Bibliographic record
Abstract
Abstract A growing recognition of the tremendous potential of vibrational spectroscopic techniques by food scientists over the past few decades has fuelled an exponential growth in the scientific literature describing research that involves the use of near‐infrared, mid‐infrared and/or Raman spectroscopy for the analysis of food systems. This chapter highlights some of the myriad applications of vibrational spectroscopy conducted to meet a diverse range of analytical needs in food science. For example, vibrational spectroscopic techniques, in conjunction with various chemometric tools, are being applied for the determination of food or beverage composition, authentication, or adulteration, the assessment and prediction of quality and process‐induced changes, and the detection of chemical or microbiological contaminants related to food safety. Applications in basic research have contributed to a better understanding of the chemical, functional, sensory, and textural properties of food. With ongoing advances in the technology and an increasing level of sophistication and expertise of users familiar with the potential advantages and challenges of these techniques, the future is promising for emergent innovative applications of vibrational spectroscopy in the areas of quality assurance, process control, and food safety management, and for fundamental research in food science.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.018 |
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".