*2.1.1.1. Instrumental peptide selection*

The first approach is to identify abundant marker peptides by high resolution mass spectrometry (HRMS). Downstream from allergen analysis by HRMS, the generated data are transferred into an algorithm for assigning peptides to MS/MS spectra (MASCOT, X!Tandem, SEQUEST) [45]. For example, Sealey-Voyksner et al. analysed 12 tree nuts and peanut-raw and roasted (176.7°C, 30 min) by time of flight (q-TOF) (Agilent 6530) spectrometry and selected two abundant peptides per tree nut and four for peanut [46]. In a previous study, ice cream spiked with peanuts was analysed by q-TOF (Waters Micromass II) to identify peptides of the Ara h1 allergen [47]. In a 2012 study, Cucu et al. identified several soybean marker peptides by matrix-assisted laser desorption ionization (MALDI-TOF/MS) [48]. The main advantage of this approach is that global peptide and protein profiles can be analysed for the different samples.

#### *2.1.1.2. In silico peptide selection*

ods such as those exploiting the real-time polymerase chain reaction (PCR) [40] are also used to detect the presence of allergens. Currently, mass spectrometry is becoming an alternative to these methods, as heat-processing induces protein denaturation and structural modifications that might result in non-recognition of the target protein by conformational antibodies and thus in the case of ELISAs, lead to false negatives or at least major underestimation of allergen content [41–44]. Mass spectrometry has the advantage of permitting simultaneous analyses for several allergens in food, including processed food products, with high sensitivity and

**Table 1.** VITAL (http://allergenbureau.net/vital/), EAACI (http://www.eaaci.org/) and NVWA (https://www.nvwa.nl/)

**Reference dose |EAACI (mg of proteins) [38]**

Peanut 0.2 0.2 0.015 4 Cow milk 0.1 0.1 0.016 2 Egg 0.03 0.03 0.0043 0.6 Hazelnut 0.1 0.1 0.011 4 Soy 1.0 1.0 0.078 20 Wheat 1.0 1.0 0.14 20 Cashew 2.0 2.0 1.4 40 Mustard 0.05 0.05 0.022 1 Lupin 4.0 4.0 0.83 80 Sesame 0.2 0.2 0.10 4 Shrimp 10 10.0 3.7 200 Fish / 0.1 / /

**Reference dose NVWA (mg of proteins) [28]**

**Reference dose VITAL (mg of proteins per kg) Portion size: 50 g**

This chapter highlights the important improvements made over the last 10 years in mass spectrometry applied to the development of allergen detection methods. It covers and discusses the mass spectrometry methods currently used to detect and quantify allergens in processed

Food allergens (except sulfites) are proteins that need to be digested by enzymes (trypsin and chymotrypsin) so as to generate peptides suitable for routine mass spectrometry analysis. Identification and selection of robust peptides are generally done first on digested raw ingredients before analysis of digested processed ingredients in food matrices. This section

specificity.

food products, including their validation.

**2. Detecting food allergens**

reference doses for different food allergens.

**Food Reference dose VITAL** 

10 Allergen

**(mg of proteins) [27, 34, 37]**

**2.1. Selecting marker peptides**

Another strategy for selecting marker peptides is to retrieve target protein sequences from a database, e.g. Uniprot (http://www.uniprot.org/), and to perform an *in silico* digestion with an open access software, e.g. Skyline or MRMaid [49, 50] (**Figure 3**).

*In silico* digestion with multiple reaction monitoring (MRM) involves generating a list of criteria that must be applied or set by the user as regards peptides, transitions and MS/MS

**Figure 3.** *In silico* peptide selection with the Uniprot database and Skyline software.

parameters (e.g. peptide length, charge states, fragmentation and enzyme). Then raw ingredients or incurred matrices can be analysed by ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS). This approach allows identification of abundant peptides. It was used by Rogniaux et al. for the analysis of wheat varieties: several gluten peptides were identified with a linear ion trap quadrupole mass filter in tandem with an orbitrap (Thermo Fisher Scientific) [51].

