Detection of sheep butter adulteration with cow butter and margarine by employing Raman spectroscopy and multivariate data analysis

Abstract

The adulteration of sheep butter with cow butter and margarine is a significant challenge in the food industry, impacting product authenticity and consumer confidence. In this research article, a comprehensive and accurate approach is proposed for detecting sheep butter adulteration by employing Raman spectroscopy in conjunction with the combined method of DD-SIMCA and PLS-DA.

Initially, Raman spectroscopy employed to collect spectral data from pure butter and margarine samples, enabling the modeling of target objects by DD-SIMCA to exclude adulterated samples. Subsequently, PLS-DA was utilized for the discrimination of sample classes.

Raman spectroscopy, combined with the classification and discrimination strategy, is a powerful tool for quickly and non-destructively detecting sheep butter adulteration, ensuring food quality control. The combined strategy successfully discriminated between pure samples, achieving high performances based on the figures of merit (sensitivity: 77.78–100% and specificity: 88.23–100%) for detecting and identifying the type of adulteration in sheep butter. These outcomes underscore the efficacy of the proposed approach in not only detecting but also identifying the specific type of adulteration present in sheep butter, thereby addressing a critical need in the food industry. This research contributes significantly to advancing the field of food quality assurance and holds great implications for support consumer confidence in product authenticity.