Screening Method for the Visual Discrimination of Olive Oil from Other Vegetable Oils by a Multispecies DNA Sensor.

Olive oil is a prominent agricultural product which, in addition to its nutritional value and unique organoleptic characteristics, offers a variety of health benefits protecting against cardiovascular disease, cancer, and neurodegenerative diseases. The assessment of olive oil authenticity is an extremely important and challenging process aimed at protecting consumers and producers. The most frequent adulteration involves blending with less expensive and readily available vegetable/seed oils. The methods for adulteration detection, whether based on changes in metabolite profiles or based on DNA markers, require advanced and expensive instrumentation combined with powerful chemometric and statistical tools. To this end, we present a simple, multiplex, and inexpensive screening method based on the development of a multispecies DNA sensor for sample interrogation with the naked eye. It is the first report of a DNA sensor for olive oil adulteration detection with other plant oils. The sensor meets the 2-fold challenge of adulteration detection, i.e., determining whether the olive oil sample is adulterated and identifying the added vegetable oil. We have identified unique, nucleotide variations, which enable the discrimination of seven plant species (olive, corn, sesame, soy, sunflower, almond, and hazelnut). Following a single PCR step, a 20 min multiplex plant-discrimination reaction is performed, and the products are applied directly to the sensing device. The plant species are visualized as red spots using functionalized gold nanoparticles as reporters. The spot position reveals the identity of the plant species. As low as <5-10% of adulterant was detected with particularly good reproducibility and specificity.

Multifold improvement in allergen detection capability of dipstick-type immunosensors via macromolecular crowding.

Dipstick-type lateral flow immunosensors are used widely for on-site detection of food allergens. The weakness of the immunosensors of this type, however, is their low sensitivity. Contrary to current methods, that focus on improving detection capability through the introduction of novel labels or multistep protocols, this work exploits macromolecular crowding to modify and regulate the microenvironment of the immunoassay, thus promoting the interactions that are responsible for allergen recognition and signal generation. The effect of 14 macromolecular crowding agents was explored using, as a model, commercially available and widely applied dipstick immunosensors, which are already optimized in terms of reagents and conditions for peanut allergen detection. An about 10-fold improvement in detection capability was achieved by using polyvinylpyrrolidone, Mr 29,000, as a macromolecular crowder without compromising simplicity and practicality. The proposed approach is complementary to other methods of improving the sensitivity by using novel labels. Because biomacromolecular interactions have a fundamental role in all types of biosensors, we foresee that the proposed strategy will also find applications in other biosensors and analytical devices.

Macromolecular crowding agents enhance the sensitivity of lateral flow immunoassays.

Lateral flow immunoassays (LFIA) have a plethora of applications in health, environmental and food sectors for low-cost, simple, and rapid point-of-need testing. Typically, the user only needs to add the sample without any other intervention from sample application to results. A compelling challenge, and a constant pursuit in LFIA is to improve the assay sensitivity without compromising the simplicity and practicality. We report that the addition of water-soluble macromolecular crowding agents leads to an enhancement of the sensitivity, which is attributed to the fact that the exposure of antibodies and micro/nanoparticle conjugates to macromolecularly crowded environment, while migrating through the confining pores of the strip-pads by capillary forces, promotes the interactions that are responsible for analyte recognition and signal generation. The effect was shown by using two of the most widely established LFIA tests worldwide, that is, detection of nucleocapsid protein from SARS-CoV-2 associated with COVID-19 and detection of Strep-A antigen from Streptococcus pyogenes associated with pharyngitis. For immediate demonstration of the sensitivity enhancement, we worked directly on commercially available devices already optimized in terms of reagents and conditions. Of the crowders used, ficoll, Mr 400000, and ficoll, Mr 70000, gave a 5-10-fold improvement of the signal without affecting the background. Because the addition of macromolecular crowding agents is complementary to other strategies of sensitivity enhancement, such as the design of novel labels and the introduction of signal amplification, we anticipate that the proposed modulation will be extended to numerous analytes with a variety of reporters and LFIA configurations.