Introduction to Vibrational Spectroscopy in Food Science
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 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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 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