The Impact of Agricultural Techniques and Modern Technological Innovations on the Quality and Processing Standards of Natural Honey (Published)
Weather patterns, agricultural advancements, and technological evolution has significantly impacted the production of natural honey. This literature review focuses on three major themes involving the impact of weather patterns due to climate change on the honey yield in various regions, the techniques and approaches used for the detection of adulteration in the honey and the optimal conditions being used in honey processing to produce a quality product that builds consumers trust and serves the beekeepers and economy. This study adhered to PRISMA guidelines to conduct qualitative literature review of peer-reviewed studies published between 2010-2020. A rigorous screening and filtering approach as used for the selection of studies with 24 studies selected for the final synthesis of the literature. The findings identified the impact of weather pattern such as drought, rainfall and temperature fluctuations to effect the nectar quality and bee activity. Several analytical methods for adulteration were found to be effective such as HPLC, HPTLC, IR-MS which precisely detected adulterant present even in trace amounts. However, technologies like IR-MS were expensive and required advanced technical expertise limiting it’s utilization for small-scale settings. Optimal conditions for honey processing were found including temperature, crystallization parameters and preserving physicochemical characteristics of honey which will lead to a quality product. Machine learning and Artificial intelligence influenced technologies were recommended to improve the manufacturing and processing of honey. Study also revealed critical insights for beekeepers, policymakers and agriculturists to foresee the long-term impact of continuously changing climate, design policies that support beekeepers financially while regulating the honey manufacturing practices.
Keywords: Beekeeping, Chromatography, Climate Change, apiculture, machine learning, technology, temprature