MS-based untargeted profiling revealed hundreds of sequences in the peptide fraction of milk and milk products, which are mostly derived from caseins and whey proteins by the activity of endogenous endoproteases. Consequently, the identification of more than 30 bioactive peptides with –for example- known mineral binding, antioxidative or ACE inhibitory activity was based on the recorded peptide profiles. [1,2] Moreover, virtual screening predicted novel antimicrobial peptides from the peptidome. Food-derived antimicrobial peptides are of interest as safe preservatives with good consumer acceptance. [3] Since the qualitative and quantitative composition of the milk peptidome is dependent on the origin of the raw materials and on various processing conditions, the peptide profile provides a fingerprint, which can be used to control product authenticity. Thus, diverse parameters, such as animal species, product type, storage, or fermentation conditions of a sample can be monitored in parallel. [4,5,6,7] For the targeted and untargeted peptidome analysis, several methods are applied in different modes including LC-ESI-QTrap MS/MS, LC-ESI-QTof MS/MS, LC-ESI LTQOrbitrap, MALDI TOF MS, or LC MS/MS sMRM. These methods, and also the subsequent data processing protocols, are characterized by specific strengths and limitations, which define their area of application. In future, peptide profiling will be extended to other foods accessing novel bioactive food components and expanding peptide fingerprinting for authenticity and process control.

MS-based untargeted profiling revealed hundreds of sequences in the peptide fraction of milk and milk products, which are mostly derived from caseins and whey proteins by the activity of endogenous endoproteases. Consequently, the identification of more than 30 bioactive peptides with –for example- known mineral binding, antioxidative or ACE inhibitory activity was based on the recorded peptide profiles. [1,2] Moreover, virtual screening predicted novel antimicrobial peptides from the peptidome. Food-derived antimicrobial peptides are of interest as safe preservatives with good consumer acceptance. [3] Since the qualitative and quantitative composition of the milk peptidome is dependent on the origin of the raw materials and on various processing conditions, the peptide profile provides a fingerprint, which can be used to control product authenticity. Thus, diverse parameters, such as animal species, product type, storage, or fermentation conditions of a sample can be monitored in parallel. [4,5,6,7] For the targeted and untargeted peptidome analysis, several methods are applied in different modes including LC-ESI-QTrap MS/MS, LC-ESI-QTof MS/MS, LC-ESI LTQOrbitrap, MALDI TOF MS, or LC MS/MS sMRM. These methods, and also the subsequent data processing protocols, are characterized by specific strengths and limitations, which define their area of application. In future, peptide profiling will be extended to other foods accessing novel bioactive food components and expanding peptide fingerprinting for authenticity and process control.

MS-based untargeted profiling revealed hundreds of sequences in the peptide fraction of milk and milk products, which are mostly derived from caseins and whey proteins by the activity of endogenous endoproteases. Consequently, the identification of more than 30 bioactive peptides with –for example- known mineral binding, antioxidative or ACE inhibitory activity was based on the recorded peptide profiles. [1,2] Moreover, virtual screening predicted novel antimicrobial peptides from the peptidome. Food-derived antimicrobial peptides are of interest as safe preservatives with good consumer acceptance. [3] Since the qualitative and quantitative composition of the milk peptidome is dependent on the origin of the raw materials and on various processing conditions, the peptide profile provides a fingerprint, which can be used to control product authenticity. Thus, diverse parameters, such as animal species, product type, storage, or fermentation conditions of a sample can be monitored in parallel. [4,5,6,7] For the targeted and untargeted peptidome analysis, several methods are applied in different modes including LC-ESI-QTrap MS/MS, LC-ESI-QTof MS/MS, LC-ESI LTQOrbitrap, MALDI TOF MS, or LC MS/MS sMRM. These methods, and also the subsequent data processing protocols, are characterized by specific strengths and limitations, which define their area of application. In future, peptide profiling will be extended to other foods accessing novel bioactive food components and expanding peptide fingerprinting for authenticity and process control.

Since food fraud has become a veritable business development of new methods to test for the authenticity of foods has increased. Traditionally, admixtures of known adulterants are detected by proving presence of an analytical marker present in the adulterant but not in the adulterated food. This approach is known as targeted testing. Many methods have been recently been introduced where the analytical markers for targeted testing were derived from an untargeted development approach, which has to be distinguished from untargeted testing itself, which detects unknown deviations in a sample in comparison to a reference group. In this talk we will provide a brief definition of the various approaches, which were here applied to develop novel methods for the authenticity testing for grains. Authenticity aspects of grains include their botanical variety, their geographical origin, their method of cultivation as well as the quality per se. Ideally, one can test for several aspects with a small number of test in a short time to ensure sufficient number of tests along the trade chain without having an adverse impact on the price of a food. Therefore, spectroscopic screening methods have become popular. However, as different technologies have different merits it is difficult to forecast, which authenticity aspect can be addressed best by which technology. Within the BMBL/BLE-funded project „AgrOr - Origin of Grains“ we therefore applied several technologies (NMR, NIR, MIR, IRMS, Raman, LCMS) to a reference set of over 1.600 carefully selected samples. Applying every sample of the reference sample collection to this analytical ecosystem allows an efficient comparison, which methods is best suited to address a particular authenticity issue. In fact, in case no single method provides satisfying results our approach offers the possibility of fusing data sets to improve authenticity testing. We provide first examples regarding the differentiation of grain varieties and cultivation methods by multi-method data fusion and evaluation by the newly developed AI(Omics)^n software package.

