Evaluation of Physicochemical and Antioxidant Properties of Peanut Protein Hydrolysate

Peanut protein and its hydrolysate were compared with a view to their use as food additives. The effects of pH, temperature and protein concentration on some of their key physicochemical properties were investigated. Compared with peanut protein, peanut peptides exhibited a significantly higher solubility and significantly lower turbidity at pH values 2–12 and temperature between 30 and 80°C. Peanut peptide showed better emulsifying capacity, foam capacity and foam stability, but had lower water holding and fat adsorption capacities over a wide range of protein concentrations (2–5 g/100 ml) than peanut protein isolate. In addition, peanut peptide exhibited in vitro antioxidant properties measured in terms of reducing power, scavenging of hydroxyl radical, and scavenging of DPPH radical. These results suggest that peanut peptide appeared to have better functional and antioxidant properties and hence has a good potential as a food additive

Effect of Peptide Size on Antioxidant Properties of African Yam Bean Seed (Sphenostylis stenocarpa) Protein Hydrolysate Fractions

Enzymatic hydrolysate of African yam bean seed protein isolate was prepared by treatment with alcalase. The hydrolysate was further fractionated into peptide sizes of <1, 1–3, 3–5 and 5–10 kDa using membrane ultrafiltration. The protein hydrolysate (APH) and its membrane ultrafiltration fractions were assayed for in vitro antioxidant activities. The <1 kDa peptides exhibited significantly better (p < 0.05) ferric reducing power, diphenyl-1-picryhydradzyl (DPPH) and hydroxyl radical scavenging activities when compared to peptide fractions of higher molecular weights. The high activity of <1 kDa peptides in these antioxidant assay systems may be related to the high levels of total hydrophobic and aromatic amino acids. In comparison to glutathione (GSH), the APH and its membrane fractions had significantly higher (p < 0.05) ability to chelate metal ions. In contrast, GSH had significantly greater (p < 0.05) ferric reducing power and free radical scavenging activities than APH and its membrane fractions. The APH and its membrane fractions effectively inhibited lipid peroxidation, results that were concentration dependent. The activity of APH and its membrane fractions against linoleic acid oxidation was higher when compared to that of GSH but lower than that of butylated hydroxyl toluene (BHT). The results show potential use of APH and its membrane fractions as antioxidants in the management of oxidative stress-related metabolic disorders and in the prevention of lipid oxidation in food products

Discovering temporal regularities in retail customers’ shopping behavior

In this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customerâ\u80\u99s temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity