Raman Spectroscopic Discrimination of Cell Response to Chemical and Physical Inactivation

2007 ◽  
Vol 61 (8) ◽  
pp. 812-823 ◽  
Author(s):  
Maria Fernanda Escoriza ◽  
Jeanne M. Van Briesen ◽  
Shona Stewart ◽  
John Maier
Keyword(s):  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Species Differentiation ◽  
Multivariate Statistical ◽  
Classification Rate ◽  
Dna And Rna ◽  
Spectral Bands ◽  
Viable Cells ◽  
Standard Plate

Raman spectroscopy was applied to study Escherichia coli and Staphylococcus epidermidis cells that were inactivated by different chemicals and stress conditions including starvation and high temperature. E. coli cells exposed to starvation conditions over several days lost viability at the same rate that spectral bands assigned to DNA and RNA bases decreased in intensity. Band intensities correlate with standard plate counts with R2 = 0.99 and R2 = 0.97, respectively. Principal components analysis and discriminant analysis multivariate statistical techniques were used to evaluate the spectral data collected. Significant changes were observed in the spectra of treated cells in comparison with their respective controls (samples without treatment). As a result, there was a significant differentiation between viable and non-viable cells (treated and non-treated cells) in the first and second principal component plots for all the treatments. Discriminant analysis was used along with PCA to estimate a classification rate based on viability status of the cells. Non-viable cells were differentiated from viable cells with classification rates that ranged between 60 and 90% for specific treatments (i.e., EDTA-treated cells versus control cells). The classification rate obtained considering all the treatments (non-viable cells) and controls (viable cells) at the same time for each of the species studied was 86%. The classification rate based on species differentiation when all the spectra (viable and non-viable) were used was 87%. These results suggest that Raman spectroscopy is a powerful tool that can be used to evaluate viability and to study metabolic changes in microorganisms. It is a robust method for bacterial identification even when high spectral variations are introduced.

scholarly journals Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4479 ◽  
Author(s):  
Xavier Cetó ◽  
Núria Serrano ◽  
Miriam Aragó ◽  
Alejandro Gámez ◽  
Miquel Esteban ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Reversed Phase ◽  
Classification Rate ◽  
Phenolic Profile ◽  
Linear Discriminant ◽  
Sample Extraction

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

scholarly journals Classification of Wines from Five Spanish Origin Denominations by Aromatic Compound Analysis

2010 ◽  
Vol 93 (6) ◽  
pp. 1916-1922 ◽  
Author(s):  
Cecilia Sáenz ◽  
Trinidad Cedráenzn ◽  
Susana Cabredo
Keyword(s):  
Discriminant Analysis ◽  
Solid Phase ◽  
Principal Component ◽  
Decanoic Acid ◽  
Multivariate Statistical ◽  
Ethyl Hexanoate ◽  
La Mancha ◽  
Diethyl Succinate ◽  
Ethyl Octanoate

Abstract Wine is a complex matrix in which aroma compounds play an important role in the characterization of the flavor pattern of a given wine. Twelve volatile compounds were determined in 244 samples of Spanish red wines from different denominations of origin: Rioja, Navarra, Valdepeas, La Mancha, and Cariena. The samples were analyzed by GC using headspace solid-phase microextraction. The concentration (mg/mL) intervals obtained were 3-methyl-butyl acetate (3.9 to 116), 3-methyl-1-butanol (93 to 724), ethyl hexanoate (0.8 to 39), 1-hexanol (0.3 to 6.7), ethyl octanoate (1.4 to 41), diethyl succinate (0.2 to 13), 2-phenyl ethyl acetate (0 to 5.3), hexanoic acid (0 to 8.3), geraniol (0 to 3.0), 2-phenylethanol (1.5 to 56), octanoic acid (0 to 20), and decanoic acid (0 to 3.3). Wines were classified by multivariate statistical methods: principal component analysis, and lineal discriminant analysis. A correct differentiation among wines according to their origin was obtained by lineal discriminant analysis.

Heating Effects of Desi Ghee Using Raman Spectroscopy

2018 ◽  
Vol 72 (6) ◽  
pp. 833-846 ◽  
Author(s):  
Naveed Ahmad ◽  
Muhammad Saleem ◽  
Mushtaq Ahmed ◽  
Shaukat Mahmood
Keyword(s):  
Principal Component Analysis ◽  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Principal Component ◽  
Nondestructive Technique ◽  
Molecular Changes ◽  
Spectral Bands ◽  
Heating Effects ◽  
Excitation Laser ◽  
First Time

