HAL (France)
open access
A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling#1266560
- Meat-Borne Bacterial Pathogen Detection: Conventional, Molecular and Emerging AI-Based Strategies.#182497
- Deep Transfer Learning Model for Classifying Different Types of Diseases in Paddy#74843
- Anti-Listerial Effects of Satureja hortensis Essential Oils in Ready-to-Eat Poultry Meat Stored at Different Temperatures#364480
- Meat Freshness Monitoring in the Modern Supply Chain: Analytical Advances, Smart Packaging, and Digital Integration.#1189237
Record · ID 1266462
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive