Raman spectroscopy combined with multiple algorithms for analysis and rapid screening of chronic renal failure

Chen, Cheng; Yang, Li; Li, Hongyi; Chen, Fangfang; Chen, Chen; Gao, Rui; Lv, XY; Tang, Jun

doi:10.1016/j.pdpdt.2020.101792

Cited by 39 publications

(19 citation statements)

References 38 publications

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“…It has strong advantages in solving the problems of fewer training samples and more categories of sample [ 35 ]. In addition, penalty coefficient C and kernel function g play a key role in finding the optimal decision plane [ 36 , 37 ]. In the experiment, particle swarm optimization (PSO), genetic algorithm (GA) and grid search (GS) algorithm were used to optimize the value of parameters C and g. The models used 10-fold cross-validation and Gaussian kernel was selected as the kernel function.…”

Section: Results and Comparisonmentioning

confidence: 99%

Identification of multiple raisins by feature fusion combined with NIR spectroscopy

Zhang

Yan

et al. 2022

PLoS ONE

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Varieties of raisins are diverse, and different varieties have different nutritional properties and commercial value. In this paper, we propose a method to identify different varieties of raisins by combining near-infrared (NIR) spectroscopy and machine learning algorithms. The direct averaging of the spectra taken for each sample may reduce the experimental data and affect the extraction of spectral features, thus limiting the classification results, due to the different substances of grape skins and flesh. Therefore, this experiment proposes a method to fuse the spectral features of pulp and peel. In this experiment, principal component analysis (PCA) was used to extract baseline corrected features, and linear models of k-nearest neighbor (KNN) and linear discriminant analysis (LDA) and nonlinear models of back propagation (BP), support vector machine with genetic algorithm (GA-SVM), grid search-support vector machine (GS-SVM) and particle swarm optimization with support vector machine (PSO- SVM) coupling were used to classify. This paper compared the results of four experiments using only skin spectrum, only flesh spectrum, average spectrum of skin and flesh, and their spectral feature fusion. The experimental results showed that the accuracy and Macro-F1 score after spectral feature fusion were higher than the other three experiments, and GS-SVM had the highest accuracy and Macro-F1 score of 94.44%. The results showed that feature fusion can improve the performance of both linear and nonlinear models. This may provide a new strategy for acquiring spectral data and improving model performance in the future. The code is available at https://github.com/L-ain/Source.

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Section: Results and Comparisonmentioning

confidence: 99%

Identification of multiple raisins by feature fusion combined with NIR spectroscopy

Zhang

Yan

et al. 2022

PLoS ONE

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“…In contrast, RS is a rapid and non-invasive method that can be used to examine expired and non-expired drugs. This is a significant problem for the medical world [72,74].…”

Section: Therapeutic Drug Monitoring (Tdm)mentioning

confidence: 99%

“…It has been discovered that RS can be used effectively in chronic renal failure (CRF) to differentiate patients with this disease from healthy patients. The group of Chen et al [74] conducted a study on 47 samples from patients with CRF and 54 samples from control subjects. There is a prospect that the application used, which can be effectively utilised as a rapid diagnostic method for CRF.…”

Section: Determination Of Metabolitesmentioning

confidence: 99%

Raman Spectroscopy and Its Modifications Applied to Biological and Medical Research

Allakhverdiev

Khabatova

Kossalbayev

et al. 2022

Cells

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Nowadays, there is an interest in biomedical and nanobiotechnological studies, such as studies on carotenoids as antioxidants and studies on molecular markers for cardiovascular, endocrine, and oncological diseases. Moreover, interest in industrial production of microalgal biomass for biofuels and bioproducts has stimulated studies on microalgal physiology and mechanisms of synthesis and accumulation of valuable biomolecules in algal cells. Biomolecules such as neutral lipids and carotenoids are being actively explored by the biotechnology community. Raman spectroscopy (RS) has become an important tool for researchers to understand biological processes at the cellular level in medicine and biotechnology. This review provides a brief analysis of existing studies on the application of RS for investigation of biological, medical, analytical, photosynthetic, and algal research, particularly to understand how the technique can be used for lipids, carotenoids, and cellular research. First, the review article shows the main applications of the modified Raman spectroscopy in medicine and biotechnology. Research works in the field of medicine and biotechnology are analysed in terms of showing the common connections of some studies as caretenoids and lipids. Second, this article summarises some of the recent advances in Raman microspectroscopy applications in areas related to microalgal detection. Strategies based on Raman spectroscopy provide potential for biochemical-composition analysis and imaging of living microalgal cells, in situ and in vivo. Finally, current approaches used in the papers presented show the advantages, perspectives, and other essential specifics of the method applied to plants and other species/objects.

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“…The extreme learning machine (ELM) is a new extended network used to train a single hidden layer feedforward neural network. Compared with other existing neural network learning methods, such as backpropagation networks (BP) [37], and typical machine learning algorithms, such as support vector machines (SVM) [10,38], the ELM performs the random initialization of input weights, and only by solving the equation can the advantage of the primary weight be determined. Therefore, the ELM algorithm has fast and powerful learning capabilities [39][40][41].…”

Section: Elm Classification Modelmentioning

confidence: 99%

Feature Fusion Combined With Raman Spectroscopy for Early Diagnosis of Cervical Cancer

Huiting

Chen

et al. 2021

IEEE Photonics J.

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Cervical cancer is a serious threat to women's health due to malignant tumours, and early detection can greatly reduce mortality. In this paper, cervical tissue was used as the research object, and Raman spectroscopy analysis of cervical inflammation and precancerous tissues was used to detect cervical cancer. This provides a clinical basis for the use of Raman spectroscopy in analysis of cervical precancerous lesions. In this study, the actual Raman spectrum signal of precancerous cervical tissue was collected, and the PLS and Relief methods were used to extract the signal characteristics of the spectrum. Then, we established and compared KNN and ELM classification models and finally achieved the early diagnosis of cervical cancer. This experiment designed a novel feature fusion method in feature extraction, and we used the first and second derivative features that reflect more peak details of the original spectrum for fusion.The accuracy rate of KNN without feature fusion is 88.17%, and the accuracy rate after fusion is 93.55%. The accuracy rate of ELM without feature fusion is 90.81%, and the accuracy rate after fusion is 93.51%. The results show that the accuracy of feature fusion has been improved to a certain extent, and this method is expected to be used as a new method of spectral data fusion.

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Raman spectroscopy combined with multiple algorithms for analysis and rapid screening of chronic renal failure

Cited by 39 publications

References 38 publications

Identification of multiple raisins by feature fusion combined with NIR spectroscopy

Identification of multiple raisins by feature fusion combined with NIR spectroscopy

Raman Spectroscopy and Its Modifications Applied to Biological and Medical Research

Feature Fusion Combined With Raman Spectroscopy for Early Diagnosis of Cervical Cancer

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