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Agricultural and Environmental Chemistry

Variance of Commercial Powdered Milks Analyzed by 1H-NMR and Impact on Detection of Adulterants James M. Harnly, Marti M. Bergana, Kristie M Adams, Jeffrey C Moore, and Zhuohong Xie J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b00432 • Publication Date (Web): 26 Apr 2018 Downloaded from http://pubs.acs.org on April 26, 2018

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Journal of Agricultural and Food Chemistry

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Variance of Commercial Powdered Milks Analyzed by 1H-

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NMR and Impact on Detection of Adulterants

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James Harnly1*, Marti Mamula Bergana2, Kristie M. Adams3,

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Zhuohong Xie3, and Jeffrey C. Moore3

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1

Food Composition and Methods Development Laboratory, Beltsville Human Nutrition Research

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Center, Agricultural Research Service U.S. Department of Agriculture Building 161,

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BARC-East, Beltsville, MD 20705

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2

Consultant, U.S. Pharmacopeia, 12601 Twinbrook Parkway, Rockville, MD 20852

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3

U.S. Pharmacopeia, 12601 Twinbrook Parkway, Rockville, MD 20852

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*Corresponding Author: James Harnly

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E-mail: [email protected]

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Tel: 301-504-8569

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ACS Paragon Plus Environment


Abstract

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1

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and pooled, crossed ANOVA. It was found that the sample type (skim milk powder or non-fat

25

dry milk), the supplier, the production site, the processing temperature (high, medium, or low

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temperature), and the day of analysis provided statistically significant sources of variation.

27

Interestingly, inexact alignment (deviations of ±0.002 ppm) of the spectral reference peak was a

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significant source of variation and fine alignment was necessary before the variation arising from

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the other experimental factors could be accurately evaluated. Using non-targeted analysis, the

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lowest detectable adulteration for dicyandiamide, melamine, and sucrose was 0.05%,

31

maltodextrin and urea was 0.5%, ammonium sulfate and whey was 5%, and soy protein isolate

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was undetectable using methods described herein. The measurement of variance and detection

33

of adulteration were relatively unaffected by the resolution. Similar results were obtained with

34

un-binned data (0.0003 ppm resolution) and binning of 333 data points (0.1 ppm resolution).

H-NMR spectra for 66 commercial powdered milk samples were analyzed by PCA, SIMCA,

35 36 37 38

Keywords NMR, milk powder, chemometrics, ANOVA, adulteration, binning

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Introduction

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US Pharmacopeia (USP) initiated a project in 2011 to develop strategies for the detection of

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adulterants in foods using powdered milk samples as a model 1. Initial efforts focused on

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characterizing commercial powdered milks in hopes of establishing a global reference material

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and developing a library of suitable methods. These studies resulted in publication of results

46

using near-infrared spectrometry 2,3 high performance liquid chromatography (HPLC-UV) 4

47

Raman spectroscopy 5, and non-protein nitrogen 6. Based on these studies, USP developed

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guidance for the development and validation of non-targeted methods for detection of

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adulteration 7. Research on the adulteration of powdered milk samples expanded with the

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development of a series of skim milk powders adulterated with 8 different compounds. The

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initial collection of commercial samples and the new adulterated samples was analyzed by

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proton nuclear magnetic resonance (1H-NMR) spectroscopy 8.

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1

H-NMR has several advantages with respect to authentication of foods and botanicals 9. Firstly,

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it detects all the sample components simultaneously, thus providing an inherently comprehensive

56

survey of the sample metabolome. Secondly, the acquired intensities of the proton spectra are

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extremely reproducible, offering the possibility of using archived spectra for authentication

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rather than constantly re-analyzing (and using up) valuable reference materials. A potential

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disadvantage of 1H-NMR is its lack of sensitivity as compared to MS 9. However, this is not a

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major obstacle with respect to economically motivated adulteration since significant levels of

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added adulterants are generally needed to achieve an economic advantage.

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NMR has been previously applied to the detection of a variety of compounds in milk 10-15.

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Lachenmeier was the first to specifically measure melamine. Most approaches were interested in

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specific compounds. Sundekilde et al. 15 were the first to take a metabolomics approach. While

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these NMR studies were termed non-targeted due to their acquisition of data for all components

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in a sample, none used non-targeted chemometric approaches to process the data.

68 69

Harnly et al, showed that spectral fingerprints acquired by IR, MS, NIR, NMR, and UV can be

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used with chemometric methods, specifically principal component analysis (PCA), for

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identification and authentication of foods and botanical supplements 3,16-18. When combined

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with sample metadata, pooled crossed analysis of variance (pcANOVA) can be used to

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determine the fraction of variance associated with each experimental factor 18,19. Authentication

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is best approached using a one-class model (i.e., only authentic or reference samples are

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modeled, the first step in soft independent modeling of class analogy) and establishing selected

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statistical limits for determining if an unknown sample is similar to or different from the

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reference samples 20. Since any spectral feature that is different from the average features of the

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reference samples will result in the unknown sample being deemed adulterated, this

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chemometrics approach is truly non-targeted.

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The collection of commercial powdered milk samples (skim milk powder and non-fat dry milk)

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analyzed by 1H-NMR in the current study were previously analyzed by NIR 2,3. Combined with

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chemometric methods, the NIR studies demonstrated that compound identification was not

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needed to detect differences in the chemical composition of samples 2,3. NIR spectra provide

85

harmonic bands for vibrational transitions and thus do not identify specific compounds or


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functional groups. Pooled (summed for the entire spectrum) and crossed (resorted for each

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factor) ANOVA of the NIR data showed that the sample type (skim milk powder or non-fat dry

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milk), the supplier, the production site, the processing temperature (high, medium, or low

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temperature), and the day of analysis provided statistically significant sources of variation. In

90

addition, the spectra correlated with residual fat and moisture content and the protein content 3.

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The results of the NIR study showed that a global reference standard consisted of variance from

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each of the sources listed above and that individual production sites were better able to detect

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variations in their product or adulteration by employing a series of in-house reference materials.

