Food Quality

Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality.
As the post-MDG era approaches in 2016, reducing child undernutrition is gaining high priority on the international development agenda, both as a maker and marker of development. Revisiting Smith and Haddad (2000), we use data from 1970 to 2012 for 116 countries, finding that safe water access, sanitation, women's education, gender equality, and the quantity and quality of food available in countries have been key drivers of past reductions in stunting. Income growth and governance played essential facilitating roles.
Tenebrio molitor in the form of mealworm (left) and beetle (right). Photos by author.
Scientists in the Netherlands are cultivating edible insects to address concerns of international food security. Committed to the One World, One Health (OWOH) movement, their research aims to create a safe and effective global solution to the conjoined problems of climate change and an increasing worldwide demand for protein. Their preliminary work is promising, as it suggests that when compared to other sources of meat, insects can be an efficient, safe, and low-impact source of nutrients. Additionally, in many sites with endemic malnutrition, people find insects tasty.