Raman Spectroscopy in Colorectal Cancer Diagnostics: Comparison of PCA-LDA and PLS-DA Models

Raman spectra of human colorectal tissue samples were employed to diagnose colorectal cancer. High-quality Raman spectra were acquired from normal and cancerous colorectal tissues from 81 patients. Subtle Raman variations, such as for peaks at 1134 cm−1 (protein, C-C/C-N stretching) and 1297 cm−1 (lipid, C-H2 twisting), were observed between normal and cancerous colorectal tissues. The average peak intensity at 1134 and 1297 cm−1 was increased from approximately 235 and 72 in the normal group, respectively, to 315 and 273 in the cancer group. The variations of Raman spectra reflected the changes of cell molecules during canceration. The multivariate statistical methods of principal component analysis-linear discriminant analysis (PCA-LDA) and partial least-squares-discriminant analysis (PLS-DA), together with leave-one-patient-out cross-validation, were employed to build the discrimination model. PCA-LDA was used to evaluate the capability of this approach for classifying colorectal cancer, resulting in a diagnostic accuracy of 79.2%. Further PLS-DA modeling yielded a diagnostic accuracy of 84.3% for colorectal cancer detection. Thus, the PLS-DA model is preferable between the two to discriminate cancerous from normal tissues. Our results demonstrate that Raman spectroscopy can be used with an optimized multivariate data analysis model as a sensitive diagnostic alternative to identify pathological changes in the colon at the molecular level.

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Identification of new spectral signatures associated with dengue virus infected sera

10.31219/osf.io/98dn4 ◽

2017 ◽

Author(s):

Ayyaz Amin ◽

Nimrah Ghouri ◽

Safdar Ali

Keyword(s):

Principal Component Analysis ◽

Raman Spectroscopy ◽

Discriminant Analysis ◽

Dengue Virus ◽

Linear Discriminant Analysis ◽

Raman Spectra ◽

Principal Component ◽

Component Analysis ◽

Optical Diagnosis ◽

Linear Discriminant

In a quest to use Raman spectroscopy as an optical diagnostic tool, we recorded Raman spectra of 32 dengue virus (DENV)-infected and 28 healthy sera samples in the near-infrared spectral range (540 to 1700 cm−1) using laser at 785 nm as the excitation source. We observed clear differences in the Raman spectra of DENV-infected sera as compared with those of healthy individuals. Here, as a result of our study, we report 12 unique Raman bands associated with DENV-infected sera that are not reported earlier. After applying analysis of variance and t-test (p < 0.05) on these 12 bands, six Raman bands at 630 (N-acetylglucosamine), 883 (in-plane bending (ring) of deoxyribose), 1218 (amide III–β conformation from C6H5–C stretching vibrations of tryptophan and phenylalanine), 1273 (amide–III), 1623 (tryptophan) and 1672 cm−1 (ceramide) were found only in the DENV-infected sera. The remaining six Raman bands at 716 (lipids), 780 (Uracil-based ring breathing mode), 828 (ring breathing tyrosine), 840 (α-anomers), 1101 (ν(C–N) of lipids and DNA) and 1150 cm−1(glycogen/carotenoids) were only found in healthy sera. Two types of classification models, principal component analysis and linear discriminant analysis, were employed to develop principal component analysis–linear discriminant analysis model that has provided diagnostic accuracy 96.50%, sensitivity 93.44%, and specificity 100%. This indicates that these 12 Raman bands have the potential to be used as biomarkers for optical diagnosis of DENV infection. This study provides a new insight for future research in the field of optical diagnosis using Raman spectroscopy.

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Diagnosis of Dental Fluorosis Using Micro-Raman Spectroscopy Applying a Principal Component-Linear Discriminant Analysis

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182010572 ◽

2021 ◽

Vol 18 (20) ◽

pp. 10572

Author(s):

Marco Antonio Zepeda-Zepeda ◽

Michel Picquart ◽

María Esther Irigoyen-Camacho ◽

Adriana Marcela Mejía-Gózalez

Keyword(s):

Raman Spectroscopy ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Raman Spectra ◽