Since food fraud has become a veritable business development of new methods to test for the authenticity of foods has increased. Traditionally, admixtures of known adulterants are detected by proving presence of an analytical marker present in the adulterant but not in the adulterated food. This approach is known as targeted testing. Many methods have been recently been introduced where the analytical markers for targeted testing were derived from an untargeted development approach, which has to be distinguished from untargeted testing itself, which detects unknown deviations in a sample in comparison to a reference group. In this talk we will provide a brief definition of the various approaches, which were here applied to develop novel methods for the authenticity testing for grains. Authenticity aspects of grains include their botanical variety, their geographical origin, their method of cultivation as well as the quality per se. Ideally, one can test for several aspects with a small number of test in a short time to ensure sufficient number of tests along the trade chain without having an adverse impact on the price of a food. Therefore, spectroscopic screening methods have become popular. However, as different technologies have different merits it is difficult to forecast, which authenticity aspect can be addressed best by which technology. Within the BMBL/BLE-funded project „AgrOr - Origin of Grains“ we therefore applied several technologies (NMR, NIR, MIR, IRMS, Raman, LCMS) to a reference set of over 1.600 carefully selected samples. Applying every sample of the reference sample collection to this analytical ecosystem allows an efficient comparison, which methods is best suited to address a particular authenticity issue. In fact, in case no single method provides satisfying results our approach offers the possibility of fusing data sets to improve authenticity testing. We provide first examples regarding the differentiation of grain varieties and cultivation methods by multi-method data fusion and evaluation by the newly developed AI(Omics)^n software package.

Since food fraud has become a veritable business development of new methods to test for the authenticity of foods has increased. Traditionally, admixtures of known adulterants are detected by proving presence of an analytical marker present in the adulterant but not in the adulterated food. This approach is known as targeted testing. Many methods have been recently been introduced where the analytical markers for targeted testing were derived from an untargeted development approach, which has to be distinguished from untargeted testing itself, which detects unknown deviations in a sample in comparison to a reference group. In this talk we will provide a brief definition of the various approaches, which were here applied to develop novel methods for the authenticity testing for grains. Authenticity aspects of grains include their botanical variety, their geographical origin, their method of cultivation as well as the quality per se. Ideally, one can test for several aspects with a small number of test in a short time to ensure sufficient number of tests along the trade chain without having an adverse impact on the price of a food. Therefore, spectroscopic screening methods have become popular. However, as different technologies have different merits it is difficult to forecast, which authenticity aspect can be addressed best by which technology. Within the BMBL/BLE-funded project „AgrOr - Origin of Grains“ we therefore applied several technologies (NMR, NIR, MIR, IRMS, Raman, LCMS) to a reference set of over 1.600 carefully selected samples. Applying every sample of the reference sample collection to this analytical ecosystem allows an efficient comparison, which methods is best suited to address a particular authenticity issue. In fact, in case no single method provides satisfying results our approach offers the possibility of fusing data sets to improve authenticity testing. We provide first examples regarding the differentiation of grain varieties and cultivation methods by multi-method data fusion and evaluation by the newly developed AI(Omics)^n software package.

In recent years, visualization has become an essential feature in plant science, enabling to study the metabolites distribution within a tissue. In this regard, mass spectrometry imaging (MSI) has become a powerful tool capable of achieving the spatial distributions and chemical specificity by enabling unprecedented details of metabolic biology to be uncovered [1]. Therefore, this research aimed to investigate the spatial tissue distribution of mycotoxins and defence metabolites, useful to assign the metabolites’ functional role. To address this challenge, transversal cross-sections obtained from wheat and maize plants were analyzed using atmospheric-pressure (AP)-scanning microprobe matrixassisted laser desorption/ionization MSI ion source ((AP-SMALDI5 AF, TransMIT GmbH, Giessen, Germany)) couple to a Q- Exactive HF orbital trapping mass spectrometer (Thermo Fisher Scientific GmbH, Bremen, Germany) [2]. Our results demonstrated the analytical potential of innovative high-throughput technique for gaining insight into the plant resistance mechanism against mycotoxin accumulation.

In recent years, visualization has become an essential feature in plant science, enabling to study the metabolites distribution within a tissue. In this regard, mass spectrometry imaging (MSI) has become a powerful tool capable of achieving the spatial distributions and chemical specificity by enabling unprecedented details of metabolic biology to be uncovered [1]. Therefore, this research aimed to investigate the spatial tissue distribution of mycotoxins and defence metabolites, useful to assign the metabolites’ functional role. To address this challenge, transversal cross-sections obtained from wheat and maize plants were analyzed using atmospheric-pressure (AP)-scanning microprobe matrixassisted laser desorption/ionization MSI ion source ((AP-SMALDI5 AF, TransMIT GmbH, Giessen, Germany)) couple to a Q- Exactive HF orbital trapping mass spectrometer (Thermo Fisher Scientific GmbH, Bremen, Germany) [2]. Our results demonstrated the analytical potential of innovative high-throughput technique for gaining insight into the plant resistance mechanism against mycotoxin accumulation.

In recent years, visualization has become an essential feature in plant science, enabling to study the metabolites distribution within a tissue. In this regard, mass spectrometry imaging (MSI) has become a powerful tool capable of achieving the spatial distributions and chemical specificity by enabling unprecedented details of metabolic biology to be uncovered [1]. Therefore, this research aimed to investigate the spatial tissue distribution of mycotoxins and defence metabolites, useful to assign the metabolites’ functional role. To address this challenge, transversal cross-sections obtained from wheat and maize plants were analyzed using atmospheric-pressure (AP)-scanning microprobe matrixassisted laser desorption/ionization MSI ion source ((AP-SMALDI5 AF, TransMIT GmbH, Giessen, Germany)) couple to a Q- Exactive HF orbital trapping mass spectrometer (Thermo Fisher Scientific GmbH, Bremen, Germany) [2]. Our results demonstrated the analytical potential of innovative high-throughput technique for gaining insight into the plant resistance mechanism against mycotoxin accumulation.