N-glycan profile as a tool in qualitative and quantitative

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Omics Technologies Applied to Agriculture and Food

N-glycan profile as a tool in qualitative and quantitative analysis of meat adulteration Zihe Shi, Binru Yin, Yuquan Li, Guanghong Zhou, Chunbao Li, Xinglian Xu, Xin Luo, Xibin Zhang, Jun Qi, Josef Voglmeir, and Li Liu J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.9b03756 • Publication Date (Web): 29 Aug 2019 Downloaded from pubs.acs.org on August 30, 2019

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N-glycan profile as a tool in qualitative and quantitative analysis of meat adulteration

Zihe Shia, Binru Yina, Yuquan Lia, Guanghong Zhoub, Chunbao Li b, Xinglian Xu b, Xin Luoc, Xibin Zhangc,d, Jun Qie, Josef Voglmeir*,a,b, Li Liu*,a,b

a

Glycomics and Glycan Bioengineering Research Center (GGBRC), College of Food

Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China b

Jiangsu Colleborative Innovation Center of Meat Production, Processing and Quality

Control, College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China c

Lab of Beef Processing and Quality Control, College of Food Science and Engineering,

Shandong Agricultural University, Taian, Shandong 271018, China d

New Hope Liuhe Co. Ltd., Beijing 100102, China

e

Anhui Engineering Laboratory for Agro-products Processing, Anhui Agricultural

University, Hefei, Anhui 230036, China *Correspondence

should be addressed to:

E-mail: [email protected]: Fax: +86 25 84399553 Tel: +86 25 84399511 or E-mail: [email protected], Fax: +86 25 84399553 Tel: +86 25 84399512


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Keywords: adulteration; N-glycan profiles; principal component analysis; PLS regression 1

Abstract: Adulteration of meat and meat products causes a concerning threat for

2

consumers. It is necessary to develop novel robust and sensitive methods which can

3

authenticate the origin of meat by qualitative and quantitative means to compensate

4

the drawbacks of the existing methods. This study has shown that the protein N-

5

glycosylation profiles of different meats are species specific and thus can be used for

6

meat authentication. Based on N-glycan pattern, the investigated five meat species (beef,

7

chicken, pork, duck and mutton) can be distinguished by principal component analysis

8

(PCA), and partial least square (PLS) regression was performed to build a calibration

9

and validation model for prediction of the adulteration ratio. Using this method, beef

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samples adulterated with the lower value duck meat could be detected down to the

11

addition ratio as low as 2.2%. The most distinguishing N-glycans from beef and duck

12

were elucidated for the detailed structures.

13 14 15 16 17



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Introduction

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Meat and meat products are highly nutritious components of the diet and are widely

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appreciated for their flavor1. More costly meats such as beef and mutton are reported to

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be fraudulently substituted by cheaper meats such as chicken, pork and duck2-4.

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Therefore, authentication of meat is essential to safeguard consumer rights, religious

24

beliefs, immune specific dietary requirements and even to some extent to protect

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wildlife5-8.

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Historically, the identification of meat species is challenging due to the change of

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external morphological features after meat pre-processing such as deboning, mincing,

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chopping, emulsification and other pre-processing procedures9. Different products and

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consumption patterns require different adulteration identification techniques. At

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present, many approaches are established for meat authentification including

31

biochemical methods

32

spectroscopic methods

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the last 25 years was achieved by using DNA-based methods such as the polymerase

34

chain reaction (PCR). DNA shows much better stability and is generally universally

35

applicable, as all tissue samples contain DNA24. However, one drawback of DNA-based

36

methods is that it is challenging to quantify the extent of meat adulteration, as even

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minute amounts of contaminants (i.e. during the meat processing) will be detected1, 25-26.

38

The comprehensive study on complex carbohydrates in tissues and cells (so-called

39

glycomics) has rapidly developed in recent years. Currently, glycomic techniques are

40

primarily applied in biomedical research for the evaluation of aberrant glycosylation

10-12, 20-23.

immunological methods13-14, molecular methods

15-19and

The greatest progress made in species identification over


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pattern as biomarkers27-28. The similar methodologies have also been proven as

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powerful tools in food safety and food quality control29-30. For example, Shim et al.

