1: This study evaluates the capabilities of near-infrared spectroscopy (NIRS) and electronic nose (E-nose) in characterizing fermented milk, focusing on the impact of feeding type and probiotic potential. Three separate trials were conducted to compare the effects of Total Mixed Ration (TMR) cow feeds enriched with polyunsaturated fatty acids against control feeds. Milk samples, collected from the feeding trials, were fermented with three Lactobacillus strains categorized based on their probiotic potential: moderate (M), non-probiotic (N), and probiotic (P). The probiotic (P) strain exhibited distinct biochemical changes that were easily identifiable by both technologies. The NIRS and E-nose datasets were analysed separately to highlight the individual strengths and unique contributions of each technique in discriminating sample attributes. Specific NIRS wavelengths (1600–1800 nm), associated with unsaturated fatty acids like oleic and linoleic acids, acted as reliable markers for distinguishing milk samples based on the feeding type, while the 1300–1600 nm range helped differentiate strains. E-nose analysis identified volatile compounds such as hexanal and 1-hexen-3-one, formed from the oxidative degradation of unsaturated fatty acids, highlighting the impact of bacterial strains and milk composition on aroma and flavor. The fatty acid profile, particularly the unsaturated fatty acids and their derivatives, played a crucial role in strain and diet selection, offering valuable insights into the development of fermented milk products with specific probiotic characteristics.

With the development of various new types of instrumental aroma sensing technologies, there is a need for methodologies that help developers and users evaluate the performance of the different devices. This study introduces a simple method that uses standard coffee beverages, reproducible worldwide, thus allowing users to compare aroma sensing devices and technologies globally. Eight different variations of commercial coffee capsules were used to brew espresso coffees (40 mL), consisting of either Arabica coffee or a blend of Robusta and Arabica coffee, covering a wide range of sensory attributes. The AlphaMOS Astree electronic tongue (equipped with sensors based on chemically modified field-effect transistor technology) and the AlphaMOS Heracles NEO and the Volatile Scout3 electronic noses (both using separation technology based on gas chromatography) were used to describe the taste and odor profiles of the freshly brewed coffee samples and also to compare them to the various sensory characteristics declared on the original packaging, such as intensity, roasting, acidity, bitterness, and body. Linear discriminant analysis (LDA) results showed that these technologies were able to classify the samples similarly to the pattern of the coffees based on the human sensory characteristics. In general, the arrangement of the different coffee types in the LDA results—i.e., the similarities and dissimilarities in the types based on their taste or smell—was the same in the case of the Astree electronic tongue and the Heracles electronic nose, while slightly different arrangements were found for the Scout3 electronic nose. The results of the Astree electronic tongue and those of the Heracles electronic nose showed the taste and smell profiles of the decaffeinated coffees to be different from their caffeinated counterparts. The Heracles and Scout3 electronic noses provided high accuracies in classifying the samples based on their odor into the sensory classes presented on the coffee capsules’ packaging. Despite the technological differences in the investigated devices, the introduced coffee test could assess the similarities in the taste and odor profiling capacities of the aroma fingerprinting technologies. Since the coffee capsules used for the test can be purchased all over the world in the same quality, these coffees can be used as global standard samples during the comparison of different devices applying different measurement technologies. The test can be used to evaluate instrumentational and data analytical developments worldwide and to assess the potential of novel, cost-effective, accurate, and rapid solutions for quality assessments in the food and beverage industry.

One of the major issues confronting the dairy industry is the efficient evaluation of the quality of feed, milk and dairy products. Over the years, the use of rapid analytical methods in the dairy industry has become imperative. This is because of the documented evidence of adulteration, microbial contamination and the influence of feed on the quality of milk and dairy products. Because of the delays involved in the use of wet chemistry methods during the evaluation of these products, rapid analytical techniques such as near-infrared spectroscopy (NIRS) has gained prominence and proven to be an efficient tool, providing instant results. The technique is rapid, nondestructive, precise and cost-effective, compared with other laboratory techniques. Handheld NIRS devices are easily used on the farm to perform quality control measures on an incoming feed from suppliers, during feed preparation, milking and processing of cheese, butter and yoghurt. This ensures that quality feed, milk and other dairy products are obtained. This review considers research articles published in reputable journals which explored the possible application of NIRS in the dairy industry. Emphasis was on what quality parameters were easily measured with NIRS, and the limitations in some instances.

