A latent variable model for identifying and characterizing food adulteration

TL;DR

This paper introduces a novel latent variable model for detecting and quantifying food adulteration using spectral data, providing detailed insights into adulterant levels and affected spectral regions, enhancing food authenticity analysis.

Contribution

The work presents a new latent variable model tailored for spectral data that not only detects adulteration but also estimates adulteration levels and identifies impacted spectral regions.

Findings

Accurately estimates adulteration levels in honey samples.

Identifies spectral regions most affected by adulterants.

Demonstrates effectiveness on synthetic and real data.

Abstract

Recently, growing consumer awareness of food quality and sustainability has led to a rising demand for effective food authentication methods. Vibrational spectroscopy techniques have emerged as a promising tool for collecting large volumes of data to detect food adulteration. However, spectroscopic data pose significant challenges from a statistical viewpoint, highlighting the need for more sophisticated modeling strategies. To address these challenges, in this work we propose a latent variable model specifically tailored for food adulterant detection, while accommodating the features of spectral data. Our proposal offers greater granularity with respect to existing approaches, since it does not only identify adulterated samples but also estimates the level of adulteration, and detects the spectral regions most affected by the adulterant. Consequently, the methodology offers deeper insights, and could facilitate the development of portable and faster instruments for efficient data collection in food authenticity studies. The method is applied to both synthetic and real honey mid-infrared spectroscopy data, delivering precise estimates of the adulteration level and accurately identifying which portions of the spectra are most impacted by the adulterant.