Health benefits and risks of fermented foods-the PIMENTO initiative.

Worldwide, fermented foods (FF) are recognized as healthy and safe. Despite the rapid increase of research papers, there is a lack of systematic evaluation of the health benefits and risks of FF. The COST Action CA20128 "Promoting innovation of fermented foods" (PIMENTO) aims to provide a comprehensive assessment on the available evidence by compiling a set of 16 reviews. Seven reviews will cover clinical and biological endpoints associated with major health indicators across several organ systems, including the cardiovascular, gastrointestinal, neurological, immune, and skeletal systems. Nine reviews will address broader biological questions associated with FF including bioactive compounds and vitamin production, nutrient bioavailability and bioaccessibility, the role of FF in healthy diets and personalized nutrition, food safety, regulatory practices, and finally, the health properties of novel and ethnic FF. For each outcome assessed in the reviews, an innovative approach will be adopted based on EFSA's published guidance for health claim submissions. In particular, each review will be composed of three parts: (1) a systematic review of available human studies; (2) a non-systematic review of the mechanism of action related to the clinical endpoints measured by the human studies identified in part 1; and (3) a non-systematic review of the characterization of the FF investigated in the human studies identified in part 1. The evidence and research gaps derived from the reviews will be summarized and published in the form of a strategic road map that will pave the way for future research on FF.

Analysis of beef quality according to color changes using computer vision and white-box machine learning techniques.

The quality of beef products relies on the presence of a cherry red color, as any deviation toward brownish tones indicates a loss in quality. Existing studies typically analyze individual color channels separately, establishing acceptable ranges. In contrast, our proposed approach involves conducting a multivariate analysis of beef color changes using white-box machine learning techniques. Our proposal encompasses three phases. (1) We employed a Computer Vision System (CVS) to capture the color of beef pieces, implementing a color correction pre-processing step within a specially designed cabin. (2) We examined the differences among three color spaces (RGB, HSV, and CIELab*) (3) We evaluated the performance of three white-box classifiers (decision tree, logistic regression, and multivariate normal distributions) for predicting color in both fresh and non-fresh beef. These models demonstrated high accuracy and enabled a comprehensive understanding of the prediction process. Our results affirm that conducting a multivariate analysis yields superior beef color prediction outcomes compared to the conventional practice of analyzing each channel independently.

Images dataset of beef meat samples with different shelf life.

Three different cuts of meat samples: inside skirt, knuckles, and sirloin were picture captioned on the first and fifth day after purchase. From each type of meat cut, ten pictures were taken at the beginning and the end of the studied shelf life, obtaining 60 different images. The images were taken under control variables in a black acrylic cabin. In addition to the original images, we proportionate another set of 60 processed images. The latter were obtained after color calibration and meat segmentation. All these images could be used for future experiments where the color in meat should be analyzed.

Sensory characterization of functional guava symbiotic petit cheese product.

The consumption of functional dairy products continues to rise due to consumer needs. This study aimed to develop a dairy guava functional symbiotic petit cheese product that included probiotics (Bifidobacterium animalis subsp. lactis BB-12, Chr. Hansen, Denmark) and prebiotics (inulin), which had adequate organoleptic characteristics. Moreover, adequate physicochemical, microbiological, and sensory characteristics during its shelf life were expected. A pasteurized skim milk curd flavored with a guava pulp was stabilized with gelatin to formulate this product. As sweeteners, iso maltol, erythritol, and Luo Han Guo extract from monk fruit (Siraitia Grosvenorii) were added. The prebiotic used was inulin, and the probiotic (Bifidobacterium animalis subsp. lactis BB-12, Chr. Hansen, Denmark). The product was kept refrigerated (4 °C) during the shelf life of 28 days. For the organoleptic analysis (100 consumers), the evaluations performed were: (1) overall liking (OL), (2) CATA (Check all that apply) testing 19 attributes, and (3) purchase intention was evaluated. Results were analyzed with FIZZ Software Biosystèmes. During shelf life, (1) physicochemical, microbiological, and sensory tests were performed. The product was evaluated as "liked much'' (7.16 out of 9); it was described as a creamy (71 %) natural product (73 %) with a fruity odor (57 %). It could be suitable for marketing because 82 % of the consumers would buy it. The product's probiotic character (over 1 × 106) was established through a microbiological count. On day one, the CFU was found to be 4.15 × 108, and after 28 days, 1.98 × 108 CFU of viable Bifidobacterium animalis subsp. lactis BB-12, leading us to establish its probiotic characteristics. The shelf life was estimated at 21 days.

Characterization of Cooked Meat Models using Grasshopper (Sphenarium purpurascens) Soluble Protein Extracted by Alkalisation and Ultrasound as Meat-Extender.

The most abundant Orthoptera in Mexico is a small grasshopper (Sphenarium purpurascens) which is considered a food source with increased nutritional value due to its high protein content. Insect proteins have gained relevance because of their high potential as gelling, texturing, and extender agents in the food industry. The objective of this study was to evaluate the effect of substituting meat with a soluble protein extract from grasshopper obtained by alkalisation or alkalisation-piezoelectric ultrasound, on the techno-functional, physicochemical, and sensory characteristics of cooked meat models (sausages). The soluble protein was extracted in NaHCO3 pH 8 and a piezoelectric ultrasound 5-mm sonotrode at 20 kHz with 99% amplitude. Different formulations with meat substitution: 0%, 5%, 10%, and 15% were prepared and characterised for their rheological behaviour, emulsion stability, weight loss by cooking, total protein content, colour, and texture. Sensory evaluation was conducted with consumers using a test involving check-all-that-apply and overall liking. The alkalisation-piezoelectric ultrasound method improved the solubility and the techno-functional properties of the soluble grasshopper protein when applied in sausages at maximum levels of 10% meat substitution. The sensory evaluation indicated that the formulation with 5% meat substitution exhibited the same acceptability as the control sample. Given these results, the soluble protein treated with alkalisation and piezoelectric ultrasound could be used as an extender in meat products.

Physicochemical and Sensory Characteristics of Sausages Made with Grasshopper (Sphenarium purpurascens) Flour.

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.

A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products.

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.

Liking Product Landscape: Going Deeper into Understanding Consumers' Hedonic Evaluations.

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.