Nanoplasmonic Decoration of Sacrificial Bacteria: Directing Interparticle Coupling Toward Multiplex Analysis of Antiseptic Alcohols

Abstract

Engineering interparticle plasmon coupling through controlling the assembly of plasmonic NPs onto the surface of sacrificial substrates is quite promising for establishing inherently absent selectivity or sensitivity toward a particular analyte. Herein, we introduce a robust sensor array strategy based upon the assembly of gold nanoparticles (AuNPs) on the cysteamine-modified surface of two Gram-positive probiotic bacteria, i.e., Lactobacillus reuteri (LBR) and Bifidobacterium lactis (BFL), as potential sacrificial substrates, for discrimination and quantification of antiseptic alcohols (AAs) comprising methanol, ethanol, and isopropanol. In fact, the damage of the bacterial membrane upon exposure to the foregoing alcohols inhibits the assembly of AuNPs, thereby precluding color variations from red to blue. Unequal resistance of the bacterial membranes against damage by the alcohols underlies independent response patterns for each analyte. The supervised classification of visible spectra and RGB data by Linear Discriminant Analysis (LDA) revealed the remarkable potential of the designed sensor array in differentiating single-component and multicomponent samples of AAs. Moreover, the Partial Least Square Regression (PLSR) technique exhibited excellent applicability to multivariate calibration of both spectral and RGB data. The intriguing attributes of the implemented approach not only hold great potential in the authentication and quality assessment of alcohol-based products but open up a new prospect for deployment of sacrificial substrates in the design of interparticle coupling-based sensors.

Fast and Facile Etching of Gold Nanorods by N-Halosuccinimides: Toward Multicolorimetric Identification and Quantification of 20 Natural Amino Acids

“…To evaluate the performance of the sensor in discrimination of all 20 amino acids, the obtained spectral responses for 10 different concentrations of 0.5, 5, 10, 15, 20, 30, 40, 50, 75, and 100 μM were used to train 10 separate LDA models. A common challenge in training an LDA model is the number of variables that should be equal to or less than the number of samples. , Therefore, at first, the dimensionality of the training set for each case (2 SEs × 600 wavelengths × 20 samples × 3 replicates) was reduced using PCA, and the first 3 PCs representing the most significant variance of each data set were used as variables for training the LDA models. Following the training step, leave-one-out cross-validation was employed to assess the validity of the models.…”