MXene-boosted plasmonic fiber optic sensor with machine learning and smartphone readout for rapid, non-destructive detection of edible oil adulteration

An MXene-enhanced plasmonic fiber optic sensor combined with machine learning and smartphone detection is developed to be an innovative method of non-destructive and quick detection of adulteration in edible oil. Its main aim was to achieve greater sensitivity to detection and portability in a multilayer nanostructured design in which Au, AuNPs, GO, and Ti₃C₂Tₓ MXene were incorporated. Mxene layer, which was developed through selective etching and delamination, offered remarkable electrical conductivity, adjustable surface chemistry and high-tunable specific surface area, and thus enhanced localized surface plasmon resonance (LSPR). The straight deposition of nanomaterials on plastic optical fibers was conducted via a series of deposition ensuring that a uniform deposition was carried out, as well as in harsh conditions. The UV-Vis spectroscopy and FTIR experimental analysis showed that both pure and adulterant oils were highly plasmonic resonant and changing in the molecular fingerprint with strong shifts. The AuGOMXene hybrid sensor has synthesized a record high sensitivity of 2145 nm/RIU and figure of merit (FOM) of 63.8 and a limit of detection (LOD) at 4.9 × 10^− 5 RIU which is better than the conventional plasmonic configurations. The built-in multilayer perceptron (MLP) model was used to classify the adulteration levels; Bluetooth Smartphone interfacing allowed real time monitoring and data logging. Such a multifunctional system shows a notable step in the direction of mobile, AI-driven, and reagent-free technologies of optical sensing food quality and safety.

The MXene enhanced plasmonic fiber-optic sensor operates by measuring light resonance variations in the case of edible oils attaching itself to multilayer-coated fiber. The layers namely gold, gold nanoparticles, graphene oxide and the MXene bolster the surface plasmon resonance (SPR) effect and result in a quantifiable wavelength shift whenever the refractive index of the oil varies as a result of adulteration. FTIR spectroscopy is used to provide a fingerprint of the molecules to be more accurate. A machine-learning model does take the form of these optical signals and estimates adulteration levels. A microcontroller, having Bluetooth capabilities, transmits real-time outputs to a smart phone, and it makes it possible to detect adulteration of oil on the spot fast, portable, non-destructive, and with a very high degree of sensitivity.