Investigating the optimum conditions for minimized 3-chloropropane-1,2-diol esters content and improved sensory attributes during savory beef flavor preparation

Maillard reaction of food-derived peptides as a potential route to generate meat flavor compounds: A review

Plant-based meat analogues (PBMA) are promising foods to address the global imbalance between the supply and demand for meat products caused by the increasing environmental pressures and growing human population. Given that the flavor of PBMA plays a crucial role in consumer acceptance, imparting meat-like flavor is of great significance. As a natural approach to generate meat-like flavor, the Maillard reaction involving food-derived peptides could contribute to the required flavor compounds, which has promising applications in PBMA formulations. In this review, the precursors of meat-like flavor are summarized followed by a discussion of the reactions and mechanisms responsible for generation of the flavor compounds. The preparation and analysis techniques for food-derived Maillard reacted peptides (MRPs) as well as their taste and aroma properties are discussed. In addition, the MRPs as meat flavor precursors and their potential application in the formulation of PBMA are also discussed. The present review provides a fundamental scientific information useful for the production and application of MRPs as meat flavor precursors in PBMA.

A predictive model for the evaluation of flavor attributes of raw and cooked beef based on sensor array analyses

There are currently no standardized objective measures to evaluate beef flavor attributes, especially the comparison between raw beef and cooked beef. Beef flavor attribute is one of the most significant parameters for consumers. This study described a predictive model using a 12-ion-sensor array and sensory properties to evaluate beef flavor attributes based on potential. Then the number of sensors was reduced to six via variance of analysis, and these six sensors were reserved with the saturated calomel reference electrode to constitute a new sensor array. Sensitive flavors of each sensor were selected through multiple comparative analysis. Results showed that the accuracy rate of classifying five basic flavors (acidity, sweetness, bitterness, saltiness, freshness) using the new sensor array was 100%. The processing methods used were based on multivariate statistical methods done with the cluster analysis (CA). Results were compared to sensory evaluation using genetic algorithm (GA). From GA, the accuracy rates of raw and cooked beef were 85.0% and 90.0%, which was consistent with the sensory analysis results. Moreover, reducing the number of sensors could decrease the data dimensionality and detection time. Also raw beef instead of cooked beef could be used in flavor attributes evaluation. This model could become an important method for evaluating beef flavor attributes repeatedly and objectively.