Surface Enhanced Raman Spectroscopy (SERS) – A New Solution for Food Quality and Safety Analysis

Yanqi Qu, Siyue Gao, Lili He*, Department of Food Science, University of Massachusetts, Amherst, MA
Winner of the 2018 Lifesciences Award in Food & Beverage Safety 2018

Introduction

In last decades, surface-enhanced Raman spectroscopy (SERS) has been extensively explored and developed as an emerging technology to detect various chemical and biological analytes in environmental, agricultural, food, and medical applications1-7. SERS combined Raman spectroscopy and nanotechnology. Raman spectroscopy measures the molecular vibration and generates a signature profile of a chemical compound. Raman scattering is relatively weak, however, after placing the analyte in the close proximity of certain noble metallic nanostructure, the Raman scattering of the analyte can be dramatically and specifically enhanced, as shown in Figure 1 (B). Compared to the standard methods for food analysis (e.g., HPLC, GC-MS, etc.), SERS showed advantages in simpler sample preparation, faster detection, easier operation, less instrumental complexity, and relative less expensive cost. Additionally, a handled or portable Raman instrument allows SERS to be an on-site solution for the field test.

Figure 1: Illustration of surface-enhanced Raman scattering mechanism, (A) Raman scattering, (B) SERS.

Illustration of surface-enhanced Raman scattering mechanism, (A) Raman scattering, (B) SERS.

SERS substrates

Colloidal nanoparticles

The most traditional SERS active substrates are colloidal nanoparticles, (i.e., silver nanoparticles and gold nanoparticles). They are commercially available and can also be easily fabricated in a lab. For sample preparation, we normally mix a few microliters of aqueous sample with a few microliters of nanoparticles and let the mixture air-dried on solid surfaces such as a gold coated glass slide or an aluminum foil covered glass slide for SERS measurement. The drying force causes the nanoparticles aggregate to form a “coffee ring” at the edge and most SERS signals are acquired from the coffee ring area. However, “coffee ring” is not always consistent and when probing the positions away from the coffee ring, the signal decreased dramatically. For example, in Figure 2 (D)9, coffee ring area showed the highest SERS signals along with a huge variation (i.e., relative standard deviation=61.12%).

Silver nanoparticles mirror substrate

To improve uniformity of the substrate, we developed a solvent mediated silver nanoparticle mirror substrate, which is fabricated using a mediating solvent (i.e., a mixture of polar solvent, and non-polar solvent) to self-assembly the nanoparticles. With the partial removal of surface charge, nanoparticles form a mirror-like structure in a moving interface formed when the mediating solvent encounter the water (as shown in Figure 3). The test with a model pesticide, fonofos, shows a much better signal consistency across the substrate (i.e., relative standard deviation = 6.56%) as compared to the coffee ring. Furthermore, due to the uniform arrangement of nanoparticles (Figure 3), the linear regression analysis also point out an improved quantitative ability of mirror substrate from the aggregate colloidal nanoparticles.

Figure 2: (A) “Coffee ring” formed by silver nanoparticles. (B) Scanning electron microscopic, (C) 5 ppm pesticides mapping of coffee ring area of the aggregated colloidal silver nanoparticles.

“Coffee ring”

 

Figure 3: Illustration of fabrication of silver nanoparticles mirror substrate, microscopic images, and SERS mapping of mirror.

Illustration of fabrication of silver nanoparticles mirror substrate, microscopic images, and SERS mapping of mirror

Filter membrane substrate

Our lab also developed a filter membrane based substrate to monitor the trace of contaminates and bacteria in food matrix4,10. In this substrate, nanoparticles or pre-formed nanoclusters are filtrated to a Millipore membrane with a 0.22 µm pore size. The filter membrane functions as both a concentrating device and a separation mechanism that eliminates molecules smaller than the pore size to eliminate the interference. It can provide a fast detection of low concentrations of targets without pre-enrichment.

Application of SERS in food analysis

Pesticides detection

Due to the special molecular structure of pesticides, SERS is known for its sensitive and fast detection of many kinds of pesticides in different food or agricultural matrix with a limit of detection lower than the regulation requirement8. In my project, I used the mirror to monitor the trace of a pesticide, fonofos, in beverages like green tea and apple juice. In both matrixes, the presence of fonofos was successfully detected at a very low concentration (i.e., 0.5 ppm) with a nearly perfect recovery percentage (i.e., 99%-106%), which illustrated the reliability of mirror and SERS in pesticides detection9. Other than the mirror, our lab also developed several methods to use SERS to monitor the distribution and penetration of pesticides on plants11, also the efficacy of pesticides removal from fruits12.