Raman spectroscopy as a fast and nondestructive technique has been used to investigate heating effects on Desi ghee during frying/cooking of food for the first time. A temperature in the range of 140–180℃ has been investigated within which Desi ghee can be used safely for cooking/frying without much alteration of its natural molecular composition. In addition, heating effects in case of reuse, heating for different times, and cooking inside pressure cookers are also presented. An excitation laser at 785 nm has been used to obtain Raman spectra and the range of 540–1800 cm−1 is found to contain prominent spectral bands. Prominent variations have been observed in the Raman bands of 560–770 cm−1, 790–1160 cm−1, and 1180–1285 cm−1 with the rise in temperature. The spectral variations have been verified using classifier principal component analysis. It has been found that Desi ghee can be reused if heated below 180℃ and it can be heated up to 30 min without any appreciable molecular changes if a controlled heating can be managed.

scholarly journals Diagnosing COVID-19 in human serum using Raman spectroscopy: a preliminary study

2021 ◽  
Author(s):  
Ana Cristina Castro Goulart ◽  
Landulfo Silveira ◽  
Henrique Cunha Carvalho ◽  
Cristiane Bissoli Dorta ◽  
Marcos Tadeu Tavares Pacheco ◽  
...  
Keyword(s):  
Amino Acids ◽  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Blood Serum ◽  
Principal Component ◽  
Label Free ◽  
Rt Pcr ◽  
Difference Spectra ◽  
Biochemical Alterations ◽  
Preliminary Study

This preliminary study proposed the diagnosis of COVID-19 by means of Raman spectroscopy. Samples of blood serum from 10 patients positive and 10 patients negative for COVID-19 by RT-PCR RNA and ELISA tests were analyzed. Raman spectra were obtained with a dispersive Raman spectrometer (830 nm, 350 mW) in triplicate, being submitted to exploratory analysis with principal component analysis (PCA) to identify the spectral differences and discriminant analysis with PCA (PCA-DA) and partial least squares (PLS-DA) for classification of the blood serum spectra into Control and COVID-19. The spectra of both groups positive and negative for COVID-19 showed peaks referred to the basal constitution of the serum (mainly albumin). The difference spectra showed decrease in the peaks referred to proteins and amino acids for the group positive. PCA variables showed more detailed spectral differences related to the biochemical alterations due to the COVID-19 such as increase in lipids, nitrogen compounds (urea and amines/amides) and nucleic acids, and decrease of proteins and amino acids (tryptophan) in the COVID-19 group. The discriminant analysis applied to the principal component loadings (PC 2, PC 4, PC 5 and PC 6) could classify spectra with 87% sensitivity and 100% specificity compared to 95% sensitivity and 100% specificity indicated in the RT-PCR kit leaflet, demonstrating the possibilities of a rapid, label-free and costless technique for diagnosing COVID-19 infection.

Comparative non-destructive classification of partial waxy wheats using near-infrared and Raman spectroscopy

2019 ◽  
Vol 70 (5) ◽  
pp. 437 ◽  
Author(s):  
Dongli Liu ◽  
Yixuan Wu ◽  
Zongmei Gao ◽  
Yong-Huan Yun
Keyword(s):  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Triticum Aestivum L ◽  
Waxy Wheat ◽  
Waxy Proteins ◽  
Sample Set

Waxy proteins play a key role in amylose synthesis in wheat. Eight lines of common wheat (Triticum aestivum L.) carrying mutations in the three homoeologous waxy loci, Wx-A1, Wx-B1 and Wx-D1, have been classified by near-infrared (NIR) and Raman spectroscopy combined with chemometrics. Sample spectra from wheat seeds were collected by using a NIR spectrometer in the wave rage 1600–2400 nm, and then Raman spectrometer in the wave range 700–2000 cm–1. All samples were split randomly into a calibration sample set containing 284 seeds (~35 seeds per line) and a validation sample set containing the remaining 92 seeds. Classification of these samples was undertaken by discriminant analysis combined with principal component analysis (PCA) based on the raw spectra processed by appropriate pre-treatment methods. The classification results by discriminant analysis indicated that the percentage of correctly identified samples by NIR spectroscopy was 84.2% for the calibration set and 84.8% for the validation set, and by Raman spectroscopy 94.4% and 94.6%, respectively. The results demonstrated that Raman spectroscopy combined with chemometrics as a rapid method is superior to NIR spectroscopy in classifying eight partial waxy wheat lines with different waxy proteins.

scholarly journals Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 1313 ◽  
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.

scholarly journals A biospectroscopic analysis of human prostate tissue obtained from different time periods points to a trans-generational alteration in spectral phenotype

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Georgios Theophilou ◽  
Kássio M. G. Lima ◽  
Matthew Briggs ◽  
Pierre L. Martin-Hirsch ◽  
Helen F. Stringfellow ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Principal Component ◽  
Attenuated Total Reflection ◽  
Prostate Tissue ◽  
Projection Algorithm ◽  
Human Prostate ◽  
Phenotypic Change ◽  
Linear Discriminant ◽  
Dna And Rna ◽  
Immunohistochemical Studies