43

described a method to characterize edible bird nest (which is made from solidified

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swiftlet saliva) adulteration based on their glycan composition31. Another recent

45

example by Wang et al. showed that protein glycosylation of edible Gingko seeds varies

46

based on their geographic origin32. However, no study was yet conducted using the

47

analysis of protein glycosylation for the determination of meat species.

48

In this study, five meat species (beef, chicken, pork, duck and mutton) were

49

investigated based on their N-glycan profiles, which were analyzed using hydrophilic

50

interaction ultra-performance liquid chromatography (HILIC-UPLC). The obtained

51

UPLC data is evaluated with PCA (principal component analysis) for discrimination of

52

samples and PLS (partial least squares) regression analysis for determination of

53

adulteration ratio. This method was then applied to discriminate duck meat, which is

54

often used for the adulteration of beef due to its intense flavor and dark meat color33-34,

55

from beef samples. Multivariate data analysis revealed the quantity of added duck meat

56

based on the glycosylation pattern present in the analyzed meat samples.

57 58

Materials and Methods

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Materials. HPLC-grade acetonitrile (ACN) was purchased from Merck (Darmstadt,

60

Germany). Supelclean ENVI-Carb solid-phase extraction (SPE) columns, trifluoroacetic

61

acid (TFA), and dextran oligomers (molecular range 300 - 3000 Da) were purchased

62

from Sigma (St. Louis, USA). 2-aminobenzamide (2-AB) was obtained from J&K



63

Chemicals (Beijing, China). PNGase F (50 mU) was provided by Qlyco Ltd. (Nanjing,

64

China).

65 66

Sample preparation. Fifteen meat samples for each species were purchased from

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local supermarkets or slaughterhouses. After removal of connective tissue and visible

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fat, approximately 200 g of each meat sample was minced to homogeneity. To mimic the

69

actual meat adulteration, duck meat in various concentrations (5%, 10%, 20%, 30%,

70

40% and 50% w/w) were added into beef for qualitative and quantitative binary

71

adulteration analysis, and chicken meat, pork and chicken + pork (in 10% and 20%

72

concentrations, respectively) were added into beef for trinary species adulteration

73

detection. All different types of samples were stored at -18 °C prior to sample analysis.

74 75

Preparation of meat N-glycans. Approximately 100 mg of minced meat was first

76

treated with 660 µL of chloroform and 330 µL of methanol, and the sample was then

77

centrifuged at 13,000 g for 5 min after vigorous mixing. The interface was recovered by

78

the careful removal the top (methanol) and bottom (chloroform) phase, and re-

79

suspended in 100 µL of 40% trichloroacetic acid (TCA, v/v). After centrifugation at

80

13,000 g for 10 min at 4°C, the supernatant was removed. The obtained (glyco-) protein

81

pellet was then washed with 1.8 mL of deionized water and centrifuged at 13,000 g for

82

5 min at 4°C. After removal of the supernatant, the pellet was then re-solubilized in 100

83

µL of 6 M urea. After the addition of 46 µL of sodium-phosphate solution (500 mM, pH

84

7.5), 23 µL of sodium dodecyl sulfate solution (2% w/v SDS in 1 M β-mercaptoethanol)

85

and 56 µL of distilled water, the mixtures were heated and boiled at 100 °C for 5 min.


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The solutions were cooled down and 38 µL of Triton-X100 solution (10% w/v) together

87

with 200 µL of PNGase F enzyme solution (50 mU) were added, prior to the incubation

88

at 37°C for 16 h. The reaction mixture was centrifuged at 13,000 g for 10 min at 4 °C to

89

remove debris and the cleared supernatant was purified using solid phase extraction

90

(SPE) columns (which were pre-treated by using 3 mL of an aqueous 80% acetonitrile

91

solution containing 0.1% TFA, and then equilibrated with 3 mL of deionized water). The

92

enzymatically released carbohydrates were then eluted from the SPE column using 1.5

93

mL of aqueous 40% acetonitrile solution containing 0.1% TFA (v/v).

