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Enregistrement W3000098311 · doi:10.17170/kobra-20190709592

Ultrasound treatment (low frequency) effects on probiotic bacteria growth in fermented milk

2019· article· en· W3000098311 sur OpenAlexaboutno aff
Alaa Kareem Niamah

Notice bibliographique

RevueKobra (Universitätsbibliothek Kassel) · 2019
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueFood Industry and Aquatic Biology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésProbioticBacteriaFood scienceFermentationBiologyMicrobiologyBiotechnology

Résumé

récupéré en direct d'OpenAlex

The effect of ultrasonic treatment at 40 kHz for 0, 5, 10, 15 and 20 minutes on the growth of five different strains of probiotic bacteria (Lactobacillus acidophilus LA-5, Lactobacillus casei LC, Lactobacillus reuteri LR-MM53, Bifidobacterium bifidum Bb-12 and Bifidobacterium loungm BB-536) in fermented milk was investigated. The study findings indicate that ultrasound treatment (10 minutes) increased the viable cells and total acidity for LA-5, LC and LR-MM53 samples but decreased viable cells and total acidity in the Bb-12 and BB-536 samples. All probiotic bacteria strains were ruptured by ultrasound treatment causing an increase in the extracellular release of β-galactosidase enzyme. Increased exposure time led to higher enzymatic activity. 2.9 unit/ml of β-galactosidase was measured in LR-MM53 after ultrasonic treatment for 20 minutes. The fermentation time of LA-5, LC and LR-MM53 samples were reduced after 10 minutes of ultrasound treatment compared with the control sample. Added 5 percent (10⁸ CFU/ml) of probiotic bacteria led to reduce at the fermentation time during ultrasonic treatment compared with control sample. The optimal time span of ultrasound treatment (40 kHz, 116 W) was 10 minutes for all fermented milk samples, which can be applied to increase the number of viable cells of probiotic bacteria and β-galactosidase enzyme. Keywords: Probiotic bacteria, Ultrasound, Fermented milk, β-galactosida Data of the article First received: 26 July 2018 | Last revision received: 27 March 2019Accepted: 20 May 2019 | Published online: 04 September 2019 DOI:10.17170/kobra-20190709592 References Abbas, S., Hayat, K., Karangwa, E., Bashari, M., & Zhang, X. (2013). An overview of ultrasound-assisted food-grade nanoemulsions. Food Engineering Reviews, 5(3), 139-157. Al-hilphy, A. R. S., Niamah, A. K., & Al-Temimi, A. B. (2012). Effect of ultrasonic treatment on buffalo milk homogenization and numbers of bacteria. International Journal of Food Science and Nutrition Engineering, 2(6), 113-118. Al-hilphy, A.R., Verma, D.K., Niamah, A. K., Billoria, S., & Srivastar, P. (2016). Principles of ultrasonic technology for treatment of milk and milk products. In M. Meghwal & M. R. Goyal (Eds.), Food process engineering: Emerging trends in research and their applications (pp. 178-202). Apple Academic Press. Al-Manhel, A. J., & Niamah, A. K. (2017). Mannan extract from Saccharomyces cerevisiae used as prebiotic in bioyogurt production from buffalo milk. International Food Research Journal, 24(5), 2259-2264. Carrillo-Lopez, L. M., Alarcon-Rojo, A. D., Luna-Rodriguez, L., & Reyes-Villagrana, R. (2017). Modification of Food Systems by Ultrasound. Journal of Food Quality, 2017. doi:10.1155/2017/5794931 Chau, Y., Suen, W. L. L., Tse, H. Y., & Wong, H. S. (2017). Ultrasound-enhanced penetration through sclera depends on frequency of sonication and size of macromolecules. 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The influence of selected prebiotics on the growth of lactic acid bacteria for bio-yoghurt production. Acta Sci Pol Technol Aliment., 10(4), 455-466. Kassem, A., Meade, J., McGill, K., Walsh, C., Gibbons, J., Lyng, J., & Whyte, P. (2018). An investigation of high intensity ultrasonication and chemical immersion treatments on Campylobacter jejuni and spoilage bacteria in chicken. Innovative Food Science & Emerging Technologies, 45, 298-305. IPCC. (2007) Climate change 2007: The physical science basis. Working Group I Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). New York: Cambridge