Cross Validation ◽

Dental Fluorosis ◽

Principal Component ◽

Control Group ◽

Linear Discriminant ◽

Micro Raman Spectroscopy

Dental fluorosis is an irreversible condition caused by excessive fluoride consumption during tooth formation and is considered a public health problem in several world regions. The objective of this study was to evaluate the capability of micro-Raman spectroscopy to classify teeth of different fluorosis severities, applying principal component analysis and linear discriminant analysis (PCA-LDA), and estimate the model cross-validation accuracy. Forty teeth of different fluorosis severities and a control group were analyzed. Ten spectra were captured from each tooth and a total of 400 micro-Raman spectra were acquired in the wavenumber range of 250 to 1200 cm–1, including the bands corresponding to stretching and bending internal vibrational modes n1, n2, n3, and n4 (PO43–). From the analysis of the micro-Raman spectra an increase in B-type carbonate ion substitution into the phosphate site of the hydroxyapatite as fluorosis severity increases was identified. The PCA-LDA model showed a sensitivity and specificity higher than 94% and 93% for the different fluorosis severity groups, respectively. The cross-validation accuracy was higher than 90%. Micro-Raman spectroscopy combined with PCA-LDA provides an adequate tool for the diagnosis of fluorosis severity. This is a non-invasive and non-destructive technique with promising applications in clinical and epidemiological fields.

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Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection

Journal of Clinical Medicine ◽

10.3390/jcm8091313 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1313 ◽

Cited By ~ 9

Author(s):

Ming-Jer Jeng ◽

Mukta Sharma ◽

Lokesh Sharma ◽

Ting-Yu Chao ◽

Shiang-Fu Huang ◽

...

Keyword(s):

Raman Spectroscopy ◽

Discriminant Analysis ◽

Oral Cancer ◽

Cross Validation ◽

Principal Component ◽

Beta Carotene ◽

Invasive Technique ◽

Protein Amino Acid ◽

Linear Discriminant ◽

Model Studies

Raman spectroscopy (RS) is widely used as a non-invasive technique in screening for the diagnosis of oral cancer. The potential of this optical technique for several biomedical applications has been proved. This work studies the efficacy of RS in detecting oral cancer using sub-site-wise differentiation. A total of 80 samples (44 tumor and 36 normal) were cryopreserved from three different sub-sites: The tongue, the buccal mucosa, and the gingiva of the oral mucosa during surgery. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to classify the samples and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. The normal and tumor tissues were differentiated under the PCA-LDA model with an accuracy of 81.25% (sensitivity: 77.27%, specificity: 86.11%). The PCA-QDA classifier model differentiated these tissues with an accuracy of 87.5% (sensitivity: 90.90%, specificity: 83.33%). The PCA-QDA classifier model outperformed the PCA-LDA-based classifier. The model studies revealed that protein, amino acid, and beta-carotene variations are the main biomolecular difference markers for detecting oral cancer.

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Using Raman Spectroscopy to Investigate the Molecular Level Characteristics of Endometriosis-=SUP=-*-=/SUP=-

Журнал технической физики ◽

10.21883/os.2020.08.49736.202-19 ◽

2020 ◽

Vol 128 (8) ◽

pp. 1223

Author(s):

Sevim Akyuz ◽

Sefa Celik ◽

Abdullah Taner Usta ◽

Aysen E. Ozel ◽

Gozde Yi lmaz -=SUP=-4-=/SUP=- ◽

...

Keyword(s):

Raman Spectroscopy ◽

Statistical Analysis ◽

Raman Spectra ◽

Young Women ◽

Molecular Level ◽

Multivariate Statistical ◽

Tissue Samples ◽

Linear Discriminant ◽

Keywords Endometriosis

Endometriosis is a benign gynecologic disorder. It is particularly common among young women and may make pregnancy difficult. In this study molecular level characterization of endometriosis tissues were performed using Raman spectroscopy in combination with multivariate statistical analysis. Three hundred sixty-six Raman spectra recorded from different points of seventy two tissue samples, taken from the cyst walls of twelve patients were examined. Principle Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) were performed on the Raman data, and the samples were then classified into three groups; severe, moderate, and weak endometriosis. In the severe endometriosis group, the relative band intensities of DNA were increased. Moreover, increase in pyrrole moieties and kynurenine were seen. The results show that endometriosis severity correlates to increase in DNA concentration, and degradation of tryptophan due to increased indoleamine-pyrrole 2, 3-dioxygenase (IDO) activity, and an increase in kynurenine concentration and pyrrole intermediate. It is concluded that Raman spectroscopy is capable of providing a quick diagnosis, ahead of the pathology result being reported. Keywords: Endometriosis; Raman spectra, PCA-LDA analysis.