An *in silico* approach also requires a complete database with available protein sequences. Uniprot inventories, however, can be too large (e.g. >145,000 proteins for the wheat species-*Triticum aestivum*), making it necessary to first select target proteins from the literature. Use of a routine UHPLC-MS/MS instrument is the main advantage of the *in silico* approach for laboratories unwilling to invest in a high-resolution mass spectrometer.

#### *2.1.2. Specificity*

**BLAST**: After this selection, blasting must be performed to guarantee the specificity of marker peptides. This step is mandatory but not always included in method development. In one study, for example, Hoofnagle et al. selected five peptides for the detection of β-casein in cookies: EMPFPK (6AA), VLPVPQK (7AA), AVPYPQR (7AA), GPFPIIV (7AA) and DMPIQAFLLYQEPVLGPVR (19AA) [52]. Only one of these peptides could be blasted, and this peptide is 100% homologous to goat, zebu, buffalo, yak and sheep β-casein (Uniprot). In proteomics, peptide blasting should be systematic, even though the international trade frequently introduces new food products and although some proteins can still be missing in the different databases.

The **specificity of selected fragments** is also paramount. To improve specificity, the mass-tocharge ratio (m/z) of the precursor should be lower than the m/z of the fragments. Too-small fragments should be avoided. At least, fragments of 1 to 2 amino acids (b1, b2, y1, y2) should be excluded, which is not always the case in published methods [53, 54].

**Blanks**: Matrices without allergens must also be analysed to ensure the specificity of the selected transitions of the target peptides. As databases do not cover all possible proteins and as new food products enter the food chain regularly, this experimental testing is crucial to proving method specificity.

#### *2.1.3. Identifying marker peptides in incurred foodstuffs*

The advantage of using mass spectrometry is detection of allergens in industrial food products. For such applications, only target peptides and proteins that will be detected in incurred and processed matrices, such as those listed in **Table 2**, need to be retained in the analytical methods. Some peptides are common to the majority of published methods: FFVAPFPEVFGK and YLGYLEQLLR (Casein αS1), and GGLEPINFQTAADQAR (ovalbumin), among others. Target peptides detected after different extraction and purification steps in several types of matrices constitute potential marker peptides for the detection of allergens in a wide variety of foodstuffs.

Food Allergen Analysis: Detection, Quantification and Validation by Mass Spectrometry http://dx.doi.org/10.5772/intechopen.69361 13


parameters (e.g. peptide length, charge states, fragmentation and enzyme). Then raw ingredients or incurred matrices can be analysed by ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS). This approach allows identification of abundant peptides. It was used by Rogniaux et al. for the analysis of wheat varieties: several gluten peptides were identified with a linear ion trap quadrupole mass filter in tandem with

An *in silico* approach also requires a complete database with available protein sequences. Uniprot inventories, however, can be too large (e.g. >145,000 proteins for the wheat species-*Triticum aestivum*), making it necessary to first select target proteins from the literature. Use of a routine UHPLC-MS/MS instrument is the main advantage of the *in silico* approach for

**BLAST**: After this selection, blasting must be performed to guarantee the specificity of marker peptides. This step is mandatory but not always included in method development. In one study, for example, Hoofnagle et al. selected five peptides for the detection of β-casein in cookies: EMPFPK (6AA), VLPVPQK (7AA), AVPYPQR (7AA), GPFPIIV (7AA) and DMPIQAFLLYQEPVLGPVR (19AA) [52]. Only one of these peptides could be blasted, and this peptide is 100% homologous to goat, zebu, buffalo, yak and sheep β-casein (Uniprot). In proteomics, peptide blasting should be systematic, even though the international trade frequently introduces new food products and

The **specificity of selected fragments** is also paramount. To improve specificity, the mass-tocharge ratio (m/z) of the precursor should be lower than the m/z of the fragments. Too-small fragments should be avoided. At least, fragments of 1 to 2 amino acids (b1, b2, y1, y2) should

