Jaya Sundaram

Surface enhanced Raman scattering (SERS) with biopolymer encapsulated silver nanosubstrates for rapid detection of foodborne pathogens

A biopolymer encapsulated with silver nanoparticles was prepared using silver nitrate, polyvinyl alcohol (PVA) solution, and trisodium citrate. It was deposited on a mica sheet to use as SERS substrate. Fresh cultures of Salmonella Typhimurium, Escherichia coli, Staphylococcus aureus and Listeria innocua were washed from chicken rinse and suspended in 10 ml of sterile deionized water. Approximately 5 μl of the bacterial suspensions was placed on the substrate individually and exposed to 785 nm HeNe laser excitation. SERS spectral data were recorded over the Raman shift between 400 and 1800 cm(-1) from 15 different spots on the substrate for each sample; and three replicates were done on each bacteria type. Principal component analysis (PCA) model was developed to classify foodborne bacteria types. PC1 identified 96% of the variation among the given bacteria specimen, and PC2 identified 3%, resulted in a total of 99% classification accuracy. Soft Independent Modeling of Class Analogies (SIMCA) of validation set gave an overall correct classification of 97%. Comparison of the SERS spectra of different types of gram-negative and gram-positive bacteria indicated that all of them have similar cell walls and cell membrane structures. Conversely, major differences were noted around the nucleic acid and amino acid structure information between 1200 cm(-1) and 1700 cm(-1) and at the finger print region between 400 cm(-1) and 700 cm(-1). Silver biopolymer nanoparticle substrate could be a promising SERS tool for pathogen detection. Also this study indicates that SERS technology could be used for reliable and rapid detection and classification of food borne pathogens.

Detection and differentiation of Salmonella serotypes using surface enhanced Raman scattering (SERS) technique

This research was conducted to prove that developed silver biopolymer nanoparticle substrate for surface enhanced Raman scattering (SERS) technique could detect and differentiate three different serotypes of Salmonella. Nanoparticle was prepared by adding 100xa0mg of silver nitrate to a 2xa0% polyvinyl alcohol solution, then adding 1xa0% trisodium citrate to reduce silver nitrate and produce silver encapsulated biopolymer nanoparticles. Then, nanoparticle was deposited on a stainless steel plate and used as SERS substrate. Fresh cultures of Salmonellatyphimurium, Salmonellaenteritidis and Salmonella infantis were washed and suspended in 10xa0mL of sterile deionized water. Approximately 5xa0μl of the bacterial suspensions were placed on the substrate individually and exposed to 785xa0nm laser excitation. SERS spectral data were recorded between 400 and 1,800xa0cm−1. SERS signals were collected from 15 different spots on the substrate for each sample. PCA model was developed to classify Salmonella serotypes. PC1 identified 92xa0% of the variation between the Salmonella serotypes, and PC2 identified 6xa0% and in total 98xa0% between the serotypes. Soft independent modeling of class analogies of validation set gave an average correct classification of 92xa0%. Comparison of the SERS spectra of Salmonella serotypes indicated that both isolates have similar cell walls and cell membrane structures which were identified by spectral regions between 520 and 1,050xa0cm−1. However, major differences were detected in cellular genetic material and proteins between 1,200 and 1,700xa0cm−1. SERS with silver biopolymer nanoparticle substrate could be a promising tool in pathogen detection and it would potentially be used to classify them.

Classification and structural analysis of live and dead Salmonella cells using Fourier transform infrared spectroscopy and principal component analysis.

