A sensitive HPLC-MS/MS screening method for the simultaneous detection of barley, maize, oats, rice, rye and wheat proteins in meat products

Jira, Wolfgang; Münch, Siegfried

doi:10.1016/j.foodchem.2018.09.041

Cited by 42 publications

(23 citation statements)

References 31 publications

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“…The production of meat products for this study focused on emulsion-type sausages due to their homogeneity and the technological experience with the addition of plant proteins [ 25 , 28 ]. All legume flours were added at the same protein levels (see Table 1 ) for the production of the four batches of processing series 1 (test sausages, T1–T4).…”

Section: Resultsmentioning

confidence: 99%

“…Approximately 2 g of each sample material was defatted and dehydrated using acetone and pressurized liquid extraction (Speed Extractor E-916 Büchi, Flawil, Switzerland), according to a method published previously [ 28 ]. About (a) 5 mg of the defatted material was used for protein extractions performed with 500 µL of one of the following buffers: Buffer T (TRIS-HCl (1 M, pH 8.2)), buffer TE-50/50 (TRIS-HCl (1 M, pH 8.2)/ethanol, 50/50, v / v ), or buffer TP-50/50 (TRIS-HCl (1 M, pH 8.2)/2-propanol, 50/50, v / v ) for 2 h with constant shaking (1400 rpm) at 60, 80, or 90 °C, respectively [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

“…The mass range of the LC-MS/MS measurements was m / z 100–1600 with a spectra scan rate of 2 Hz. Selected precursors analyzed more than twice were actively excluded from analysis for 60 s. The collision energy of the quadrupole ranged between 25 and 50 V [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

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A UHPLC-MS/MS Method for the Detection of Meat Substitution by Nine Legume Species in Emulsion-Type Sausages

Spörl

Speer²,

Jira

2021

Foods

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Meat substitution by legume proteins in various types of meat products is a common practice. A reliable detection and quantification of these additives is required to control food specifications, especially regarding food fraud. Consequently, a UHPLC-MS/MS method for the simultaneous detection of alfalfa (Medicago sativa), broad bean (Vicia faba), chickpea (Cicer arietinum), lentil (Lens culinaris), lupine (Lupinus albus and Lupinus angustifolius), pea (Pisum sativum), peanut (Arachis hypogaea), and soy (Glycine max) proteins in meat products was developed. After protein extraction and tryptic digestion, three marker peptides for each legume species were measured by multiple reaction monitoring (MRM) using an optimized extraction protocol. To the best of our knowledge, the marker peptides for alfalfa, broad bean, chickpea, and lentil have not been reported previously. Emulsion-type sausages with 0.1, 0.4, 0.7, 1.0, 1.3, 1.6, 1.9, 2.2, and 2.5% meat substitution by each legume species, representing the concentration range between inadvertently transferred cross-contaminations and the conscious use for meat substitution, were produced for matrix calibration. No false-positive results were recorded in blank samples. In the quantification of alfalfa, broad bean, chickpea, lentil, pea, peanut, and soy, 673 of 756 measuring data of the recovery rate in unknown sausages were in the accepted range of 80–120%.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A UHPLC-MS/MS Method for the Detection of Meat Substitution by Nine Legume Species in Emulsion-Type Sausages

Spörl

Speer²,

Jira

2021

Foods

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“…Conversely, ESI is powerful technique for the analysis of complex protein and peptide mixtures that benefit from the additional separation. Jira and Münch (2019) used LC-ESI-MS/MS for the simultaneous MS detection of the six most important grain species (barley, maize, oats, rice, rye, and wheat) in meat products based on marker peptides. ESI was also suitable to detect traces of immunogenic gluten marker peptides in a variety of foods (Sealey-Voyksner et al, 2010) and gluten marker peptides (e.g., Manfredi et al, 2015; Colgrave et al, 2016; Schalk et al, 2018a; Schalk et al, 2018b).…”