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In this study, the 1H-NMR data are analyzed by PCA, one-class modeling, and pcANOVA to

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characterize the variance of the reference samples, i.e., to determine the fraction of variance from

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each experimental factor (type of milk powder, supplier, production site, processing temperature,

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and day of analysis). Newly prepared samples with systematically increasing concentrations of 8

99

adulterants were analyzed to determine the level of detection using non-targeted 1H-NMR. In

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addition, the impact of resolution on variance and detection of adulterants was determined using

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variable bin sizes.

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Materials and Methods

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Standards and Solvents: Details were previously described 8. Briefly, ampoules of 99.8%

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deuterium dimethyldisulfide (DMSO-d6) containing 0.05% v/v tetramethylsilane (TMS) were

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purchased from Cambridge Isotope Labs (Tewksbury, MA). Sucrose, dicyandiamide, urea,

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melamine, maltodextrin, and ammonium sulfate were reagent grade and commercially purchased



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from Sigma-Aldrich (St. Louis, MO). Whey protein concentrate and soy protein isolate were

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obtained from industrial ingredient suppliers.

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Milk Powder Samples: Samples were acquired from commercial samples all over the world as

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previously described 2,3. The metadata is summarized in Table 1. Briefly, there were two

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sample types, skim milk powder (SMP) and non-fat dry milk (NFDM), 3 processing

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temperatures, high (H), medium (M), and low (L), 11 suppliers (national and international), 15

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production sites, and samples were analyzed over a period of 7 days 2.

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Milk Powder Solution Preparation: Samples were prepared for NMR analysis by weighing

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6.0 ± 0.1 mg MP and dissolving in 650 µL DMSO-d6 in an Eppendorf tube (powders did not

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fully dissolve) 8. This mixture was shaken at 2800 rpm on a touch-sensitive miniRoto vortex

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mixer (Thermo Fisher Scientific, Waltham, MA (formerly Fischer Scientific, Pittsburgh, PA))

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for one min. After ensuring that no dry powder remained, centrifugation at 2000 x g for 10 min

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was applied using a C1301 centrifuge (Labnet, Edison, NJ), and the resulting clear, colorless

121

supernatant liquid was decanted into a clean Eppendorf tube. Finally, 600 µL of each supernate

122

was transferred via autopipette into a high-quality, 5-mm NMR tube for analysis.

123

Adulterant-Spiked Milk Powder Solution Preparation: As previously described 8, adulterant,

124

synthetic mixtures were prepared using replicate solution preparations (per Milk Powder

125

Solution Preparation section) of sample S097 and two adulterant spiking solutions (a

126

concentrated adulterant stock solution, CAS, 10 mg/mL) and a dilute adulterant stock solution

127

(DAS, 0.1 mg/mL) (Table 2) 8. The CAS was prepared similarly as the milk powder samples (all

128

powders were not fully dissolved) using 600 µL of DMSO-d6. The DAS was prepared by

129

dissolving six µL of CAS in 594 µL DMSO-d6. Adulterated solutions were prepared at 0.5, 1.0

130

and 5.0% w/w concentrations by sequentially spiking three µL, three µL, and 24 µL of CAS,


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respectively, into the same NMR tube containing 600 µL of MP S097 solution. The NMR tube

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was inverted after each spike to aid in mixing. Data collection was performed before the first

133

spike (pre-spike) and after each sequential adulterant CAS solution spike (adulterant spiked).

134

This cyclic data collection-spike procedure was carried out with all eight adulterants in eight

135

nonadulterated S097 sample solution replicates for a total of 32 spectra. This same process was

136

repeated using DAS yielding a second set of eight pre-spike replicate spectra and spectra of eight

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adulterated S097 preparations each at 0.005, 0.01, and 0.5% w/w. Sample solutions for the eight

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adulterants (referred to as 100% spike or adulterant standard) were prepared by combining 30 µL

139

of CAS and 570 µL of DMSO-d6 in an NMR tube and mixing with tube inversion.NMR

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Data Acquisition: Data were acquired with a Bruker AVANCE III 600 MHz NMR

141

spectrometer (Bruker Biospin, Rheinstetten, Germany) equipped with a 5 mm TXI inverse probe

142

with Z-gradient coils and a Bruker SampleXpress Lite sample changer. The instrumentation and

143

analytical conditions have been previously described 8. Instrument performance was verified

144

daily by performing 1H lineshape, 1H sensitivity, 13C sensitivity tests, and temperature

145

reference was completed in automation using the spectrometer’s Assure-System Suitability Test

146

routine and standard samples. All NMR spectra were acquired at 298.0 K in an approximate 20-

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ppm spectral range between approximately -4 and 16 ppm. 1H NMR spectra were acquired in

148

automation under the control of ICON-NMR software (Bruker Biospin, Rheinstetten, Germany)

149

using a single 90°-pulse experiment with 32 scans and a 10 s relaxation delay, requiring about 12

150

min per sample.

151

Batch Sample Preparation and Data Collection Timeline: Batch sample preparation and

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NMR data collection were carried out in two studies (Part I and Part II) as previously described 8.

153

Preparation and analysis took place several months apart. The reference data and replicate



154

sample S097 data were collected in Part I (Table 3) and the spiked sample (S097 and adulterant

155

standard data) data were collected in Part II (Table 2). For Part I, each sample was prepared in

156

triplicate on day one, and 1H NMR spectra were acquired on each day of the five days of batch

157

analysis along with nine randomly selected authentic samples. 1H NMR spectra of a separate

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S097 replicate on day nine was analyzed continuously at six different times on a single day. In

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total, 66 data sets representing 46 commercial MP samples were collected in Part I. For Part II

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(spiking study), the data were collected over an approximate 10-day period, randomly selecting

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each day one or more adulterant spiking set or adulterant standard sample to be prepared and

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analyzed. An adulterant spiking set consisted of S097 solutions (0.0% w/w) and its

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corresponding three serially adulterant-spiked S097 solutions (either 0.005, 0.01, and 0.05 or 0.5,

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1, and 5% w/w) yielding a total of four spectra. In total, 72 spectra (9 spectra (i.e., 2 spiking sets

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plus the adulterant standard) x 8 adulterants) were collected in Part II.