**Blanks**: Matrices without allergens must also be analysed to ensure the specificity of the selected transitions of the target peptides. As databases do not cover all possible proteins and as new food products enter the food chain regularly, this experimental testing is crucial to

The advantage of using mass spectrometry is detection of allergens in industrial food products. For such applications, only target peptides and proteins that will be detected in incurred and processed matrices, such as those listed in **Table 2**, need to be retained in the analytical methods. Some peptides are common to the majority of published methods: FFVAPFPEVFGK and YLGYLEQLLR (Casein αS1), and GGLEPINFQTAADQAR (ovalbumin), among others. Target peptides detected after different extraction and purification steps in several types of matrices constitute potential marker peptides for the detection of allergens in a wide variety

laboratories unwilling to invest in a high-resolution mass spectrometer.

although some proteins can still be missing in the different databases.

be excluded, which is not always the case in published methods [53, 54].

an orbitrap (Thermo Fisher Scientific) [51].

*2.1.2. Specificity*

12 Allergen

proving method specificity.

of foodstuffs.

*2.1.3. Identifying marker peptides in incurred foodstuffs*


**Table 2a.** List of target marker peptides used to detect several allergens in bread and cookies [55–59].

#### **2.2. Developing mass spectrometry methods**

After selection of marker peptides, the developed method must be able to detect traces of the allergen proteins in the 'mg allergen proteins per kg food product' range. The real chalFood Allergen Analysis: Detection, Quantification and Validation by Mass Spectrometry http://dx.doi.org/10.5772/intechopen.69361 15


**2.2. Developing mass spectrometry methods**

After selection of marker peptides, the developed method must be able to detect traces of the allergen proteins in the 'mg allergen proteins per kg food product' range. The real chal-

**Table 2a.** List of target marker peptides used to detect several allergens in bread and cookies [55–59].

**Authors Matrix Allergen Protein Peptide Fragments**

SQSDNFEYVSFK y3, y10 FYLAGNQEQEFLK y9, y10, y11

ADIYTEQVGR y6, y7 ALPDDVLANAFQISR y7, y8, y13

FFVAPFPEVFGK y8, y9, y10 HQGLPQEVLNENLLR y11, y12

DLAFPGSGEQVEK y10, y9, y8, b4,

NLPQQCGLR y7, y6, y5, a2 CDLEVESGGR y8, y6, y5, y4 CMCEALQQIMENQSDR y14, y11, y10, y8,

SPDIYNPQAGSLK ymax, y12, y9, y8,

LSAEFGLR 432.3/ 779.4/ 579.3 LNALKPDNR 742.4/ 629.3/ 501.2

AFYLAGGVPR 556.3/ 485.3/ 669.4 SPLAGYTSVIR 795.4/ 866.5/ 575.4

NTLEATFNTR 951.5/838.4/ 709.4 NPYHFSSQR 761.4/ 624.3/ 477.2

AHVQVVDSNGNR b7, y6, b5

y4, y2

b3, b2

y7, y6, y5, b2

y5, b2

y7, y5, b3

Milk αS1-casein YLGYLEQLLR y8, y9, y10

Peanut Ara h1 VLLEENAGGEQEER y9, y8, y7, y6,

Ara h2 CCNELNEFENNQR y8, y6, y5, y4

Ara h3 LNAQRPDNR ymax, y8, y7,

Soy Gly m6 VFDGELQEGR 903.6/ 489.2/ 788.5

Sesame Ses i6 ISGAQPSLR 472.3/ 728.4/ 671.4

Lupine β-conglutin LLGFGINADENQR 846.4/661.3/ 797.4

Soy Glycinin G1-G2

Lamberti et al. [57]

14 Allergen

Pedreschi et al. [58]

Huschek et al. [59]

Cookie (180°C, 10 min)

Cookie (180°C, 16 min)

Cookie (190°C, 13 min) Hazelnut 11S globulin-

like protein


**Table 2b.** List of target marker peptides used to detect several allergens in sauce, ice cream, chocolate, cookies and muffins [60–62].

lenge for laboratories is to achieve this sensitivity with processed foodstuffs. To reach this sensitivity, two factors must be considered: instrument sensitivity and optimization of sample preparation. The different strategies used to evaluate sensitivity are described below.

