The agri-food sector is an endless source of expansion for nourishing a vast population, but there is a considerable need to develop high-standard procedures through intelligent and innovative technologies, such as artificial intelligence (AI) and big data. This paper addresses the research concerning AI and big data analytics in the food industry, including machine learning, artificial neural networks (ANNs), and various algorithms. Logistics, supply chain, marketing, and production patterns are covered along with food sub-sector applications for artificial intelligence techniques. It is found that utilization of AI techniques and the intelligent optimization algorithm also leads to significant process and production management. Thus, digital technologies are a boon for the food industry, where AI and big data have enabled us to achieve optimum results in realtime.

Nowadays, big data is an emerging area of computer science. Data are generated through different sources such as social media, e-commerce, blogs, banking, healthcare, transactions, apps, websites, opinion platforms, etc. It is processed for effective utilization in different industries, including healthcare. These enormous generated data are essential for data analysis and processing for industrial needs. This paper reviews the work of various authors who have contributed to data collection, analyzing, processing, and viewing to explore the importance and possibilities of big data in industrial processing applications and healthcare sectors. It identifies different opportunities and challenges (data cleaning, missing values, and outlier analysis) along with applications and features of big data. This systematic review further proposed dirty data detection and cleaning and outlier detection models that can be used for many applications. The data cleaning and outlier detection models use the optimizations concept to solve the optimal centroid selection problem and suspected data.

In the present study, the effects of orange peel essential oils (EOs) on the physiochemical, rheological, particle size and zeta potential distribution of the developed aloe vera gel-based edible coating were investigated. We also investigated the effects of prepared aloe vera gel-based edible coating (with or without incorporation of orange peel essential oil) on the postharvest shelf life and characteristics such as physiological loss of weight (PLW), color, respiration rate, firmness, total phenolic content (TPC), total flavonoid content (TFC), antioxidant activity, and antimicrobial activity of button mushroom (Agaricus bisporus) at 4 °C during 16 days of storage. The results revealed that the 50% concentration of aloe vera gel-based edible coating without addition of essential oil had significantly superior properties with higher stability zeta potential (− 9.7 mV) as compared to other concentrations of aloe vera gel-based formulation. It also showed the highest potential to maintain the postharvest quality attributes of mushrooms throughout storage. The maximum concentration of orange peel essential oil (1500 µL/L) incorporated in the 50% aloe vera gel (F3)-based treatment significantly improved the postharvest quality attributes of mushrooms as compared to coating prepared with incorporation of 500 µL/L and 1000 µL/L concentration of EOs (orange peel essential oils) and helped extend the shelf life of mushrooms up to 4 days as compared to the control (50% AV (aloe vera gel) only). Further research should be performed to develop water and gas barrier composite edible coatings to further extend mushroom shelf life.

Data analysis and processing is playing an important role because of the large amount of data generated through various sources of big data. It is an important component in big data-based applications. Data qualities are the main concern in the data acquisition, transformation and data pre-processing under big data applications. Data pre-processing is required because of inconsistent, noisy and incomplete data generation in big data applications. Data analysis basically encompasses different methods and a function applicable to data’s to detect characteristics such as data type, size, format, patterns and so on. Based on data format, it’s easy to identify data qualities for further use in various applications. Moreover, data analysis and processing includes various steps such as data qualities identification, statistical analysis of data, defining modeling, and hypothetical testing of model and result from analysis. Raw data is unused data and required analysis, filtering, and processing in any system. This paper deals with the analysis and processing aspects of raw data and cleaned data in big data applications. This paper also deals with data cleaning and its implementation concepts.