What's in this drink? Classification and adulterant detection in Irish Whiskey samples using near infrared spectroscopy combined with chemometrics.

BACKGROUND: Near-infrared (NIR) spectroscopy coupled with principal component analysis (PCA) and partial least squares (PLS) regression was used to analyse a series of different Irish Whiskey samples in order to define their spectral profile and to assess the capability of the NIR method to identify samples based on their origin and storage (e.g. distiller, method of maturation). The ability of NIR spectroscopy to quantify the level of potential chemical adulterants was also investigated. Samples were spiked with 0.1%, 0.5%, 1.0%, 1.5% and 2.0% v/v of each adulterant (e.g. methanol, ethyl acetate, etc.) prior to NIR analysis. RESULTS: The results of this study demonstrated the capability of NIR spectroscopy combined with PLS regression to classify the whiskey samples and to determine the level of adulteration. Moreover, the potential of NIR coupled with chemometric analysis as a rapid, portable, and non-destructive screening tool for quality control, traceability, and food/beverage adulteration for customs and other regulatory agencies, to mitigate beverage fraud was illustrated. CONCLUSION: Given the non-specificity of the NIR technique, these positive preliminary results indicated that this method of analysis has the potential to be applied to identify the level of adulteration in distilled spirits. The rapid nature of the technique and lack of consumables or sample preparation required allows for a far more time and cost-effective analysis per sample. © 2021 Society of Chemical Industry.

Non aggregated colloidal silver nanoparticles for surface enhanced resonance Raman spectroscopy.

Silver nanoparticles with a tuneable λ max were produced as colloids by heterogeneous nucleation. The synthesis process is both fast and repeatable, producing stable PVA capped nanoparticles. The colloid's effectiveness in the SERRS system was investigated using Rhodamine 6G, R6G, Crystal Violet, CV, and Malachite Green, MG, as probe molecules. A clear sensing trend was observed, where the Raman signal emitted was significantly enhanced by the addition of silver nanoparticles. A build up of signal intensity is observed until an optimum ratio is achieved, followed by a decline in signal intensity as the concentration of nanoparticles is further increased. The sensing trend appeared to be dependant on the structure of these model molecules with similarly structured compounds exhibiting similar trends. Thus a maximum enhancement with the Ag: analyte molar ratio of ∼ 5.56: 1, was seen for CV and MG whereas R6G had a maximum enhancement at the Ag: analyte molar ratio of ∼ 2.25: 1.

Silver nanoparticle polymer composite based humidity sensor.

Silver nanoparticles were synthesised by a chemical reduction process in order to produce an aqueous colloidal dispersion. The resulting colloids were then characterised by a combination of UV-Vis spectroscopy, dynamic light scattering, X-ray diffraction and transmission electron microscopy and the nanoparticles were found to have an average diameter of 20-22 nm. The Ag/polymer nanocomposites were then applied to platinum interdigital electrodes as sensor coatings and the capability of the resulting sensor as a humidity detector investigated. With the application of 1 V, a current developed which was found to be directly proportional to humidity levels. The sensor gives a reversible, selective and rapid response which is proportional to levels of humidity within the range of 10% RH to 60% RH. An investigation into the mechanism of the sensor's response was conducted and the response was found to correlate well with a second order Langmuir adsorption model.