Investigating variability of craft microbreweries spent grains for classification and incorporation into precision diet formulation through multivariate analyses
Bibliographic record
Abstract
Alternative feedstuffs offer a cost-effective and sustainable option for livestock nutrition, playing a crucial role in niche market development. Brewer's spent grains (BSG), a byproduct of the expanding craft microbrewery industry, are a particularly promising feed source due to their availability and nutrient content. However, variability in BSG composition poses challenges for their effective incorporation into precision diet formulations. This study aimed to evaluate the variability in the nutrient composition of BSG from craft microbreweries and classify them for precision diet formulation using multivariate analyses. BSG samples from 29 craft microbreweries were collected and analysed for their nutrient composition using wet chemistry methods. Principal components analysed included crude protein (CP), ash and protein corrected neutral detergent fiber (apNDFom), non-fibrous carbohydrates (NFC), and ether extract (EE). Principal component analysis (PCA) was employed to identify the most significant nutrient variations, and hierarchical clustering of the principal components was used to group the samples into four distinct clusters. These clusters were further evaluated through in vitro fermentation tests, assessing gas production, digestibility, and fermentation characteristics. Statistical analyses were conducted using R software. The principal components (energy (PC1) and protein (PC2) were the primary factors driving BSG variability. Hierarchical clustering produced four distinct feed clusters, which showed significant differences ( P < 0.05) in fermentation profiles, The apNDFom digestibility varied across clusters, with energy-dense feeds (higher and lower energy grains) demonstrating higher digestibility ( P < 0.05). The third cluster (CL3), characterized by low protein content, had significantly lower NH 3 -N concentrations after fermentation ( P < 0.05). Regarding gas and volatile fatty acids (VFA) production, clusters exhibited significant differences ( P < 0.05) compared to an alfalfa standard, highlighting the diverse fermentation characteristics of BSG. The variability in energy and protein content among BSG samples results in distinct fermentation profiles, which can influence animal performance and environmental outcomes. These findings emphasize the importance of classifying BSG and incorporating precision formulation to mitigate adverse effects and maximize the benefits of this alternative feedstuff.
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 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".