Controversies surrounding the name and how the electronics nose (e-nose) works have been at the center stage since the advent of the technology. Notwithstanding the controversies, the technology has gained popularity in the sensory analysis of dairy foods, because of its rapid results delivery on product aroma profile or pattern, which can be used to assess quality. This review critically evaluated the advances made in the application of the e-nose or artificial sensory system in the dairy industry, focusing on the evaluation of milk, yoghurt and cheese properties, and the trends and prospects of the technology. Most of the e-nose devices applied in the available scientific publications used sensors such as metal oxide semiconductor sensors (MOS), metal-oxide-semiconductor field-effect transistor (MOSFET), conducting polymers composites and quartz microbalance (QMB), and flame ionization detector FID, in a recent study. Though known for aroma sensing, the technology has been applied to evaluate the shelf life or microbial spoilage and to discriminate dairy products based on the volatile profile composition, as determined by the sensors. In most cases, the limitation of the technology is the inability of it to provide information on the nature of constituting compounds, except in gas chromatography and mass spectrometry-based e-nose systems.

The objective of our research was to evaluate the possible alteration of the organoleptic properties of eggs produced by hens (Lohmann Brown-Classic) fed with diets containing different doses of an industrial by-product enriched with organic zinc (Zincoppyeast, ZP): Control 0%, ZP 2.5%, and ZP 5.0%. Eggs were collected after 30 days (batch 1) and 60 days (batch 2) of feeding with the experimental diets and subjected to chemical, microbiological, human sensory, e-nose, and e-tongue analyses. There was no significant difference among the microbiological status of eggs of the three groups, but there were significant differences (p < 0.05) in the fat (9.5% vs. 9.3%) and protein contents (12.7% vs. 13.4%) of the Control and ZP 5.0% groups, respectively. Human sensory analysis showed no clear change in the organoleptic characteristics of the eggs. Using linear discriminant analysis (LDA), the e-tongue could recognize the three groups of eggs in batch 1 and batch 2 with 95.9% and 100% accuracy and had a prediction accuracy of 64.8% and 56.2%, respectively. When the eggs were incubating at 50 °C or 80 °C before the e-nose analysis, the groups of eggs could be recognized with 98.0% and 82.7% accuracy, and predicted with 68.5% and 62.2% accuracy, respectively, using principal component analysis-based discriminant analysis (PCA–DA). The aroma compounds and respective sensory descriptors showing changes among the different groups of eggs (batch, storage, and feeding) were identified based on the e-nose analysis. The sup-plementation of laying hens’ feed with the investigated industrial by-product can be applied without any substantial effect on egg quality, which can, however, be detected with advanced analytical methods.

The effects of n-6 and n-3 fatty acid supplementation on the fatty acid profile of sow milk were investigated using traditional fatty acid analysis and a novel method of the electronic nose (EN). The control group received 6.3 g of sunflower oil (SO) rich in n-6 fatty acids per kg feed, and experimental animals received the same amount of fish oil (FO) as an n-3 fatty acid source. The diets were corn- and soybean meal-based diets. Supplementation of SO enhanced the amount of linoleic acid (C18:2, n-6) (SO: 8.43 mg/mL vs. FO: 6.63 mg/mL milk), and significantly increased (p < 0.02) the amount of total polyunsaturated fatty acids (SO: 9.92 mg/mL vs. FO: 8.61 mg/mL milk) in the sow’s milk. On the contrary, FO significantly increased the amount of n-3 polyunsaturated fatty acids (FO: 1.17 mg/mL vs. SO: 0.69 mg/mL milk), especially eicosapentaenoic acid (C20:5, n-3), docosapentaenoic acid (C22:5, n-3), and docosahexaenoic acid (C22:6, n-3), in the milk (p < 0.001). FO and SO supplementation did not affect the analytical composition of milk. Milk samples collected from the differently fed individuals could be clearly separated according to the feeding groups based on the odour profile described by the EN.