Integrating advanced computing techniques into food safety management has attracted significant attention recently. Machine learning (ML) algorithms offer innovative solutions for Hazard Analysis Critical Control Point (HACCP) monitoring by providing advanced data analysis capabilities and have proven to be powerful tools for assessing the safety of Animal-Source Foods (ASFs). Studies that link ML with HACCP monitoring in ASFs are limited. The present review provides an overview of ML, feature extraction, and selection algorithms employed for food safety. Several non-destructive techniques are presented, including spectroscopic methods, smartphone-based sensors, paper chromogenic arrays, machine vision, and hyperspectral imaging combined with ML algorithms. Prospects include enhancing predictive models for food safety with the development of hybrid Artificial Intelligence (AI) models and the automation of quality control processes using AI-driven computer vision, which could revolutionize food safety inspections. However, handling conceivable inclinations in AI models is vital to guaranteeing reasonable and exact hazard assessments in an assortment of nourishment generation settings. Moreover, moving forward, the interpretability of ML models will make them more straightforward and dependable. Conclusively, applying ML algorithms allows real-time monitoring and predictive analytics and can significantly reduce the risks associated with ASF consumption.

The aim of the present study was to evaluate Marisol strawberries’ (Fragaria × ananassa) physicochemical quality and shelf-life during storage, using an integrated analytical approach. More specifically, the research aimed to assess the strawberries’ color, texture, and nutritional quality, over an 11-day storage period, employing physicochemical analyses, spectrophotometric assays, Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy, image analysis, and statistical tools. The results revealed significant changes in the outer surface texture and color characteristics, indicating spoilage progression. Physicochemical parameters such as water activity, moisture content, soluble solids, titratable acidity, and ascorbic acid content exhibited significant alterations, influencing the taste profile and freshness perception. Antioxidant and antiradical activities showed fluctuations, suggesting a potential decrease in phenolic content during storage. Moreover, the ATR-FTIR spectra findings confirmed the results regarding the moisture content, soluble solids, and total phenolic content. The integration of physicochemical and image analysis-derived features through a principal component analysis (PCA) enabled the accurate classification of samples based on storage days. Regression analysis, using these features, successfully predicted the storage day with high accuracy. Overall, this integrated analytical approach provided valuable information on the estimation of Marisol strawberries’ shelf-life and the prediction of their quality deterioration, contributing to better fruit management and the minimization of discards.

Currently, the evaluation of fruit ripening progress in relation to physicochemical and texture-quality parameters has become an increasingly important issue, particularly when considering consumer acceptance. Therefore, the purpose of the present study was the application of rapid, nondestructive, and conventional methods to assess the quality of banana peels and flesh in terms of ripening and during storage in controlled temperatures and humidity. For this purpose, we implemented various analytical techniques, such as attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for texture, colorimetrics, and physicochemical features, along with image-analysis methods and discriminant as well as statistical analysis. Image-analysis outcomes showed that storage provoked significant degradation of banana peels based on the increased image-texture dissimilarity and the loss of the structural order of the texture. In addition, the computed features were sufficient to discriminate four ripening stages with high accuracy. Moreover, the results revealed that storage led to significant changes in the color parameters and dramatic decreases in the texture attributes of banana flesh. The combination of image and chemical analyses pinpointed that storage caused water migration to the flesh and significant starch decomposition, which was then converted into soluble sugars. The redness and yellowness of the peel; the flesh moisture content; the texture attributes; Brix; and the storage time were all strongly interrelated. The combination of these techniques, coupled with statistical tools, to monitor the physicochemical and organoleptic quality of bananas during storage could be further applied for assessing the quality of other fruits and vegetables under similar conditions.

Commercially available common and balsamic vinegars were examined, using a combination of spectrophotometric, chromatographic, colorimetric and spectroscopic methods. Total phenolic content, antioxidant activity, radical scavenging capacity, phenolic profile, colour parameters, Fourier Transform Infrared (FT-IR) absorbance spectra and Nuclear Magnetic Resonance (¹H NMR) spectra were comparatively studied. The main scope was the assessment of vinegar antioxidant and metabolic profiles and the identification of the most appropriate features influencing their type and subtypes. Red grape balsamic vinegars exhibited the strongest antioxidant profile. High total phenolic content and radical scavenging-antioxidant activity of vinegars was strongly correlated with high hue-angle and colour density values and low lightness and a* values. FT-IR spectra analysis confirmed the presence of organic acids and carbohydrates and, in combination with Gas Chromatography-Mass Spectrometry (GC-MS), the occurrence of phenolic compounds. NMR spectroscopy enabled the identification of 27 characteristic metabolites in each type of vinegar. The combination of all applied techniques provides critical information on compositional differences among the vinegars and could serve as an application tool for similar fermentation products.

Carotenoids are important antioxidant compounds, present in many foods of plant, animal and marine origin. The aim of the present study was to describe the carotenoid composition of tomato waste, prawn muscle and cephalothorax and avian (duck and goose) egg yolks through the use of a modified gradient elution HPLC method with a C₃₀ reversed-phase column for the efficient separation and analysis of carotenoids and their cis-isomers. Elution time was reduced from 60 to 45 min without affecting the separation efficiency. All-trans lycopene predominated in tomato waste, followed by all-trans-β-carotene, 13-cis-lutein and all-trans lutein, while minor amounts of 9-cis-lutein, 13-cis-β-carotene and 9-cis-β-carotene were also detected. Considering the above findings, tomato waste is confirmed to be an excellent source of recovering carotenoids, especially all-trans lycopene, for commercial use. Xanthophylls were the major carotenoids of avian egg yolks, all-trans lutein and all-trans zeaxanthin in duck and goose egg yolk, respectively. In the Penaeus kerathurus prawn, several carotenoids (zeaxanthin, all-trans-lutein, canthaxanthin, cryptoxanthin, optical and geometrical astaxanthin isomers) were identified in considerable amounts by the same method. A major advantage of this HPLC method was the efficient separation of carotenoids and their cis-isomers, originating from a wide range of matrices.

Background: The identification of bacterial species in fermented PDO (protected designation of origin) cheese is important since they contribute significantly to the final organoleptic properties, the ripening process, the shelf life, the safety and the overall quality of cheese. Methods: Ten commercial PDO feta cheeses from two geographic regions of Greece, Epirus and Thessaly, were analyzed by 16S metagenomic analysis. Results: The biodiversity of all the tested feta cheese samples consisted of five phyla, 17 families, 38 genera and 59 bacterial species. The dominant phylum identified was Firmicutes (49% of the species), followed by Proteobacteria (39% of the species), Bacteroidetes (7% of the species), Actinobacteria (4% of the species) and Tenericutes (1% of the species). Streptococcaceae and Lactobacillaceae were the most abundant families, in which starter cultures of lactic acid bacteria (LAB) belonged, but also 21 nonstarter lactic acid bacteria (NSLAB) were identified. Both geographical areas showed a distinctive microbiota fingerprint, which was ultimately overlapped by the application of starter cultures. In the rare biosphere of the feta cheese, Zobellella taiwanensis and Vibrio diazotrophicus, two Gram-negative bacteria which were not previously reported in dairy samples, were identified. Conclusions: The application of high-throughput DNA sequencing may provide a detailed microbial profile of commercial feta cheese produced with pasteurized milk.