Data-driven pipeline modeling for predicting unknown protein adulteration in dairy products

This publication has 21 references indexed in Scilit:

• A suspect screening strategy with automated data processing tools for the comprehensive detection of emerging chemical hazards in food
  Food Control, 2024
• Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents
  Food Chemistry, 2024
• Application of QSAR models for acute toxicity of tetrazole compounds administrated orally and intraperitoneally in rat and mouse
  Toxicology, 2023
• Detection and quantification of dicyandiamide (DCD) adulteration in milk using infrared spectroscopy: A rapid and cost-effective screening approach
  Food and Humanity, 2023
• A rapid method to predict type and adulteration of coconut milk by near-infrared spectroscopy combined with machine learning and chemometric tools
  Microchemical Journal, 2023
• Recent developments in DNA nanostructure-based biosensors for the detection of melamine adulteration in milk
  Microchemical Journal, 2023
• Mass spectrometry-based protein and peptide profiling for food frauds, traceability and authenticity assessment
  Food Chemistry, 2021
• Swans and elephants: A typology to capture the challenges of food supply chain risk assessment
  Trends in Food Science & Technology, 2020
• FADB-China: A molecular-level food adulteration database in China based on molecular fingerprints and similarity algorithms prediction expansion
  Food Chemistry, 2020
• Melamine in milk products in China: Examining the factors that led to deliberate use of the contaminant
  Food Policy, 2010