The mycotoxin ochratoxin A is degraded during coffee roasting by up to 90%. During this process, the two known degradation products, 14R-ochratoxin A and 14-decarboxy-ochratoxin A are formed. However, there is still an unexplained loss of more than 50% ochratoxin A. Here, we describe the binding of ochratoxin A to coffee polysaccharides via esterification as a further thermal reaction. This ester formation was studied by heating ochratoxin A with methyl α-d-glucopyranoside, a model compound to mimic polysaccharides. From this experiment, (22 → 6') ochratoxin A-methyl-α-d-glucopyranoside ester was isolated and characterized as a reaction product, showing the general ability of ochratoxin A for esterification with carbohydrates at roasting temperatures. Subsequently, a sample preparation protocol for the detection of ochratoxin A saccharide esters based on an enzymatic cleavage and purification using immunoaffinity chromatography was developed and applied. The detection was carried out by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Using this method, it was possible to detect ochratoxin A polysaccharide esters formed during roasting of artificially contaminated coffee, confirming the results of the previous model experiments. Thus, the formation of ochratoxin A esters is a further explanation for the loss of ochratoxin A during coffee roasting.
The mycotoxin ochratoxin A is a secondary metabolite occurring in a wide range of commodities. During the exposure of ochratoxin A to white and blue light, a cleavage between the carbon atom C-14 and the nitrogen atom was described. As a reaction product, the new compound ochratoxin α amide has been proposed based on mass spectrometry (MS) experiments. In the following study, we observed that this compound is also formed at high temperatures such as used for example during coffee roasting and therefore represents a further thermal ochratoxin A degradation product. To confirm the structure of ochratoxin α amide, the compound was prepared in large scale and complete structure elucidation via nuclear magnetic resonance (NMR) and MS was performed. Additionally, first studies on the toxicity of ochratoxin α amide were performed using immortalized human kidney epithelial (IHKE) cells, a cell line known to be sensitive against ochratoxin A with an IC50 value of 0.5 μM. Using this system, ochratoxin α amide revealed no cytotoxicity up to concentrations of 50 μM. Thus, these results propose that the thermal degradation of ochratoxin A to ochratoxin α amide might be a detoxification process. Finally, we present a sample preparation and a HPLC-tandem mass spectrometry (HPLC-MS/MS) method for the analysis of ochratoxin α amide in extrudates and checked its formation during the extrusion of artificially contaminated wheat grits at 150 and 180 °C, whereas no ochratoxin α amide was detectable under these conditions.
Sensor-based sorting is a well-established single particle separation technology. It has found wide application as a quality assurance and control approach in food processing, mining, and recycling. In order to assure high sorting quality, a high degree of control of the motion of individual particles contained in the material stream is required. Several system designs, which are tailored to a sorting task at hand, exist. However, the suitability of a design for a sorting task is assessed by empirical observation. The required thorough experimentation is very time consuming and labor intensive. In this paper, we propose an instrumented bulk material particle for the characterization of motion behavior of the material stream in sensor-based sorting systems. We present a hardware setup including a 9-axis absolute orientation sensor that is used for data acquisition on an experimental sorting system. The presented results show that further processing of this data yields meaningful features of the motion behavior. As an example, we acquire and process data from an experimental sorting system consisting of several submodules such as vibrating conveyor channels and a chute. It is shown that the data can be used to train a model which enables predicting the submodule of a sorting system from which an unknown data sample originates. To our best knowledge, this is the first time that this IIoT-based approach has been applied for the characterization of material flow properties in sensor-based sorting.
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