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Isothermal amplification techniques offer an alternative to “classical” PCR and are more suitable to point-of-use technology.  For a general overview, see FAN’s analytical method explainers.  Recent advances have enabled the development of microfluidic chip platforms, which integrate micro-scale channels, pumps, chambers, valves, and sensors onto a single substrate for fluidic control. This integration enables simultaneous sample pretreatment, component separation, detection, and biochemical analysis on a single platform.

Recombinase polymerase amplification (RPA) is an isothermal method that has gained attention due to its low instrument dependency, high sensitivity, and rapidity. RPA reactions can be conducted at near-ambient temperatures (37–42 °C) within 20 minutes, and results can be interpreted via fluorescence signals or lateral flow dipstick by incorporating sequence-species probes. The exo probe, typically 46–52 nucleotides in length, is widely used in real-time RPA detection. The design of primers and exo probes in RPA assays offers potential for seamless integration with microfluidic chip platforms.

In this study (purchase required) the researchers integrated RPA into a microfluidic chip to develop an assay for identifying commonly marketed codfish species prone to adulteration: Atlantic cod (G. morhua), sablefish (A. fimbria) and toothfish (D. eleginoides and D. mawsoni).

They reported that the assay demonstrated high specificity and sensitivity, with detection limits of 10 copies/μL recombinant plasmid or 10³ fg/μL genomic DNA. Application to 141 commercial seafood products resulted in 100 % identification accuracy for the three target species, and revealed a 32 % inconsistency between product labels and genetic identities, involving substitutions with Pacific cod, pollock, and other species.

They conclude that on-chip RPA assay offers a rapid, high-throughput, and reliable tool for seafood authentication and potential mislabeling surveillance.

Photo by Patrick Boucher on Unsplash

Jaggery is one of the most popular foods in India.

 This research (purchase required) presents a classical, novel colour-based method for detecting  adulteration in jaggery. A colour sensor is used to detect the colour of melted jaggery samples, and an Arduino Uno (opensource microcontroller board) is used to further analyse the colour. This research exploits the direct relationship between the captured pixel intensities of the jaggery and its purity in order to develop a linear regression model. The developed product is validated using samples having varying percentages of adulterations (10% to 70%) caused due to single and multiple adulterants (sugar and food colour) in jaggery. The abstract does not describe how these reference samples were sourced or prepared. 

The authors report that their machine learning approach gave promising results with accuracy of 94.67% and precision as 92.6%. The developed method for identifying tampered jaggery is user friendly, affordable, portable and non-destructive.

Photo by Prchi Palwe on Unsplash

In this paper (open access) the authors demonstrate an optical, contactless method to discriminate different types of commercial milk (whole, partially skimmed and skimmed) and identify its adulteration with water and 12.5% water-glucose solution.  This adulterant was selected since it exhibits a refractive index comparable to that of whole milk, rendering such adulteration unnnoticed when performing a routine quality test based on refractive index measurements.

The prototype sensor employs a CMOS digital camera to acquire speckle pattern images generated by shining the beam of a red semiconductor laser onto milk samples placed in a plastic cuvette. The collected data are then analyzed to extract informative parameters, such as the average intensity and the speckle grain size.

The authors report that the system can distinguish between different types of milk and detect diluted samples with both water and glucose.

This paper (purchase required) reports the use of a portable optical sensor (Multi-Spectral Imaging) to build a classification model for detecting milk adulteration. This encompassed mixtures of milk from different species (cow, goat, and sheep), as well as dilution of cow’s milk with water. The study's scope also included milk with diverse heat treatments, fat content, and commercial brands.

The authors report that discriminant analyses provided reliable predictive models, with Accuracy and Cohen's Kappa values ranging between 0.80 and 1. In quantitative studies, the quantification of milk mixtures at a minimum percentage interval of 10% was detected with Mean Absolute Error (MAE) values between 0.14 and 0.05, and 0.03 for cow's milk adulterated with water at adulteration levels of 5%.

The authors conclude that the portability of these instruments adds a significant advantage by enabling on-site and real-time determination and quantification of milk adulteration.

Impedance is a complex Cartesian function describing the difference between an inputting and exiting sinusoidal electrical signal.  It can be depicted graphically as a plot (vector) of resistance vs reactance.  The linearity of this plot, and the angle of the vector, are distinctive.  In a sample of meat or fish, impedance is affected by the cell structure and the water content.  Both of which are an indicator of freshness.  An impedance sensor, comparing the result with a “normal” database, can therefore be used to detect unfresh meat or meat that has been prior frozen and defrosted without declaration.

This review (open access) describes published applications, comparing the technique with other approaches such as HADH Enzyme measurement (see FAN method explainers).  It concludes that the development of Impedance Sensor methods is now at a stage where the technique is ideal as a cheap, non-destructive inline check in the food industry, particularly if coupled with machine learning to spot unusual or anomalous samples.

Photo by Victoria Shes on Unsplash

Measuring the profile of trace metals in food is one approach to discriminating its geographic origin.  Analytical tools for trace-metal measurement tend to be laboratory-based with expensive capital equipment.

There has been recent research to develop point-of-use sensors for metal ions using specific chemical binders (often based on the chemistry of human olefaction) with fluorescent markers.  This paper (purchase requires) takes the work a significant step forward.  Their sensor is based upon the principle that different metal ions induce different degrees of aggregation in perylene diimide derivative based supramolecular nanoaggregates.  This enabled the construction of a multi-analyte sensor which they report as having ease of preparation, rapid response, and high sensitivity originating from large specific surface areas.  The authors report that they used the sensor to build a successful classification model of geographic origin for both drinking water and apples.