Insects are currently of interest due to their high nutritional value, in particular for the high concentration of quality protein. Moreover, it can also be used as an extender or binder in meat products. The objective was to evaluate grasshopper flour (GF) as a partial or total replacement for potato starch to increase the protein content of sausages and achieve good acceptability by consumers. GF has 48% moisture, 6.7% fat and 45% total protein. Sausages were analyzed by NIR and formulations with GF in all concentrations (10, 7, 5 and 3%) combined with starch (3, 5 and 7%) increased protein content. Results obtained for the sausages formulations with grasshoppers showed an increase in hardness, springiness, gumminess and chewiness through a Texture-Profile-Analysis. Moreover, a* and b* are similar to the control, but L* decreased. The check-all-that-apply test showed the attributes highlighted for sausages with GF possessed herbal flavor, brown color, and granular texture. The liking-product-landscape map showed that the incorporation of 7 and 10% of GF had an overall liking of 3.2 and 3.3, respectively, considered as “do not like much”. GF can be used as a binder in meat products up to 10% substitution. However, it is important to improve the overall liking of the sausage.
Sensory experiences play an important role in consumer response, purchase decision, and fidelity towards food products. Consumer studies when launching new food products must incorporate physiological response assessment to be more precise and, thus, increase their chances of success in the market. This paper introduces a novel sensory analysis system that incorporates facial emotion recognition (FER), galvanic skin response (GSR), and cardiac pulse to determine consumer acceptance of food samples. Taste and smell experiments were conducted with 120 participants recording facial images, biometric signals, and reported liking when trying a set of pleasant and unpleasant flavors and odors. Data fusion and analysis by machine learning models allow predicting the acceptance elicited by the samples. Results confirm that FER alone is not sufficient to determine consumers’ acceptance. However, when combined with GSR and, to a lesser extent, with pulse signals, acceptance prediction can be improved. This research targets predicting consumer’s acceptance without the continuous use of liking scores. In addition, the findings of this work may be used to explore the relationships between facial expressions and physiological reactions for non-rational decision-making when interacting with new food products.
Machine vibrations often occur due to dynamic unbalance inducing wear, fatigue, and noise that limit the potential of many machines. Dynamic balancing is a main concern in mechanism and machine theory as it allows designers to limit the transmission of vibrations to the frames and base of machines. This work introduces a novel method for representing a four-bar mechanism with the use of Fully Cartesian coordinates and a simple definition of the shaking force (ShF) and the shaking moment (ShM) equations. A simplified version of Projected Gradient Descent is used to minimize the ShF and ShM functions with the aim of balancing the system. The multi-objective optimization problem was solved using a linear combination of the objectives. A comprehensive analysis of the partial derivatives, volumes, and relations between area and thickness of the counterweights is used to define whether the allowed optimization boundaries should be changed in case the mechanical conditions of the mechanism permit it. A comparison between Pareto fronts is used to determine the impact that each counterweight has on the mechanism’s balancing. In this way, it is possible to determine which counterweights can be eliminated according to the importance of the static balance (ShF), dynamic balance (ShM), or both. The results of this methodology when using three counterweights reduces the ShF and ShM by 99.70% and 28.69%, respectively when importance is given to the static balancing and by 83.99% and 8.47%, respectively, when importance is focused on dynamic balancing. Even when further reducing the number of counterweights, the ShF and ShM can be decreased satisfactorily.
The use of graphical mapping for understanding the comparison of products based on consumers’ perceptions is beneficial and easy to interpret. Internal preference mapping (IPM) and landscape segmentation analysis (LSA) have successfully been used for this propose. However, including all the consumers’ evaluations in one map, with products’ overall liking and attributes’ perceptions, is complicated; because data is in a high dimensional space some information can be lost. To provide as much information as possible, we propose the liking product landscape (LPL) methodology where several maps are used for representing the consumers’ distribution and evaluations. LPL shows the consumers’ distribution, like LSA, and also it superimposes the consumers’ evaluations. However, instead of superimposing the average overall liking in one map, this methodology uses different maps for each consumer’s evaluation. Two experiments were performed where LPL was used for understanding the consumers’ perceptions and compared with classic methodologies, IPM and cluster analysis, in order to validate the results. LPL can be successfully used for identifying consumers’ segments, consumers’ preferences, recognizing perception of product attributes by consumers’ segments and identifying the attributes that need to be optimized.
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