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Data Processing: As previously described 8, each raw signal was multiplied by an exponential

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line-broadening factor of 0.3 Hz before Fourier transformation. Resulting NMR spectra were

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manually phase corrected and referenced to TMS at a chemical shift (δ) of 0.0 ppm and saved in

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Bruker NMR data file format. For data analysis, the processed Bruker data files were converted

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to tab-separated value (TSV) text files, retaining 65K data points per sample. For visual

171

evaluation, the TSV files were analyzed using MestReNova NMR software (v. 11.0.2, Mestrelab

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Research, Santiago de Compostela, Spain). For the current study, the TSV files were transferred

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and combined into a single Excel file. Although all the files were the same length, they did not

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have the same starting and ending points. Positive or negative shifts of 0 to 7 units (0.0 to

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0.0021 ppm) were necessary to align the TMS peaks.


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Data Range: For all the results presented in this study, intensities from 9.0 ppm to 0.2 ppm

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were used.

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Solvent Peaks: For all the results presented in this study, intensities between 2.47 and 2.56 ppm

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and 3.30 and 3.40 ppm were set to 0.0 to eliminate the signals from DMSO and HDO.

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Data Analysis: The raw data consisted of 65,636 data points for each sample for the range of

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16.0 ppm to -3.0 ppm. The selected data range (9ppm - 0.2 ppm) resulted in files that were 66

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samples x 28,175 data points. Binning of 100 points and 333 points gave files of 66 x 295 and

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66 x 86, respectively. Data were analyzed using principal component analysis (PCA) 20. Soft

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independent modeling of class analogy (SIMCA) was applied using one-class modeling 20. Data

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for chemometric analysis were pre-processed by a square root transformation, normalization

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(ΣX2 = 1.0), and mean centering. Pooled and crossed analysis of variance (ANOVA) was

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applied as previously described 19.

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Results

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This research consisted of two parts. Part 1 determined the variance associated with each of the

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experimental factors (i.e. sample metadata): sample type (SMP or NFDM), processing

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temperature (high, medium, or low), supplier, production site, and repeat analyses of a selected

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sample (S097). Part 2 examined the ability to detect samples spiked with a series of

194

concentrations for 8 adulterants, ammonium sulfate (AS), dicyandiamide (DCD), maltodextrin

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(Malt), melamine (Mel), soy protein isolate (SPI), sucrose (Sucr), urea, and whey using a non-

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targeted approach.

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Sources of Variance.



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Principal Component Analysis: PCA and pcANOVA provide easy means of examining the

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similarity of samples and the variance associated with each of the experimental factors. Figure

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1A shows the PCA scoress plot obtained for the 1H-NMR spectra of the 46 samples in Table 1.

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The samples are labeled with respect to their processing temperature and show a visual

203

correlation with PC2 (i.e., the processing temperatures appear to separate vertically), which

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provided 22% of the total variance. None of the experimental factors (type, supplier, production

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site, or day of analysis) showed a correlation with PC1 which provided 65% of the variance.

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Close examination of the PCA loadings plot (loadings as a function of magnetic shift) for the

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first principal component (PC1) showed a first derivative shape for every peak. Figure 2 shows

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an expanded view of the 1H-NMR spectrum around the peak at 5.74 ppm and illustrates how

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misalignment of the spectra can produce loadings with a first derivative structure. Since the

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peaks are not aligned, the first half of the leading peak gives a negative error and the last half of

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the trailing peak gives a positive error.

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Processing of spectra routinely references the chemical shift of the data to an internal standard,

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e.g., tetramethylsilane (TMS). Processing of the data produced spectra containing 65,636 data

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points for each sample for the range of 16.0 ppm to -3.0 ppm, a resolution of 0.0003 ppm. Close

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examination of the spectra showed that the TMS peak was not always labeled as 0.0 (Figure 3A).

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Instead, the peaks ranged from -1 to +6 resolution units away from 0.0, corresponding to a shift

218

of -0.0003 to +0.0018 ppm. Shifts of this magnitude are generally deemed inconsequential,

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especially if the spectra are binned to 0.02 ppm. Misalignment of the TMS peak with respect to

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0.0 linearly affected the entire spectrum (Figure 3B). Consequently, alignment of all the TMS

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peaks at 0.0 (Figure 3C) served to align the whole spectrum (Figure 3D).


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Figure 1B shows the PCA scores plot for the same data plotted in Figure 1A after alignment of

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the TMS peaks. Now the processing temperature is correlated with PC1. In addition, the PCA

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loadings plot (data not shown) no longer had any first derivative structures. However, even with

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alignment of the TMS peaks, none of the other experimental factors showed a clear visual

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correlation with any of the principal components.

228 229

Binning: In this study, the patterns of the 1H-NMR spectra were used to determine sample

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similarity. Since there is no intent to identify or quantify specific compounds there was no

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inherent necessity for high resolution. To determine the impact of resolution on determination of

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similarity, the data were binned at 2 levels. Initial bins of 100 data points for the aligned spectra

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(a resolution of 0.03 ppm) produced little difference in the PCA scores plot (data not shown).

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Figure 1C shows the PCA scores plot obtained with binning of 333 points (a resolution of 0.1

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ppm). There is little difference between Figures 1B and 1C.

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Binning was also applied to the un-aligned data. Whereas alignment of the TMS peaks shifted

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the processing temperature from a vertical correlation (Figure 1A) to a horizontal correlation

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(Figure 1B), binning with 333 data fell short of a strictly horizontal correlation and retained a

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vertical component of approximately 30° (data not sown). Thus, binning minimizes the

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uncertainty introduced by the spectral shift characterized by misalignment of the TMS peaks, but

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does not eliminate it.

243



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Analysis of Variance: A second method for evaluating the variance of the dry milks analyzed by

245

1

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every data point in the spectra and crossed because the spectra are re-sorted for the calculation

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for each factor. Crossed ANOVA necessitates the need to compute the variance arising from

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cross factor interactions. In this study, spectra for repeat preparations and analyses of sample

249

S097 (Table 3) were combined with the results for the analyses of the original 46 samples (Table

250

1).

H-NMR spectra is pcANOVA. pcANOVA is described as pooled because it is summed for

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Table 4 summarizes the pcANOVA results for the aligned, un-binned high resolution data. The

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variance arising from each experimental factor (type, processing temperature, supplier,

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production site, day of analysis, and preparation) was statistically significant at well above the

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95% confidence limit. The final residual of 3% for preparation reflects the variance due to

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repeat analysis. This value is probably high since the repeat analyses were run over a period of 5

257

hours. For 1H-NMR, repeat analyses without removing the sample are generally close to

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identical. Repeat analyses with the sample removed and the re-inserted have been reported as

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low as 1% over extended periods of operation and between platforms. Regardless, the variance

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for repeat preparations (18%) far exceeds that for repeat analysis of the same sample (3%).