**Instrument sensitivity:** No comparison of the sensitivities of different instruments with the same peptide extract has yet been published for allergen analysis, although the sensitivity of the instrument is crucial to the sensitivity of the method, as in the case of other contaminants. One should bear in mind, however, that the most sensitive research-dedicated instrument might not be the best choice for routine analysis (automated injection and short analytical run).

**Extraction and purification of proteins**: The ideal sample preparation protocol should allow extraction of 100% of the target compounds, the final extract used for MS analysis being as pure as possible. Yet, the preparation of samples for food allergen analysis is difficult, because it should be applicable to a very broad range of food matrices and because the extractability of proteins might be altered in a processed food [63]. In addition, several modifications can occur, e.g. asparagine deamination, the Maillard reaction and several reactions of lysine. Such modifications cause a mass shift of tryptic peptides, resulting in non-recognition of several peptides by mass spectrometry [64–66]. To improve protein extraction, different parameters can be optimized: the composition of extraction buffers, the temperature, the sample-to-buffer ratio and the presence of detergents. Furthermore, the purification step is as important as extraction in order to concentrate proteins in and eliminate interferences from the supernatant. Purification usually involves solid phase extraction (SPE), protein precipitation, ultrafiltration and size exclusion chromatography (SEC), among others. Optimizing extraction and purification is a key step in developing sensitive methods for the detection of allergens by mass spectrometry (**Table 3**).

**Determining the sensitivity**: The sensitivity of food allergen analysis can be determined on spiked samples (obtained by incorporating extracted proteins into a matrix after processing), fortified samples (obtained by incorporating raw ingredients into a matrix after processing) or processed samples (obtained by incorporating raw ingredients into a matrix before processing). For spiked and fortified samples ('non-processed samples'), examples of the limit of quantification (LOQ) reached are 0.1 mg milk protein, 0.3 mg egg protein and 2 mg soy protein per kg cookies [67] and 0.1–1.3 mg tree nuts per kg biscuit [68]. Although these studies demonstrate the sensitivity of mass spectrometry, the real challenge is to reach this sensitivity in thermally processed samples. Important improvements have been made over the last 5 years in the detection of allergens in processed samples. Recently, developed methods allow reaching an LOQ near or below the VITAL threshold (**Table 1**), e.g. 0.5 mg for milk protein, 3.4 mg egg protein, 5 mg soy protein and 2.5 mg peanut protein per kg incurred cookie (180°C, 18 min, with SPE purification) [60]. In another study, the LOQs achieved were 30 mg egg (13.8 mg proteins), 20 mg milk (7.2 mg proteins), 19 mg soy (6.8 mg proteins), 20 mg hazelnut (3 mg proteins) and 40 mg peanut (10 mg proteins) per kg incurred cookie (200°C, 12 min, with SEC purification) [56].

As described above, the sensitivity reached for processed samples is lower than that obtained with spiked or fortified samples. The same applies to ELISAs, which can show up to 100-fold lower sensitivity when applied to processed food than when applied to raw food, as demonstrated by the poor performance of several ELISAs for egg detection in cookies after processing. In 2010, Dumont et al. showed that one ELISA kit was not even able to detect 1000 mg egg powder per kg baked cookie, and four others strongly underestimated the amount of egg in

lenge for laboratories is to achieve this sensitivity with processed foodstuffs. To reach this sensitivity, two factors must be considered: instrument sensitivity and optimization of sample preparation. The different strategies used to evaluate sensitivity are described below.

