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Related Experiment Video

Updated: Oct 12, 2025

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

Multivariate analysis for organic milk authentication.

Siyan Xu¹, Chaomin Zhao², Xiaojun Deng²

¹School of Life Sciences, Shanghai University, Shanghai 200444, China; Technical Center for Animal, Plant and Food Inspection and Quarantine, Shanghai Customs, Shanghai 200135, China.

Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences

|November 19, 2021

Summary

This summary is machine-generated.

A new model using stable isotopes, elements, and fatty acids can effectively differentiate organic milk from conventional milk. This approach provides a more robust method for verifying milk authenticity and quality.

Keywords:

Discriminant analysis Multivariate determination Nutritional quality Organic milk Stable isotope ratios

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Area of Science:

Food Science
Analytical Chemistry
Biochemistry

Background:

Distinguishing organic milk (OM) from conventional milk (CM) is crucial for consumer trust and regulatory compliance.
Previous methods often rely on limited variables, potentially leading to less reliable models for milk authenticity testing.

Purpose of the Study:

To develop and validate a robust multivariate model for differentiating OM from CM.
To identify key discriminating parameters between organic and conventional milk sources.

Main Methods:

Construction of an orthogonal projection to latent structure-discriminant analysis (OPLS-DA) model.
Utilizing a comprehensive dataset including stable isotopes (δ13C, δ15N, δ18O), 51 elements, and 35 fatty acids (FAs).

Main Results:

The multivariate model demonstrated superior explanatory and predictive abilities (R2X = 0.647, R2Y = 0.962, Q2 = 0.821) compared to models with fewer variables.
Key discriminators identified include δ15N, Selenium (Se), δ13C, Europium (Eu), Potassium (K), and α-Linolenic acid (ALA).

Conclusions:

A multivariate approach integrating stable isotopes, elemental composition, and fatty acid profiles offers a powerful tool for identifying organic milk.
This method enhances the reliability of milk authenticity testing and can contribute to global databases for milk quality assessment.