LC‐MS/MS for simultaneous detection and quantification of Amadori compounds in tomato products and dry foods and factors affecting the formation and antioxidant activities
Bibliographic record
Abstract
In order to establish an efficient detection method to evaluate the formation of Amadori compounds (ACs) in food products and study the potential health effects, an ultra-high performance liquid chromatography-tandem mass spectrometric (UPLC-MS/MS) method using caffeine as internal standard was developed to determine eight ACs. The detection limits ranged from 0.0179 to 0.0887 mg/L for the ACs. The accuracy of the method was tested through measuring recovery of the spiked samples that varied from 81.90 ± 2.98% to 108.74 ± 2.34%. This method was further applied to detect ACs in 10 food products. Results showed that dry fruits and vegetables were rich in ACs, the total content of ACs varied from 1.36 ± 0.26 to 3415.91 ± 147.96 mg/100 g. The total amount of ACs in tomato juice heated under vacuum condition showed significant increment (P < 0.05) in 25 min at 80 °C comparing with that under atmospheric pressure due to the rapid loss of water. Besides, the amino acid content shows positive correlation with the corresponding AC formation in Maillard reaction during food drying. After heated at fixed water activity (Aw) for 4 hr by sous-vide process, the ACs content in tomato powder increased significantly and the antioxidant activity improved as well. PRACTICAL APPLICATION: Results of this study provided a valuable tool to evaluate the formation of ACs in complex dry food products, facilitated the quality control of food products. The knowledge obtained will offer useful information to food processors. The synthesized ACs would facilitate further study into the antioxidant activities and potential health effects of specified AC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".