Review on Food Safety Analysis Using Spectroscopy and Machine Learning

A 2026 review paper by Qing-Xiao Ma, available on figshare, synthesizing recent advances in applying spectroscopic techniques and machine learning models to food safety analysis. The 4.3 MB document discusses applications in adulteration identification, classification of unsafe components, and contaminant detection. It is published under a CC-BY-4.0 license.

Use Cases

• Surveying machine learning models for food adulteration identification based on spectroscopic data.
• Reviewing qualitative classification techniques for unsafe components in food products.
• Examining quantitative detection methods for contaminants or residues using spectroscopic analysis.

Strengths

• Document is 4.3 MB, indicating a substantive review.
• Explicitly licensed under CC-BY-4.0 for open sharing and reuse.
• Focuses on three defined application areas: adulteration, classification, and contaminant detection.

Limitations

• Row count and column-level documentation are absent; data structure must be inferred after download.
• The dataset is a review paper, not a primary data collection, limiting direct analytical use.
• Last updated 2026-04-21 03:54:57; freshness should be verified.

Provenance

Source

figshare, authored by Qing-Xiao Ma.

Collection Method

Literature review and synthesis.

Freshness

Last updated 2026-04-21 03:54:57.