1,720,972 research outputs found
Data on antimicrobial, barrier, and mechanical properties of biocomposites prepared from carrot pomace and wheat gluten with varied eugenol content
No full textThis article presents analyzed data on the antimicrobial, barrier, and mechanical properties inherent to films created by blending carrot pomace with wheat gluten and polyglycerol-3 plasticizer and combined with varying contents (0 wt.%, 3 wt.%, and 5 wt.%) of eugenol, a natural antimicrobial compound derived from essential oils. The integration of carrot pomace, wheat gluten, plasticizer, and eugenol involved meticulous mortar and pestle processing, ensuring a homogenous blend. Subsequently, the mixture was compression-molded in a hydraulic press to fabricate the films. Standard bacteria strains-Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 6538-are used in the antimicrobial evaluation, and antimicrobial efficacy is measured using OD600 measurements. Water vapor permeability (WVP) measurement effectively defines the films' potential to prevent water vapor infiltration. Mechanical properties are assessed by determining elastic modulus, tensile strength, and elongation at break, which together reveal the films' adaptive flexibility and durability. The dataset presented herein holds substantial promise for food packaging applications. Researchers in the food packaging industry can leverage the antimicrobial and barrier property data to design novel packaging materials, potentially enhancing shelf-life and food safety. Engineers and material scientists can utilize the mechanical properties data to develop structurally robust and flexible materials. (c) 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/
Comprehensive assessment of bacterial community viability in natural whey starter cultures by flow cytometric approach
No full textThe natural whey starter consists of a lactic microbial community
perpetuated daily by fermenting the whey from the previous day’s
processing. These natural cultures play a crucial role in the acidification
process of the cheese mass, which must lead to the complete breakdown of
lactose in the first few hours after its production. The lactic acid bacteria in
the whey starter act first, through their metabolism, as living cells and then
as released enzymes, participating in the complex biochemical phenomena
that occur during cheese ripening. The viability of the cells plays a key role
in the acidifying activity required during the first hours of curd acidification,
in parallel to the microbial composition. Analytical methods based on
plate counts have long been used to evaluate the number of cultured
cells. The disadvantage of these methods is that they have high variability
and take days to obtain results. In this work, we focused on evaluating the
physiological state of the lactic bacterial cell community of natural whey
starter samples. This work involved a comparison of bacterial enumeration
by classical methods using whey agar medium, MRS, and M17, and flow
cytometry on natural whey samples. The flow cytometry results were in good
agreement with a tendency towards overestimation. Flow cytometry has
also introduced other parameters to assess natural whey by measuring cell
physiological status. In addition, two fluorescent dyes used in flow cytometry
resulted in the assessment of cell wall damage and metabolic activity and
therefore enabled the estimation of the number of cells even under suboptimal
physiological states. In conclusion, we discovered that determining
the physiological condition of the cells served as a potential indicator for
predicting its acidifying activity
Bifidobacterium fermentum sp. nov. and Bifidobacterium aquikefiricola sp. nov., isolated from water kefir
No full text: Four strains, representing two novel Bifidobacterium species, were isolated from water kefir, a fermented beverage. 16S rRNA gene analysis suggested that the novel species share high identities (98.82-98.89%) with Bifidobacterium aquikefiri LMG 28769T. Complete genomes were assembled with a short- and long-read hybrid sequencing approach. In agreement with the 16S rRNA gene analysis, phylogenetics with 117 marker genes places the novel species closest to B. aquikefiri LMG 28769T as well. The isolates have average nucleotide identity (ANI) scores ranging from 81.46 to 84.84% and digital DNA-DNA hybridization (dDDH) scores from 23.9 to 38.5% with the closest related species, as well as ANI scores between the proposed new species of 80.50%, indicating that the isolates represent two novel species. Matrix-assisted laser desorption/ionization-time of flight chemotaxonomic analysis supported the gene-based taxonomic placement. We propose the names Bifidobacterium fermentum sp. nov. and Bifidobacterium aquikefiricola sp. nov. for these novel species within the Bifidobacterium genus. The proposed type strain B. fermentum WK012_4_13T (= LMG 33104T = DSM 116073T; GenBank accession number GCF_041080835.1) has a genome size of 2.43 Mbp, with a G+C content of 56.00 mol%. The proposed type strain for B. aquikefiricola WK041_4_12T (= LMG 33105T = DSM 116074T; GenBank accession number GCF_041080795.1) has a genome size of 2.36 Mbp and a G+C content of 53.94 mol%. B. fermentum cells are Gram-positive staining, non-motile, non-spore-forming, fructose-6-phosphate phosphoketolase (F6PPK)-positive, catalase- and oxidase-negative and bacillary club shaped. B. aquikefiricola cells are Gram-positive staining, non-motile, non-spore-forming, F6PPK-positive, catalase- and oxidase-negative and square rod shaped
The genus Microbacterium: A bacterial community resistant to sanitisation processes in long-life milk
