Hina Ali

Raman spectroscopy based discrimination of NS1 positive and negative dengue virus infected serum

This study is intended to develop a multivariate statistical model for the prediction of nonstructural protein 1 (NS1) in dengue virus (DENV) infected blood serum in humans. The model has been developed on the basis of partial least squares regression using the Raman spectra of NS1 positive and NS1 negative samples. Human blood sera of 218 subjects is included in this study, of which 95 were NS1 positive and 123 were NS1 negative, which was confirmed with the enzyme linked immunosorbent assay method. For model development, 80 NS1 positive and 98 NS1 negative samples were used, while 40 DENV suspected samples were used for double blind testing of the model. This selection of samples was performed by the code in an automatic manner to avoid biasing. A laser at 785 nm was used as the excitation source to acquire Raman spectra of samples with an integration time of 15 s. The multivariate model yields coefficients of regression at corresponding Raman shifts. These coefficients represent changes in the molecular structures associated with NS1 positive and negative samples. The analysis of the regression coefficients which differentiate NS1 positive and NS1 negative groups shows an increasing trend for phosphatidylinositol, ceramide, and amide-III, and a decreasing trend for thiocyanate in the DENV infected serum. The R-squared value of the model was found to be 0.91, which is clinically acceptable. The blind testing of 40 suspected samples yields an accuracy, sensitivity, and specificity of about 100% each.

Evaluation of Raman spectroscopy in comparison to commonly performed dengue diagnostic tests

Abstract. This study demonstrates the evaluation of Raman spectroscopy as a rapid diagnostic test in comparison to commonly performed tests for an accurate detection of dengue fever in human blood sera. Blood samples of 104 suspected dengue patients collected from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study. Out of 104 samples, 52 (50%) were positive based on immunoglobulin G (IgG), whereas 54 (52%) were positive based on immunoglobulin M (IgM) antibody tests. For the determination of the diagnostic capabilities of Raman spectroscopy, accuracy, sensitivity, specificity and false positive rate have been calculated in comparison to normally performed IgM and IgG captured enzyme-linked immunosorbent assay tests. Accuracy, precision, specificity, and sensitivity for Raman spectroscopy in comparison to IgM were found to be 66%, 70%, 72%, and 61%, whereas based on IgG they were 47%, 46%, 52%, and 43%, respectively.

Identification of cow and buffalo milk based on Beta carotene and vitamin-A concentration using fluorescence spectroscopy

The current study presents the application of fluorescence spectroscopy for the identification of cow and buffalo milk based on β-carotene and vitamin-A which is of prime importance from the nutritional point of view. All samples were collected from healthy animals of different breeds at the time of lactation in the vicinity of Islamabad, Pakistan. Cow and buffalo milk shows differences at fluorescence emission appeared at band position 382 nm, 440 nm, 505 nm and 525 nm both in classical geometry (right angle) setup as well as front face fluorescence setup. In front face fluorescence geometry, synchronous fluorescence emission shows clear differences at 410 nm and 440 nm between the milk samples of both these species. These fluorescence emissions correspond to fats, vitamin-A and β-carotene. Principal Component Analysis (PCA) further highlighted these differences by showing clear separation between the two data sets on the basis of features obtained from their fluorescence emission spectra. These results indicate that classical geometry (fixed excitation wavelength) as well as front face (synchronous fluorescence emission) of cow and buffalo milk nutrients could be used as fingerprint from identification point of view. This same approach can effectively be used for the determination of adulterants in the milk and other dairy products.

Lactate based optical screening of dengue virus infection in human sera using Raman spectroscopy

This study presents the screening of dengue virus (DENV) infection in human blood sera based on lactate concentration using Raman spectroscopy. A total of 70 samples, 50 from confirmed DENV infected patients and 20 from healthy volunteers have been used in this study. Raman spectra of all these samples have been acquired in the spectral range from 600 cm-1 to 1800 cm-1 using a 532 nm laser as an excitation source. Spectra of all these samples have been analyzed for assessing the biochemical changes resulting from infection. In DENV infected samples three prominent Raman peaks have been found at 750, 830 and 1450 cm-1. These peaks are most probably attributed to an elevated level of lactate due to an impaired function of different body organs in dengue infected patients. This has been proven by an addition of lactic acid solution to the healthy serum in a controlled manner. By the addition of lactic acid solution, the intense Raman bands at 1003, 1156 and 1516 cm-1 found in the spectrum of healthy serum got suppressed when the new peaks appeared around 750, 830, 925, 950, 1123, 1333, 1450, 1580 and 1730 cm-1. The current study predicts that lactate may possibly be a potential biomarker for the diagnosis of DENV infection.

