Non-targeted NIR spectroscopy and SIMCA classification for commercial milk powder authentication: A study using eleven potential adulterants

A non-targeted detection method using near-infrared (NIR) spectroscopy combined with chemometric modeling was developed for the rapid screening of commercial milk powder (MP) products as authentic or potentially mixed with known and unknown adulterants. Two benchtop FT-NIR spectrometers and a handheld NIR device were evaluated for model development. The performance of SIMCA classification models was then validated using an independent test set of genuine MP samples and a set of gravimetrically prepared mixtures consisting of MPs spiked with each of eleven potential adulterants. Classification models yielded 100% sensitivities for the benchtop spectrometers. Better specificity, which was influenced by the nature of the adulterant, was obtained for the benchtop FT-NIR instruments than for the handheld NIR device, which suffered from lower spectral resolution and a narrower spectral range. FT-NIR spectroscopy and SIMCA classification models show promise for the rapid screening of commercial MPs for the detection of potential adulteration

Non-targeted detection of milk powder adulteration using Raman spectroscopy and chemometrics: melamine case study

<p>Raman spectroscopy in combination with chemometrics was explored as a rapid, non-targeted screening method for the detection of milk powder (MP) adulteration using melamine as an example contaminant. Raman spectroscopy and an unsupervised pattern-recognition method, principal component analysis (PCA), allowed for the differentiation of authentic MPs from adulterated ones at concentrations > 1.0% for dry-blended (DB) samples and > 0.30% for wet-blended (WB) ones. Soft independent modelling of class analogy (SIMCA), a supervised pattern-recognition method, was also used to classify test samples as adulterated or authentic. Combined statistics at a 97% confidence level from the SIMCA models correctly classified adulteration of MP with melamine at concentrations ≥ 0.5% for DB samples and ≥ 0.30% for WB ones, while no false-positives from authentic MPs were found when the spectra in the 600–700 cm^–¹ range were pre-processed using standard normal variate (SNV) followed by a gap-segment derivatisation. The combined technique of Raman spectroscopy and chemometrics proved to be a useful tool for the rapid and cost-efficient non-targeted detection of adulteration in MP at per cent spiking levels.</p

Effects of Wet-Blending on Detection of Melamine in Spray-Dried Lactose

During the development of rapid screening methods to detect economic adulteration, spray-dried milk powders prepared by dissolving melamine in liquid milk exhibited an unexpected loss of characteristic melamine features in the near-infrared (NIR) and Raman spectra. To further characterize this “wet-blending” phenomenon, spray-dried melamine and lactose samples were produced as a simplified model and investigated by NIR spectroscopy, Raman spectroscopy, proton nuclear magnetic resonance (¹H NMR), and direct analysis in real time Fourier transform mass spectrometry (DART–FTMS). In contrast to dry-blended samples, characteristic melamine bands in NIR and Raman spectra disappeared or shifted in wet-blended lactose–melamine samples. Subtle shifts in melamine ¹H NMR spectra between wet- and dry-blended samples indicated differences in melamine hydrogen-bonding status. Qualitative DART–FTMS analysis of powders detected a greater relative abundance of lactose–melamine condensation product ions in the wet-blended samples, which supported a hypothesis that wet-blending facilitates early Maillard reactions in spray-dried samples. Collectively, these data indicated that the formation of weak, H bonded complexes and labile, early Maillard reaction products between lactose and melamine contribute to spectral differences observed between wet- and dry-blended milk powder samples. These results have implications for future evaluations of adulterated powders and emphasize the important role of sample preparation methods on adulterant detection