A. R. M. Radzol

Classification of salivary based NS1 from Raman Spectroscopy with support vector machine

Non-Structural Protein 1 (NS1) antigen has been recognized as a biomarker for diagnosis of flavivirus viral infections at early stage. Surface Enhanced Raman Spectroscopy (SERS) is an optical technique capable of detecting up to a single molecule. Our previous work has established the Raman fingerprint of NS1 with gold as substrate. Our current study aims to classify NS1 infected saliva samples from healthy samples, a first ever attempt. Saliva samples from healthy subjects, NS1 protein and NS1-saliva mixture samples were analyzed using SERS. The SERS spectra were then pre-processed prior to classification with support vector machine (SVM). NS1-saliva mixture at concentration of 10ppm, 50ppm and 100ppm were examined. Performance of SVM classifier with linear, polynomial and radial basis function (RBF) kernels were compared, in term of accuracy, sensitivity, and specificity. From the results, it can be concluded that SVM classifier is able to classify the samples into NS1 infected samples and normal saliva samples. Of the three kernels, performance in using polynomial and RBF kernel is found surpassing the linear kernel. The best performance is attained with RBF kernel with accuracy of [97.1% 93.4% 81.5%] for 100ppm, 50ppm and 10ppm respectively.

Raman molecular fingerprint of non-structural protein 1 in phosphate buffer saline with gold substrate

SERS is a form of Raman spectroscopy that is enhanced with nano-sensing chip as substrate. It can yield distinct biochemical fingerprint for molecule of solids, liquids and gases. Vice versa, it can be used to identify unknown molecule. It has further advantage of being non-invasive, non-contact and cheap, as compared to other existing laboratory based techniques. NS1 has been clinically accepted as an alternative biomarker to IgM in diagnosing viral diseases carried by virus of flaviviridae. Its presence in the blood serum at febrile stage of the flavivirus infection has been proven. Being an antigen, it allows early detection that can help to reduce the mortality rate. This paper proposes SERS as a technique for detection of NS1 from its scattering spectrum. Contribution from our work so far has never been reported. From our experiments, it is found that NS1 protein is Raman active. Its spectrum exhibits five prominent peaks at Raman shift of 548, 1012, 1180, 1540 and 1650cm-1. Of these, peak at 1012cm-1 scales the highest intensity. It is singled out as the peak to fingerprint the NS1 protein. This is because its presence is verified by the ring breathing vibration of the benzene ring structure side chain molecule. The characteristic peak is found to vary in proportion to concentration. It is found that for a 99% change in concentration, a 96.7% change in intensity is incurred. This yields a high sensitivity of about one a.u. per ppm. Further investigation from the characterization graph shows a correlation coefficient of 0.9978 and a standard error estimation of 0.02782, which strongly suggests a linear relationship between the concentration and characteristic peak intensity of NS1. Our finding produces favorable evidence to the use of SERS technique for detection of NS1 protein for early detection of flavivirus infected diseases with gold substrate.

Optimization of Savitzky-Golay smoothing filter for salivary surface enhanced Raman spectra of non structural protein 1

Raman spectroscopy is a vibration based spectroscopic technique for identifying chemical constituent. The Raman signal can be enhanced by binding with metal nanostructures, resulting in so-called surface-enhanced Raman scattering (SERS). Non-structural protein 1 (NS1), a biomarker for flavivirus origin diseases, is found to present in saliva in low concentration. SERS is promising as an early detection method for NS1 in saliva. However, unwanted features are found to embed in the SERS spectrum, making the characteristic features undetected with visual or automated means. In particular, at low concentration of NS1, the intensity at characteristic peak (Imax) and its signal-to-noise ratio (SNR) is so low that the noisy features actually bury the fingerprint characteristics. In addition to background subtraction and baseline removal, spectrum smoothing using suitable filter is found to improve the spectra SNR. In this study, NS1 dilution dataset from the UiTM-NMRR-12868-NS1-DENV spectral database is used. It is found that SG smoothing filter optimized with a span of 13 and a polynomial degree of order 3 produces the most intense smoothing effect while preserving most of the characteristics of the original spectral waveform, with SNR of 3.76, %RMSE of 33.49% and maximum intensity (Imax) of 98%.