Fourier transform infrared spectroscopy (FT-IR) was used to detect Salmonella Typhimurium and Salmonella Enteritidis food-borne bacteria and to distinguish between live and dead cells of both serotypes. Bacteria cells were prepared in 10(8) cfu/mL concentration, and 1 mL of each bacterium was loaded individually on the ZnSe attenuated total reflection (ATR) crystal surface (45° ZnSe, 10 bounces, and 48 mm × 5 mm effective area of analysis on the crystal) and scanned for spectral data collection from 4000 to 650 cm(-1) wavenumber. Analysis of spectral signatures of Salmonella isolates was conducted using principal component analysis (PCA). Spectral data were divided into three regions such as 900-1300, 1300-1800, and 3000-2200 cm(-1) based on their spectral signatures. PCA models were developed to differentiate the serotypes and live and dead cells of each serotype. Maximum classification accuracy of 100% was obtained for serotype differentiation as well as for live and dead cells differentiation. Soft independent modeling of class analogy (SIMCA) analysis was carried out on the PCA model and applied to validation sample sets. It gave a predicted classification accuracy of 100% for both the serotypes and its live and dead cells differentiation. The Mahalanobis distance calculated in three different spectral regions showed maximum distance for the 1800-1300 cm(-1) region, followed by the 3000-2200 cm(-1) region, and then by the 1300-900 cm(-1) region. It showed that both of the serotypes have maximum differences in their nucleic acids, DNA/RNA backbone structures, protein, and amide I and amide II bands.

AOTF hyperspectral microscopic imaging for foodborne pathogenic bacteria detection

Hyperspectral microscope imaging (HMI) method which provides both spatial and spectral information can be effective for foodborne pathogen detection. The AOTF-based hyperspectral microscope imaging method can be used to characterize spectral properties of biofilm formed by Salmonella enteritidis as well as Escherichia coli. The intensity of spectral imagery and the pattern of spectral distribution varied with system parameters (integration time and gain) of HMI system. The preliminary results demonstrated determination of optimum parameter values of HMI system and the integration time must be no more than 250 ms for quality image acquisition from biofilm formed by S. enteritidis. Among the contiguous spectral imagery between 450 and 800 nm, the intensity of spectral images at 498, 522, 550 and 594 nm were distinctive for biofilm; whereas, the intensity of spectral images at 546 nm was distinctive for E. coli. For more accurate comparison of intensity from spectral images, a calibration protocol, using neutral density filters and multiple exposures, need to be developed to standardize image acquisition. For the identification or classification of unknown food pathogen samples, ground truth regions-of-interest pixels need to be selected for spectrally pure fingerprints for the Salmonella and E. coli species.

Estimation of mass ratio of the total kernels within a sample of in-shell peanuts using RF impedance method

It would be useful to know the total kernel mass within a given mass of peanuts (mass ratio) while the peanuts are bought or being processed. In this work, the possibility of finding the mass ratio while the peanuts were in their shells was investigated. Capacitance, phase angle, and dissipation factor measurements on a parallel-plate capacitor holding in-shell peanut samples were made at frequencies from 1 to 10 MHz insteps of 1 MHz. A calibration equation was developed by multilinear regression analysis correlating the percentage ratio of the kernel weight with the measured capacitance, dissipation factor, and phase angle values of in-shell peanut samples with known kernel weights. The equation was used to predict the percentage mass ratio in the validation groups. Fitness of calibration model was verified using standard error of calibration, root mean square error of calibration, and leverage and influence plots. The predictability percentage, within 1% and 2% of the visual determination, was calculated by comparing the kernel mass ratio, obtained by the model equation and the reference value obtained by visual determination. Cross-validation gave 96% and 100% predictability, and external validation gave 87% and 98% predictability within 1% and 2% difference, respectively.

Analysis of Moisture Content, Total Oil and Fatty Acid Composition by NIR Reflectance Spectroscopy: A Review

Near Infrared (NIR) Reflectance spectroscopy has established itself as an important analytical technique in the field of food and agriculture. It is quicker and easier to use, and does not require processing the samples with corrosive chemicals such as acids or hydroxides. However, in earlier times, the samples had to be ground into powder form before making any measurements. Thanks to the development of new soft ware packages for use with NIR instruments, NIR techniques could be used in the analysis of intact grains and seeds. While most of the commercial instruments presently available work well with small grain size materials such as wheat and corn, they were found to be unsuitable for large kernel size products such as shelled or in-shell peanuts. In this chapter, principles of NIR Reflectance spectroscopy were reviewed, in particular reference to the water and oil bands. Also presented are some recent applications of NIR for the rapid and nondestructive measurement of moisture and total oil contents in shelled and in-shell peanuts. Applicability, and limitations of NIR reflectance method in the analysis of fatty acid composition of different varieties of peanuts while they are in their shells was also discussed. Ability to rapidly and nondestructively measure the water and total oil content, and analyze the fatty acid composition, will be immensely useful in the grading process of grains and nuts.

