ABSTRACT
Reducing the production of food in current times due to increasing population, shrinking land space, and other resources have led to increased global concern. Moreover, trade across countries has opened up avenues but also posed some problems of adulteration, ranging from mislabeling to mixing poor-quality additives. This necessitates rapid detection methods for screening. These methods should be able to screen out a large number of samples.
Recent advancements in computational biology have revolutionized every data processing technique. It is efficient in solving complex problems with easy algorithm-based methods that can give results for a large number of samples in very less duration of time. Moreover, it can give predictions using various bioinformatic tools that are the need of the hour. More focus is given to the machine learning tools used in handling the chemometric data. A wide spectrum of molecular data in food analysis leads to a difficult task in handling and finding a right solution to the problem. Hence, various types of computational tools and their importance in the food industry are depicted.
Prevention of huge economic losses due to food spoilage is possible with the right usage of data analytical tools. Integrating signal analytical approach with conventional analysis minimizes the time required for an analysis.
