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Saffron is a high value spice and hence susceptible to adulteration and fraud. In this study, a machine vision system based on smartphone image analysis and deep learning was used to detect saffron authenticity and quality. A dataset of 1869 images was created of 6 types of saffron/adulterants including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stems of saffron; sunflower; saffron stems mixed with food colouring; and corn silk mixed with food colouring. The deep learning system developed for grading and authenticity determination of saffron in images captured by smartphones and applied to these images, was a Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN). After applying further data augmentation and comparison against regular CNN-based methods and traditional classifiers, the results showed that the proposed LAII-v4 CNN approach gave an accuracy of 99.5%.

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Chinese researchers have applied an electronic nose (E-nose) system to detect beef adulteration with pork. The E-nose system uses a colourimetric sensors, which give different colours with different volatile compounds emmitted by the meat, the resulting coloured pattern is analysed by image analysis before and after exposure to the meat sample. The resulting signals are then analysed chemometrically to predict both qualitatively and quantitatively, the adulteration of beef with pork. This system was tested using samples of raw minced beef and pork mixed at different levels from 0%  to 100%  adulteration at 20%  increments. The system was able to accurately identify adulteration and give good quantitative correlation.  Read the article and the full paper.