Identification of Milk Adulteration in Camel Milk Using FT-Mid-Infrared Spectroscopy and Machine Learning Models
Problem
Camel milk is adulterated with cheaper cow milk, threatening food quality and safety, and a rapid detection method was needed.
Why it matters
Milk adulteration deceives consumers and undermines food safety in a high-value nutritional product.
What the AI did
Machine learning models, including Linear Discriminant Analysis and neural networks, were trained on FT-MIR spectra of camel milk samples spiked with cow milk to detect and distinguish adulteration.
Results
LDA maintained a 100% detection rate at cow milk addition levels of 10 g/100 g
Impact
The study demonstrates a rapid spectroscopy-plus-ML screening method for camel milk authenticity under controlled lab conditions.
Record
| Venue | Foods |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Zhiqiu Yao, Xinxin Zhang, Pei Nie, Haimiao Lv, Ying Yang, Wenna Zou, Liguo Yang |
| Citations | 20 (OpenAlex, at import time) |
| References | 44 |
| Model or method | Linear Discriminant Analysis (LDA), neural networks |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/foods12244517 |
| Open access | gold |
| Keywords | FT-MIR spectroscopy, camel milk, adulteration, LDA, food safety |
Importance rating
incremental / niche
Abstract states camel milk adulteration poses a significant threat to food quality and safety; a real but narrow food-integrity problem for one product category, demonstrated only in lab-spiked samples.