94 95

N-glycan labeling and analysis. N-glycans derived from the meat samples were

96

fluorescently labeled with 10 µL of 2-AB labeling reagent (48 mg 2-AB and 64 mg of

97

NaCNBH3 dissolved in 7 mL dimethylsulfoxide and 3 mL acetic acid), and the reaction

98

mixtures were incubated at 65°C for 2 h. 10 µL of the 2-AB labeled sample were diluted

99

with 30 μL deionized water and 35 μL of acetonitrile prior to HILIC-UPLC analysis

100

(Nexera, Shimadzu Corporation, Kyoto, Japan), and separated by an Acquity UPLC BEH

101

Glycan column (2.1×150 mm, 1.7 mm particle size; Waters, Ireland) at a column

102

temperature of 60°C. Fluorescence detection was set at the excitation/emission

103

wavelengths of 330 and 420 nm, respectively. The separation of N-glycans was carried

104

out using a previously developed linear gradient of aqueous ammonium formate (50

105

mM, pH 4.5) and acetonitrile35 (Supplementary Table S1). Elution profiles were

106

compared with a 2AB-labeled dextran oligomers (2–20 glucose units), and the absolute

107

retention time of each N-glycan peak was converted to relative retention times of the

108

dextran oligomers (GU values).



109 110

MALDI-TOF MS/MS analysis. Peak fractions of the 2AB-labeled UPLC fractions were

111

manually collected, dried by centrifugal evaporation, and analyzed using matrix-

112

assisted laser desorption/ionization time of-flight mass spectrometry (MALDI-TOF MS).

113

These analyses were performed on a Bruker Autoflex Speed instrument (equipped with

114

a 1000 Hz Smartbeam-II laser) in a positive ion mode using with 2, 5-dihydroxybenzoic

115

acid (10 mg/ml in acetone) as matrix. Mass fragmentation experiments (MS/MS) were

116

performed by laser induced dissociation. The acquired data were processed using the

117

Bruker Flex Analysis software (version 3.3), and the mass peaks derived from the MS-

118

and MS/MS-spectra were evaluated using GlycoWorkbench (version 1.1)36 .

119 120

Data analysis. N-glycan peaks exceeding a relative peak area over 1% were

121

considered for statistical analysis. To identify the various types of meat and the ratios of

122

adulterated meat blends, multivariate data analysis was carried out. PCA and PLS

123

analysis were performed using SPSS statistics package (version 19) and Matlab (version

124

2016b). The limit of detection (LOD) in the multivariate domain was calculated

125

according to latest development in PLS calibration37-38. The RSD of the relative peak

126

areas of each peak from 15 replicates was calculated using Microsoft Excel 2010.

127 128

Results

129

Meat N-glycan analysis and species classification.


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N-glycans were released by PNGase F and separated using a 55 min HILIC-UPLC

131

program with the N-glycans being eluted between 15 and 40 min. For comparing UPLC

132

chromatograms, the retention time of glycan peaks was standardized into GU (glucose

133

unit) value, based on the retention times of dextran oligomer standards, using a linear

134

regression model (Fig. S1). Each of the five tested meat samples showed a distinct

135

overall N-glycan profile (Fig. 1A). The N-glycan profile of chicken sample bore the

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largest number of peaks, whereas the beef sample showed the least number of peaks.

137

Duck and chicken meat N-glycans contained more relatively smaller structures

138

compared to others. Generally, although the majority of the peaks were commonly

139

shared among the tested samples, the presence of some species specific peaks and the

140

quantitative difference in relative peak area percentage of each peak made the N-

141

glycome profile of each species meat sample different from others.

142

In order to distinguish different meat species based on the protein glycosylation, the

143

N-glycan UPLC patterns of all samples were compared in terms of the relative level of

144

each target peak (percentage of the UPLC area of the target peak over the areas of all

145

peaks). Due to the difference of relative level of each peak in different meats, the overall

146

UPLC profile enables the discrimination of different meats. The identification of

147

characteristic peaks in each species from a total of 15 replicates was a critical step, and

148

may greatly affect the accuracy of the species prediction22. The UPLC N-glycan profiles

149

of 15 individuals of each species were shown in Fig. S2. The N-glycan peak with a

150

relative peak area percentage above 1% and presents in all samples was selected for

151

the PAC dataset building up. Variability of RPA of each peak within one species was

152

examined by the relative standard deviations (RSD) value of the relative peak areas



153

(RPA). For each species dataset, the RSD values of most peaks (account for over 60% of

154

the total peak area) were under 10%, with the maximum value being less than 17%,

155

indicating the low variability of the dataset (Table 1).