Abstract Prostate cancer is the most commonly-diagnosed malignancy in males worldwide; however, there is marked geographic variation in incidence that may be associated with a Westernised lifestyle. We set out to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) or Raman spectroscopy combined with principal component analysis-linear discriminant analysis or variable selection techniques employing genetic algorithm or successive projection algorithm could be utilised to explore differences between prostate tissues from differing years. In total, 156 prostate tissues from transurethral resection of the prostate procedures for benign prostatic hyperplasia from 1983 to 2013 were collected. These were distributed to form seven categories: 1983–1984 (n = 20), 1988–1989 (n = 25), 1993–1994 (n = 21), 1998–1999 (n = 21), 2003–2004 (n = 21), 2008–2009 (n = 20) and 2012–2013 (n = 21). Ten-μm-thick tissue sections were floated onto Low-E (IR-reflective) slides for ATR-FTIR or Raman spectroscopy. The prostate tissue spectral phenotype altered in a temporal fashion. Examination of the two categories that are at least one generation (30 years) apart indicated highly-significant segregation, especially in spectral regions containing DNA and RNA bands (≈1,000–1,490 cm−1). This may point towards alterations that have occurred through genotoxicity or through epigenetic modifications. Immunohistochemical studies for global DNA methylation supported this. This study points to a trans-generational phenotypic change in human prostate.

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

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Wenjing Liu ◽  
Zhaotian Sun ◽  
Jinyu Chen ◽  
Chuanbo Jing
Keyword(s):  
Colorectal Cancer ◽  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Diagnostic Accuracy ◽  
Raman Spectra ◽  
Principal Component ◽  
Cancer Diagnostics ◽  
Analysis Model ◽  
Tissue Samples ◽  
Linear Discriminant

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.

scholarly journals Geographical origin identification of teas using UV-VIS spectroscopy

2021 ◽  
Vol 265 ◽  
pp. 05013
Author(s):  
Thi Hue Tran ◽  
Quoc Toan Tran ◽  
Thi Thao Ta ◽  
Si Hung Le
Keyword(s):  
Discriminant Analysis ◽  
Principal Component ◽  
Ann Model ◽  
Multivariate Statistical ◽  
Identification Rate ◽  
Tea Quality ◽  
Alternative Approach ◽  
Tea Infusions ◽  
Vis Spectroscopy ◽  
Artificial Neural Network Ann

In this work we proposed a method to verify the differentiating characteristics of simple tea infusions prepared in boiling water alone, which represents the final product as ingested by the consumers. For this purpose, total of 125 tea samples from different geographical provines of Vietnam have been analyzed in UV-Vis spectroscopy associated with multivariate statistical methods. Principal Component Analysis-Discriminant Analysis (PCA-DA), Partial Least Squares Discriminant Analysis (PLS-DA) and Artificial Neural Network (ANN) were compared to construct the identification model. The experimental results showed that the performance of ANN model was better than PCA-DA and PLS-DA model. The optimal ANN model was achieved when neuron numbers were 200, identification rate being 99% in the training set and 84% predition set. The proposed methodology provides a simpler, faster and more affordable classification of simple tea infusions, and can be used as an alternative approach to traditional tea quality evaluation.

Bayesian Discriminant Analysis of Yogurt Products Based on Raman Spectroscopy

2020 ◽  
Vol 103 (5) ◽  
pp. 1435-1439 ◽  
Author(s):  
Zheng-Yong Zhang ◽  
An-Yang Yao ◽  
Tong-Tong Yue ◽  
Min-Qiu Niu ◽  
Hai-Yan Wang
Keyword(s):  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Posterior Probability ◽  
Dairy Product ◽  
Principal Component ◽  
Mean Values ◽  
Discriminant Model ◽  
Important Basis ◽  
Component Feature

Abstract Background The quality discrimination of dairy products is an important basis on which to achieve quality assurance. Objective Taking the discriminant analysis of brand yogurt products as an example, a new rapid discriminant method can be constructed. Method The first three principal components were selected as the pattern vectors of the samples. Then, at random, 75% of the samples were collected as a training set, and their mean values and covariance matrices were calculated to construct a Gauss Bayesian discriminant model. The remaining 25% of samples were employed as a test set, and the pattern vectors of each sample were input into the above model. Next, the posterior probability of each sample in relation to each category could be obtained. Results: The category corresponding to the maximum posterior probability as the brand classification of each sample was defined. Conclusions We constructed a Gauss Bayesian discriminant model to discriminate these different yogurt products after the principal component feature extraction of Raman properties. The results indicate the rationality and wide application prospects of this approach. Highlights A fast dairy product discriminant method based on Gauss Bayesian model and Raman spectroscopy was established.

Sign in / Sign up

Export Citation Format

Share Document