Non-destructive testing techniques (NDTTs) for microbial contamination in cheese: A review
Bibliographic record
Abstract
: Background As per the World Health Organisation, about 600 million cases of foodborne illness and 420,000 fatalities happen every year due to the consumption of food contaminated with microorganisms or chemicals. In cheese production, microbiological contamination poses a significant challenge at every stage of processing, from raw milk collection to ripening. Pathogens and spoilage microbes namely Listeria monocytogenes, Clostridium butyricum, Escherichia coli, Streptococcus spp., and Penicillium spp. can invade cheese despite its acidic or saline nature. Hence, timely detection of such microbes is essential to ensure safety for consumer consumption. Traditional microbiological testing methods such as culture-based techniques are time-consuming due to longer incubation duration. On the contrary, advanced detection methods like nucleic acid-based techniques (PCR), immunological techniques like ELISA, and liquid chromatography provide rapid results but require expensive equipment and skilled personnel. Scope and approach Recent emerging non-destructive testing techniques (NDTTs) like hyperspectral imaging, spectroscopic methods (Near Infrared, FTIR, fluorescence, and Surface enhanced Raman), and electronic-nose (E-nose) have been widely used for quick detection of microbial contaminants in cheese, without physically altering or damaging the sample. Moreover, they have high accuracy, sensitivity and proven potential to differentiate microbial species in cheese samples. Key findings and conclusion The integration of spectroscopy with chemometrics resulted in an acceptable correlation coefficient of 0.89-0.96. This review summarizes the existing application of various conventional methods and NDTTs in cheese microbial detection. Furthermore, this study also highlights the merits and demerits of NDTTs. The utilization of suitable NDTT along with chemometrics could help in real-time monitoring of microbial contaminants in the cheese industry.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".