Visible feature engineering to detect fraud in black and red peppers.

Visible imaging is a fast, cheap, and accurate technique in the assessment of food quality and safety. The technique was used in the present research to detect sea foam adulterant levels in black and red peppers. The fraud levels included 0, 5, 15, 30, and 50%.

Detection of chlorine in potassium chloride and potassium sulfateusing chemical imaging and artificial neural network.

Chlorine in potassium chloride and potassium sulfate must be detected due to its negative effect on soil. Although the laboratory-based chlorine measurement tests are reliable, they are time-consuming, expensive, and requires chemical agents and highly skilled operators. Therefore, the novelty of the present research is developing a fast, accurate, and cheap machine-based method to measure the amount of chlorine.

Ability of visible imaging and machine learning in detection of chickpea flour adulterant in original cinnamon and pepper powders.

Adulteration detection in plant-based medicinal powders is necessary to provide high quality products due to the economic and health importance of them. According to advantages of imaging technology as non-destructive tool with low cost and time, the present research aims to evaluate the ability of the visible imaging combined with machine learning for distinguish original products and the adulterated samples with different levels of chickpea flour. The original products were black pepper, red pepper, and cinnamon, the adulterant was chick pea, and the adulteration levels were 0, 5, 15, 30, and 50 %.