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
Abstract The recent application of fluorescence spectroscopy to food analysis is reviewed and future trends in fluorometry are discussed. For food proteins, two techniques, i.e. intrinsic fluorometry and extrinsic fluorometry, are contrasted. Changes in the fluorescence intensity due to tryptophan and the anisotropy were measured in food proteins, including β‐lactoglobulin, α‐lactalbumin and lysozyme, to estimate changes in molecular structure during environmental alterations. Major applications of extrinsic fluorometry to food proteins are surface hydrophobicity measurements using hydrophobic probes. For this purpose, this approach is currently the most popular method in food protein chemistry probably because of the simplicity of the analytical technique. Advantages and disadvantages of various fluorescence probes and their applications were compared. Toxins are one of the most appropriate applications of sensitive fluorometry. Examples of the application shown here are mycotoxins and toxins of shellfish poisoning. For the former, capillary electrophoresis was introduced as a new tool. For the latter, the methods of derivatization are critical and therefore compared. For enumerating bacterial infection, bioluminescence using luciferase and direct epifluorescent filter technique (DEFT) are being used. Further, immunofluorescence is useful for specifically detecting pathogenic bacteria such as Salmonella, Listeria and enterotoxigenic Escherichia coli. Fluorescence caused by heating foods and fat oxidation was measured to assess their intensity. The thiobarbituric acid (TBA) reaction for measuring fat oxidation by colorimetry has been replaced frequently by fluorometry to improve the accuracy and specificity. Vitamins have been other popular analytes for fluorometry. Water‐soluble and fat‐soluble vitamins are discussed separately. High‐performance liquid chromatography (HPLC) combined with fluorometric detectors is popular for the simultaneous analysis of multivitamins or multiforms of vitamins. Normal‐phase HPLC of fat‐soluble vitamins eliminated the need for solvent extraction and, in some cases, even the saponification process. As food additives, antibiotics (although they may be considered contaminants), aspartame and salicylates are discussed in this chapter. In amino acids analysis, reversed‐phase high‐performance liquid chromatography (RPHPLC) with fluorometric detectors has replaced the traditional ion exchange–ninhydrin colorimetric detector systems because of simpler, quicker elution and higher sensitivity of detection. In enzyme chemistry, alkaline phosphatase (ALP) determinations of the adequacy of milk pasteurization and proteolytic enzyme activity are the most frequently reported methods in the recent literature. A fluorometric substrate is used for the former, which converts to a fluorescent form upon loss of a phosphate radical due to the action of ALP. For the latter, the same reagent as in amino acid analysis emits fluorescence upon reaction with an α‐amino group yielded from proteins by proteolysis. During the past one or two decades, fluorometry has in many cases replaced traditional colorimetry because of its higher sensitivity and selectivity. However, the modern automated, quicker separation technique achieved by RPHPLC has, in some cases, allowed fluorometric detectors to be replaced by ultraviolet (UV) detectors. Although HPLC fluorometric detector systems will remain as sensitive, versatile methods for analytes at very low concentrations, such as toxins and antibiotics, a substantial enhancement of sensitivity is achieved by using laser‐induced fluorometry. This new trend is worth considering as a replacement for traditional radiolabeling techniques.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 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 it