Consumer perception of natural hot-pepper cheeses

Consumer perception of Cheddar cheese color

The color of Cheddar cheese in the United States is influenced by many factors, primarily the amount of annatto added as a colorant. The US Food and Drug Administration is currently reviewing its definition of the term "natural" on food labels, which may result in the use of colorants being restricted in natural cheeses. The objective of this study was to evaluate how consumers perceive Cheddar cheese color to better understand how changes to legislation surrounding colorants in natural Cheddar cheese may affect consumption. We were also interested in determining if a relationship exists between color and other perceived characteristics of Cheddar cheese. Two online surveys on Cheddar cheese color and flavor attributes (n = 1,226 and n = 1,183, respectively) were conducted, followed by a consumer acceptance test on 6 commercially available Cheddar cheeses (n = 196). Overall, consumers preferred light orange color in Cheddar cheese over dark orange or white Cheddar cheese, but segmentation was observed for Cheddar color preference. Light orange Cheddar and white Cheddar were perceived as approximately equal in terms of "naturalness." White and light orange Cheddars were perceived as more natural than dark orange Cheddars conceptually and in consumer acceptance testing. White Cheddar was considered most natural by 50.3% of n = 1,283 survey participants and 43.4% of n = 196 consumer acceptance test participants, whereas light orange Cheddar was perceived as most natural by 40.6% and 45.9% of these groups, respectively. A bimodal distribution was observed in both the online survey and in consumer acceptance testing for the naturalness of Cheddar cheese color, with a subset of consumers (31.4% of n = 1,183 survey participants and 30.6% of n = 196 consumer testing participants) indicating that white Cheddar was the least natural option. Consumers associated orange color in Cheddar cheese with a sharper flavor both in an online survey format and consumer acceptance testing.

Plant-Based Alternatives Need Not Be Inferior: Findings from a Sensory and Consumer Research Case Study with Cream Cheese

Reliance on animal foods must be reduced to improve planetary and human well-being. This research studied plant-based cheese alternatives (PBCA) relative to dairy cheese in a consumer taste test with 157 consumers in New Zealand. A case study approach used cream cheese (commercially available) as the focal product category (2 PBCA, 2 dairy) and implemented a multi-response paradigm (hedonic, sensory, emotional, conceptual, situational). "Beyond liking" insights were established, including drivers of liking (sensory, non-sensory) and sensory drivers of non-sensory product associations. Two consumer segments were identified, of which the largest (n = 111) liked PBCA and dairy samples equally (6.5-6.7 of 9). In this PBCA Likers cluster, the key sensory drivers of liking were 'creamy/smooth mouthfeel', 'dissolves quickly in mouth', and 'sweet', while a significant penalty was associated with 'mild/bland flavour'. The non-sensory data contributed additional consumer insights, including the four samples being perceived as differently appropriate for 9 of 12 use situations, with PBCA being regarded as less appropriate. In the limited confines of this case on cream cheese, the findings show that PBCA need not be inferior to their dairy counterparts despite a general narrative to the contrary. Of note, the results were obtained among participants who were open to eating a more PB diet but were not vegetarian or vegan.

Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics

In this study, an Artificial Neural Network (ANN) model is used to solve the complex task of producing fresh cheese with the desired quality parameters. The study focuses on kombucha fresh cheese samples fortified with ground wild thyme, supercritical fluid extract of wild thyme, ground sage and supercritical fluid extract of sage and optimizes the parameters of chemical composition, antioxidant potential and microbiological profile. The ANN models demonstrate robust generalization capabilities and accurately predict the observed results based on the input parameters. The optimal neural network model (MLP 6-10-16) with 10 neurons provides high r2 values (0.993 for training, 0.992 for testing, and 0.992 for validation cycles). The ANN model identified the optimal sample, a supercritical fluid extract of sage, on the 20th day of storage, showcasing specific favorable process parameters. These parameters encompass dry matter, fat, ash, proteins, water activity, pH, antioxidant potential (TP, DPPH, ABTS, FRAP), and microbiological profile. These findings offer valuable insights into producing fresh cheese efficiently with the desired quality attributes. Moreover, they highlight the effectiveness of the ANN model in optimizing diverse parameters for enhanced product development in the dairy industry.