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Table 5 shows pcANOVA results for aligned, binned data of 333 points (resolution of 0.1 ppm)

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that are very similar to those for the un-binned high resolution spectra in Table 4. With the

264

reduced resolution, the major experimental factors (type, temperature, supplier, and production

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site) are shifted with respect to their percent contribution to the total variance, but are still

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statistically significant. The day of analysis and preparation are no longer significant. These


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data show that a dramatic reduction in resolution has little impact on our ability to determine the

268

variance introduced by the various experimental factors. Thus, the diminished resolution still

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preserves the inherent pattern of the spectra.

270 271

When pcANOVA was applied to the un-aligned, un-binned data, the misalignment of the

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reference peak accounted for 64% of the total variance (data not shown). With binning of the

273

unaligned data, the variance was reduced to 11%. With alignment of the high resolution data,

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this variance was reduced to 6%. Some residual variance persisted because the alignment wasn't

275

perfect as shown in Figure 3C. Adjustment of a fraction of a unit would be desirable but the

276

effort was not justified. Peak alignment is not shown in Tables 4 and 5 and is incorporated in the

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cross factor interaction residual.

278 279

Adulteration.

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Sample S097 was spiked with six different concentrations of eight different adulterants (Table

281

2). As previously described 8, there were 4 classes of adulterants: class 1 consisted of small

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molecules with a high N ratio (dicyandiamide, melamine, and urea) class 2 was an organic salt

283

(ammonium sulfate), class 3 consisted of carbohydrates (maltodextrin and sucrose), and class 4

284

consisted of food-grade proteins (soy protein isolate and whey). Two preparations of un-

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adulterated sample S097 were run with each set of adulterated samples and provided a statistical

286

basis for judging the difference in the spectra arising from adulteration. The NMR spectra were

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processed in the same manner used in the previous section. No attempt was made to target the

288

adulterants. For each adulterant, the spectra of six spiked samples and the 16 un-adulterated

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samples of S097 were subjected to PCA and then to SIMCA, one-class modeling based on the 16



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un-adulterated samples of S097. The adulterants provided 3 distinct spectral patterns

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corresponding to the small N containing adulterants (class 1), the carbohydrates (class 3), and the

292

organic salt and proteins (classes 2 and 4).

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Class 1, dicyandiamide, melamine, and urea: Figure 4A shows the PCA scores plot for the un-

295

binned high resolution 1H-NMR spectra for the 16 un-adulterated S097 samples and S097 spiked

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with 5%, 1%, 0.5%, 0.05%, 0.01%, and 0.005% dicyandiamide (DCD). Spikes of 5%, 1%, and

297

0.5% are well separated from the un-adulterated samples on the horizontal axis while spikes

298

0.05%. 0.01%, and 0.005% fall among the un-adulterated samples. The vertical axis reflects the

299

variance associated with the 16 repeat preparations and analyses of sample S097. The PCA

300

loadings plot for the first principal component (PC1) in Figure 4B shows that a single peak at

301

6.58 ppm is primarily responsible for the separation of spikes of 5%, 1%, and 0.5% from the un-

302

adulterated samples on the horizontal axis. All the spectra for adulterated samples in this

303

category were characterized by a single, dominant spectral feature (Table 6) that serves to

304

discriminate between the un-adulterated and adulterated samples as illustrated by DCD.

305 306

A better statistical evaluation of the differences between the clusters of samples and outliers can

307

be obtained using SIMCA with one-class modeling 21. With this approach, a PCA model is

308

applied to the 16 un-adulterated samples and the loadings are used to compute scoress for all the

309

un-adulterated and adulterated samples. Two statistical variables, the Hotelling T2 statistic and

310

the Q statistic, characterize the variance associated with the model and the variance outside the

311

model, respectively. The Q statistic is more informative and takes precedence over the Hotelling

312

T2 statistic. Consequently, only the Q residuals are plotted in Figure 4C for the un-binned 1H-


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NMR spectra for the 16 un-adulterated samples (on which the model is based) and the 6 samples

314

spiked with DCD. Spikes of 5%, 1%, 0.5%, and 0.05% lie above the 95% confidence limit while

315

spikes of 0.01% and 0.005% lie below the 95% confidence limit. The Q residuals plot for binned

316

data (resolution of 0.1 ppm) in Figure 4D shows similar results as the un-binned plot in Figure

317

4C.

318 319

The Q residual plots in Figures 4C and 4D are based on a PCA model using only the first

320

principal component. Use of more PCs to construct the model provides a better fit to the un-

321

adulterated samples and improves the ability to detect adulterated samples. However, use of too

322

many PCs can result in over-fitting of the data. The computer program (i.e., a pot of

323

eigenvectors versus PC) suggested the use of 6 PCs to model the 28,175 data points of the

324

aligned and un-binned spectra and 3 PCs to model the 87 data points of the aligned and binned

325

spectra with 0.1 ppm resolution. Using 6 PCs for the model for the un-binned spectra, the 0.01%

326

and 0.005% spikes fell above the 95% confidence for all three adulterants (DCD, Mel, and Urea)

327

(data not shown). For the binned data using a model based on 3 PCs, the 0.01% spike fell above

328

the 95% confidence limit while the 0.005% spike remained below (data not shown). Table 6

329

summarizes the lowest detectable adulterant concentration based on a 1 PC model.

330 331

Table 6 shows that the results for melamine and urea were similar to those for dicyandiamide.

332

The loadings for the PCA plots of melamine and urea identified peaks at shifts of 5.95 and 5.40

333

ppm, respectively, as being primarily responsible for discrimination between the adulterated and

334

un-adulterated samples. In each case, a single peak dominated the spectrum as shown for

335

dicyandiamide in Figure 4B. The lowest detectable concentrations with models based on 1 PC



336

were similar for all 3 compounds and, as discussed above, lower concentrations could be

337

detected with models based on more PCs.