Colorants and adulterants analysis5

Food colorant is another popular target due to the increasing focus of natural or artificial colorants. We developed a SERS database including a wide variety of artificial and natural food coloring agents currently approved or banned in the United States. All colorants showed discriminative SERS signals and can be easily differentiated. Further tests confirmed the capability of SERS in quantifying adulteration with chemically and visually similar colorants with a concentration as low as 1 ppm within 10 min. Additionally, both artificial and natural colorants were successfully identified in commercially available food products. This database5 indicates the great potential of SERS in fast differentiation and authentication of food colors.

Wine analysis

We also explored the application of the mirror substrate and SERS in the food quality analysis. Two facile methods were developed to profile the quality-related chemicals in red wines which only took 10 minutes including the analysis. The first approach (Figure 4 (A)) included the directly incubation of mirror with red wine samples. The signal obtained through this approach was mainly contributed from adenine (i.e., a DNA fraction) which was found to be correlated with the condensed tannins. This was confirmed by the standard method (i.e., Bate Smith) as shown in Figure 4 (B). The second approach (Figure 4 (C)) was based on a solvent extraction which gave more characteristic information that is beneficial for wine chemical profiling and discrimination. The solvent was applied not only as an extracting solvent to obtain chemicals from red wines but also as a mediating solvent to form the mirror substrate. Signature peaks in wine extract spectra were found to match condensed tannin, resveratrol, anthocyanins, gallic acid, and catechin, and the unique chemical information creates a specific bar code for each tested red wine (Figure 4 (D)). Two SERS approaches to obtain rich spectral information for the red wine provide a new solution for wine discrimination and quality assessment. The combination between the SERS bar code and the data science and machine learning could establish a new wine quality screening system to quickly differentiate and authenticate red wines from their origins and vine species.

Figure 4: (A) Direct analysis approach (B) Condensed tanning content measured by SERS method and Bate Smith method (C) Extraction approach (D) Peak assignment of red wines and the bar code for each red wine.

(A) Direct analysis approach (B) Condensed tanning content measured by SERS method and Bate Smith method (C) Extraction approach (D) Peak assignment of red wines and the bar code for each red wine.

Bacteria detection

In addition to chemical detection, our lab also investigated the potential of SERS in microbial analysis10,13. For the application of Millipore filter membrane substrate, we developed a rapid bacteria screening method using SERS non-specifically and specifically. Based on the unique signal from an indicator chemical, 4-mercaptophenylboronic acid (4-mpba) or an aptamer specifically designed for the target bacteria, which can specifically bind to the surface of bacteria and give off consistent SERS signal. As shown in Figure 5 (A), the filter membrane functions as both a concentrating device and a separation mechanism that eliminates molecules smaller than the pore size. As shown in Figure 5, with SERS mapping, the indicator chemical gives characteristic signal, indicating the presence of bacteria (displayed as red dots), whereas no SERS signal indicating the absence of bacteria (displayed as blue dots). The developed method was applied to detect Escherichia coli, Salmonella enterica, and Listeria monocytogenes on a filter membrane non-selectively and selectively in 80 min. The quantitative ability of this technique was also tested for a set of bacteria with different concentrations. Filter membranes along with SERS can be a very fast solution to determine the presence of bacteria in water and beverage matrix compared to the standard plate count method.

Figure 5: (A) Schematic illustration of aptamer-based filtration assay for the detection of bacteria using aptamer. (B) SERS mappings of Salmonella detected by aptamers. Results displayed in positive/negative pattern, with red spots indicating presence of Salmonella bacterial cells.

Conclusion

SERS as an emerging analytical technique, has shown its superior capacity of rapid and sensitive analysis of chemical and microbial targets in food with facile sample preparation, inexpensive cost, simple operation, and reliable quantification. Advanced with nanotechnology, more and more sensitive and reliable SERS substrates are fabricated, and innovative detection methods are developed. With the establishment of standardized substrates, protocols and databases, SERS is becoming a powerful tool with a huge potential in industrial practical applications.

 

References

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