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Novel Quantitative Analysis Using Optical Imaging (VELscope) and Spectroscopy (Raman) Techniques for Oral Cancer Detection

Cancers ◽

10.3390/cancers12113364 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3364

Author(s):

Ming-Jer Jeng ◽

Mukta Sharma ◽

Lokesh Sharma ◽

Shiang-Fu Huang ◽

Liann-Be Chang ◽

...

Keyword(s):

Raman Spectroscopy ◽

Quantitative Analysis ◽

Discriminant Analysis ◽

Oral Cancer ◽

Raman Spectra ◽

Cross Validation ◽

Error Rates ◽

Control Group ◽

Optical Techniques ◽

Linear Discriminant

In this study, we developed a novel quantitative analysis method to enhance the detection capability for oral cancer screening. We combined two different optical techniques, a light-based detection technique (visually enhanced lesion scope) and a vibrational spectroscopic technique (Raman spectroscopy). Materials and methods: Thirty-five oral cancer patients who went through surgery were enrolled. Thirty-five cancer lesions and thirty-five control samples with normal oral mucosa (adjacent to the cancer lesion) were analyzed. Thirty-five autofluorescence images and 70 Raman spectra were taken from 35 cancer and 35 control group cryopreserved samples. The normalized intensity and heterogeneity of the 70 regions of interest (ROIs) were calculated along with 70 averaged Raman spectra. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to differentiate the cancer and control groups (normal). The classifications rates were validated using two different validation methods, leave-one-out cross-validation (LOOCV) and k-fold cross-validation. Results: The cryopreserved normal and tumor tissues were differentiated using the PCA–LDA and PCA–QDA models. The PCA–LDA of Raman spectroscopy (RS) had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while ROIs on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. The combination of two optical techniques differentiated cancer and normal group with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. Conclusion: In this study, we combined the data of two different optical techniques. Furthermore, PCA–LDA and PCA–QDA quantitative analysis models were used to differentiate tumor and normal groups, creating a complementary pathway for efficient tumor diagnosis. The error rates of RS and VELcope analysis were 17.10% and 10%, respectively, which was reduced to 3% when the two optical techniques were combined.

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Spectrochemical differentiation of meningioma tumours based on attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy

Analytical and Bioanalytical Chemistry ◽

10.1007/s00216-019-02332-w ◽

2019 ◽

Vol 412 (5) ◽

pp. 1077-1086

Author(s):

Taha Lilo ◽

Camilo L. M. Morais ◽

Katherine M. Ashton ◽

Ana Pardilho ◽

Charles Davis ◽

...

Keyword(s):

Fourier Transform ◽

Discriminant Analysis ◽

Ftir Spectroscopy ◽

Principal Component ◽

Fourier Transform Infrared ◽

Total Reflection ◽

Attenuated Total Reflection ◽

Tissue Samples ◽

Linear Discriminant ◽

Grade Ii

AbstractMeningiomas are the commonest types of tumours in the central nervous system (CNS). It is a benign type of tumour divided into three WHO grades (I, II and III) associated with tumour growth rate and likelihood of recurrence, where surgical outcomes and patient treatments are dependent on the meningioma grade and histological subtype. The development of alternative approaches based on attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy could aid meningioma grade determination and its biospectrochemical profiling in an automated fashion. Herein, ATR-FTIR in combination with chemometric techniques is employed to distinguish grade I, grade II and grade I meningiomas that re-occurred. Ninety-nine patients were investigated in this study where their formalin-fixed paraffin-embedded (FFPE) brain tissue samples were analysed by ATR-FTIR spectroscopy. Subsequent classification was performed via principal component analysis plus linear discriminant analysis (PCA-LDA) and partial least squares plus discriminant analysis (PLS-DA). PLS-DA gave the best results where grade I and grade II meningiomas were discriminated with 79% accuracy, 80% sensitivity and 73% specificity, while grade I versus grade I recurrence and grade II versus grade I recurrence were discriminated with 94% accuracy (94% sensitivity and specificity) and 97% accuracy (97% sensitivity and 100% specificity), respectively. Several wavenumbers were identified as possible biomarkers towards tumour differentiation. The majority of these were associated with lipids, protein, DNA/RNA and carbohydrate alterations. These findings demonstrate the potential of ATR-FTIR spectroscopy towards meningioma grade discrimination as a fast, low-cost, non-destructive and sensitive tool for clinical settings.