156

As shown in Figure 1B, the dataset obtained was transformed into 3 principle

157

components named PC1, PC2 and PC3. PC1 explained 45.82% of variance, and PC2 and

158

PC3 explained 33.98% and 10.76% of variance, respectively. The percentage of the total

159

variances which are able to be explained by the 3 PCs was over 90%, and therefore the

160

PCA results was regarded as reliable (>85%)39. The resulting score plot, based on the

161

PCA analysis of N-glycan profiles of the meat samples, allowed the obvious

162

discrimination of the 5 tested meat species.

163 164

Quantitative and qualitive identification of binary adulterated meat samples.

165

After the analysis of the meat samples of individual species, samples of beef blended

166

with various levels (between 0% and 50%) of added duck meat were examined (Fig.

167

2A). The overall N-glycan UPLC profile of the analyzed samples gradually changed with

168

increasing amounts of added duck meat. When compared with the N-glycan profile

169

derived from the pure beef sample, the peak area of each of the four peaks (peak 1

170

eluted at GU 6.12, peak 2 eluted at GU 6.89, peak 3 eluted at GU 7.30, and peak 4 eluted

171

at GU 7.67) over all peak areas increased, whereas the relative area of peak 5 (eluted at

172

GU 8.92) and peak 6 (eluted at GU 9.12) decreased. The relative area of peaks 1-6 were

173

further visualized using boxplot analysis (Fig. 2B) and it was shown that the peak area

174

percentage of the target peaks obviously changed with the added level of duck meat,


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indicating the possible ability of discriminating the meat adulteration based on the

176

change of relative amount of each peak.

177

Principal component analysis was further applied in qualitative discrimination of

178

mixed meat samples. Beef sample was mixed with lower value duck meat at various

179

percentages including 5%, 10%, 20%, 30%, 40% and 50%. First, three principal

180

components were chosen and the resulted scatter plot showed (Fig. 3A) that the mixed

181

beef-duck samples can be separated from pure beef and other pure lower value meat

182

species. Importantly, cluster of mixed beef-duck samples were obviously placed closer

183

to beef and duck, while far from pork and chicken, indicating the ability of identifying

184

the spiked meat from the mixed sample using meat N-glycan profile. Subsequently, 2D

185

principal component analysis of beef, duck and mixed beef-duck samples exhibited

186

better ability of distinguishing all mixed samples from pure beef and pure duck (Fig. 3B).

187

Furthermore, when comparing with beef only, 2D principal components analysis of

188

mixed beef-duck samples containing various levels of duck meat showed that the mixed

189

sample with each different level of spiked duck can be isolated from others, indicating

190

the ability of discriminating the addition level of adulterated meat using this method

191

(Fig. 3C). Generally, the built PCA models based on meat N-glycan UPLC profile allowed

192

to effectively tell whether the known meat samples are pure or adulterated.

193

Furthermore, it can also identify which meat species and to which level this meat

194

species is used in adulteration.

195

In order to quantify the amount of duck meat spiked in beef, a PLS (partial least

196

squares) regression model, which is an effective and convenient quantification method

197

for binary mixtures, was employed (Fig. 4). Beef spiked with seven different levels of



198

duck meat (0%, 5%, 10%, 20%, 30%, 40% and 50%) were investigated for the

199

quantification of the adulteration. 114 of mixed duck/beef meat samples were used for

200

calibration and 28 mixed meat samples were used for validation. The correlation

201

coefficients of calibration (R2C >0.99), the validation (R2V>0.99), the lower root mean

202

square errors of calibration (RMSEC, 1.35%) and the prediction (RMSEP, 1.58%) of the

203

PLS model confirmed its effectiveness and accuracy40. In addition, the residual

204

predictive deviation (RPD) values calculated to be 13.06 (>8) for both calibration and

205

validation, indicating the suitability of this method for analytical tasks41-42. This result

206

showed that the built statistical model was robust enough to predict the addition of

207

duck meat in beef samples with a predicted limit of detection (LOD) of 2.2%. However,

208

given that adulteration of meat samples in industry or on the market usually contains

209

higher amounts of contaminant, we suggest that this method is feasible for the

210

detection of duck meat in beef samples in practice.

