Localized Surface Plasmon Resonance-Based Nanosensor for Rapid Detection of Glyphosate in Food Samples

In this study, we developed a biosensor based on the localized surface plasmon resonance (LSPR) phenomenon of gold nanoparticles (AuNPs) to detect the widely used herbicide glyphosate in food samples. To do so, either cysteamine or a specific antibody for glyphosate were conjugated to the surface of the nanoparticles. AuNPs were synthesized using the sodium citrate reduction method and had their concentration determined via inductively plasma coupled mass spectrometry. Their optical properties were analyzed using UV-vis spectroscopy, X-ray diffraction, and transmission electron microscopy. Functionalized AuNPs were further characterized via Fourier-transform infrared spectroscopy, Raman scattering, Zeta potential, and dynamic light scattering. Both conjugates succeeded in detecting the presence of glyphosate in the colloid, although nanoparticles functionalized with cysteamine tended to aggregate at high concentrations of the herbicide. On the other hand, AuNPs functionalized with anti-glyphosate functioned at a broad concentration range and successfully identified the presence of the herbicide in non-organic coffee samples and when it was added to an organic coffee sample. This study demonstrates the potential of AuNP-based biosensors to detect glyphosate in food samples. The low-cost and specificity of these biosensors make them a viable alternative to current methods for detecting glyphosate in foodstuffs.

Fiber-Optic Hydrophone Based on Michelson's Interferometer with Active Stabilization for Liquid Volume Measurement

Sensing technologies using optical fibers have been studied and applied since the 1970s in oil and gas, industrial, medical, aerospace, and civil areas. Detecting ultrasound acoustic waves through fiber-optic hydrophone (FOH) sensors can be one solution for continuous measurement of volumes inside production tanks used by these industries. This work presents an FOH system composed of two optical fiber coils made with commercial single mode fiber (SMF) working in the sensor head of a Michelson's interferometer (MI) supported by an active stabilization mechanism that drives another optical coil wound around a piezoelectric actuator (PZT) in the reference arm to mitigate external mechanical and thermal noise from the environment. A 1000 mL glass graduated cylinder filled with water is used as a test tank, inside which the sensor head and an ultrasound source are placed. For detection, amplitudes and phases are measured, and machine learning algorithms predict their respective liquid volumes. The acoustic waves create patterns electronically detected with resolution of 1 mL and sensitivity of 340 mrad/mL and 70 mvolts/mL. The nonlinear behavior of both measurands requires classification, distance metrics, and regression algorithms to define an adequate model. The results show the system can determine liquid volumes with an accuracy of 99.4% using a k-nearest neighbors (k-NN) classification with one neighbor and Manhattan's distance. Moreover, Gaussian process regression using rational quadratic metrics presented a root mean squared error (RMSE) of 0.211 mL.