338 339

Class 3, maltodextrin and sucrose: These adulterants produced PCA scores plots similar to the

340

Class 1 adulterants as shown for sucrose in Figure 5A. However, the PCA loadings showed a

341

series of spectral features, with only one peak slightly more dominant than the others. Sucrose

342

did not show any first derivative shaped peaks but maltodextrin did. These features are

343

summarized in Table 6. The PCA scores plots and the Q residuals for the binned data suggested

344

that both carbohydrates could be detected at the 0.5% level.

345 346

Classes 2 and 4, ammonium sulfate, soy protein isolate, and whey: These classes of adulterants

347

showed no separation of adulterated and un-adulterated samples in the PCA plots and were

348

characterized by the lack of dominant spectral features unique to the adulterant in the loadings

349

plots. Instead, the loadings plots were dominated by features with a first derivative shape arising

350

from peak shifts approximately 0.0015 ppm for the adulterated samples. These observations are

351

illustrated in Figures 6 for whey. Figure 6A shows that there is no obvious separation of the un-

352

adulterated and adulterated samples. Figure 6B shows the first derivative shaped peaks arising

353

from small spectral shifts. Thus, the primary impact of these adulterants was a slight shift in

354

some of the peaks in the ample spectra.

355 356

Validation of the Q Statistic: The major concern regarding the use of one-class modeling is

357

over fitting the data. In this study, the model was validated in two ways, by cross validation with


Page 16 of 45

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358

a second set of un-adulterated samples and by measuring the linearity of the response of the Q

359

residuals as a function of the spike concentration.

360 361

Cross validation was achieved by constructing a one-class model using the 66 samples of SMP

362

and NFDM samples analyzed in Phase 1 (Table 1) and determining the sensitivity (ability to

363

classify un-adulterated samples as un-adulterated samples) of the method based on identification

364

of the un-adulterated samples from Phase 2 (Table 2). Over fitting (selection of too many PCs

365

for the model) will be evidenced by a decrease in sensitivity from 100%. Figure 7A shows the Q

366

residuals computed for all the samples based on a one-class model with 4 PCs for the 66 samples

367

analyzed in Phase 1. The dashed line provides the 95% confidence limit established by only the

368

Phase 1 samples. It can be seen that all the un-adulterated S097 samples (samples 97-112) run

369

with the adulterated samples in Phase 2 fell below the 95% confidence level; the sensitivity was

370

100%. When 5 PCs were used, the sensitivity fell to 52%. Figure 7B shows similar results for

371

binned data (0.1 ppm resolution). Sensitivity was 100% with 4 PCs (Figure 7B) and fell to 45%

372

with 5 PCs. It will be noted that since the 95% confidence limit was chosen for the models in

373

Figures 7A and 7B, the sensitivity for the Phase 1 samples will, by definition, be close to 95%.

374 375

A different perspective can be achieved by plotting the Q residual for each spiked sample as a

376

function of the spike concentration. Previous research demonstrated that a systematic addition of

377

an adulterant, with all other variables held constant, produces a linear response in the Q residuals

378

22

379

the Q residuals have been plotted as a function of the logs of the adulterant concentrations for the

380

un-binned high resolution 1H-NMR spectra. The 95% confidence limits are the same as those in

. This is demonstrated for all the adulterants for classes 1 and 3 in Figure 8A where the logs of



381

Figure 7 and are based on the samples analyzed in Phase 1. Only the spiked samples falling

382

above or close to the 95% confidence limit have been plotted. No plots are shown for the

383

adulterants of Classes 2 and 4 since none of them fell above the confidence limit. Similar results

384

were observed for the binned spectra in Figure 8B.

385 386

The linearity of the slopes demonstrates the proportional relationship between the concentration

387

of the adulterant and the Q residual. Some variability was observed for the slopes of the un-

388

binned data in Figure 8A which is reasonable considering how few points are plotted. The linear

389

relationships were independent of the number of PCs used to build the PCA model (data not

390

shown). The slopes for the plots of the binned data in Figure 8B were more consistent,

391

undoubtedly the result of binning 333 data points. Thus, the linearity of the plots supports the

392

validity of the measurements and the validity of the detection of the adulterated samples.

393 394

The least detectable concentration for each adulterant was computed from the Q residuals shown

395

in Figures 7 or 8 and are summarized in Table 6. A slight advantage was seen for the un-binned

396

spectra. For un-binned spectra, melamine, dicyandiamide, and sucrose samples adulterated at the

397

0.05% level exceeded the 95% confidence limit. Maltose and urea adulteration were observed at

398

the 0.5% level and ammonium sulfate and whey were observed at the 5% level. No response for

399

the soy protein isolate was observed that was different from the un-adulterated samples.

400 401

Similar results were seen with binning with the results for dicyandiamide and sucrose being

402

worse by one standard; observing the 0.05% spike with un-binned spectra and 0.5% spike with

403

binned spectra. This apparently large jump would be reduced with more spikes over the


Page 18 of 45

Page 19 of 45


404

concentration interval. Additionally, lower limits using the Q residuals could be achieved by

405

restricting the shift range to be examined, i.e., with a targeted approach. For the un-binned

406

spectra, more than 28,000 data points contributed to the background level. Reducing this to a

407

few hundred points over the range of the dominant peaks identified by the PCA loadings for the

408

category 1 and 2 samples would reduce the background noise and reduce the level of spike that

409

could be observed. Thus, targeted analysis can evolve from the non-targeted analysis using

410

chemometrics. However, initial screening with a non-targeted method allows inspection of the

411

full spectrum.

412

Discussion

413

Spectral fingerprinting combined with PCA and pcANOVA and with no attempt at identification

414

of individual components, is a rapid method for classifying samples (Figures 4-8) and

415

understanding the variance attributable to the samples and the method (Tables 2 and 3). Spectra

416

for 1H-NMR, like those for IR, FIMS, NIR, and UV, can be acquired be acquired in minutes and

417

support high throughput analyses. In each case, the grinding and/or extraction of the sample is

418

the rate limiting step. The complexity of the spectra and the resolution of the method are

419

irrelevant as only the pattern is important. These are key features for a high throughput, non-

420

targeted method.