**Table 2b.** List of target marker peptides used to detect several allergens in sauce, ice cream, chocolate, cookies and

**Authors Matrix Allergen Protein Peptide Fragments**

NNPFYFPSR

NLPQQCGLR

SPDIYNPQAGSLK WLGLSAEYGNLYR

CMCEALQQIMENQSDR

Milk αS1-casein HQGLPQEVLNENLLR best

not selected αS2-casein NAVPITPTLNR

HNIGQTSSPDIYNPQAGSVTTATSLDFPALSWLR TNDTPMIGTLAGANSLLNALPEEVIQHTFNLK

HQQEEENEGGSILSGFTLEFLEHAFSVDK

DLDIFLSIVDMNEGALLLPHFNSK

AIVILVINEGDANIELVGLK

YFIALPVPSQPVDPR LLVAPGQCNLATIHNVR

LPIVVDASGDGAYVCK SGNVGESGLIDLPGCPR

DYVLQQTCGTFTPGSK

LTAASITAVCR

EMQWDFVR

TNDRPSIGNLAGANSLLNALPEEVIQHTFNLK QNIGQNSSPDIYNPQAGSITTATSLDFPALWLLK transitions

Ara h2 CCNELNEFENNQR

Ara h3 FNLAGNHEQEFLR

LNFLK

κ-casein YIPIQYVLSR

Soy Glycinin G1 precursor

> Glycinin G2 precursor

Wheat Alpha amylase

Beta conglycinin alpha chain precursor

trypsin inhibitor

ALNEINQFYQK

VLIVPQNFVVAAR

EGDLIAVP…DQMPR

Peanut Ara h1 GTGNLELVAVR

Gomaa et al. 2014 [62]

16 Allergen

muffins [60–62].

Cookie (177°C, 12 min)



**Authors** Heick et al.

Milk, egg,

Bread

2 g/20 ml

(2010) [55]

soy, peanut,

(200°C, 60

hazelnut,

min)

walnut, almond

**Allergen**

**Matrix**

**Extraction**

**Purification** Ultrafiltration

Dilution: 1 mg of proteins

LC: 1200 HPLC (Agilent)

LOD (S/N>3)

by ml with NH4HCO3

(Amilcon Ultra

15 mL, 5 kDa

(100 mM)

molecular

weight cut-off)

Aliquot: 100 μl

Reduction: 50 μl DTT

(200 mM), 45 min

Alkylation: 40 μl IA (1

M), 45 min in the dark

20 μl DTT (200 mM) + 50

μl NH4HCO3 (100 mM)

Digestion: 10 μl trypsin

(1 μg/μl) 12 h - 37°C

2 μl formic acid

Sciex)

Column: Xbridge C18 3.5 μm

5 mg of soluble milk

proteins by kg

42 mg of soluble egg

proteins by kg

24 mg of soluble soy

proteins by kg

11 mg of soluble peanut proteins by kg

5 mg of soluble

hazelnut proteins

by kg

70 mg of soluble

walnut proteins by kg

3 mg of soluble

almond proteins

by kg

LOD (S/N>3)

Pilolli et al.

milk,

Cookie

2.5 g/50 ml

1.2 μm acetate

Elution SEC: 3.5 ml

LC: -

NH4HCO3 (50 mM)

cellulose

membrane,

Size exclusion

Aliquot: 300 μl

Column: Aeris Peptide XB-C18 (150 × 2.1 mm)

7 mg of milk by kg

(Phenomenex)

MS: Dual pressure Linear

9 mg of egg by kg

Ion Trap Spectrometer

Velos Pro (Thermo Fisher

6 mg of soy by kg

13 mg of peanut

by kg

Scientic)

Reduction: 15 μl of 50

mM DTT 30 min at 60°C

Alkylation: 30 μl of 100

mM IAA 30 min in the

dark at room temperature

Buffer: 20 mM TRIS-HCl pH 8.2

column (SEC)

(G25 Sephadex

column)

Ultrasound: 30 min

Protein denaturation:

15 min at 95°C

[56]

egg,soy,peanut,

(200°C, 12

hazelnut

min)

(2.1×150 mm) (Waters)

MS: API 4000QTrap (MDS

Buffer: TRIS-HCl

pH 8.2

Agitation: 60°C

for 3h

(Millipore)

**Digestion**

**Instrument**

**Sensitivity**

18 Allergen

**Table 3a.** Mass-spectrometry-based methods (extraction, purification, digestion, and analysis) for detecting allergens in processed food products [55–58].