Nonstructural protein 1 characteristic peak from NS1-saliva mixture with Surface-Enhanced Raman spectroscopy

Surface Enhanced Raman spectroscopy (SERS) is an enhanced technique of Raman spectroscopy, which amplifies the intensity of Raman scattering to a practical range with adsorption of analyte onto nano-size plasmonic material such as gold, silver or copper. This feature of SERS has given it a niche in tracing molecular structure, especially useful for marking diseases specific biomarker. NS1 protein has been clinically accepted as an alternative biomarker for diseases caused by flavivirus. Detection of Nonstructural Protein 1 (NS1) will allow early diagnosis of the diseases. Its presence in the blood serum has been reported as early as first day of infection. With gold substrate, our work here intends to explore if SERS is suitable to detect NS1 from saliva, with saliva becoming the most favored alternative to blood as diagnostic fluid due to its advantages in sample collection. Our experimental results find both gold coated slide (GS) and saliva being Raman inactive, but the molecular fingerprint of NS1 protein at Raman shift 1012cm-1, which has never been reported before. The distinct peak is discovered to be attributed by breathing vibration of the benzene ring structure of NS1 side chain molecule. The characteristic peak is also found to vary in direct proportion to concentration of the NS1-saliva mixture, with a correlation coefficient of +0.96118 and a standard error estimation of 0.11382.

Principal component analysis for detection of NS1 molecules from Raman spectra of saliva

NS1 is an early biomarker for detection of flavivirus related diseases such as Japanese Encephalitis, Murray Valley Encephalitis, Tick-borne Encephalitis, West Nile Encephalitis, Dengue Fever and Yellow Fever. At present, it is detected in the infected blood serum through ELISA and immune-chromatographic lateral flow test. As a preliminary study, we are using PCA to extract NS1 feature from SERS spectra of NS1 adulterated saliva. NS1 characteristic peak at about 1000cm-1 is extracted by the most significant principal component, PC1. Using PCA adhoc stopping rules, data dimension is significantly reduced to more than 90% without losing important features from the original data. Furthermore, PCA score plots of the dataset is also showing clear separation between NS1 adulterated saliva and healthy saliva. This encouraging finding is suggesting the possibility to develop a SERS based automatic classification algorithm for detection of NS1 in saliva. Being a salivary based technique, this will lead to a novel, rapid, non-invasive and non-infectious detection method, dispense of problem arising from blood sampling.

Surface-Enhanced Raman spectral analysis of substrates for salivary based disease detection

Raman Spectroscopy is a mean to study the molecular structure property of solid, liquid and gases from its scattering spectrum. It offers a detailed biochemical fingerprint for identifying the unknown molecule. Since the amount of inelastic scattering is infinitesimally small relative to that of the elastic scattering, the Raman signal emitted is extremely weak, rendering limited applications. Surface Enhanced Raman Spectroscopy augments the detection sensitivity of Raman spectroscopy with nano-sensing chip as substrate, enabling amplification of Raman signal by a factor of 104 to 109. This innovation is made possible with the integration of the latest in optical sensing technology and nanotechnology. With this improvement, SERS has shown its niche in tracing molecular structure, especially in marking abnormal biological molecules such as cancer, conjunctivitis, AIDS. The quality of spectrum is highly dependent on the substrate type and laser wavelength. Our work here examines the spectral characteristics of 3 candidate substrate types and wavelength for application in salivary based disease detection. The spectrum intensity enhancement, oxidation and fluorescence effects are also investigated. This study finds that adoption of gold or Klarite® as substrate and 785nm as wavelength are appropriate for salivary based disease detection using SERS technique. The latter will be preferred if cost effectiveness is important.