421 422

Understanding the sources of variance for an analysis is important for determining the impact of

423

experimental factors and the extent to which an unknown sample can deviate from the reference

424

samples before it is detected. Eliminating unknown sources of variance allows better evaluation

425

of the data and better detection limits for adulterants. This is true for both targeted, mono-variate

426

data and non-targeted, multi-variate data, i.e., spectral fingerprints. Thus, the presence of



427

unexplained variance in Figure 1A was a cause for major concern. In retrospect, the source of

428

variance is an artifact of the data processing program. However, we have observed this artifact

429

in a number of different programs. Fortunately, a simple shift of each spectrum to align the TMS

430

peaks provides an easy solution (Figure 3).

431 432

There are numerous publications describing the impact of analytical parameters, such as

433

temperature and acidity, on peak position 22-24. However, none of the publications address the

434

issue of alignment of the TMS peaks directly. In general, the issue is minimized by binning and

435

by use of a reference spectrum 24. In the latter case, a single spectrum is selected as a template to

436

which the rest of the spectra are aligned. In the case of binning, a shift of ±5 units (±0.0015 ppm

437

with a 0.0003 ppm resolution) is obscured if the binned interval is 0.01 to 0.03 ppm. This degree

438

of shift is not trivial when un-binned data are processed as demonstrated by the comparison of

439

Figures 1A and 1B. Aligning the TMS peaks reduces the relative variance arising from

440

alignment in pcANOVA from 60% to 8% (data not shown). Tables 4 and 5 show that the

441

percentage of variance for each experimental factor changes with binning but the level of

442

significance does not. With and without binning (but with TMS peak alignment) the variance

443

attributable to milk powder type, processing temperature, supplier, and processing site were

444

highly significant and day of analysis was not.

445 446

Binning had a mixed impact on detection of adulteration. Table 6 shows that the detection limits

447

for dcyandiamide, melamine, and urea were worse, maltodextrin was the same, and sucrose was

448

slightly better. In addition, with binning it was possible to positively identify 5% contamination

449

for ammonium sulfate and whey. The degree of change with binning is difficult to quantify


Page 20 of 45

Page 21 of 45


450

because of the coarseness (large concentration gaps) between the spikes. The effect of binning is

451

most pronounced in Figure 8 where the alignment of the signals and the slopes of the plots are

452

much more uniform. Binning serves as a smoothing algorithm that reduces the random noise.

453

This is advantageous when the spectral features are subtle, but are a disadvantage when the

454

adulterant produces a single strong peak (dcyandiamide, melamine, and urea). In general, there

455

appears to be little difference between the results, binned versus non-binned, especially when

456

looking for patterns, i.e., non-targeted analysis. For detailed analyses of spectral features, non-

457

binned provides greater information.

458 459

It is interesting to note that the loadings for PCA, when plotted as a function of the magnetic

460

shift (Figures 2A, 4B, 5B, and 6B) can serve as a diagnostic for peak alignment. Differences in

461

peak intensities are seen as a single positive or negative going peak. Differences arising from

462

peak shifts produce a 1st derivative shaped signal in the loadings plot. In theory, peak

463

broadening, with no sift in the peak maximum position, would produce a 2nd derivative shaped

464

signal. Figures 5B and 6B suggest that spectral differences induced by adulteration with sucrose

465

and whey include peak shifts.

466 467

Detection of adulteration based on spectral fingerprints and data processing using one-class

468

modeling and a test for variance outside the model (Q statistic) is a truly non-targeted method of

469

analysis. A difference in a spectral feature in an unknown sample can produce a scores that falls

470

outside the confidence limit established by the researcher. The disadvantage of the method is

471

that the variance for the reference sample model, upon which the confidence limit is based, is

472

determined by all the data points in the spectra. When the limiting noise is random, the variance



473

of the samples will increase in proportion to the number of variables. Thus, the variance for

474

28,000 points (9.0 to 0.2 ppm) is 170 times greater than for 167 points, a range of 0.05 ppm in

475

Figure 3B and 3D. This means that the method's ability to detect a difference in a spectral

476

feature improves as the spectral window is narrowed down. This constitutes a targeted method

477

and is dependent on a priori knowledge of the researcher. Unfortunately, the researcher often

478

lacks prior knowledge and needs a truly non-targeted method. A method such as the one

479

described in this report.


Page 22 of 45

Page 23 of 45

480


Funding Sources

481

This work was supported financially by the U.S. Pharmacopeia, the Agricultural Research

482

Service of the U.S. Department of Agriculture, and by an InterAgency Agreement with the

483

Office of Dietary Supplements of the National Institutes of Health.

484



References

485 486

1. Moore, J.C.; Ganguly, A.; Smeller, J.; Botros, L.; Mossoba, M.; Bergana, M.M.

487

Standardisation of Non-Targeted Screening Tools to Detect Adulterations in Skim Milk

488

Powder Using NIR Spectroscopy and Chemometrics. NIR News, 2012, 23, 9–13.

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2. Botros, L.; Jablonski, J.; Chang, C.; Bergana, M.; Wehling, P.; Harnly, J.; Downey, G.;

490

Harrington, P.; Potts, A.; Moore, J. Exploring Authentic Nonfat and Skim Milk Powder

491

Variance for the Development of Nontargeted Adulterant Detection Methods Using NIR

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Spectroscopy and Chemometrics. J. Agric. Food Chem. 2013 61, 9810-9818.

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3. Harnly, J.; Harrington, P.; Botros, L.; Jablonski, J.; Chang, C.; Bergana, M.; Wehling, P.;

494

Downey, G.; Potts ,A.; Moore, J. Characterization of Near Infrared Spectral Variance in the

495

Authentication of Skim and Nonfat Dry Milk Powder Collection Using ANOVA-PCA,

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Pooled-ANOVA, and Partial Least Squares Regression. J. Agric. Food Chem. 2014, 62,

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8060−8067.

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4. Jablonski, J.; Harnly, J.; Moore, J. Non-targeted detection of adulteration of skim milk powder with foreign proteins using UHPLC−UV. J. A. Food Chem. 2013, 62, 5198-5206.

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5. Sanjeewa, R.K., Farris, S., Mossoba, M.M., Moore, J.C., Yakes, B.J. Non-targeted detection

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of milk powder adulteration using Raman spectroscopy and chemometrics: melamine case

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study. Food Additives and Contaminants: Part A, 2016, 33:6, 921-932.