Identification and characterization of Salmonella serotypes using DNA spectral characteristics by fourier transform infrared

Analysis of DNA samples of Salmonella serotypes were performed using FT-IR spectrometer by placing directly in contact with a diamond attenuated total reflection (ATR) crystal. Spectra were recorded from 4000 cm-1 to 525 cm-1 wavenumber with the resolution of 4 cm-1 and data spacing of 1.928 cm-1. Collected spectra were subtracted from the background spectra of empty diamond crystal surface. Principal Component Analysis (PCA) was conducted at four different spectral regions to differentiate the different serotypes of Salmonella on the basis of difference in their spectral features of DNA structure macromolecules. PCA was used to show the natural clusters in the data set and to describe the difference between the sample clusters. At the region 1800 - 1200 cm-1, PC1 distinguished 93 % and PC2 distinguished 7 % of the serotypes. Therefore, maximum classification of 100 % in total was obtained at this region. For all the Salmonella serotypes, the frequency between 1000-1150 cm-1 and 1170 -1280 cm-1 had higher loading values which showed their significant contribution in the serotype classification.

Rapid detection of salmonella using SERS with silver nano-substrate

Surface Enhanced Raman Scattering (SERS) can detect the pathogen in rapid and accurate. In SERS weak Raman scattering signals are enhanced by many orders of magnitude. In this study silver metal with biopolymer was used. Silver encapsulated biopolymer polyvinyl alcohol nano-colloid was prepared and deposited on stainless steel plate. This was used as metal substrate for SERS. Salmonella typhimurium a common food pathogen was selected for this study. Salmonella typhimurium bacteria cells were prepared in different concentrations in cfu/mL. Small amount of these cells were loaded on the metal substrate individually, scanned and spectra were recorded using confocal Raman microscope. The cells were exposed to laser diode at 785 nm excitation and object 50x was used to focus the laser light on the sample. Raman shifts were obtained from 400 to 2400 cm-1. Multivariate data analysis was carried to predict the concentration of unknown sample using its spectra. Concentration prediction gave an R2 of 0.93 and standard error of prediction of 0.21. The results showed that it could be possible to find out the Salmonella cells present in a low concentration in food samples using SERS.

Estimation of moisture and oil content of in-shell nuts with a capacitance sensor using discrete wavelet analysis

Moisture and oil contents are important quality factors often measured and monitored in the processing and storage of food products such as corn and peanuts. For estimating these parameters for peanuts nondestructively a parallel-plate capacitance sensor was used in conjunction with an impedance analyzer. Impedance, phase angle and dissipation factor were measured for the parallel-plate system, holding the in-shell peanut samples between its plates, at frequencies ranging between 1MHz and 30 MHz in intervals of 0.5 MHz. The acquired signals were analyzed with discrete wavelet analysis. The signals were decomposed to 6 levels using Daubechies mother wavelet. The decomposition coefficients of the sixth level were passed onto a stepwise variable selection routine to select significant variables. A linear regression was developed using only the significant variables to predict the moisture and oil content of peanut pods (inshell peanuts) from the impedance measurements. The wavelet analysis yielded similar R2 values with fewer variables as compared to multiple linear and partial least squares regressions. The estimated values were found to be in good agreement with the standard values for the samples tested. Ability to estimate the moisture and oil contents in peanuts without shelling them will be of considerable help to the peanut industry.