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Raman spectroscopy to differentiate between fresh tissue samples of glioma and normal brain: a comparison with 5-ALA–induced fluorescence-guided surgery

Journal of Neurosurgery ◽

10.3171/2020.5.jns20376 ◽

2020 ◽

pp. 1-11

Author(s):

Laurent J. Livermore ◽

Martin Isabelle ◽

Ian M. Bell ◽

Oliver Edgar ◽

Natalie L. Voets ◽

...

Keyword(s):

Raman Spectroscopy ◽

Predictive Model ◽

Raman Spectra ◽

Aminolevulinic Acid ◽

Temporal Lobectomy ◽

Normal Brain ◽

Fresh Tissue ◽

Tissue Samples ◽

Linear Discriminant ◽

Resection Cavity

OBJECTIVERaman spectroscopy is a biophotonic tool that can be used to differentiate between different tissue types. It is nondestructive and no sample preparation is required. The aim of this study was to evaluate the ability of Raman spectroscopy to differentiate between glioma and normal brain when using fresh biopsy samples and, in the case of glioblastomas, to compare the performance of Raman spectroscopy to predict the presence or absence of tumor with that of 5-aminolevulinic acid (5-ALA)–induced fluorescence.METHODSA principal component analysis (PCA)–fed linear discriminant analysis (LDA) machine learning predictive model was built using Raman spectra, acquired ex vivo, from fresh tissue samples of 62 patients with glioma and 11 glioma-free brain samples from individuals undergoing temporal lobectomy for epilepsy. This model was then used to classify Raman spectra from fresh biopsies from resection cavities after functional guided, supramaximal glioma resection. In cases of glioblastoma, 5-ALA–induced fluorescence at the resection cavity biopsy site was recorded, and this was compared with the Raman spectral model prediction for the presence of tumor.RESULTSThe PCA-LDA predictive model demonstrated 0.96 sensitivity, 0.99 specificity, and 0.99 accuracy for differentiating tumor from normal brain. Twenty-three resection cavity biopsies were taken from 8 patients after supramaximal resection (6 glioblastomas, 2 oligodendrogliomas). Raman spectroscopy showed 1.00 sensitivity, 1.00 specificity, and 1.00 accuracy for predicting tumor versus normal brain in these samples. In the glioblastoma cases, where 5-ALA–induced fluorescence was used, the performance of Raman spectroscopy was significantly better than the predictive value of 5-ALA–induced fluorescence, which showed 0.07 sensitivity, 1.00 specificity, and 0.24 accuracy (p = 0.0009).CONCLUSIONSRaman spectroscopy can accurately classify fresh tissue samples into tumor versus normal brain and is superior to 5-ALA–induced fluorescence. Raman spectroscopy could become an important intraoperative tool used in conjunction with 5-ALA–induced fluorescence to guide extent of resection in glioma surgery.

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Spontaneous Raman spectroscopy for intracranial tumors diagnostics ex vivo

Biomedical Photonics ◽

10.24931/2413-9432-2020-9-3-4-12 ◽

2020 ◽

Vol 9 (3) ◽

pp. 4-12

Author(s):

I. D. Romanishkin ◽

L. R. Bikmukhametova ◽

T. A. Savelieva ◽

S. A. Goryaynov ◽

A. V. Kosyrkova ◽

...

Keyword(s):

