Partial least squares discrimination applied to a few samples dataset: A case for predicting the presence of pesticide in lettuce
Bibliographic record
Abstract
Abstract To perform discriminant analysis through partial least squares (PLS‐DA), it is important to note that the smaller the set of samples and the higher the variable to sample ratio, the higher the chance of a too optimistic classification model. In this sense, it is necessary to use strategies to check for the possibility that the classification achieved is done by chance. In metabolomics studies, it is not uncommon to work with a reduced number of samples, in which discrimination approaches must be evaluated according to its reliability. Considering this issue, this study aimed to show a case study to deal with few samples using PLS‐DA to discriminate lettuce cultivated in the absence and presence of imidacloprid (IMI). The data were acquired by using ultra‐high‐performance liquid chromatography coupled to a quadrupole‐time of flight mass spectrometry, and the model prediction ability was evaluated by permuting the classes. The performance of the PLS‐DA model built using all the variables reached 100% correct classification. Nonetheless, the reliability tests (Wilcoxon, sign test, and Rand t test) indicated that the model has been build choosing variables by chance. By using the variable importance in projection, it was possible to build a model with reliable specificity and sensitivity equals 1. The study showed the need to check the classification ability in PLS‐DA models through strategies such as variable selection and the permutation test in order to allow for the evaluation of the reliability of the results, even in cases in which the classification reaches 100% in the target.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".