Section: Proteomics As a Tool For The Screening For Immunogenic Peptidesmentioning

confidence: 99%

Modern Approaches in the Identification and Quantification of Immunogenic Peptides in Cereals by LC-MS/MS

et al. 2019

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Celiac disease (CD) is an immunogenic disorder that affects the small intestine. It is caused by the ingestion of gluten, a protein network formed by prolamins and glutelins from cereals such as wheat, barley, rye and, possibly, oats. For predisposed people, gluten presents epitopes able to stimulate T-cells causing symptoms like nausea, vomiting, diarrhea, among others unrelated to the gastrointestinal system. The only treatment for CD is to maintain a gluten-free diet, not exceeding 20 mg/kg of gluten, what is generally considered the safe amount for celiacs. Due to this context, it is very important to identify and quantify the gluten content of food products. ELISA is the most commonly used method to detect gluten traces in food. However, by detecting only prolamins, the results of ELISA tests may be underestimated. For this reason, more reliable and sensitive assays are needed to improve gluten quantification. Because of high sensitivity and the ability to detect even trace amounts of peptides in complex matrices, the most promising approaches to verify the presence of gluten peptides in food are non-immunological techniques, like liquid chromatography coupled to mass spectrometry. Different methodologies using this approach have been developed and described in the last years, ranging from non-targeted and exploratory analysis to targeted and specific methods depending on the purpose of interest. Non-targeted analyses aim to define the proteomic profile of the sample, while targeted analyses allow the search for specific peptides, making it possible to quantify them. This review aims to gather and summarize the main proteomic techniques used in the identification and quantitation of gluten peptides related to CD-activity and gluten-related allergies.

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“…Among the above methods, some (e.g., traditional spectroscopic methods) can be used for nondestructive testing, but their accuracies are low; some methods (e.g., conventional gas chromatography (GC), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS)) have high accuracy, but they cannot perform detections quickly and nondestructively and require complex sample pretreatment processes [13], [14]. In recent years, high-performance liquid chromatography (HPLC) [15] and HPLC/MS [16], [17] have been used to detect and study rice, and some achievements have been produced. We also hope to find a simple, rapid, accurate and sensitive rice quality detection technology through our research.…”

Section: Introductionmentioning

confidence: 99%

Identification of Rice Varieties and Adulteration Using Gas Chromatography-Ion Mobility Spectrometry

Lian

et al. 2021

IEEE Access

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To solve the problems existing in traditional biochemical methods, such as complex sample pretreatment requirements, tedious detection processes and low detection accuracies with respect to rice species and adulteration, the volatile flavor substances of five kinds of rice are detected using headspace-gas chromatography-ion mobility spectrometry (HGC-IMS) to effectively identify the quality of rice and adulterated rice. The ion migration fingerprint spectra of five kinds of rice are identified using a semi-supervised generative adversarial network (SSGAN). We replace the output layer of the discriminator in a GAN with a softmax classifier, thus extending the GAN to a semi-supervised GAN. We define additional category tags for generated samples to guide the training process. Semi-supervised training is used to optimize the network parameters, and the trained discriminant network is used for classifying HGC-IMS images. The experimental results show that the prediction accuracy of the model reaches 98.00%, which is significantly higher than the rates achieved by other models, such as a decision tree, a support vector machine (SVM), improved SVM models (LS-SVM and PCA-SVM) and local geometric structure Fisher analysis (LGSFA); 98.00% is also higher than the prediction accuracies of the VGGNet, ResNet and Fast RCNN deep learning models. The experimental results also show that the accuracy of HGC-IMS image classification for identifying adulterated rice reaches 97.30%, which is higher than those of traditional chromatographic or spectral methods. The proposed method overcomes the shortcomings of some intelligent algorithms regarding the application of ion migration spectra and is feasible for accurately predicting rice varieties and adulterated rice.

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A sensitive HPLC-MS/MS screening method for the simultaneous detection of barley, maize, oats, rice, rye and wheat proteins in meat products

Cited by 42 publications

References 31 publications

A UHPLC-MS/MS Method for the Detection of Meat Substitution by Nine Legume Species in Emulsion-Type Sausages

A UHPLC-MS/MS Method for the Detection of Meat Substitution by Nine Legume Species in Emulsion-Type Sausages

Modern Approaches in the Identification and Quantification of Immunogenic Peptides in Cereals by LC-MS/MS

Identification of Rice Varieties and Adulteration Using Gas Chromatography-Ion Mobility Spectrometry

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