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6. DeVries, J.W., Greene, G.W., Payne, A., Zbyluta, S., Scholld, P.F., Wehling, P., Evers, J.M.,

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Moore, J.C. Non-protein nitrogen determination: A screening tool for nitrogenous compound

505

adulteration of milk powder. International Dairy Journal 2017, 68, 46-51.

506

7. U.S. Pharmacopeial Convention. Appendix XVIII: USP Guidance on Developing and

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Validating Non-Targeted Methods for Adulteration Detection. Food Chemical codex 10 3S,

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2017, 2053-2066.


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8. Bergana, M.; Adams, K.; Harnly, J.; Xie, Z, Moore, J. Nontargeted Detection of Milk

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Powder Adulteration by 1H NMR spectroscopy and Conformity Index Analysis. Food

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Chem. 2017 (submitted).

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9. Harnly, J., Chen, P., Sun, J., Huang, H., Colson, K.L. Comparison of Flow Injection MS,

513

NMR, and DNA Sequencing: Methods for Identification and Authentication of Black Cohosh

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(Actaea racemosa). Planta Med 2016, 82, 250–262..

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10. Hu, F., Furihata, K., Ito-Ishida, M., Kaminogawa, S., & Tanokura, M. Nondestructive

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observation of bovine milk by NMR spectroscopy: Analysis of existing states of compounds

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and detection of new compounds. Journal of agricultural and food chemistry, 2004, 52(16),

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4969-4974.

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11. Hu, F., Furihata, K., Kato, Y., & Tanokura, M. Nondestructive quantification of organic

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compounds in whole milk without pretreatment by two-dimensional NMR spectroscopy.

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Journal of agricultural and food chemistry, 2007, 55(11), 4307-4311.

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12. Lachenmeier, D. W., Humpfer, E., Fang, F., Schütz, B., Dvortsak, P., Sproll, C., & Spraul,

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M. NMR-spectroscopy for nontargeted screening and simultaneous quantification of health-

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relevant compounds in foods: the example of melamine. Journal of Agricultural and Food

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Chemistry, 2009, 57(16), 7194-7199.

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13. Belloque, J., & Ramos, M. Application of NMR spectroscopy to milk and dairy products. Trends in Food Science & Technology, 1999, 10(10), 313-320. 14. Monakhova, Y. B., Kuballa, T., Leitz, J., Andlauer, C., & Lachenmeier, D. W. NMR

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spectroscopy as a screening tool to validate nutrition labeling of milk, lactose-free milk, and

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milk substitutes based on soy and grains. Dairy science & technology, 2012, 92(2), 109-120.



531 532

15. Sundekilde, U. K., Larsen, L. B., & Bertram, H. C. NMR-based milk metabolomics. Metabolites, 2013, 3(2), 204-222.

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16. Harnly, J.M.; Mukhopadhyay, S.; Lin, L.-Z.; Luthria, D.L. A comparison of analytical and

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data preprocessing methods for spectral fingerprinting. Appl. Spec. 2011, 65, 250-259.

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17. .Harnly, J.M., Chen, P., Luthria, D. Detection of Adulterated Ginkgo biloba Supplements

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Using Chromatographic and Spectral Fingerprints. J AOAC International, 2012, 95, 1579-

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1587.

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18. Chen, P., Harnly, J.M., Lester, G.E. Flow injection mass spectral fingerprints demonstrate

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chemical differences in Rio Red grapefruit with respect to year, harvest time, and

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conventional versus organic farming. J Ag Food Chem, 2010, 58, 4545-4553.

541 542 543 544 545 546 547 548 549 550 551 552

19. Harnly, J.M., Pastor-Corrales, M.S., Luthria, D.L. Variance in the chemical composition of dry beans determined from UV spectral fingerprints. J Ag Food Chem 2009, 57, 8705-8710. 20. Brerton, R. Chemometric for Pattern Recognition; Publisher: John Wiley & Sons, West Sussex, UK, ISBN 978-0-470-98725-4; 2009, pp 233-287. 21. Harnly, J.; Harrington, P. Probability of identification: Adulteration of American ginseng with Asian ginseng. J AOAC Int. 2013, 96,1258-1265. 22. Edwards J.C. Principles of NMR. http://process-nmr.com/pdfs/NMR%20Overview.pdf (accessed April 16, 2018). 23. Vu, T.N.; Laukens, K. Getting your peaks in a line: a review of alignment methods for NMR spectral data. Metabolites, 2013, 3, 259-276. 24. Vu, T.N.; Valkenborg, D.; Smets, K.; Verwaest, K.A.; Dommisse, R.; Lemière, P.; Verschoren, A.; Goethals, B.; Laukens, K. An integrated workflow for robust alignment and


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simplified quantitative analysis of NMR spectrometry data. BMC Bioinformatics 2011, 12,

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405.

555



Figure Captions

556 557 558

Figure 1 PCA scores plots for 1H-NMR spectra for 46 samples from 9.0 to 0.2 ppm, minus the

559

solvent peaks (2.47 to 2.56 ppm and 3.30 to 3.40 ppm) with A) original spectra, un-aligned with

560

0.0003 ppm resolution and B) aligned spectra with 0.0003 ppm resolution, and C) aligned

561

spectra with 333 points per bin. Samples were processed at low (∗), medium (■), and high (▼)

562

temperatures.

563

Figure 2 Illustration of source of the first derivative shape for PCA loadings: A) spectral profiles

564

for un-aligned peaks for whey at 5.74 ppm and B) the resultant PCA loadings.

565

Figure 3 Spectral profiles for A) TMS un-aligned near 0.00 ppm, B) quartet at 4.18 ppm un-

566

aligned, C) TMS aligned at 0.00 pm, and D) quartet at 4.18 ppm after alignment of TMS.

567

Figure 4 Characterization of samples adulterated with dicyandiamide (DCD): A) PCA scores

568

plot. B) PCA loadings plot for PC1, C) Q residuals versus sample for un-binned high resolution

569

(0.0003 ppm) spectra, and D) Q residuals versus sample for binned samples with 0.1 ppm

570

resolution. Both PCA models are based on 1 PC.

571

Figure 5 Characterization of samples adulterated with sucrose: A) PCA scores plot and B) PCA

572

loadings plot for PC1.

573

Figure 6 Characterization of samples adulterated with whey: A) PCA scores plot and B) PCA

574

loadings plot for PC1.

