Characterization of Chemical and Bacterial Compositions of Dairy Wastewaters
Bibliographic record
Abstract
The dairy industry produces large amounts of wastewater, including white and cleaning wastewater originating principally from rinsing and cleaning-in-place procedures. Their valorization into process water and non-fat milk solids, in the case of white wastewater, or the renewal of cleaning solutions could be achieved using pressure-driven membrane processes. However, it is crucial to determine the intrinsic characteristics of wastewaters, such as proximate composition and bacterial composition, to optimize their potential for valorization. Consequently, white and cleaning wastewaters were sampled from industrial-scale pasteurizers located in two different Canadian dairy processing plants. Bacterial profiles of dairy wastewaters were compared to those of tap waters, pasteurized skim milk and unused cleaning solutions. The results showed that the physicochemical characteristics as well as non-fat milk solids contents differed drastically between the two dairy plants due to different processing conditions. A molecular approach combining quantitative real-time polymerase chain reaction (qPCR) and metabarcoding was used to characterize the bacteria present in these solutions. The cleaning solutions did not contain sufficient genomic DNA for sequencing. In white wastewater, the bacterial contamination differed depending on the dairy plant (6.91 and 7.21 log10 16S gene copies/mL). Psychrotrophic Psychrobacter genus (50%) dominated white wastewater from plant A, whereas thermophilic Anoxybacillus genus (56%) was predominant in plant B wastewater. The use of cold or warm temperatures during the pasteurizer rinsing step in each dairy plant might explain this difference. The detailed characterization of dairy wastewaters described in this study is important for the dairy sector to clearly identify the challenges in implementing strategies for wastewater valorization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".