Treatment of spent wash water derived from shredded lettuce processing using a combination of electrocoagulation and germicidal ultraviolet light
Bibliographic record
Abstract
Water recycling is a significant part of an overall water management system. The current study evaluated electrocoagulation, used in combination with ultraviolet light (at 254 nm), to reduce the organic content and enhance the microbiological quality, of wash water derived from shredded lettuce processing. The composition of spent wash water derived from a commercial lettuce processing operation was used to prepare a simulated solution to be applied to validate the water recycling system. The simulated spent wash water was subjected to an electrocoagulation process followed by filtration and a tertiary ultraviolet (254 nm) treatment. The efficacy of the recycling treatment to decrease turbidity (nephelometric turbidity units, biological oxygen demand (BOD), chemical oxygen demand (COD) and decrease in introduced bacterial numbers. Spent wash water sampled from a commercial processing line was found to be colloidal in nature (78 ± 26 NTU) with low total solids content (544 ± 87 mg/L), BOD (230 ± 53 mg/L) and COD (309 ± 53 mg/L). An electrocogaultion process performed for 10 min using 3.48 A/m2 current density at pH 6.5 and conductivity of >100 µS/cm supported an 87% removal of turbidity, 38% reduction in BOD along with 49% decrease in COD. The electrocoagulation process was also found to reduce the levels of Escherichia coli, Salmonella and Listeria monocytogenes by 1–2 log cfu. The tertiary UV treatment of water derived from the electro coagulation process, supported further reduction in model pathogens, although it was noted that the D values for inactivation were in the order of 1.01–1.60 mJ/cm2, which compares to 0.22–0.31 mJ/cm2 in saline. The apparent increase in bacterial resistance to ultraviolet was likely due to the UV absorbing low molecular weight constituents within wash water that provided protection against inactivation. In conclusion, the study demonstrated the feasibility of applying electrocoagulation and UV to rapidly treat spent lettuce wash water to facilitate in-process recycling within shredded lettuce processing operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".