Advanced hyphenated chromatographic‐mass spectrometry in mycotoxin determination: Current status and prospects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mass spectrometric techniques are essential for advanced research in food safety and environmental monitoring. These fields are important for securing the health of humans and animals, and for ensuring environmental security. Mycotoxins, toxic secondary metabolites of filamentous fungi, are major contaminants of agricultural products, food and feed, biological samples, and the environment as a whole. Mycotoxins can cause cancers, nephritic and hepatic diseases, various hemorrhagic syndromes, and immune and neurological disorders. Mycotoxin-contaminated food and feed can provoke trade conflicts, resulting in massive economic losses. Risk assessment of mycotoxin contamination for humans and animals generally depends on clear identification and reliable quantitation in diversified matrices. Pioneering work on mycotoxin quantitation using mass spectrometry (MS) was performed in the early 1970s. Now, unambiguous confirmation and quantitation of mycotoxins can be readily achieved with a variety hyphenated techniques that combine chromatographic separation with MS, including liquid chromatography (LC) or gas chromatography (GC). With the advent of atmospheric pressure ionization, LC-MS has become a routine technique. Recently, the co-occurrence of multiple mycotoxins in the same sample has drawn an increasing amount of attention. Thus, modern analyses must be able to detect and quantitate multiple mycotoxins in a single run. Improvements in tandem MS techniques have been made to achieve this purpose. This review describes the advanced research that has been done regarding mycotoxin determination using hyphenated chromatographic-MS techniques, but is not a full-circle survey of all the literature published on this topic. The present work provides an overview of the various hyphenated chromatographic-MS-based strategies that have been applied to mycotoxin analysis, with a focus on recent developments. The use of chromatographic-MS to measure levels of mycotoxins, including aflatoxins, ochratoxins, patulin, trichothecenes, zearalenone, and fumonisins, is discussed in detail. Both free and masked mycotoxins are included in this review due to different methods of sample preparation. Techniques are described in terms of sample preparation, internal standards, LC/ultra performance LC (UPLC) optimization, and applications and survey. Several future hyphenated MS techniques are discussed as well, including multidimensional chromatography-MS, capillary electrophoresis-MS, and surface plasmon resonance array-MS.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it