Characterising variances of milk powder and instrumentation for the development of a non-targeted, Raman spectroscopy and chemometrics detection method for the evaluation of authenticity

Abstract

There is a need to develop rapid tools to screen milk products for economically motivated adulteration. An understanding of the physiochemical variability within skim milk powder (SMP) and non-fat dry milk (NFDM) is the key to establishing the natural differences of these commodities prior to the development of non-targeted detection methods. This study explored the sources of variance in 71 commercial SMP and NFDM samples using Raman spectroscopy and principal component analysis (PCA) and characterised the largest number of commercial milk powders acquired from a broad number of international manufacturers. Spectral pre-processing using a gap-segment derivative transformation (gap size = 5, segment width = 9, fourth derivative) in combination with sample normalisation was necessary to reduce the fluorescence background of the milk powder samples. PC scores plots revealed no clear trends for various parameters, including day of analysis, powder type, supplier and processing temperatures, while the largest variance was due to irreproducibility in sample positioning. Significant chemical sources of variances were explained by using the spectral features in the PC loadings plots where four samples from the same manufacturer were determined to likely contain an additional component or lactose anomers, and one additional sample was identified as an outlier and likely containing an adulterant or differing quality components. The variance study discussed herein with this large, diverse set of milk powders holds promise for future use as a non-targeted screening method that could be applied to commercial milk powders.

Detection of decomposition in mahi‐mahi, croaker, red snapper, and weakfish using an electronic‐nose sensor and chemometric modeling

“…captures the next largest variances. Often two or three principal components provide an adequate representation of the data which is most commonly presented on a PCA scores plot (Karunathilaka et al., 2016). In this study, PCA was used to explore the possibility of differentiating the samples that failed the sensory evaluation (SIs 5–7) from those that passed (SIs 1–4) and to identify the most informative sensors that best discriminated between these two sample groups.…”

Insights into chemometric algorithms for quality attributes and hazards detection in foodstuffs using Raman/surface enhanced Raman spectroscopy

“…On the other hand, PCA through analysis of variance produced much lower accuracy for the strains with both linear discrimination analysis (72.7%) and linear SVM classification (68.2%). In addition to different beverages (Mandrile et al., 2016; Mendes et al., 2003; Nordon et al., 2005; Pierna et al., 2012; Silveira Jr et al., 2009; Wu et al., 2015; Zanuttin et al., 2019), the chemometric‐based Raman/SERS approach has also been extended to all sorts of foodstuffs quality monitoring, such as dairy products, (Almeida et al., 2011; Caponigro et al., 2019; de Oliveira Mendes et al., 2019; Júnior et al., 2016; Karunathilaka et al., 2016; Liu et al., 2020; Moros et al., 2007; Nedeljkovic et al., 2017; Nieuwoudt et al., 2017; Richardson et al., 2019; Stefanov et al., 2013; Taylan et al., 2020; Zhao et al., 2020), vegetables (Sebben et al., 2018), wheat, flour (Cebi et al., 2017; Czaja et al., 2016; Liu et al., 2019), tea, and coffee (Buyukgoz et al., 2016; El‐Abassy et al., 2011; Figueir, 2019; Liao & Chen, 2017; Luna et al., 2019). The wide literature indicates the applicability of chemometric algorithms in Raman/SERS‐based quality monitoring of foodstuffs (Table 2).…”

Rapid Identification between Two Fish Species Using UV-Vis Spectroscopy for Substitution Detection

“…Olive oils from the Mediterranean region were evaluated using UV-Vis spectroscopy and independent component analysis [15]. Raman chemical imaging method has been developed to authenticate skim milk powder where identification and distribution of the multiple adulterant particles in the milk powder could be visualized using Raman chemical images [16].…”