211 212

Analysis of trinary adulterated meat samples.

213

In order to further verify the applicability of the N-glycosylation based method in

214

multi-species meat authentification, trinary-species mixed meat samples were also

215

examined. Beef samples were blended with the same concentration of both pork and

216

chicken meat (10% and 20% were used) and compared with pure beef and the binary

217

beef mixtures containing either pork or chicken meat. As shown in Figure 5, the overall

218

N-glycan UPLC profile of all the samples showed differences in relative amount of some

219

peaks. For instance, the peak area of the five highlighted peaks (peak 1 eluted at GU

220

5.20, peak 2 eluted at GU 5.96, peak 3 eluted at GU 6.96, and peak 4 eluted at GU 7.67,


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peak 6 eluted at GU 9.98) over all peaks areas increased when compared with the N-

222

glycan profile derived from the pure beef sample, whereas the relative area of peak 5

223

(eluted at GU 8.12) decreased.

224

To further analyze the difference of the samples more accurately, PCA was performed

225

to discriminate all the mixed meat samples. Beef-pork-chicken samples presented to be

226

closer to beef, pork and chicken meat samples, whereas to be farther to duck meat

227

samples, indicating the ability of preliminarily identifying the species of trinary-species

228

meat samples (Fig. 6A). Meanwhile, when compared with binary mixed meat samples

229

such as beef-pork and beef-chicken, clusters of beef-pork-chicken meat samples were

230

able to be distinguished from others (Fig. 6B). Furthermore, Figure 6C displayed that

231

both mixed binary and ternary samples containing different concentrations of spiked

232

meats can all be distinguished from each other, indicating the ability of quantitatively

233

discriminating the trinary-species adulterated meat using this method.

234 235

N-glycan structural identification.

236

In order to know the N-glycan structures which are different between beef and duck

237

samples, the 6 N-glycan peaks, experienced the most significant change during the

238

adulteration of beef with duck, were further analyzed by MALDI-TOF-MS and MS/MS

239

for detailed structures. As shown in Table 2, the mass spectra of these 6 peculiar peaks

240

could be identified by the obtained m/z values (Fig. 7), and the detailed structures were

241

proposed based on the MS/MS analysis of the fragmentation of the peaks (Fig.S3). It can

242

be seen from Figure 2A that peak 2 and peak 4, featured with tri-antenna bearing core

243

fucosylation and terminal galactosylation in β1,4 linkage, are the most abundant



244

structures specific for duck meat. Peak 5 and 6 were the most abundant structures in

245

beef, with the former being a hybrid type N-glycan which is not widely present in

246

nature and the latter containing α1,3 linked terminal galactose which is known to be the

247

antigen epitope when enters into human body43. These structures are all the key factors

248

responsible for the change of N-glycan UPLC profile when meat samples are mixed. It

249

can be seen from these detailed characteristic structures that there are significant

250

compositional differences between different species. The structural specificity is

251

determined by the genes encoding the glycoenzymes which are involved in the

252

synthesis of N-glycans. Therefore, the significance of N-glycan structural difference

253

between meats is stable and reliable for species discrimination. Furthermore, by

254

knowing the detailed structures of these peaks, it will enable the detection of beef

255

adulteration with duck using mass spectrometry to directly detect the characteristic

256

structures in duck, instead of using UPLC which requires fluorescent labeling and time-

257

cost pretreatment procedure.

258 259 260

Discussion

261

The protein N-glycosylation of biological materials is generally species-specific and

262

therefore extensively studied on its potential for acting as biomarkers in various areas,

263

especially in medical biology44-46. In first studies, identification of protein N-

264

glycosylation has been reported to be a reliable tool in food safety and food quality

265

control research30,

266

structural difference was found between human milk and bovine milk49. These findings

47-48.

