Advanced Detection Techniques Using Artificial Intelligence in Processing of Berries

Abstract

Berries are delicious and nutritious, making them among the popular fruits. There are various types of berries, the most common ones include blueberries, strawberries, raspberries, blackberries, grapes, and currants. Fresh berries combine high nutritional value and perishability. The processing of berries ensures high quality and enhanced marketability of the product. Sorting, disinfection, and decontamination are essential processes that many types of fruits such as citrus fruits, berries, pomes, and drupes must undergo to ensure improved quality, uniformity, and microbiological safety of the product. Drying and freezing are excellent processing methods to extend the shelf life of berries which also provide new options to the consumer of a wide variety of berries. With the demand for high quality and automatic high-throughput detection of the quality of fruit products, intelligent and rapid detection of various parameters during processing has become the development direction of modern food processing. Therefore, this paper reviews the application of advanced detection technologies, artificial intelligence-based methods for detection and prediction during berry sorting, drying, disinfecting, sterilizing, and freezing processing. These advanced detection techniques include computer vision system, near infrared, hyperspectral imaging, thermal imaging, low-field nuclear magnetic resonance, magnetic resonance imaging, electronic nose, and X-ray computed tomography. These artificial intelligence methods include mathematical modeling, chemometrics, machine learning, deep learning, and artificial neural networks. In general, advanced detection techniques incorporating artificial intelligence have not yet penetrated into all aspects of commercial berry processing, which include drying, disinfecting, sterilizing, and freezing processes.