**Author** Huschek

Soy,

Wheat, cookie

1 g

(190°C, 13 min),

bread (220°C,

30 min)

et al. [59]

sesame,

lupin

**Allergens**

**Matrices**

**Extraction**

**Purification** SPE cardridge

Alkylation: IAA 20 min

(LiChrolut

at 50°C

RP-18 Merck

Millipore)

Digestion: Trypsin

formic acid 2%

Buffer: 100 mM

NH4HCO3, 4M urea

5 mM DTT pH 8.2

Agitation 30 min RT

Planque

Milk,

Tomato sauce

2 g / 20 ml

Sep-Pack tC18

10 ml extract + 10 ml

LC: UPLC Acquity

LOQ (S/N > 10)

(Waters)

NH4HCO3 (200 mM)

Reduction: 1 ml DTT

Column: BEH130 (2.1

0.5 mg of milk proteins by kg

× 150 mm) (Waters)

(400 mM), 45 min

Alkylation: 2 ml IAA (500

MS: Xevo TQS

3.4 mg of egg proteins by kg

5 mg of soy proteins by kg

(Waters)

mM), 45 min in the dark

Digestion: Ratio

protein:trypsin 1:20 16

h, 37°C

300 μl formic acid 20%

2.5 mg of peanut proteins by kg

6cc (Waters)

et al. [33,

egg, soy,

(95°C, 45

min), cookie

(180°C, 18

Buffer: 200 mM

TRIS-HCl pH 9.2,

2M urea

Agitation:30 min

Ultrasound: 15 min

Parker et

Milk, egg,

Muffin (177°C,

Buffer: 2 M urea, 50

Microcentrifuge

Filter-aided sample

LC: nano Acquity

/

UPLC (Waters)

preparation (FASP)

sample cocentration and

digestion protocol

Amicon Ultra

Reduction: 10 mM DTT

Column: nano Acquity

BEH130 C18 1.7 μm

(100 μm × 100 mm)

0.5 ml

vortex: 5 min at 1400

Utracel-10

Alkylation:25 mM IAA

Digestion: Ratio

protein:trypsin 1:100 16

h -37°C

0.1% trifluoroacetic acid

and 2% acetonitrile

(Sciex)

MS:6500 QTRAP

membrane

rpm

Ultrasound: 10 min

at 4°C

mM TRIS Ph 8.0, 25

tubes

mM DTT

al. [61]

peanut

48 min)

min), ice cream,

chocolate

peanut

60]

**Digestion**

**Instrument** LC: Nexera XR

UHPLC (Shimadzu)

Column: Aeris Peptide

10–20 mg of soy per kg

10–50 mg of sesame per kg

XB-C18 (100 × 2.1 mm,

1.7 μm) (Phenomenex)

MS: Qtrap 5500 MS/

10–50 mg of lupine per kg

MS (Sciex)

**Sensitivity**

20 Allergen

LOQ (S/N > 10)

**Table 3b.** Mass-spectrometry-based methods (extraction, purification, digestion, and analysis) for detecting allergens in processed chocolate, sauce, ice cream, muffins and cookies [59–62].

**Figure 4.** Analytical results for 1000 mg spray-dried whole egg powder (National Institute of Standards and Technology RM 8445) per kg incurred cookies, obtained with the different enzyme-linked immunosorbent assay test kits for egg detection (A–E) (from Ref. [69]).

the samples (**Figure 4** of Ref. [69]). While mass spectrometry and ELISAs show comparable sensitivities when applied to unbaked products, mass spectrometry seems to be the method of choice for the analysis of allergens in baked food products.
