Multivariate Calibration for the Determination of Total Azadirachtin-Related Limonoids and Simple Terpenoids in Neem Extracts Using Vanillin Assay
Bibliographic record
Abstract
Two-component and multivariate calibration techniques were developed for the simultaneous quantification of total azadirachtin-related limonoids (AZRL) and simple terpenoids (ST) in neem extracts using vanillin assay. A mathematical modeling method was also developed to aid in the analysis of the spectra and to simplify the calculations. The mathematical models were used in a two-component calibration (using azadirachtin and limonene as standards) for samples containing mainly limonoids and terpenoids (such as neem seed kernel extracts). However, for the extracts from other parts of neem, such as neem leaf, a multivariate calibration was necessary to eliminate the possible interference from phenolics and other components in order to obtain the accurate content of AZRL and ST. It was demonstrated that the accuracy of the vanillin assay in predicting the content of azadirachtin in a model mixture containing limonene (25% w/w) can be improved from 50% overestimation to 95% accuracy using the two-component calibration, while predicting the content of limonene with 98% accuracy. Both calibration techniques were applied to estimate the content of AZRL and ST in different parts of the neem plant. The results of this study indicated that the relative content of limonoids was much higher than that of the terpenoids in all parts of the neem plant studied.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".