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This paper (open access) reports the development of a label-free electrochemical immunosensor for the detection of low quantities of porcine gelatin.  The sensor is based on a boron-doped diamond electrode modified with aryl diazonium salt. The diazonium electrografting enabled stable covalent immobilization of anti-porcine gelatin antibodies via protein A, preserving anti­body orientation and activity.

The optimised conditions were a 500× antibody concentration, 60 minute antibody incubation, and 15 minute gelatin incubation. Detection was performed using differential pulse voltammetry with [Fe(CN)₆]3-/4- as a redox probe, allowing label-free monitoring of anti­body-antigen interactions based on changes in current.

The authors report that the immunosensor demonstrated excellent analytical performance, with a detection limit of 142.15 pg mL-1. Specificity testing showed no cross-reactivity with bovine gelatin.

Although suitable validation would be required, the authors conclude that this immunosensor has potential to form the basis of a rapid, highly sensitive, and specific platform for porcine gelatin detection, offering great potential for food authentication and halal verification.

This paper (purchase required) reports an electroanalytical approach for measuring apple juice or apple cider in white wine.

After addition of LiClO₄ as electrolyte and deoxygenation, reference samples were analysed using a screen-printed carbon electrode modified with gold nanoparticles (cyclic voltammetry).

The authors report that cyclic voltammograms (CVs) of white wine samples displayed consistency regardless of their grape variety, mono-, bi- or multi-varietal status as well as geographical origin. In contrast, CVs of apple juice and apple cider exhibited similarities but were distinct from those of white wine. They were particularly characterized by the presence of a cathodic peak at about -0.50 V, attributed to sugars and organic acids, predominantly malic acid.

The authors then exported these reference, cyclic voltammograms into data points and classified them using chemometric analysis. Principal Component Analysis effectively grouped samples into two clusters: white wine and apple juice/ apple cider. Class-modelling demonstrated the ability to detect adulteration in white wine samples, with a detection threshold of 5% v/v or lower, contingent upon the adulterant type (apple juice or apple cider). Partial Least Squares regression facilitated approximate quantification of the adulteration level.

They conclude that this approach is both cost-effective and straightforward, involving minimal sample preparation.

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