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The physicochemical and microbiological properties of traditional Portuguese ready-to-eat dry fermented sausage chouriça de carne samples from 14 regional producers were analysed and subjected to multivariate analysis to determine the relationships between them and to evaluate how the quality and safety of these sausages is affected by these properties. Producer-mean values for physicochemical analyses were quite variable, with intervals of 4.87–6.11 for pH, 0.803–0.965 for aw, moisture 19.5–48.5%, protein 32.0–60.1% (db), fat 22.0–53.3% (db), ash 3.52–9.69% (db), and carbohydrates 1.66–13.5% (db). Mesophilic counts varied (5.61–8.68 log CFU/g), while lactic acid bacteria were generally high (MRS: 8.21–10.2; M17: 7.66–10.0 log CFU/g). S. aureus was enumerated in levels up to 2.55 log CFU/g, while presumptive C. perfringens never surpassed 2 log CFU/g. Salmonella spp. and Listeria spp. were also detected in the samples tested. Principal component (PC) analysis yielded a three-dimension solution that explained 60% of the data variation; PC1 (26%) characterized chorizo formulations with more meat, while PC2 (19.3%) described sausages with longer/rapid fermentation, and PC3 (14.5%) highlighted chouriços with poorer hygiene. Cluster analysis identified three quality groups: (i) chorizos with high moisture, high protein content, and lowest pH; (ii) sausages with low moisture, high fat content, and elevated pH; and (iii) chouriças with high moisture and high protein but lower fat contents, low pH, and improved hygiene. Lastly, factor analysis yielded a varimax-rotated three-factor solution that explained 65% of the data, with similar results to PCA; factor 1 (23.5%) depicted chorizos with low pH but high moisture, factor 2 (20.8%) described sausages with more meat in the formulation, and factor 3 (20.6%) longer or rapid fermentation. Overall, the results evidenced the great variability in the quality attributes of Portuguese chorizo sausages, very likely to arise from multiple recipes, ingredients, and manufacturing practices. The definition of quality clusters is expected to play a crucial role for the self-denominated “artisanal” food companies to benchmark their chouriço sausages against the proper artisanal quality group.

In this study, we aimed to carry out the efficient fortification of vanilla milkshakes with micro-encapsulated microalgae oil (brand: S17-P100) without distorting the product’s odor. A 10-step oil-enrichment protocol was developed using an inclusion rate of 0.2 to 2 w/w%. Fatty acid (FA) profile analysis was performed using methyl esters with the GC-MS technique, and the recovery of docosahexaenoic acid (C22:6 n3, DHA) was robust (r = 0.97, p < 0.001). The enrichment process increased the DHA level to 412 mg/100 g. Based on this finding, a flash-GC-based electronic nose (e-nose) was used to describe the product’s odor. Applying principal component (PC) analysis to the acquired sensor data revealed that for the first four PCs, only PC3 (6.5%) showed a difference between the control and the supplemented products. However, no systematic pattern of odor profiles corresponding to the percentages of supplementation was observed within the PC planes. Similarly, when discriminant factor analysis (DFA) was applied, though a classification of the control and supplemented products, we obtained a validation score of 98%, and the classification pattern of the odor profiles did not follow a systematic format. Again, when a more targeted approach such as the partial least square regression (PLSR) was used on the most dominant sensors, a weak relationship (R2 = 0.50) was observed, indicating that there was no linear combination of the qualitative sensors’ signals that could accurately describe the supplemented concentration variation. It can therefore be inferred that no detectable off-odor was present as a side effect of the increase in the oil concentration. Some volatile compounds of importance in regard to the odor, such as ethylacetate, ethyl-isobutarate, pentanal and pentyl butanoate, were found in the supplemented product. Although the presence of yeasts and molds was excluded from the product, ethanol was detected in all samples, but with an intensity that was insufficient to cause an off-odor.

Textural characteristics of fruit are important for their quality, storability, and consumer acceptance. While texture can be evaluated instrumentally or sensorially, instrumental measurements are preferred if they can be reliably related to human perception. The objectives of this research were to validate instrumental measurements with sensory determinations, develop a classification scheme to group apples by their textural characteristics, and create models to predict sensory attributes from instrumental and compositional analyses. The textural characteristics (crispness, hardness, juiciness, and skin toughness) of 12 apple cultivars were evaluated on new and established cultivars. Fruit was also evaluated using five instrumental measurements from TA.XTplus Texture Analyzer, and three compositional determinations. The experiment was repeated for analysis and validation purposes. Principal component (PC) analysis revealed that 95.88% of the variation in the instrumental determinations could be explained by two components (PC 1 and PC 2); which were highly correlated with flesh firmness and skin strength, respectively. Four textural groups of apples were identified, and the accuracy of classification was established at 94.44% by using linear discriminant analysis. The predictive models that were developed between the sensory and instrumental-compositional data explained more than 85% of the variation in the data for hardness and crispness, while models for juiciness and skin toughness were more complex. The work should assist industry personnel to reduce time-consuming and costly sensory testing, yet have an appreciation of the textural traits as perceived by the consumer.