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Injera is a dietary staple in Ethiopia, eaten with most meals.  It is a flatbread made with teff flour.  Injera is vulnerable to adulteration with cheaper gesso or cassava flours.

This paper (purchase required) reports a simple, affordable, portable, and easy-to-use method based on a paper analytical device to indicate adulteration qualitatively.

The authors report that the developed test card generated a red-orange colour on lane B (ferric detection), red on lane D (ferrous detection), Prussian blue on lane F (ferric detection), and Turnbull’s blue colour on lane H (ferrous detection) for pure teff injera. The colour barcodes generated by pure teff injera differ from those produced by teff injera that contain gesso or cassava.

In a survey of local market produce, the test card colour result was less intense or inactive in most cases. It indicates that inexpensive cereals might be used in place of authentic teff flour or flours have been blended before baking.

The authors cross-validated their method by analysing the elemental composition of samples using microwave plasma atomic emission spectrometers.

Photo by Syed F Hashemi on Unsplash

This short communication describes a simple chemical-based colour test for detecting fake saffron.  No details are provided in the abstract about the basis of the test, or whether it discriminates botanically-related adulterants such as safflower.  We have not purchased the full article in order to review it.

The abstract describes it as a panel of 4 simple chemical tests which take around 30 minutes to perform and can be read by eye from a colour card.  The attached image is from a correction (open access) to the original publication (which remains behind a paywall).

Dehydrated bee pollen is a premium product with a growing market as a food supplement, functional food ingredient, and is also an ingredient in biomedical and healthcare formulations.  Pollen from certain species of stingless bees is higher priced than the equivalent from the much more common honey bee, as the former is considered to have enhanced nutritional and health benefits.  Pollens from different bee species are visually identical, giving an incentive for potential fraud.

In this paper (purchase required) the authors built a classification model to discriminate pollens from different bee species.  The researchers integrated digital image processing and machine learning to classify pollen loads produced by honey bees (Apis mellifera) and pot-pollen from stingless bee species based on their colour patterns. A total of 246 pollen loads and pot-pollen samples from five bee species (Apis mellifera, Melipona marginata, Melipona quadrifasciata quadrifasciata, Scaptotrigona bipunctata, and Tetragona clavipes) were collected, and high-resolution images were captured using a smartphone. Colour parameters extracted from images such as R, B, H, and V were analyzed, and classification models employing CatBoost, XGBoost, Random Forest, and k-Nearest Neighbors (kNN) algorithms were tested.

They reported that CatBoost achieved the highest performance, with accuracies of 100 % in the training phase and 98.9 % in the testing phase. Linear Discriminant Analysis (LDA) further validated the classification by grouping the pollen samples into five distinct clusters corresponding to the bee species studied.

They conclude that combining digital image processing from a smartphone with machine learning offers an effective approach to classifying pollen from different bee species, with promising applications in apiculture to ensure product quality and authenticity.

Photo by Aaron Burden 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