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Mozuriene E, Mockus E, Klupsaite D, Starkute V, Tolpeznikaite E, Gruzauskas V, Gruzauskas R, Paulauskaite-Taraseviciene A, Raudonis V, Bartkiene E. Physical and Chemical Characteristics of Droppings as Sensitive Markers of Chicken Health Status. Animals (Basel) 2024; 14:1389. [PMID: 38731394 PMCID: PMC11083088 DOI: 10.3390/ani14091389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/03/2024] [Accepted: 05/04/2024] [Indexed: 05/13/2024] Open
Abstract
The aim of this study was to analyze the physical and chemical characteristics of chicken droppings (n = 73), which were collected during different age periods and classified by visual inspection into normal (N) and abnormal (A). Significant differences were found in the texture, pH, dry matter (DM), fatty acids (FAs), short-chain fatty acids (SCFAs), and volatile compounds (VCs) between the tested dropping groups (p ≤ 0.05). The age period of the chicken had a significant influence on the color coordinates, texture, pH, DM, and SCFA contents in N and A as well as on all FAs content in N (p ≤ 0.05). Droppings from the N group had a harder texture, lower values of a* and b* color coordinates, higher DM content, higher level of linoleic FA, and lower level of α-linolenic FA than the droppings from the A group in each age period (p ≤ 0.05). The predominant SCFA was acetic acid, the content of which was significantly lower in the N group compared to that of the A group. The alcohol and organic acid contents were the highest in most of the A group at different age periods, while ketones dominated in the N and A groups. In conclusion, the majority of the tested dropping characteristics were influenced by the age period. While certain characteristics demonstrate differences between N and A, a likely broader range of droppings is required to provide more distinct trends regarding the distribution of characteristics across different droppings.
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Affiliation(s)
- Erika Mozuriene
- Institute of Animal Rearing Technologies, Faculty of Animal Sciences, Lithuanian University of Health Sciences, Mickeviciaus Str. 9, LT-44307 Kaunas, Lithuania; (E.M.); (E.M.); (D.K.); (V.S.); (E.T.)
| | - Ernestas Mockus
- Institute of Animal Rearing Technologies, Faculty of Animal Sciences, Lithuanian University of Health Sciences, Mickeviciaus Str. 9, LT-44307 Kaunas, Lithuania; (E.M.); (E.M.); (D.K.); (V.S.); (E.T.)
| | - Dovile Klupsaite
- Institute of Animal Rearing Technologies, Faculty of Animal Sciences, Lithuanian University of Health Sciences, Mickeviciaus Str. 9, LT-44307 Kaunas, Lithuania; (E.M.); (E.M.); (D.K.); (V.S.); (E.T.)
| | - Vytaute Starkute
- Institute of Animal Rearing Technologies, Faculty of Animal Sciences, Lithuanian University of Health Sciences, Mickeviciaus Str. 9, LT-44307 Kaunas, Lithuania; (E.M.); (E.M.); (D.K.); (V.S.); (E.T.)
- Department of Food Safety and Quality, Faculty of Veterinary Medicine, Lithuanian University of Health Sciences, Mickeviciaus Str. 9, LT-44307 Kaunas, Lithuania
| | - Ernesta Tolpeznikaite
- Institute of Animal Rearing Technologies, Faculty of Animal Sciences, Lithuanian University of Health Sciences, Mickeviciaus Str. 9, LT-44307 Kaunas, Lithuania; (E.M.); (E.M.); (D.K.); (V.S.); (E.T.)
| | - Valentas Gruzauskas
- Artificial Intelligence Centre, Kaunas University of Technology, K. Barsausko 59, LT-51423 Kaunas, Lithuania; (V.G.); (R.G.); (A.P.-T.); (V.R.)
| | - Romas Gruzauskas
- Artificial Intelligence Centre, Kaunas University of Technology, K. Barsausko 59, LT-51423 Kaunas, Lithuania; (V.G.); (R.G.); (A.P.-T.); (V.R.)
| | - Agne Paulauskaite-Taraseviciene
- Artificial Intelligence Centre, Kaunas University of Technology, K. Barsausko 59, LT-51423 Kaunas, Lithuania; (V.G.); (R.G.); (A.P.-T.); (V.R.)
| | - Vidas Raudonis
- Artificial Intelligence Centre, Kaunas University of Technology, K. Barsausko 59, LT-51423 Kaunas, Lithuania; (V.G.); (R.G.); (A.P.-T.); (V.R.)
| | - Elena Bartkiene
- Institute of Animal Rearing Technologies, Faculty of Animal Sciences, Lithuanian University of Health Sciences, Mickeviciaus Str. 9, LT-44307 Kaunas, Lithuania; (E.M.); (E.M.); (D.K.); (V.S.); (E.T.)
- Department of Food Safety and Quality, Faculty of Veterinary Medicine, Lithuanian University of Health Sciences, Mickeviciaus Str. 9, LT-44307 Kaunas, Lithuania
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Zarebska A, Amor ÁC, Ciurkot K, Karring H, Thygesen O, Andersen TP, Hägg MB, Christensen KV, Norddahl B. Fouling mitigation in membrane distillation processes during ammonia stripping from pig manure. J Memb Sci 2015. [DOI: 10.1016/j.memsci.2015.03.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Aust MO, Thiele-Bruhn S, Eckhardt KU, Leinweber P. Composition of organic matter in particle size fractionated pig slurry. BIORESOURCE TECHNOLOGY 2009; 100:5736-5743. [PMID: 19604689 DOI: 10.1016/j.biortech.2009.06.065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2009] [Revised: 06/18/2009] [Accepted: 06/18/2009] [Indexed: 05/28/2023]
Abstract
Pig slurry is a heterogeneous mixture of different particle sizes that will have different mobility in soil. Therefore, a physically fractionated pig slurry sample was analysed, e.g. using pyrolysis-field ionisation mass spectrometry (Py-FIMS) in an effort to identify relationships between particle size and composition of organic matter. The presumably most mobile fractions in soils (<63 microm) accounted for approximately 50% of slurry dry matter and were dominated by lignins, and N-containing compounds. Sterols were especially abundant in the larger-sized fractions, which corresponds to their reported distribution in soils and surface waters. The averaged molecular masses indicated similarities of fractions <10 microm to aquatic humic substances and increasing content of plant material with increasing particle size. A statistical analysis of the compound class distribution revealed that the analysis of three particle size fractions is essential for the assessment of the composition and properties of slurry constituents.
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Affiliation(s)
- Marc-Oliver Aust
- University of Trier, FB VI: Geography/Geosciences, Soil Science Section, Campus II, 54286 Trier, Germany.
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