Exploring Authentic Skim and Nonfat Dry Milk Powder Variance for the Development of Nontargeted Adulterant Detection Methods Using Near-Infrared Spectroscopy and Chemometrics

Botros, Lucy; Jablonski, Joseph E.; Chang, Claire; Bergana, Marti Mamula; Wehling, Paul; Harnly, James M.; Downey, Gerard; Harrington, Peter de B.; Potts, Alan R.; Moore, Jeffrey C.

doi:10.1021/jf4023433

Cited by 30 publications

(49 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…1 Varimax rotations provided variable loadings that correlated with the bands of all three families of compounds. For the partial least-squares regression (PLSR) study, spectra from the 24 samples that had complete COA concentrations for moisture, fat, and protein were used.…”

Section: Results and Discussionmentioning

confidence: 99%

“…Initial analysis by PCA showed little variation for the variable loadings of the first and second principal components in the region between 1000 and 1700 nm. 1 Consequently, only the region from 1700 to 2500 nm was used in this study.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…In this study, the previously reported NIR data for SMP and NFDM 1 are subjected to analysis by ANOVA-PCA, pooled-ANOVA, and PLSR coupled with pooled-ANOVA. The percentages of the total variance according to day of analysis, supplier, production site, milk class, process type, between sample variation, and instrumental variation were determined.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Characterization of Near-Infrared Spectral Variance in the Authentication of Skim and Nonfat Dry Milk Powder Collection Using ANOVA-PCA, Pooled-ANOVA, and Partial Least-Squares Regression

Harnly

Harrington

Botros

et al. 2014

J. Agric. Food Chem.

Self Cite

View full text Add to dashboard Cite

Forty-one samples of skim milk powder (SMP) and nonfat dry milk (NFDM) from 8 suppliers, 13 production sites, and 3 processing temperatures were analyzed by NIR diffuse reflectance spectrometry over a period of 3 days. NIR reflectance spectra (1700–2500 nm) were converted to pseudoabsorbance and examined using (a) analysis of variance-principal component analysis (ANOVA-PCA), (b) pooled-ANOVA based on data submatrices, and (c) partial least-squares regression (PLSR) coupled with pooled-ANOVA. ANOVA-PCA score plots showed clear separation of the samples with respect to milk class (SMP or NFDM), day of analysis, production site, processing temperature, and individual samples. Pooled-ANOVA provided statistical levels of significance for the separation of the averages, some of which were many orders of magnitude below 10–3. PLSR showed that the correlation with Certificate of Analysis (COA) concentrations varied from a weak coefficient of determination (R2) of 0.32 for moisture to moderate R2 values of 0.61 for fat and 0.78 for protein for this multinational study. In this study, pooled-ANOVA was applied for the first time to PLS modeling and demonstrated that even though the calibration models may not be precise, the contribution of the protein peaks in the NIR spectra accounted for the largest proportion of the variation despite the inherent imprecision of the COA values.

show abstract

Section: Results and Discussionmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Characterization of Near-Infrared Spectral Variance in the Authentication of Skim and Nonfat Dry Milk Powder Collection Using ANOVA-PCA, Pooled-ANOVA, and Partial Least-Squares Regression

Harnly

Harrington

Botros

et al. 2014

J. Agric. Food Chem.

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, developing robust methods for the non-targeted detection of adulteration in SMP based on NIRS is complicated by the expected physical-chemical variability in SMP composition which reduces the ability of the method to detect low amount of any adulterant. Such variability depends more on minor constituent composition and drying conditions than on SMP proximate composition (Botros et al 2013). Therefore, a considerable database needs to be built for use in practice.…”

Section: Development Of the Predictive Modelsmentioning

confidence: 99%

Targeted and Untargeted Detection of Skim Milk Powder Adulteration by Near-Infrared Spectroscopy

et al. 2015

View full text Add to dashboard Cite

In the present study, near-infrared spectroscopy (NIRS) was explored as a fast and reliable screening method for the detection of adulteration of skim milk powder (SMP). Sixty genuine SMP were adulterated with acid whey (1-25 % w/w), starch (2 and 5 %) and maltodextrin (2 and 5 %) for a total of 348 adulterated samples. Two chemometric approaches were employed. In the first approach, an untargeted one class model for genuine skim milk powder was developed by Soft Independent Modelling of Class Analogy. In the second approach, adulterant-specific regression models were developed to assess the amount of each adulterant by partial least square regression and principal component regression. The class modelling approach had the advantage that several adulterants could be detected with the same chemometric model, including situations where multiple adulterants are present in the test sample or where yet unknown adulterants are present. Regression models showed a better sensitivity with genuine SMP samples completely discriminated from samples adulterated with 5 % acid whey and 2 % of starch or maltodextrin. NIRS proved to be a useful tool for the rapid and cost-efficient untargeted and/or targeted detection of adulterations in SMP.

show abstract

“…3 This study examines principal component analysis (PCA) and the related soft independent modeling of class analogy (SIMCA) approach as potential chemometric tools that could be used to assess spectral similarity, which would indicate comparability of HOS in protein samples. These chemometric methods are well known and have been widely used to deconvolute spectral data of complex mixtures, including detection of impurities, 4 analysis of tissue composition, 5 analysis of complex foodstuffs, 6 and changes in structure upon aggregation. 7 It should be noted that this study focuses on demonstrating the utility of chemometric methods for quantitative comparison and visualization of spectral differences as a function of changing solution conditions.…”

Section: Introductionmentioning

confidence: 99%

Comparability of Higher Order Structure in Proteins: Chemometric Analysis of Second-Derivative Amide I Fourier Transform Infrared Spectra

Stockdale

Murphy

D’Antonio

et al. 2015

Journal of Pharmaceutical Sciences

View full text Add to dashboard Cite

Exploring Authentic Skim and Nonfat Dry Milk Powder Variance for the Development of Nontargeted Adulterant Detection Methods Using Near-Infrared Spectroscopy and Chemometrics

Cited by 30 publications

References 13 publications

Characterization of Near-Infrared Spectral Variance in the Authentication of Skim and Nonfat Dry Milk Powder Collection Using ANOVA-PCA, Pooled-ANOVA, and Partial Least-Squares Regression

Characterization of Near-Infrared Spectral Variance in the Authentication of Skim and Nonfat Dry Milk Powder Collection Using ANOVA-PCA, Pooled-ANOVA, and Partial Least-Squares Regression

Targeted and Untargeted Detection of Skim Milk Powder Adulteration by Near-Infrared Spectroscopy

Comparability of Higher Order Structure in Proteins: Chemometric Analysis of Second-Derivative Amide I Fourier Transform Infrared Spectra

Contact Info

Product

Resources

About