Internet of things (IoT) results in massive amount of streaming data, often referred to as "big data", which brings new opportunities to monitor agricultural and food processes. Besides sensors, big data from social media is also becoming important for the food industry. In this review we present an overview of IoT, big data, and artificial intelligence (AI) and their disruptive role in shaping the future of agri-food systems. Following an introduction to the fields of IoT, big data, and AI, we discuss the role of IoT and big data analysis in agriculture (including greenhouse monitoring, intelligent farm machines, and drone-based crop imaging), supplychain modernization, social media (for open innovation and sentiment analysis) in food industry, food quality assessment (using spectral methods and sensor fusion), and finally, food safety (using gene sequencing and blockchain based digital traceability). A special emphasis is laid on the commercial status of applications and translational research outcomes.

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Meat and meat products are popular foods due to their balanced nutritional nature and their availability in a variety of forms. In recent years, due to an increase in the consumer awareness regarding product quality and authenticity of food, rapid and effective quality control systems have been sought by meat industries. Near-Infrared (NIR) spectroscopy has been identified as a fast and cost-effective tool for estimating various meat quality parameters as well as detecting adulteration. This review focusses on the on/inline application of single and multiprobe NIR spectroscopy for the analysis of meat and meat products starting from the year 1996 to 2017. The article gives a brief description about the theory of NIR spectroscopy followed by its application for meat and meat products analysis. A detailed discussion is provided on the various studies regarding applications of NIR spectroscopy and specifically for on/inline monitoring along with their advantages and disadvantages. Additionally, a brief description has been given about the various chemometric techniques utilized in the mentioned studies. Finally, it discusses challenges encountered and future prospects of the technology. It is concluded that, advancements in the fields of NIR spectroscopy and chemometrics have immensely increased the potential of the technology as a reliable on/inline monitoring tool for the meat industry.

This study optimises the degradation of a cocktail of the dyes methyl orange and bromothymol blue by atmospheric air plasma. Response surface methodology (RSM) was employed to investigate the efficacy of the plasma process parameters on degradation efficiency. A Box–Behnken design (BBD) was employed to optimise the degradation of dyes by air plasma discharge. A second order polynomial equation was proposed to predict process efficiency. It was observed that the predicted values are significant (p<0.001) with coefficients of determination 0.98, 0.96, 0.98 for dye degradation, pH value and ozone concentration, respectively. The analysis of variance results showed that the coefficients of the polynomials for the percentage degradation and ozone concentration responses indicated positive linear effects (p<0.001), whereas a negative linear effect was found for pH. The positive linear effect of variable emphasises that voltage and treatment time were the most dominant factors (p<0.001), meaning that higher degradation efficiencies are achieved with an increase in treatment duration. This study showed that a BBD model and RSM could be employed to optimize the colour degradation parameters of non-thermal plasma treated model dyes while minimising the number of experiments required.

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