Our previous study also showed that significant N-glycan


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267

suggest the idea that N-glycan profiles may be of interest for meat species specific

268

biomarker and therefore can be used as a tool in meat authentication. As shown in this

269

study, the meat protein N-glycan UPLC profile is shown to be species specific and thus a

270

reliable method for meat species identification and for the detection of meat

271

adulteration which normally occurs in high value meat such as beef. Additionally, it can

272

also tell the species of the meat which is spiked in the major carrier and even the ratio

273

of the adulteration, for both binary and multi-species adulteration. This is for the first

274

time, to the authors’ knowledge, that protein N-glycosylation is explored in meats and

275

used to establish a novel robust and versatile method for qualitative and quantitative

276

analysis of meat authentication detection. On the other hand, only 5 meat species were

277

employed in this study and it certainly needs to extend the range to validate the

278

applicability of this method when used for meat species other than the ones listed in

279

this study.

280

The most commonly used approaches for meat species identification at present

281

include PCR, a DNA-based molecular method, and spectroscopic methods. However,

282

sample pollution and DNA degradation during meat processing may greatly affect the

283

PCR results50. The N-glycan profiles of meats were shown to stay stable upon various

284

processing procedures such as boiling, roasting, frying, microwave and high-pressure

285

treatments (data now shown), indicating that our method probably bears advantage

286

over other methods such as PCR when used for processed meat adulteration detection.

287

Spectroscopic methods perform a rapid analysis of meat samples with minimum pre-

288

processing requirements, while the data processing is complicated. For example, each

289

type of product matrix (species and processing) needs a separate calibration which is



290

time consuming. In this study, the established protein glycosylation based method

291

presents to be relatively sensitive, specific and repeatable. The length of workflow

292

reported here was used to establish reliability of the analysis, but this procedure can be

293

shortened based on our previous study, according to which a rapid sample preparation

294

can be done within 4 h35.

295

The limit of detection (LOD) level for duck meat adulterated beef sample was found

296

to be 2.2% using the N-glycan profile based method in this study, which is low enough

297

for meat authentication on market as the adulteration ratio in reality is generally over

298

10% for the producers to make profit. However, this LOD value is not as low as those of

299

other meat adulteration methods and optimization of this method, such as the

300

optimization of meat sample preparation, N-glycan purification and enrichment, novel

301

derivatization and even the UPLC detection, needs to be performed to realize the low

302

LOD comparable with that of other methods.

303

The structures of the six peculiar peaks from beef and duck meat N-glycans were also

304

identified in details based on MALDI-TOF-MS and MS/MS analysis. The structural

305

differences between beef and duck are relatively big. It is well known that

306

glycoenzymes are responsible for the glycan synthesis and their abundance specificity

307

varies with species, organs and even tissues. It therefore can be deducted from the

308

structures that the level of each of glycosyltransferases differs between animals which

309

eventually resulted in the significant structural difference. In addition to understanding

310

the working principle of the novel UPLC-based method established in this study, the

311

detailed structure identification also reveals that the N-glycan based approach for meat

312

adulteration is not only limited to UPLC detection, but can also be cooperated with


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MALDI-TOF mass spectrometry analysis. In other words, this finding will enable the

314

detection of meat adulteration using mass spectrometry through directly detecting the

315

characteristic structures species without the pretreatments required by UPLC. The MS-

316

based method will thus be more rapid, easy and robust than the UPLC-based method.

317

Given all the above-stated reliabilities, this proof-of-concept work may also be of

318

interest for the analysis of other adulterated meat blends, or even other type of foods, in

319

future.

320 321

Abbreviations

322

HILIC-UPLC, hydrophobic interaction ultra-performance liquid chromatography; PCA,

323

principal component analysis; PLS, partial least square; RMSEC, root mean square

324

errors of calibration; RMSEP, root mean square errors of prediction; RPD, residual

325

predictive deviation; LOD, limit of detection; PCR, polymerase chain reaction; ACN,

326

acetonitrile;

327

aminobenzamide; TCA, trichloroacetic acid; MALDI-TOF MS, matrix-assisted laser

328

desorption/ionization time of-flight mass spectrometry; GU, glucose unit; PC, principle

329

component; R2C, correlation coefficients of calibration; R2V, correlation coefficients of

330

validation.

SPE,

supelclean

ENVI-Carb

solid-phase

331 332

Funding Sources


extraction;

2-AB,

2-


333

This work was financially supported by the National Natural Science Foundation of

334

China (NSFC, grant number 31871793 to J.V.) and National Key Research and

335

Development Plan (grant number 2018YFC1602804 to C.B. L.).