No full textMicrofiltration and pasteurization are treatments carried out to guarantee the safety of milk and to elongate the shelf life. These sanitization processes strongly modify the composition of the microbial population in raw milk. Microfiltered milk samples produced over three months and in two different industrial dairy plants were studied. The microbial analysis allowed us to identify Microbacterium sp. as dominant and other spoiling bacteria belonging to Bacillus, Acinetobacter, Micrococcus, Staphylococcus, Pantoea, and Escherichia. Microorganisms were identified after colony isolation and by 16S rRNA sequencing. To accurately identify Microbacterium species, gyrB gene similarity analysis was performed, and Microbacterium lacticum and Microbacterium paulum resulted being the two dominant species. The role played by Microbacterium sp. in milk is unknown, and a lab-scale experiment was carried out to evaluate the effects. Most Microbacterium isolated strains can grow in milk with moderate acidification activity. The strains were exposed to pasteurization treatment, and the isolated strains survived when a combination of time and temperature similar to industrial conditions was used. Antibiotic resistance was evaluated in the isolated Microbacterium strains, and resistance to clindamycin and ciprofloxacin was found
Combining UHPLC‐HRMS targeted and suspect screening for a comprehensive analysis of nisin A and its variants in cow milk
No full textA liquid chromatography method coupled with an Orbitrap analyser was used to detect nisin A and its variants in partially skimmed and raw cow milk. A suspect screening method enabled the identification of F, Q and Z variants. Adequate linearity was achieved (r(2) = 0.9998-1 up to 250 mu g/L) with a limit of quantitation for nisin A of 2.5 mu g/L. Method accuracy (at 2.5, 25 and 250 mu g/L) in milk ranged from 94 to 98% and precision in the range 0.4-1.9%. Nisin Z was abundant in partially skimmed samples. The method was suitable for determining these bacteriocins in cow milk
Milky WAI: Unlocking the Secrets of Raw Cow Milk Through Speckle Pattern and AI
Full text linkThis paper introduces a novel method for raw cow milk classification combining speckle pattern (SP) imaging and AI-based processing of statistical parameters. Raw cow milk has been classified considering (i) 6 statistical features extracted from raw SP images, and (ii) 4 features extracted from the Gray-Level Co-occurrence Matrix (GLCM) computed on each SP image. We conducted 4 experimental campaigns, resulting in 24, 000 SP images retrieved from 20 milks produced by 20 cows. We then considered two different datasets: (i) a Complete dataset (made of all 24,000 frames), and (ii) a Reduced dataset, built by excluding data from one acquisition campaign. The two datasets were then split into training and testing sub-datasets. We trained three Wide Neural Networks (WNN) using different strategies. WNN1 and WNN2 have been trained and tested using the Complete and the Reduced datasets respectively, while WNN3 has been trained on the Reduced tested on the Complete dataset. WNN3 only attained 72% accuracy (revealing sensitivity to environmental conditions), while WNN1 and WNN2 achieved a test accuracy higher than 90%, demonstrating effectiveness without relying on computationally expensive models such as CNNs. This approach could be considered as a promising initial step for cow milk classification using SP imaging
Natural creaming significantly modulates the metabolomic profile and bacterial community of raw milk: A case study on organic milk for Parmigiano Reggiano PDO
No full textThis study analyzed raw milk's microbial community and chemical profile during the natural creaming process of Parmigiano Reggiano production by comparing milk from farms following two different organic certifications. Specifically, the natural creaming process underlined the positive accumulation of potentially pro-dairy bacteria, particularly those of the genera Lactococcus and Streptococcus, and a significant reduction of negative bacterial genera, such as Acinetobacter and Rothia, in the final mix milk. Meanwhile, untargeted metabolomic analysis confirmed the representativeness of lipids and lipid-derivatives as chemical markers involved in the overnight creaming process, with fatty acid esters and long fatty acids enriched in the evening samples. Finally, by using a multi-omics approach, we integrated microbial and metabolomic datasets and identified correlations between specific microbial populations and metabolite changes. This integrative analysis revealed microbial-metabolite interactions that may be a starting point to better understand the pivotal role exerted by milk creaming on the final cheese quality
Case Study on the Microbiological Quality, Chemical and Sensorial Profiles of Different Dairy Creams and Ricotta Cheese during Shelf-Life
No full textThis work investigated the microbiological quality and chemical profiles of two different dairy creams obtained by centrifugation vs. natural creaming separation systems. To this aim, an untargeted metabolomics approach based on UHPLC-QTOF mass spectrometry was used in combination with multivariate statistical tools to find potential marker compounds of the two different types of two dairy creams. Thereafter, we evaluated the chemical, microbiological and sensorial changes of a ricotta cheese made with a 30% milk cream (i.e., made by combining dairy creams from centrifugation and natural creaming separation) during its shelf-life period (12 days). Overall, microbiological analysis revealed no significant differences between the two types of dairy creams. On the contrary, the trend observed in the growth of degradative bacteria in ricotta during shelf-life was significant. Metabolomics revealed that triacylglycerols and phospholipids showed significant strong down-accumulation trends when comparing samples from the centrifugation and natural creaming separation methods. Additionally, 2,3-Pentanedione was among the best discriminant compounds characterising the shelf-life period of ricotta cheese (VIP score = 1.02), mainly related to sensorial descriptors, such as buttery and cheesy. Multivariate statistics showed a clear impact of the shelf-life period on the ricotta cheese, revealing 139 potential marker compounds (mainly included in amino acids and lipids). Therefore, the approach used showed the potential of a combined metabolomic, microbiological and sensory approach to discriminate ricotta cheese during the shelf-life period
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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