University Press. Retrieved from https://www.ipcc.ch/report/ar4/wg1/ Nguyen, T. M. P., Lee, Y. K., & Zhou, W. (2009). Stimulating fermentative activities of bifidobacteria in milk by high intensity ultrasound. International dairy journal, 19(6), 410-416. Niamah, A. K. (2017). Physicochemical and microbial characteristics of yogurt added with Saccharomyces boulardii. Current Research in Nutrition and Food Science Journal, 5(3), 300-307. Niamah, A. K., Al-Sahlany, S. T. G., & Al-Manhel, A. J. (2016). Gum Arabic uses as prebiotic in yogurt production and study effects on physical, chemical properties and survivability of probiotic bacteria during cold storage. World Applied Sciences Journal, 34(9), 1190-1196. Niamah, A. K., Sahi, A. A., & Al-Sharifi, A. S. (2017). Effect of feeding soy milk fermented by probiotic bacteria on some blood criteria and weight of experimental animals. Probiotics and Antimicrobial Proteins, 9(3), 284–291. Ojha, K. S., Granato, D., Rajuria, G., Barba, F. J., Kerry, J. P., & Tiwari, B. K. (2018). Application of chemometrics to assess the influence of ultrasound frequency, Lactobacillus sakei culture and drying on beef jerky manufacture: Impact on amino acid profile, organic acids, texture and colour. Food Chemistry, 239, 544-550. Ojha, K. S., Mason, T. J., O’Donnell, C. P., Kerry, J. P., & Tiwari, B. K. (2017). Ultrasound technology for food fermentation applications. Ultrasonics Sonochemistry, 34, 410-417. O'Leary, V. S., & Woychik, J. H. (1976). Utilization of lactose, glucose, and galactose by a mixed culture of Streptococcus thermophilus and Lactobacillus bulgaricus in milk treated with lactase enzyme. Applied and Environmental Microbiology, 32(1), 89-94. Pitt, W. G., & Ross, S. A. (2003). Ultrasound increases the rate of bacterial cell growth. Biotechnology Progress, 19(3), 1038-1044. Pohlman, F. W., Dikeman, M. E., & Zayas, J. F. (1997). The effect of low-intensity ultrasound treatment on shear properties, color stability and shelf-life of vacuum-packaged beef semitendinosus and biceps femoris muscles. Meat Science, 45(3), 329-337. Racioppo, A., Corbo, M. R., Piccoli, C., Sinigaglia, M., Speranza, B., & Bevilacqua, A. (2017). Ultrasound attenuation of lactobacilli and bifidobacteria: Effect on some technological and probiotic properties. International Journal of Food Microbiology, 243, 78-83. Ranadheera, R. D. C. S., Baines, S. K., & Adams, M. C. (2010). Importance of food in probiotic efficacy. Food Research International, 43(1), 1-7. doi:10.1016/j.foodres.2009.09.009 Shershenkov, B., & Suchkova, E. (2015). Upgrading the technology of functional dairy products by means of fermentation process ultrasonic intensification. Agronomy Research, 13(4), 1074-1085. Tabatabaie, F., & Mortazavi, A. (2008). Studying the effects of ultrasound shock on cell wall permeability and survival of some LAB in milk. World Applied Sciences Journal ,3(1), 119-121. Unver, A. (2016). Applications of ultrasound in food processing. Green Chemistry & Technology Letters, 2(3), 121-126. Vercet, A., Oria, R., Marquina, P., Crelier, S., & Lopez-Buesa, P. (2002). Rheological properties of yoghurt made with milk submitted to manothermosonication. Journal of Agricultural and Food Chemistry, 50(21), 6165-6171. Wang, D., & Sakakibara, M. (1997). Lactose hydrolysis and b-galactosidase activity in sonicated fermentation with Lactobacillus strains. Ultrasonics Sonochemistry, 4, 255-261. Wu, X., & Narsimhan, G. (2017). Synergistic effect of low power ultrasonication on antimicrobial activity of melittin against Listeria monocytogenes. LWT-Food Science and Technology, 75, 578-581. Yang, M., & Li, L. (2010). Physicochemical, textural and sensory characteristics of probiotic soy yogurt prepared from germinated soybean. Food Technology and Biotechnology, 48(4), 490-496.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,856
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,011
Tête enseignante GPT0,192
Écart entre enseignants0,181 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations32
Publié2019
Routes d'admission1
Résumé présentoui

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