Study on the Raman spectral characteristics of dynamic and static blood and its application in species identification

Wang, Hongpeng; Fang, Peipei; Yan, Xinru; Zhou, Yiming; Cheng, Yulong; Yao, Lei; Jia, Jianjun; He, Jin; Xiong, Wei

doi:10.1016/j.jphotobiol.2022.112478

Cited by 9 publications

(8 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The core part of spectral data preprocessing is the total decomposition of the LIBS spectral lines, baseline, and noise. The baseline estimation and denoising using sparsity (BEADS) algorithm removes each channel's baseline and noise [18,19]. The baseline is modeled as a low-frequency signal, the noise is modeled as a high-frequency signal, and the LIBS spectral peaks are modeled as sparse signals, whose first and second derivatives also have sparse characteristics.…”

Section: Spectral Data Preprocessingmentioning

confidence: 99%

LIBS-MLIF Method: Stromatolite Phosphorite Determination

et al. 2023

Self Cite

View full text Add to dashboard Cite

The search for biominerals is one of the core targets in the deep space exploration mission. Stromatolite phosphorite is a typical biomineral that preserves early life on Earth. The enrichment of phosphate is closely related to microorganisms and their secretions. Laser-induced breakdown spectroscopy (LIBS) has become an essential payload in deep space exploration with the ability to analyze chemical elements remotely, rapidly, and in situ. This paper aims to evaluate the rapid identification of biological and non-biological minerals through a remote LIBS payload. LIBS is used for element analysis and mineral classification determination, and molecular laser-induced fluorescence (MLIF) is used to detect halogenated element F to support the existence of fluorapatite. This paper analyzes the LIBS-MLIF spectral characteristics of stromatolites and preliminarily evaluates the feasibility of P element quantification. The results show that LIBS technology can recognize biological and non-biological signals. This discovery is significant because it is not limited to detecting and analyzing element composition. It can also realize the detection of molecular spectrum based on selective extraction of CaF molecule. Therefore, the LIBS payload still has the potential to search for biomineral under the condition of adjusting the detection strategy.

show abstract

Section: Spectral Data Preprocessingmentioning

confidence: 99%

LIBS-MLIF Method: Stromatolite Phosphorite Determination

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…It has the characteristics of high efficiency, fast iterative convergence, and stability [19]. BEADS has been successfully applied to baseline correction of Raman spectra and spectral data denoising [20][21][22][23]. The cut-off frequency Fc, filtering order parameter D, and asymmetry parameter R of the BEADS algorithm used in this paper were 0.05, 1.00, and 6.00, respectively.…”

Section: Spectral Data Pre-processingmentioning

confidence: 99%

Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion

Wang

Yan

Xin

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

This paper evaluates the potential application of Raman baselines in characterizing organic deposition. Taking the layered sediments (Stromatolite) formed by the growth of early life on the Earth as the research object, Raman spectroscopy is an essential means to detect deep-space extraterrestrial life. Fluorescence is the main factor that interferes with Raman spectroscopy detection, which will cause the enhancement of the Raman baseline and annihilate Raman information. The paper aims to evaluate fluorescence contained in the Raman baseline and characterize organic sedimentary structure using the Raman baseline. This study achieves spectral image fusion combined with mapping technology to obtain high spatial and spectral resolution fusion images. To clarify that the fluorescence of organic matter deposition is the main factor causing Raman baseline enhancement, 5041 Raman spectra were obtained in the scanning area of 710 μm × 710 μm, and the correlation mechanism between the gray level of the light-dark layer of the detection point and the Raman baseline was compared. The spatial distribution of carbonate minerals and organic precipitations was detected by combining mapping technology. In addition, based on the BI-IHS algorithm, the spectral image fusion of Raman fluorescence mapping and reflection micrograph, polarization micrograph, and orthogonal polarization micrograph are realized, respectively. A fusion image with high spectral resolution and high spatial resolution is obtained. The results show that the Raman baseline can be used as helpful information to characterize stromatolite organic sedimentary structure.

show abstract

“…In 2018, Kyle C. Doty [13] and colleagues successfully utilized Partial Least Squares Discriminant Analysis (PLS-DA) to distinguish blood from 17 different organisms, including humans, providing crucial assistance for forensic investigations at crime scenes and paving the way for future research into differentiating blood data from various organisms. Subsequently, researchers such as Wang [14] applied the Support Vector Machine (SVM) method to successfully inspect the blood spectral data of four avian species, offering a novel solution for analyzing the presence of food additives in blood. However, traditional machine learning algorithms often fail to achieve the expected results when handling large sample datasets.…”

Section: Introductionmentioning

confidence: 99%

RepDwNet: Lightweight Deep Learning Model for Special Biological Blood Raman Spectra Analysis

He,

Zhou,

Ren

et al. 2024

Chemosensors

View full text Add to dashboard Cite

The Raman spectroscopy analysis technique has found extensive applications across various disciplines due to its exceptional convenience and efficiency, facilitating the analysis and identification of diverse substances. In recent years, owing to the escalating demand for high-efficiency analytical methods, deep learning models have progressively been introduced into the realm of Raman spectroscopy. However, the application of these models to portable Raman spectrometers has posed a series of challenges due to the computational intensity inherent to deep learning approaches. This paper proposes a lightweight classification model, named RepDwNet, for identifying 28 different types of biological blood. The model integrates advanced techniques such as multi-scale convolutional kernels, depth-wise separable convolutions, and residual connections. These innovations enable the model to capture features at different scales while preserving the coherence of feature data to the maximum extent. The experimental results demonstrate that the average recognition accuracy of the model on the reflective Raman blood dataset and the transmissive Raman blood dataset are 97.31% and 97.10%, respectively. Furthermore, by applying structural reparameterization to compress the well-trained model, it maintains high classification accuracy while significantly reducing the parameter size, thereby enhancing the speed of classification inference. This makes the model more suitable for deployment in portable and mobile devices. Additionally, the proposed model can be extended to various Raman spectroscopy classification scenarios.

show abstract

Study on the Raman spectral characteristics of dynamic and static blood and its application in species identification

Cited by 9 publications

References 22 publications

LIBS-MLIF Method: Stromatolite Phosphorite Determination

LIBS-MLIF Method: Stromatolite Phosphorite Determination

Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion

RepDwNet: Lightweight Deep Learning Model for Special Biological Blood Raman Spectra Analysis

Contact Info

Product

Resources

About