-associated fibroblasts (CAFs) are activated fibroblasts that form a large component of the tumor microenvironment and undergo metabolic reprogramming to provide critical metabolites for tumor growth. However, it is still unknown how obesity, characterized by a surplus of free fatty acids drives the modifications of CAFs lipid metabolism which may provide the mechanistic link between obesity and EC progression. The present study aims to evaluate the utility of Raman spectroscopy, an emerging nondestructive analytical tool to detect signature changes in lipid metabolites of CAFs from EC patients with varying body mass index. We established primary cultures of fibroblasts from human EC tissues, and CAFs of overweight/obese and nonobese women using antibody-conjugated magnetic beads isolation. These homogeneous fibroblast cultures expressed fibroblast markers, including α-smooth muscle actin and vimentin. Analysis was made in the Raman spectra region best associated with cancer progression biochemical changes in lipids (600–1800 cm^–1 and 2800–3200 cm^–1). Direct band analysis and ratiometric analysis were conducted to extract information from the Raman spectrum. Present results demonstrated minor shifts in the CH₂ symmetric stretch of lipids at 2879 cm^–1 and CH₃ asymmetric stretching from protein at 2932 cm^–1 in the overweight/obese CAFS compared to nonobese CAFs, indicating increased lipid content and a higher degree of lipid saturation. Principal component analysis showed that CAFs from overweight/obese and nonobese EC patients can be clearly distinguished indicating the capability of Raman spectroscopy to detect changes in biochemical components. Our results suggest Raman spectroscopy supported by chemometric analysis is a reliable technique for characterizing metabolic changes in clinical samples, providing an insight into obesity-driven alteration in CAFs, a critical stromal component during EC tumorigenesis.

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Handheld Fourier transform infrared (FT-IR) spectrometers are very promising candidates for several applications where accurate real-time material detection and quantification are needed. Due to their compact size, their mode of operation which does not allow for long warm-up time, and changing environmental conditions, these spectrometers suffer from short-term noise and long-term instabilities which affect their performance. In this work, the effect of long-term multiplicative instabilities on the signal-to-noise ratio (S/N), measured using the 100% line-method, is studied. An expression for the variance, in this case, is deduced. The Allan variance technique is used to identify and quantify the presence of the different types of noises. The methodology is applied to a commercial NeoSpectra scanner module from Si-Ware Systems, Inc.

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In confocal Raman microscopy, depth profiling is a key application that enables analysis of the structural and chemical composition and size of three-dimensional (3D) transparent objects. However, the precise interpretation of a probed sample's Raman depth profile measurement can be significantly affected by both its size and surrounding objects. This study provides a more comprehensive understanding of the observed optical effects at the interface between polymer spheres and different substrates. Ray- and wave-optical simulations support our results. We derive a correction factor that, depending on the instrumental configuration, allows us to determine the nominal dimensions of the scanned objects more accurately from Raman depth profiles. Our studies support the need for careful consideration when employing depth profiling in confocal Raman microscopy for nondestructive, quantitative tomography of 3D objects.

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Turquoise is one of the key ingredients in some magical Tibetan medicines, and its quality and content directly affect the medicine's effectiveness. In this paper, laser-induced breakdown spectroscopy (LIBS) technology was first applied to detect the raw materials of Tibetan medicine. The traditional data analysis methods could not meet the practical requirements of modern Tibetan medicine factories due to matrix effects. The concept of correlation coefficient (ρ) in pattern recognition technique was introduced as an evaluation index, and the model was established based on the intensities of the four characteristic Al and Cu spectral lines of the samples for different contents of turquoise, which was applied to estimate the contents of turquoise in the samples to be tested. We detected the LIBS on 126 samples of raw ore from 42 areas in China and evaluated the turquoise content using self-developed software with an error of <10%. This paper's technical testing process and methods can also be applied to test other mineral compositions and provide technical support for modernizing and standardizing Tibetan medicines.

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Ice cream is a complex product containing four different phases that affect its microstructure. Viscosity is a critical ice cream quality parameter that is typically measured using off-line methodologies, such as rheometry. In-line viscosity measurements allow continuous and instant analysis compared to off-line methodologies, yet they still constitute a challenge. This work focused on the preliminary study of the potential application of near-infrared (NIR) and Raman spectroscopy as analytical tools to assess the viscosity of ice cream mixes. Historically, partial least squares regression (PLSR) is a standard algorithm used for analysis of spectral data and in the development of predictive models. This methodology was implemented over a range of viscosity values, obtained by varying the ice cream fat content and homogenization conditions. Individual PLSR models showed some predictive ability and better performance compared to the integrated model obtained by data fusion. Lower prediction errors and higher coefficients of determination were obtained for NIR, making this technique more suitable based on model performance. However, other considerations should be accounted during the selection of the best method, such as implementation limitations. This study offers a preliminary comparison of the spectroscopic methods for quantitative analysis of viscosity of aged ice cream mixes and a starting point for an in-situ application study.

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Herbal powder preparations (HPPs) are common forms of traditional medicine made by blending the powder of two or more ingredients. The first step to ensure the safety and efficacy of HPPs is to confirm the prescribed ingredients and screen the abnormal ingredients. With the help of attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) imaging or mapping, the particles of different ingredients in an HPP sample can be measured individually. In this way, the overlapped absorption signals of different ingredients in the ATR FT-IR spectrum of the bulk sample can be isolated in the ATR FT-IR spectra of the microscopic particles, which leads to the substantial increase of the specificity and sensitivity of the infrared spectral identification method. The characteristic particles of each ingredient can be identified by the objective comparison of the microscopic ATR FT-IR spectra against the reference spectra based on the correlation coefficients. Since the ATR FT-IR imaging or mapping tests of HPPs are free of the separation preprocess, multiple organic and inorganic ingredients are able to be recognized by a single identification procedure simultaneously rather than by different separation and identification procedures. As an example, the ATR FT-IR mapping method was used in this research to successfully identify three prescribed ingredients and two abnormal ingredients in oral ulcer pulvis, which is a classic HPP for oral ulcer in traditional Chinese medicine. The results show the feasibility of the ATR FT-IR microspectroscopic identification method for the objective and simultaneous identification of the prescribed and abnormal ingredients of HPPs.