We evaluated the temporal profile of the flavor enhancers monosodium glutamate (MSG), disodium inosinate (IMP), disodium guanylate (GMP), and monoammonium glutamate (MAG). We also evaluated the ability of these flavor enhancers to enhance salty taste in solutions containing different reductions of sodium chloride. Four experiments were conducted using Central Composite Rotational Design (CCRD) with focus on two objectives: concentration of flavor enhancers (0% to 1%) and reduction of sodium chloride content (0% to 100%). A 0.75% saline solution of NaCl was used as a control. In each experiment, the treatments were evaluated by the intensity of salty and umami tastes using an intensity scale. Treatments, selected according to the results of CCRD, were analyzed using time‐intensity (TI) and temporal dominance of sensations (TDS) analyses. Glutamates (MSG/MAG) showed greater capacity to enhance salty taste than treatments containing nucleotides (IMP/GMP). The intensity of umami taste, using all the examined flavor enhancers, showed a similar sensory profile. Temporal perception curves (TI and TDS) of salty and umami tastes also showed a similar temporal profile. The glutamic acid amino acids were better able to improve salty taste than nucleotides in any range of sodium chloride reduction. Flavor enhancers showed greater ability to increase salty taste in smaller reductions in sodium chloride content.Practical ApplicationThis research expand the knowledge about the ability to enhance the salty taste of flavor enhancers in different reductions in sodium content, Beside that, will provide information about the time profile of flavor enhancers. This study provides scientific technical information on the ability to intensify the salty taste of flavor enhancers and can assist the industry to develop new low sodium products and encourage the scientific community to conduct future research on this subject.

The effect of the flavor enhancers monoammonium glutamate (MAG), monosodium glutamate (MSG), disodium guanylate (GMP), and disodium inosinate (IMP) on intensifying salty taste in food matrices (shoestring potatoes, requeijão cheese, and beef burgers) with a reduction in the amount of sodium chloride (NaCl) present was evaluated. Experiments were conducted using a central composite rotational design with two variables: the concentrations of flavor enhancer and NaCl added in the food matrix. The effect of IMP was not significant (P > 0.05) on the intensity of salty taste in any of the matrices analyzed. GMP presented lower performance compared to MAG and MSG in intensifying the salty taste of the treatments, regardless of the reduction of NaCl. Compared to MSG and GMP, MAG showed greater efficiency in intensifying the salty taste in requeijão cheese and beef burger with a reduction of 25%, 50%, 75%, and 100% of NaCl. MSG presented higher efficiency compared to MAG and GMP when applied in shoestring potatoes for all reductions of NaCl tested (25%, 50%, and 75%). The ability of flavor enhancers to improve the salty taste depends on the effect of the flavor enhancer, the complexity of the food matrix, and the reduction of NaCl in foods. Practical Applications The complexity of the food matrix plays a significant role in the perception of salty taste in sodium‐reduced products. In these products, sodium reduction may affect the taste enhancer's effect of enhancing salty taste. Therefore, this study broadens the knowledge of the effects of flavor enhancers on different foods, as well as the ability to enhance salty taste in food matrices with NaCl reduction. Moreover, it provides information on how to reduce the sodium content in these matrices while maintaining the same perception of salty taste as a conventional matrix.

The objective of this work was to evaluate the expectations of consumers and specialists of craft beers regarding the visual impact of different styles of craft beer served in different beer glasses. An online survey was designed to determine the effect of different shaped glasses on consumer expectations of craft beer. A total of 252 answers from craft beer consumers and 19 responses from beer experts were collected. Respondents were asked about their impressions and expectations regarding five craft beers of different colors, served in seven beer glasses. The responses were summarized and analyzed using descriptive and multivariate statistics. The data revealed that the color of craft beers influenced the expectations of consumers and experts, so that craft beer with similar colors was associated with the same glass shape. It was concluded that the beer glass shape could directly influence the consumer expectancy on craft beers. Practical applications This is the first work in the literature that explores the visual aspect of photos of different‐colored craft beers served into distinct glass shapes. These photos were used to understand the expectations of craft beer consumers regarding the craft beer/glass shape combination. This study demonstrates that the shape of the glass can influence consumer expectations regarding different craft beers. Thus, bar or pub owners can use the most expected combination of beer glasses and craft beer to deliver a better experience to their consumers. Additionally, our findings can also lead to market strategies by defining a specific combination of craft beer and glass shape that the consumers expect to see.

