An ultrasensitive bacterial imprinted electrochemical sensor for the determination of Lactobacillus rhamnosus GG

Sodium Deoxycholate-Propidium Monoazide Droplet Digital PCR for Rapid and Quantitative Detection of Viable Lacticaseibacillus rhamnosus HN001 in Compound Probiotic Products

As a famous probiotic, Lacticaseibacillus rhamnosus HN001 is widely added to probiotic products. Different L. rhamnosus strains have different probiotic effects, and the active HN001 strain is the key to exerting probiotic effects, so it is of great practical significance for realising the detection of L. rhamnosus HN001 at the strain level in probiotic products. In this study, strain-specific primer pairs and probes were designed. A combined treatment of sodium deoxycholate (SD) and propidium monoazide (PMA) inhibited the amplification of dead bacterial DNA, establishing a SD-PMA-ddPCR system and conditions for detecting live L. rhamnosus HN001 in probiotic powders. Specificity was confirmed using type strains and commercial strains. Sensitivity tests with spiked samples showed a detection limit of 10⁵ CFU/g and a linear quantification range of 1.42 × 10⁵-1.42 × 10⁹ CFU/g. Actual sample testing demonstrated the method's efficiency in quantifying HN001 in compound probiotic products. This method offers a reliable tool for the rapid and precise quantification of viable L. rhamnosus HN001, crucial for the quality monitoring of probiotic products.

Recent advance of nanomaterials modified electrochemical sensors in the detection of heavy metal ions in food and water

As one of the main pollutants, heavy metal ions can accumulate in the human body and cause a cascade of damage. Electrochemical sensors provide great prospects for tracing heavy metal ions because of their properties of high sensitivity, low detection limits and fast response. Electrode surface modification materials play a key role in enhancing the performance of electrochemical sensors. Herein, we summarize in detail the recent work on electrochemical sensors modified by carbon nanomaterials (graphene and its derivatives, carbon nanofibers and carbon nanotubes), metal nanomaterials (gold, silver, bismuth and iron), complexes (MOFs, ZIFs and MXenes) and their composites for the detection of heavy metal ions (mainly include Cd(II), Hg(II), Pb(II), As(III), Cu(II) and Zn(II)) in food and water. The synthetic strategies, mechanisms, innovations, advantages, challenges and prospects of various electrode modification nanomaterials for the detection of heavy metal ions in food and water are discussed.

Optimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning

Molecularly imprinted polymers (MIPs) rely on synthetic engineered materials able to selectively bind and intimately recognise a target molecule through its size and functionalities. The way in which MIPs interact with their targets, and the magnitude of this interaction, is closely linked to the chemical properties derived during the polymerisation stages, which tailor them to their specific target. Hence, MIPs are in-deep studied in terms of their sensitivity and cross-reactivity, further being used for monitoring purposes of analytes in complex analytical samples. As MIPs are involved in sensor development within different approaches, a systematic optimisation and rational data-driven sensing is fundamental to obtaining a best-performant MIP sensor. In addition, the closer integration of MIPs in sensor development requires that the inner properties of the materials in terms of sensitivity and selectivity are maintained in the presence of competitive molecules, which focus is currently opened. Identifying computational models capable of predicting and reporting the best-performant configuration of electrochemical sensors based on MIPs is of immense importance. The application of chemometrics using design of experiments (DoE) is nowadays increasingly adopted during optimisation problems, which largely reduce the number of experimental trials. These approaches, together with the emergent machine learning (ML) tool in sensor data processing, represent the future trend in design and management of point-of-care configurations based on MIP sensing. This review provides an overview on the recent application of chemometrics tools in optimisation problems during development and analytical assessment of electrochemical sensors based on MIP receptors. A comprehensive discussion is first presented to cover the recent advancements on response surface methodologies (RSM) in optimisation studies of MIPs design. Therefore, the recent advent of machine learning in sensor data processing will be focused on MIPs development and analytical detection in sensors.