Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra

Surface-enhanced Raman spectroscopy (SERS) has been well explored as a highly effective characterization technique that is capable of chemical pollutant detection and identification at very low concentrations. Machine learning has been previously used to identify compounds based on SERS spectral data. However, utilization of SERS to quantify concentrations, with or without machine learning, has been difficult due to the spectral intensity being sensitive to confounding factors such as the substrate parameters, orientation of the analyte, and sample preparation technique. Here, we demonstrate an approach for predicting the concentration of sample pollutants from SERS spectra using machine learning. Frequency domain transform methods, including the Fourier and Walsh-Hadamard transforms, are applied to spectral data sets of three analytes (rhodamine 6G, chlorpyrifos, and triclosan), which are then used to train machine learning algorithms. Using standard machine learning models, the concentration of the sample pollutants is predicted with >80% cross-validation accuracy from raw SERS data. A cross-validation accuracy of 85% was achieved using deep learning for a moderately sized data set (∼100 spectra), and 70-80% was achieved for small data sets (∼50 spectra). Performance can be maintained within this range even when combining various sample preparation techniques and environmental media interference. Additionally, as a spectral pretreatment, the Fourier and Hadamard transforms are shown to consistently improve prediction accuracy across multiple data sets. Finally, standard models were shown to accurately identify characteristic peaks of compounds via analysis of their importance scores, further verifying their predictive value.

Metal organic frameworks for electrochemical sensor applications: A review

Metal-organic frameworks (MOFs) are broadly known as porous coordination polymers, synthesized by metal-based nodes and organic linkers. MOFs are used in various fields like catalysis, energy storage, sensors, drug delivery etc., due to their versatile properties (tailorable pore size, high surface area, and exposed active sites). This review presents a detailed discussion of MOFs as an electrochemical sensor and their enhancement in the selectivity and sensitivity of the sensor. These sensors are used for the detection of heavy metal ions like Cd²⁺, Pb²⁺, Hg²⁺, and Cu²⁺ from groundwater. Various types of organic pollutants are also detected from the water bodies using MOFs. Furthermore, electrochemical sensing of antibiotics, phenolic compounds, and pesticides has been explored. In addition to this, there is also a detailed discussion of metal nano-particles and metal-oxide based composites which can sense various compounds like glucose, amino acids, uric acid etc. The review will be helpful for young researchers, and an inspiration to future research as challenges and future opportunities of MOF-based electrochemical sensors are also reported.