Raman Spectroscopy ◽

Raman Spectra ◽

Ex Vivo ◽

Principal Component ◽

Intraoperative Navigation ◽

Optical Biopsy ◽

Optical Methods ◽

Intracranial Tumors ◽

Tissue Samples ◽

Endogenous Fluorophores

Neurosurgery of intracranial tumors, especially of glial origin, is a non-trivial task due to their infiltrative growth. In recent years, optical methods of intraoperative navigation have been actively used in neurosurgery. However, one of the most widely used approaches based on the selective accumulation of fluorescent contrast medium (5-ALA-induced protoporphyrin IX) by the tumor cannot be applied to a significant number of tumors due to its low accumulation. On the contrary, Raman spectroscopy, which allows analyzing the molecular composition of tissues while preserving all the advantages of the method of fluorescence spectroscopy, does not require the use of an exogenous dye and may become a method of choice when composing a system for intraoperative navigation or optical biopsy. This work presents the first results of using the principal component method to classify Raman spectra of human glioblastoma with intermediate processing of spectra to minimize possible errors from the fluorescence of both endogenous fluorophores and photosensitizers used in fluorescence navigation. As a result, differences were found in the principal component space, corresponding to tissue samples with microcystic components, extensive areas of necrosis, and foci of fresh hemorrhages. It is shown that this approach can serve as the basis for constructing a system for automatic intraoperative tissue classification based on the analysis of Raman spectra.

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Investigation of Preeclampsia Using Raman Spectroscopy

Spectroscopy An International Journal ◽

10.1155/2012/376793 ◽

2012 ◽

Vol 27 ◽

pp. 239-252 ◽

Cited By ~ 11

Author(s):

Günay Başar ◽

Uğur Parlatan ◽

Şeyma Şeninak ◽

Tuba Günel ◽

Ali Benian ◽

...

Keyword(s):

Raman Spectroscopy ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Principal Component ◽

Component Analysis ◽

Control Group ◽

Serum Samples ◽

Linear Discriminant ◽

Significant Difference ◽

And Control

Preeclampsia is associated with increased perinatal morbidity and mortality. There have been numerous efforts to determine preeclampsia biomarkers by means of biophysical, biochemical, and spectroscopic methods. In this study, the preeclampsia and control groups were compared via band component analysis and multivariate analysis using Raman spectroscopy as an alternative technique. The Raman spectra of serum samples were taken from nine preeclamptic, ten healthy pregnant women. The Band component analysis and principal component analysis-linear discriminant analysis were applied to all spectra after a sensitive preprocess step. Using linear discriminant analysis, it was found that Raman spectroscopy has a sensitivity of 78% and a specificity of 90% for the diagnosis of preeclampsia. Via the band component analysis, a significant difference in the spectra of preeclamptic patients was observed when compared to the control group. 19 Raman bands exhibited significant differences in intensity, while 11 of them decreased and eight of them increased. This difference seen in vibrational bands may be used in further studies to clarify the pathophysiology of preeclampsia.

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Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition

Plants ◽

10.3390/plants10081542 ◽

2021 ◽

Vol 10 (8) ◽

pp. 1542

Author(s):

Moisés Roberto Vallejo-Pérez ◽

Jesús Antonio Sosa-Herrera ◽

Hugo Ricardo Navarro-Contreras ◽

Luz Gabriela Álvarez-Preciado ◽

Ángel Gabriel Rodríguez-Vázquez ◽

...

Keyword(s):

Machine Learning ◽

Raman Spectroscopy ◽

Raman Spectra ◽

High Performance ◽

Principal Component ◽

Rrna Gene ◽

Bacterial Canker ◽

Isolation And Identification ◽

Linear Discriminant ◽

Bacterial Canker Of Tomato

Bacterial canker of tomato is caused by Clavibacter michiganensis subsp. michiganensis (Cmm). The disease is highly destructive, because it produces latent asymptomatic infections that favor contagion rates. The present research aims consisted on the implementation of Raman spectroscopy (RS) and machine-learning spectral analysis as a method for the early disease detection. Raman spectra were obtained from infected asymptomatic tomato plants (BCTo) and healthy controls (HTo) with 785 nm excitation laser micro-Raman spectrometer. Spectral data were normalized and processed by principal component analysis (PCA), then the classifiers algorithms multilayer perceptron (PCA + MLP) and linear discriminant analysis (PCA + LDA) were implemented. Bacterial isolation and identification (16S rRNA gene sequencing) were realized of each plant studied. The Raman spectra obtained from tomato leaf samples of HTo and BCTo exhibited peaks associated to cellular components, and the most prominent vibrational bands were assigned to carbohydrates, carotenoids, chlorophyll, and phenolic compounds. Biochemical changes were also detectable in the Raman spectral patterns. Raman bands associated with triterpenoids and flavonoids compounds can be considered as indicators of Cmm infection during the asymptomatic stage. RS is an efficient, fast and reliable technology to differentiate the tomato health condition (BCTo or HTo). The analytical method showed high performance values of sensitivity, specificity and accuracy, among others.

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