Raman spectra of drop coating deposition Raman for melamine solution

Melamine is an organic compound that is often combined with formaldehyde (chemical compound) to produce melamine resin, a synthetic polymer that is fire resistant and heat tolerant. Melamine can be easily molded while warm but will set into a fixed form, which makes it suitable for certain industrial applications. This compound is considered safe for its normal uses, but food products that are contaminated with it can be unsafe for consumption. Routine consumption can cause acute renal failure, kidneys damage and death. Focusing on the milk with melamine in the market, a rapid and easy method is needed to detect melamine from the milk. Now with Surface Enhanced Raman Spectroscopy (SERS), which is capable of detection up to a single molecule, this problem can be surmounted. This paper describes the characterization of dried melamine sample prepared via drop coating deposition Raman (DCDR) method for Raman analysis. Two chosen substrates were used for sample deposition. The samples were analyzed using Dispersive Raman Spectrometer to observe the Raman spectra for different concentration of diluted melamine solution. Using statistical analysis, our results demonstrate that the melamine peak intensity is linearly proportional to the melamine solution concentration. In addition, the SERS detection method proves to be a fast, highly sensitive and quantitative detection for melamine.

Automatic Non-Structural Protien 1 recognition based on LDA classifier

This paper discusses the possibilities of Non Structural Protein 1 (NS1) fingerprint can be classified from Raman spectra of saliva using Linear Discriminant Analysis (LDA). LDA is a supervised statistical method that can be used to classify two or more groups of data. In this research, Raman spectra of saliva and NS1-saliva mixture are obtained using Surface Enhanced Raman Spectroscopy (SERS) technique where gold coated slides (GS) are used as substrates. The NS1-saliva mixtures are prepared into different concentration of 10ppm, 50ppm and 100ppm. After applying simple LDA algorithm, the transformed data of saliva and NS1-saliva mixture are overlapping. However the overlapped data is reduced as the concentration of the mixture increase. It indicate that the algorithm is more suitable for samples with higher amount of NS1. Integration of LDA with other algorithm need to be considered for better classification.

Model Selection for PCA-Linear SVM for automated detection of NS1 molecule from Raman spectra of salivary mixture.

Of recent, detection of Non-structural Protein 1 (NS1) in saliva has become appealing, as it may lead to a noninvasive detection method for NS1-related diseases at the febrile phase, before complication developed. NS1 is found to have a molecular fingerprint with the use of SERS technique. Our work here intends to determine an optimum PCA-Linear SVM model for automated detection of NS1 molecules from Raman spectra of NS1 adulterated saliva. Raman spectra of normal saliva (n=64) and saliva adulterated with low concentration NS1 (n=64) are used. Since Raman features extracted for each spectrum numbered at 1801, ranking and selection of features in order of their contribution is important prior to classification, for efficient computation. Hence, PCA for feature selection and SVM with linear kernel for classification are integrated. It is found that the Cattels Scree test is the best stopping criteria for PCA with a selection of 5 PCs and a box constraint of 20 is optimum for Linear SVM. Together they achieve a classification performance, [accuracy sensitivity, specificity], of [98.71% 98.97% 98.44%].

Melamine in milk with surface enhanced Raman spectroscopy

Melamine is rich in nitrogen and easily confound with natural protein present in dairy products. It is added to diluted milk to increase the protein concentration. However, interaction of melamine and cyanuric acid in our bladder triggers the formation of kidney stones, which results in acute kidney failure. The significance of this is accentuated in the case of infant formula, which is the sole source of food for infants, with several feedings a day. Raman spectroscopy is a photonic method capable of identifying unknown molecule through a biochemical fingerprint, from its scattering spectrum. It is simple, rapid, portable and pre-treatment is unnecessary. Our work here explores detection for traces of melamine in infant formula using Raman spectroscopy with gold substrate. A Raman spectra unique of melamine is first established. The characteristic peak at 676cm− 1 signatures the Raman fingerprint for melamine. Then mixtures of milk and melamine, in liquid and powder form, are examined. The characteristic peak of melamine is found in the spectra of all the mixtures adulterated with melamine. It can be concluded that Raman spectroscopy with gold substrate is capable to detect for traces of melamine in infant formula, in different forms, to ensure a safe complementary and substitute for breast milk.