Raman Spectroscopy Combined with Principal Component Analysis for Screening Nasopharyngeal Cancer in Human Blood Sera

This study demonstrates the analysis of nasopharyngeal cancer (NPC) in human blood sera using Raman spectroscopy combined with the multivariate analysis technique. Blood samples of confirmed NPC patients and healthy individuals have been used in this study. The Raman spectra from all these samples were recorded using 785 nm laser for excitation. Important Raman bands at 760, 800, 815, 834, 855, 1003, 1220–1275, and 1524 cm−1, have been observed in both normal and NPC samples. A decrease in the lipids content, phenylalanine, and β-carotene, whereas increases in amide III, tyrosine, and tryptophan have been observed in the NPC samples. The two data sets were well separated using principal component analysis (PCA) based on Raman spectral data. The spectral variations between the healthy and cancerous samples have been further highlighted by plotting loading vectors PC1 and PC2, which shows only those spectral regions where the differences are obvious.

Analysis of hepatitis B virus infection in blood sera using Raman spectroscopy and machine learning

This study presents the analysis of hepatitis B virus (HBV) infection in human blood serum using Raman spectroscopy combined with pattern recognition technique. In total, 119 confirmed samples of HBV infected sera, collected from Pakistan Atomic Energy Commission (PAEC) general hospital have been used for the current analysis. The differences between normal and HBV infected samples have been evaluated using support vector machine (SVM) algorithm. SVM model with two different kernels i.e. polynomial function and Gaussian radial basis function (RBF) have been investigated for the classification of normal blood sera from HBV infected sera based on Raman spectral features. Furthermore, the performance of the model with each kernel function has also been analyzed with two different implementations of optimization problem i.e. Quadratic programming and least square. 5-fold cross validation method has been used for the evaluation of the model. In the current study, best classification performance has been achieved for polynomial kernel of order-2. A diagnostic accuracy of about 98% with the precision of 97%, sensitivity of 100% and specificity of 95% has been achieved under these conditions.

Raman spectroscopy combined with a support vector machine for differentiating between feeding male and female infants mother’s milk

This study presents differentiation in milk samples of mothers feeding male and female infants using Raman spectroscopy combined with a support vector machine (SVM). Major differences have been observed in the Raman spectra of both types of milk based on their chemical compositions. Overall, it has been found that milk samples of mothers having a female infant are richer in fatty acids, phospholipids, and tryptophan. In contrast, milk samples of mothers having a male infant contain more carotenoids and saccharides. Principal component analysis and SVM further highlighted the differences between the two groups on the basis of differentiating features obtained from their Raman spectra. The SVM model with two different kernels, i.e. polynomial kernel function (order-2) and Gaussian radial basis function (RBF sigma-2), are used for gender based milk differentiations. The performance of the proposed model in terms of accuracy, precision, sensitivity, and specificity using the polynomial kernel function of order-2 have been found to be 86%, 88%, 85% and 88%, respectively.

Validation of Fluorescence Spectroscopy to Detect Adulteration of Edible Oil in Extra Virgin Olive Oil (EVOO) by Applying Chemometrics

Due to high price and nutritional values of extra virgin olive oil (EVOO), it is vulnerable to adulteration internationally. Refined oil or other vegetable oils are commonly blended with EVOO and to unmask such fraud, quick, and reliable technique needs to be standardized and developed. Therefore, in this study, adulteration of edible oil (sunflower oil) is made with pure EVOO and analyzed using fluorescence spectroscopy (excitation wavelength at 350 nm) in conjunction with principal component analysis (PCA) and partial least squares (PLS) regression. Fluorescent spectra contain fingerprints of chlorophyll and carotenoids that are characteristics of EVOO and differentiated it from sunflower oil. A broad intense hump corresponding to conjugated hydroperoxides is seen in sunflower oil in the range of 441–489 nm with the maximum at 469 nm whereas pure EVOO has low intensity doublet peaks in this region at 441 nm and 469 nm. Visible changes in spectra are observed in adulterated EVOO by increasing the concentration of sunflower oil, with an increase in doublet peak and correspondingly decrease in chlorophyll peak intensity. Principal component analysis showed a distinct clustering of adulterated samples of different concentrations. Subsequently, the PLS regression model was best fitted over the complete data set on the basis of coefficient of determination (R2), standard error of calibration (SEC), and standard error of prediction (SEP) of values 0.99, 0.617, and 0.623 respectively. In addition to adulterant, test samples and imported commercial brands of EVOO were also used for prediction and validation of the models. Fluorescence spectroscopy combined with chemometrics showed its robustness to identify and quantify the specified adulterant in pure EVOO.