Probing of ultrasonic assisted pasteurization (UAP) effects on physicochemical profile and storage stability of jambul (Syzygium cumini L.) squash

Role and mechanism of fruit waste polyphenols in diabetes management

Among various diseases in humans, diabetes is one of the most complicated disorders resulting either from the malfunctioning of β cells, causing a poor discharge of insulin from them, or poor functioning of the liberated insulin. A wide array of chemical compounds so-called secondary metabolites are present in plants. These phytochemicals are produced as by-products of metabolism and play a key role in plant protection. However, in humans, they offer several beneficial functions. Polyphenols are an important class of phytochemicals and apart from fruits, they are also found in their major wastes mainly including the peel, pomace, and seed. The current review is aimed to focus on the potential sources, distribution, and extraction/isolation of polyphenols from major fruit wastes along with highlighting their medicinal and therapeutic benefits, especially in the management of diabetes.

A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products

Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.