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. 2018 Sep 21;4(9):e00806.
doi: 10.1016/j.heliyon.2018.e00806. eCollection 2018 Sep.

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

Sanjeewa R Karunathilaka  1 , Betsy Jean Yakes  1 , Keqin He  2 , Jin Kyu Chung  1 , Magdi Mossoba  1
Affiliations

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

Sanjeewa R Karunathilaka et al. Heliyon. .

Abstract

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.

Keywords: Analytical chemistry; Food analysis; Food safety.

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Figures

Fig. 1
Fig. 1
a) NIR second derivative spectra for a representative MP sample and for each of the eleven, pure component, potential MP adulterants collected with the PE Frontier benchtop FT-NIR spectrometer. Second derivative spectra are in arbitrary units and spatially offset for visual clarity. b) Second derivative spectra for each type of gravimetric blend at their highest concentrations with a MP spectrum shown for comparison.
Fig. 2
Fig. 2
a) PCA scores plot for genuine and adulterated MP spectral data collected with PE FT-NIR spectrometer. Spectra were pretreated by the use of SNV followed by a second derivative transformation. b) Loading plot for PC1 with arrows and labels for unique spectral bands attributed to three MP adulterants.
Fig. 3
Fig. 3
Principal component (PC) loading plots for the non-supervised principal component analysis (PCA) performed for the data collected from the PE FT-NIR spectrometer. a) Loading plot for PC3 that illustrates higher positively and negatively loaded bands at 5300–4900 cm−1. b) Loading plot for PC7 that has higher positive loadings at approximately 6950 cm−1.
Fig. 4
Fig. 4
Q residuals (reduced) versus Hotelling's T2 (reduced) plots for the SIMCA models developed for the data collected from the two benchtop FT-NIR spectrometers. Data are shown for genuine MP and five MP samples each spiked with a low molecular weight, nitrogen-rich adulterant. a) Bruker MPA and b) PE Frontier FT-NIR spectrometers. The optimized 95% confidence limits are indicated by blue lines.
Fig. 5
Fig. 5
Q residuals (reduced) versus Hotelling's T2 (reduced) plot for a SIMCA model built in the 6005–4406 cm−1 range for the handheld NIR device. The 99% confidence limits are indicated by blue lines. In the plot, MP = milk powder; DC = dicyandiamide; BU = biuret; AMT = aminotriazole; Mel = melamine; CA = cyanuric acid. The optimized 99% confidence limits are indicated by blue lines.
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