Short communication: Potential prediction of vitamin B12 concentration based on mid-infrared spectral data using Holstein Dairy Herd Improvement milk samples
Bibliographic record
Abstract
The purpose of this study was (1) to predict the quantitative concentration of vitamin B 12 in milk using mid-infrared (MIR) spectrometry, and (2) to evaluate the potential of MIR spectra to discriminate different clusters of records based on their B 12 concentration. Milk samples were collected from 4,340 Holstein cows between 3 and 592 d in milk and located in 100 herds. Samples were taken using in-line milk meters and divided into 2 aliquots: one for MIR spectrometry and the other for B 12 concentration reference analyses by radioassay. Analyses were performed on 311 selected spectral wavelengths. A partial least squares regression model was built to quantify B 12 concentration. Discriminant analysis was used to isolate B 12 concentration clusters. A B 12 concentration threshold was set at 442 ng/dL, because this represents the cutoff value for a 250-mL glass of milk to fulfill 46% of the daily vitamin B 12 recommended dietary allowance for individuals 14 yr or older. For each analysis, records coming from two-thirds of herds were used to calibrate prediction equations, and the remaining records (one-third of herds for validation) were used to assess the prediction performance. In the case of discriminant analysis, validation sets were divided into evaluation sets (one-third of herds) to obtain alternate probability cutoffs and in test sets (two-thirds of herds) to validate equations. Spectral and B 12 concentration outliers were identified by calculating standardized Mahalanobis distance and with a residual analysis, respectively (n = 3,154). Regarding quantitative B 12 concentration, cross-validation and validation coefficients of determination averaged 0.51 and 0.46, respectively, which are relatively low, which would limit the potential use of the developed quantitative equations. In addition, root mean square errors of prediction of cross validation and validation sets averaged 88.9 and 94.7 ng/dL, respectively. Area under the receiver operating characteristic curve of test sets averaged 0.81 based on the 442 ng/dL threshold, which could be considered to represent good accuracy of classification. However, the false discovery rate averaged 36%. In summary, models predicting quantitative B 12 concentration had low cross-validation and validation coefficients of determination, limiting their use, but the proposed discriminant models could be used to identify milk samples with naturally high B12.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".