BACKGROUND Strawberry quality is one of the most important factors that guarantees consistent commercialization of the fruit and ensures the consumer's satisfaction. This work makes innovative use of random forest (RF) to predict sensory measures of strawberries using physical and physical‐chemical variables. Furthermore, it also employs these same physical and physical‐chemical variables to classify strawberries in the classes "satisfied" or "not satisfied" and "would pay more" or "wouldn't pay more. The RF‐based model predicts the acceptance, expectation, ideal of sweetness, ideal of acidity, and the ideal of succulence based on the physical and physical‐chemical data. Then, the predicted parameters are used as input for the RF‐based classification model. RESULTS The RF achieved a coefficient of determination R2 > 0.72 and a root‐mean‐squared error (RMSE) smaller than 0.17 for the prediction task, which indicates that one can estimate the sensory measures of strawberries using physical and physical–chemical data. Furthermore, the RF was able to classify 87.95% of the strawberry samples correctly into the classes ‘satisfied’ and ‘not satisfied’ and 78.99% in the classes ‘would pay more’ or ‘would not pay more’. A two‐step RF model, which employed both physical and physical–chemical data to classify strawberry samples regarding the consumer's response also correctly classified 100% and 90.32% of the samples with respect to consumers’ satisfaction and their willingness to pay more, respectively. CONCLUSION The results indicate that the developed models can be used in the quality control of strawberries, supporting the establishment of quality standards that consider the consumer's response. The proposed methodology can be extended to control the sensory quality of other fruits. © 2021 Society of Chemical Industry

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Coriander (Coriandrum sativun L.) is a vegetable widely produced and consumed in Brazil being widely used as a condiment in many dishes. It has an excellent nutritional value as a source of calcium, iron, vitamin C. Furthermore it has functional properties such as anti-inflammatory, antioxidant and antimicrobial. Although it has so many benefits, there are few studies in this field that specifically analyze the use of that vegetable as condiment in food exploring its potential to add flavor. This way, this research carried out a survey on the consumption of coriander as condiment and evaluated the profile of consumers and their knowledge about that vegetable. It was found that coriander is consumed by 67.7% of interviewees mainly those who live in the Northeast and North regions where coriander is traditionally used in cooking. However, there is still a portion (32.3%) of consumers who still reject it. As a substitute for other condiments, coriander is more often used instead of parsley and the main food to which it is added is fish. When one takes into account the purchase intention of industrialized products with the addition of coriander, it is observed that seasoned cheese and snacks are the most cited. In addition, it was possible to observe that flavor is the main motivator for consumption ahead of health-related attributes. Thus, it was possible to observe that consumers are still unaware of the main beneficial characteristics of coriander. In this way, there is the possibility of developing new products using coriander as well as the relevance of carrying out studies with an emphasis on this flavouring.

Machine learning algorithms are widely used for predicting the consumer response to several food products. Recent studies in the literature demonstrated that it is possible to predict the consumer response to fruits using the physical, chemical, and physical–chemical data of fruits as input for the machine learning algorithms. However, a myriad of machine learning algorithms exists, and there is no consensus on which algorithm is the best for this task. This work evaluates and compares the results of six of the most used machine learning algorithms, the Random Forest, the Decision Tree, the Support Vector Machine, the Multilayer Perceptron neural network, the K‐Nearest Neighbors, and the Multivariate Linear Regression, in predicting the consumers’ acceptance, expectation, and their ideal of sweetness, succulency, and acidity for three different fruits. The results demonstrated that, indeed, there is no algorithm that outperforms all others for this task. Every algorithm has its advantages and disadvantages and performs differently according to the fruit and the corresponding dataset. Therefore, it highlights the importance of carefully selecting, optimizing, and comparing several algorithms when one is interested in predicting the consumer response to fruits. Practical applications Fruits are mostly commercialized without a strong assurance of their quality, which is their physical–chemical and sensory aspects. The loss of control of these aspects may impact the consumer, which can acquire low‐quality products and, thus, lead to unsatisfaction. This research shows that machine learning algorithms can be employed to effectively predict the consumer's sensory response to fruits. The use of these algorithms, which are based on easy‐to‐obtain physical and physical–chemical data, can improve the quality control of fruits in the market. Therefore, fruit producers and markets can commercialize their products based on their quality, thus providing a better experience to the consumer, which, in turn, can improve their satisfaction.