336 337 338 339 340

Supporting Information The Supporting Information is available.

341 342 343

References

344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360

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Tables Table 1 The relative peak area (RPA) of characteristic peaks in five meat species samples (n = 15).

Peak

Beef

Chicken

Pork

Duck meat

Mutton

Average

RSD

Average

RSD

Average

RSD

Average

RSD

Average

RSD

of RPA (%)

(%)

of RPA (%)

(%)

of RPA (%)

(%)

of RPA (%)

(%)

of RPA (%)

(%)

1

2.18

13.27

5.75

14.80

2.03

14.64

5.34

15.44

1.24

12.33

2

1.91

13.54

2.82

10.10

4.01

12.08

1.69

6.79

1.44

15.19

3

3.54

8.34

9.79

7.26

4.75

11.42

11.84

6.40

4.40

9.75

4

2.85

4.05

9.54

5.37

4.65

9.22

4.31

13.99

3.45

9.36

5

5.07

11.04

8.73

7.84

2.06

16.61

8.42

6.05

4.71

8.38

6

7.98

8.63

7.89

8.59

4.53

14.30

7.86

8.81

1.84

15.44

7

9.27

6.06

8.60

7.10

5.59

12.61

2.62

15.54

5.24

9.78

8

10.12

6.45

5.53

12.81

13.75

2.86

2.24

7.94

3.62

13.15

9

12.21

8.76

9.51

9.69

13.52

8.03

13.45

9.20

15.31

9.79

10

12.33

6.51

6.50

8.52

4.10

9.54

5.73

8.14

12.16

9.84

11

10.47

9.72

3.54

13.97

4.53

9.02

5.58

9.84

14.49

8.10

12

6.00

9.59

6.54

9.10

22.15

7.62

2.77

12.73

4.87

14.21

13

3.23

11.63

7.15

8.12

3.18

9.64

8.02

9.45

8.76

8.30

14

0.00

0.00

3.18

12.82

0.00

0.00

0.00

0.00

6.46

6.82

15

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

4.15

8.47

No.


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Table 2 Structures of beef and duck N-glycans based on the results of MALDI-TOF-MS/MS. Number Structure Name GU M/Z Source Beef ,

M5

6.12

1377.92

FA3G1

6.89

1970.58

Duck

A3G2

7.30

1987.48

Duck

FA3G2

7.67

2132.83

Duck

FM5A1G1S1

8.92

2359.47

Beef

FA2G(4)2G(3)2

9.12

2254.20

Beef

1

duck

2

3

4

5

6 M: mannose; A: N-acetyl-hexosamine; G: galactose; F: fucose; S: sialic acid.



Figures

Figure 1 Identification of five meat species. A: UPLC- profiles of N-glycomes derived from five different species of meat samples. B: PCA score plot of UPLC N-glycome profiles of five meat species. (One column)


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Figure 2 UPLC-based N-glycosylation pattern analysis of beef samples adulterated with different levels of duck. A: UPLC- profiles of N-glycome derived from mixed meat samples. B: boxplot of relative peak areas of 6 selected N-glycans. In each box, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. (Two column)



Figure 3 PCA score plot of UPLC-profiles of different mixed meat samples. A: classification of mixed meat samples, beef, pork, chicken meat and duck meat. B: separation of mixed meat samples from beef and duck meat. C: identification of seven different levels of mixed meat samples. (Two column)


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Figure 4 PLS calibration and validation models for quantitative determination of duck adulterated beef samples. (One column)



Figure 5 UPLC- profiles of N-glycome derived from mixed meat samples. (One column)


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Figure 6 PCA score plot of UPLC N-glycome profiles of mixed meat samples. A: classification of trinary mixed meat samples, beef, pork, chicken meat and duck meat. B: separation of mixed meat samples from each pure species beef, pork and chicken meat. C: identification of mixed meat samples with different concentrations of adulterated meat. (Two columns)



Figure 7 Structural analysis of 2-AB labeled N-glycans derived from beef and duck by MALDI-TOFMS. （Two Columns）


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Graphic for table of contents


N-glycan profile as a tool in qualitative and quantitative

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