Demonstrating the application of Raman spectroscopy together with chemometric technique for screening of asthma disease

Ullah, Rahat; Khan, Saranjam; Farman, Fizah; Bilal, Muhammad; Krafft, Christoph; Shahzad, Shaheen

doi:10.1364/boe.10.000600

Cited by 30 publications

(13 citation statements)

References 34 publications

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“…The decrease in β-carotene in the diseased serum samples is consistent with previous research [29] . In addition, some weak difference Raman peaks appear at 962, 1127, 1297, 1335, 1447, 1584, and 1653cm −1 can also be found.…”

Section: Raman Spectra Of Serum Samplessupporting

confidence: 92%

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Based on Serum Raman and Fluorescence Spectra to Diagnose Liver Cancer

Yang

et al. 2022

Preprint

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Background: Raman and fluorescence spectra techniques are potential tools for disease diagnosis. In recent years, the application of Raman and fluorescence spectra techniques in biological studies has increased a great deal, and clinical investigations relevant to cancer detection by spectroscopic means have attracted particularly attention from both clinical and non-clinical researchers. Methods: In this article, Raman and fluorescence spectra were employed for the detection of liver cancer and healthy individuals using their serum samples. These serum samples were compared with their spectral features acquired by Raman and fluorescence spectroscopy to initially establish spectral features that can be considered spectral markers of liver cancer diagnosis. Resuits: The intensity differences from characteristic peaks of carotene, protein and lipid associated Raman spectra were clearly observed in liver cancer patient serum samples versus normal human serum. The changes in the serum fluorescence profiles of liver cancer patients were also analyzed. To probe the capacity and contrast of Raman spectroscopy as an analytical implement for the early diagnosis of liver cancer, principal component analysis (PCA) was used to analyze the Raman spectra of controls , liver cancer patients and healthy individuals. Furthermore, the Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to compare the diagnostic performance of Raman spectroscopy for the classification of disease samples and healthy samples.Conclusion: Compare with the existing diagnostic techniques, the Raman spectroscopy technique has an excellent advantage in extremely low sample requirements, ease of use and ideal screening procedures. Thus, Raman spectroscopy has great potential to be developed as a powerful tool for distinguishing between healthy and liver cancer serum samples.

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Section: Raman Spectra Of Serum Samplessupporting

confidence: 92%

“…4 and porphyrin content variation [28] . Two high intensity Raman peaks at 1156 and 1519 cm −1 due to the resonance raman effect belonging to β-carotene are strongly enhanced under excitation at 532 nm [28,29] .…”

Section: Raman Spectra Of Serum Samplesmentioning

confidence: 99%

Based on Serum Raman and Fluorescence Spectra to Diagnose Liver Cancer

Yang

et al. 2022

Preprint

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“…The peak assignments are listed in Table 3 according to the reported literature. [11,14,[21][22][23][24][25][26][27][28] The HGG and LGG group peak intensity changes compared with the normal group are shown in Table 2. The HGG group had lower intensities than the LGG group at all specific peaks except 749 cm −1 .…”

Section: Spectral Analysismentioning

confidence: 99%

“…T A B L E 3 Raman peak attribution in normal and glioma serum [11,14,[21][22][23][24][25][26][27][28] Raman shift (cm − PCA-LDA for glioma diagnosis, an ROC curve was produced, as shown in Figure 3b, and the accuracy of the discriminant analysis results was evaluated by the area under the ROC curve. Therefore, we performed a pairwise comparison of the PCA-LDA diagnostic algorithm results.…”

Section: Principal Component Analysis (Pca) and Linear Discriminantmentioning

confidence: 99%

Label‐free serum detection based on Raman spectroscopy for the diagnosis and classification of glioma

Zhang

Han²,

Sun

et al. 2020

J Raman Spectroscopy

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Glioma is the most prevalent malignant cancer in the central nervous system and can cause significant mortality and morbidity. A rapid, convenient, accurate, and relatively noninvasive diagnostic method for glioma is important and urgently needed. In this study, we investigated the feasibility of using Raman spectroscopy to discriminate patients with glioma from healthy individuals. Serum samples were collected from healthy individuals (n = 86) and patients with glioma [high-grade glioma (HGG) n = 75, low-grade glioma (LGG) n = 60]. All spectra were collected with a 785-nm wavelength laser in the range of 400-1800 cm −1. A total of three spectra were recorded for each sample, and every spectrum was integrated for 12 s and averaged over five accumulations. Principal component analysis and linear discriminant analysis models were combined to classify the Raman spectra of different groups. The correct classification ratios were 95.35, 93.33, and 93.34% for the normal, HGG, and LGG groups, respectively, and the total accuracy was 94.12%. The sensitivity, specificity, and accuracy of differentiating the HGG group from the normal group were 96.00, 96.51, and 96.27%, respectively, with an area under the curve of 0.997; in addition, the sensitivity, specificity, and accuracy of differentiating the LGG group from the normal group were 96.67%, 98.84%, and 97.95%, respectively, with an area under the curve of 0.999. Our study results suggested that the rapid and noninvasive detection method based on principal component analysis and linear discriminant analysis combined with Raman spectroscopy is a highly promising tool for the early diagnosis of glioma. K E Y W O R D S classification, diagnosis, glioma, Raman spectroscopy, serum 1 | BACKGROUND Brain tumors have become one of the top 10 malignant tumors that endanger the health of people as they age and with changes in environmental factors, and the incidence rate of these tumors is still on the rise. [1] Glioma, the most prevalent malignant cancer in the central nervous system, accounts for 40-50% of all intracranial Abbreviations: ANOVA, one-way analysis of variance; AUC, area under curve; HGG, high-grade glioma; LGG, low-grade glioma; PCA-LDA, principal component analysis and linear discriminant analysis; ROC, receive operating characteristic; WHO, World Health Organization.

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“…6 Ullah et al reported the ability of Raman technique for asthma screening by demonstrating the effective classication of serum spectra from asthmatic and healthy individuals using PLS-DA. 7 Raman spectroscopy analysis have been reported as a potential tool in order to evaluate the therapeutic response in patients suffering from breast cancer. The relative changes of biomolecules such as tryptophan, tyrosine, phenylalanine, proteins, carotenoids and lipids in blood plasma have been observed in the Raman spectrum which was good enough to discriminate pre and post treatment conditions.…”

Section: Introductionmentioning

confidence: 99%