Label free detection of multiple trace antibiotics with SERS substrates and independent components analysis.

Surface enhanced Raman spectroscopy (SERS) has been widely studied and recognized as a powerful label-free technique for trace chemical analysis. However, its drawback in simultaneously identifying several molecular species has greatly limited its real-world applications. In this work, we reported a combination between SERS and independent component analysis (ICA) to detect several trace antibiotics which are commonly used in aquacultures, including malachite green, furazolidone, furaltadone hydrochloride, nitrofurantoin, and nitrofurazone. The analysis results indicate that the ICA method is highly effective in decomposing the measured SERS spectra. The target antibiotics could be precisely identified when the number of components and the sign of each independent component loading were properly optimized. With SERS substrates, the optimized ICA can identify trace molecules in a mixture at a concentration of 10-6 M achieving the correlation values to the reference molecular spectra of 71-98%. Furthermore, measurement results obtained from a real-world sample demonstration could also be recognized as an important basis to suggest this method is promising for monitoring antibiotics in a real aquatic environment.

An efficient and simple SERS approach for trace analysis of tetrahydrocannabinol and cannabinol and multi-cannabinoid detection.

Many countries have legalized cannabis and its derived products for multiple purposes. Consequently, it has become necessary to develop a rapid, effective, and reliable tool for detecting delta-9-tetrahydrocannabinol (THC) and cannabinol (CBN), which are important biologically active compounds in cannabis. Herein, we have fabricated SERS chips by using glancing angle deposition and tuned dimensions of silver nanorods (AgNRs) for detecting THC and CBN at low concentrations. Experimental and computational results showed that the AgNR substrate with film thickness (or nanorod length) of 150 nm, corresponding to nanorod diameter of 79 nm and gap between nanorods of 23 nm, can effectively sense trace THC and CBN with good reproducibility and sensitivity. Due to limited spectral studies of the cannabinoids in previous reports, this work also explored towards identifying characteristic Raman lines of THC and CBN. This information is critical to further reliable data analysis and interpretation. Moreover, multianalyte detection of THC and CBN in a mixture was successfully demonstrated by applying an open-source independent component analysis (ICA) model. The overall method is fast, sensitive, and reliable for sensing trace THC and CBN. The SERS chip-based method and spectral results here are useful for a variety of cannabis testing applications, such as product screening and forensic investigation.

Tuberculosis determination using SERS and chemometric methods.

Nanostructures have been multiplying the advantages of Raman spectroscopy and further amplify the advantages of Raman spectroscopy is a continuous effort focused on the appropriate design of nanostructures. Herein, we designed different shapes of plasmonic nanostructures such as Vertical, Zig Zag, Slant nanorods and Spherical nanoparticles employing the DC magnetron sputtering system as SERS-active substrates for ultrasensitive detection of target molecules. The fabricated plasmonic nanostructures sensitivity and uniformity were exploited by reference dye analyte. These nanostructures were utilized in the label free detection of infectious disease, Tuberculosis (TB). For the first time, TB detection from serum samples using SERS has been demonstrated. Various multivariate statistical methods such as principal component analysis, support vector machine, decision tree and random forest were developed and tested their ability to discriminate the healthy and active TB samples. The results demonstrate the performance of the SERS spectra, chemometric methods and potential of the method in clinical diagnosis.