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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

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.

This thesis (open access) set out to prove the concept that multi-spectral imaging  (MSI) could be used to build a classification model to differentiate chicken breast with undeclared added water from that with no, or legally-permitted low, added water content.

The researcher built a model based upon an in-house reference set of chicken breast samples; 12 with no added water, 12 with water added at a level that need not be legally declared (3 – 5%) and 12 with water added at a level that should be legally declared on-pack (9-11%).  The protein/water content of the samples was then calculated using classical analysis, in order to label the MSI scans.  MSI used two cameras , FX10 and  FX17.  After annotation, the samples were saved and analysed in MATLAB for model development

The researcher concluded that the method holds promise but would need a much more robust database.  With this limited database, the model could distinguish added-water from non-added-water samples but could not robustly distinguish between amounts of added water which would be legal if undeclared and those which would not be legal.

Photo by Philippe Zuber on Unsplash