Detecting fraudulent additions in skimmed milk powder using a portable, hyphenated, optical multi-sensor approach in combination with one-class classification. (March 2021)

• Record Type: Journal Article
• Authors: Müller-Maatsch, Judith; Alewijn, Martin; Wijtten, Michiel; Weesepoel, Yannick
• Is Part Of: Food control. Volume 121(2021)
• Publication Date: 2021-03
• Subjects: Near-infrared -- Ultraviolet–visible -- Fluorescence -- Photonics -- Data fusion -- Hyphenated device -- Food fraud -- One-class classification; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2020.107744
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: The detection of fraudulent additions to milk powder is an ongoing research subject for governmental agencies, industry and academia. Current developments steer towards the application of so-called fingerprint approaches, describing authentic, reference samples with spectroscopy and using one-class classification (OCC) to identify "out-of-class", or adulterated samples. Within this article we describe the application of a novel, portable device hyphenating ultraviolet–visible, fluorescence and near-infrared spectroscopy in combination with OCC modelling to discriminate authentic skimmed milk powders from adulterated ones. As adulterated samples we analyzed skimmed milk powder with the addition of plant protein powder, whey powder, starch, lactose, glucose, fructose as well as non-protein nitrogen like ammonium chloride, ammonium nitrate, melamine and urea in different concentrations. After fusion of the classification results from the three spectral techniques and several models two scenarios are presented. 100% (scenario 1) or 80% (scenario 2) of the authentic skimmed milk powders were correctly identified as "in-class", whereas respectively 64% or 86% of the adulterated samples were correctly classified as "out-of-class". In brief, this article provides insights in the application of novel, portable devices that may be applied in a non-invasive manner and gives an outlook on data handling and a new data fusion strategy. Highlights: A hyphenated photonics device was successfully applied to detect food fraud. Combining near-infrared, ultraviolet–visible, fluorescence spectroscopy and imaging. High-level data fusion and one-class classification algorithms were applied. Three sensor signals improved classification scores compared to the individual ones. Fraudulent additions ranging from 0.1 to 50% ( w/w ) in skimmed milk powder were analyzed.