575

Figure 7 Q Residuals for A) un-biinned and B) binned samples as a function of the sample

576

number. The one-class model was based on S097 samples analyzed in Phase 1 (▼). Duplicate

577

analyses of S097 (+) analyzed with each set of adulterants are plotted separately and were used


Page 28 of 45

Page 29 of 45


578

for validation of the analyses. Adulterants are labeled as W-whey, U-urea, S-sucrose, SPI-soy

579

protein isolate, Me-melamine, Ma-maltodextrin, D-dicyandiamide, and AS-ammonium sulfate.

580

Figure 8 Q Residuals for adulterated samples versus spike concentration: A) un-binned

581

(resolution = 0.0003 ppm) and B) binned (resolution = 0.1 ppm). Horizontal dashed line

582

provides 95% confidence limit for Q value of un-adulterated S097 samples from Figure 7.

583

Adulterants are (♦) melamine, (■) dicyandiamide, (▲) urea, (x) sucrose, and (∗ ∗) maltodextrin



Page 30 of 45

Table 1 Samples Analyzed

Supplier, Milk Heat Production Sample Production Powder Process Date Code Location* Class Temp (mm/dd/yy) S021 S022 S023 S024 S030 S031 S032 S033 S047 S051 S053 S054 S055 S061 S068 S070 S076 S077 S080 S081 S082 S084 S085 S086

B,1 B,1 B,1 B,1 B,2 B,2 B,2 B,2 B,1 B,1 B,1 B,1 B,1 C,3 C,3 C,3 B,2 B,1 A,4 A,5 A,5 A,4 A,4 A,5

N S N N N N N N N N N N N N N N N S S N N S S N

LH -MH MH HH HH LH LH LH LH LH LH LH LH LH LH HH LH LH HH LH MH MH HH

07/12/10 02/27/10 05/05/10 05/05/10 07/18/10 11/16/09 06/19/10 02/26/10 06/07/10 ---------08/26/08 03/08/11 02/21/11 02/07/11 01/14/11 02/21/11 03/27/11 03/09/11 02/27/11 01/30/11 03/08/11 01/15/11

Supplier, Milk Heat Production Sample Production Powder Process Date Code Location Class Temp (mm/dd/yy) S087 S089 S091 S093 S094 S095 S096 S097 S098 S106 S107 S108 S110 S116 S117 S136 S145 S147 S149 S154 S157 S170 S171 S172

A,5 A,6 A,6 B,1 B,1 D,7 A,8 A,8 A,8 E.9 E.9 F,F,G,10 G,10 H,12 I,11 I,11 I,11 J,13 J,13 --K,14

N N N N N S S S S S S N N S S S N N N N N N N N

*Samples were collected from 11 suppliers (A-K) with 14 processing locations (1-14).


LH MH MH LH MH MH MH LH LH MH MH --MH MH -LH LH HH LH LH LH LH LH

03/07/11 03/12/11 12/26/10 02/01/11 02/13/11 10/20/10 02/08/11 03/12/11 08/29/11 08/17/10 01/15/10 ------04/02/11 03/15/11 11/01/11 05/12/12 07/05/12 05/15/12 07/06/12 07/06/12 ----------

Page 31 of 45


Table 2 Spiked Adulterants of Sample S097

Whey

Urea

Sucrose

Soy Protein DicyanIsolate Melamine Maltodextrin Diamide

Ammonium Sulfate

Sample S097 Whey Adulterant Whey_ref Sample + Adulterant 5.000% Whey 5 1.000% Whey 1 0.500% Whey 05 0.050% Whey 005 0.010% Whey 001 0.005% Whey 0005

S097 Urea S097 Sucrose S097 SPI

S097 Mel

S097 Malto

S097 DCD S097 AS

Urea_ref

Sucrose_ref

SPI_ref

Mel_ref

Malto_ref

DCD_ref

AS_ref

Urea 5 Urea 1 Urea 05 Urea 005 Urea 001 Urea 0005

Sucrose 5 Sucrose 1 Sucrose 05 Sucrose 005 Sucrose 001 Sucrose 0005

SPI 5 SPI 1 SPI 05 SPI 005 SPI 001 SPI 0005

Mel 5 Mel 1 Mel 05 Mel 005 Mel 001 Mel 0005

Malto 5 Malto 1 Malto 05 Malto 005 Malto 001 Malto 0005

DCD 5 DCD 1 DCD 05 DCD 005 DCD 001 DCD 0005

AS 5 AS 1 AS 05 AS 005 AS 001 AS 0005



Table 3 Repeat Analyses of Sample S097

Sample

Prep

Prep Date

S097 S097 S097 S097 S097 S097 S097 S097 S097 S097 S097 S097 S097 S097 S097 S097 S097 S097 S097 S097 S097

1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 4

12/3/12 12/4/12 12/5/12 12/6/12 12/7/12 12/3/12 12/4/12 12/5/12 12/6/12 12/7/12 12/3/12 12/4/12 12/5/12 12/6/12 12/7/12 12/9/12 12/9/12 12/9/12 12/9/12 12/9/12 12/9/12

Day of Analysis Analysis Time 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 9 9 9 9 9 9

------------------------------1 hr 2 hr 0.5 hr 3 hr 4 hr 5 hr


Page 32 of 45

Page 33 of 45


Table 4 Pooled, Crossed, ANOVA for Aligned, Un-Binned Data (Resolution = 0.0003 ppm)

Total Sum of Squares SMP vs NFDM Mean Res Processing Temp Mean Res Supplier Mean Res Site Mean Res Cross Factors Mean Res Analysis Day Mean Res Preparation Mean Res

n 66 2 66 3 66 11 66 15 66 21 66 7 66 45 66

df 1 65 2 64 10 55 14 51 20 140 6 59 44 21

Total %Total Mean Var Var Var F value 0.4053 100% 0.0251 6% 0.0251 50.68 0.3802 0.0680 17% 0.0340 68.65 0.3121 0.0583 14% 0.0058 11.77 0.2538 0.0900 22% 0.0064 12.99 0.1638 0.0601 15% 0.0030 6.06 0.1037 0.0189 5% 0.0032 6.36 0.0848 0.0744 18% 0.0017 3.41 0.0104 3% 0.0005


p

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