Browsing by Person "Zhang, Youfeng"

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    Characterization of the aroma properties in fragrant rapeseed oil and aroma variation during critical roasting phase
    (2023) Zhang, Youfeng; Zhang, Yanyan
    Rapeseed oil is one of the third most-produced vegetable oil in the world, which is appreciated for its characteristic flavor and high nutritional value. Fragrant rapeseed oil (FRO) produced by a typical roasting process is popular for its characteristic aroma, which has an annual consumption exceeding 1.5 million tons. However, the changes in aroma blueprint of FRO during the typical roasting processing are still unclear, which challenges rapeseed oil quality and consumer acceptance. Accordingly, the aim of this work was to investigate the aroma characteristics and their precursors pyrolysis behavior of FRO to provide a basis and guidance for the control of FRO aroma quality during production processing. First, a systematic review on summarizing, comparing, and critiquing the literature regarding the flavor of rapeseed oil, especially about employed analysis techniques (i.e., extraction, qualitative, quantitative, sensorial, and chemometric methods), identified representative/off-flavor compounds, and effects of different treatments during the processes (dehulling, roasting, microwave, flavoring with herbs, refining, oil heating, and storage) was performed. One hundred and thirty-seven odorants found in rapeseed oil from literature are listed, including aldehydes, ketones, acids, esters, alcohols, phenols, pyrazines, furans, pyrrolines, indoles, pyridines, thiazoles, thiophenes, further S-containing compounds, nitriles, and alkenes, and possible formation pathways of some key aroma-active compounds are also proposed. Nevertheless, some of these compounds require further validation (e.g., nitriles) due to lack of recombination experiments in the previous work. To wrap up, advanced flavor analysis techniques should be evolved toward time-saving, portability, real-time monitoring, and visualization, which aims to obtain a “complete” flavor profile of rapeseed oil. Aparting from that, studies to elucidate the influence of key roasting processing on the formation of aroma-active compounds are needed to deepen understanding of factors resulting in flavor variations of rapeseed oil. Following, a systematic comparison among five flavor trapping techniques including solid-phase microextraction (SPME), SPME-Arrow, headspace stir bar sorptive extraction (HSSE), direct thermal desorption (DTD), and solvent-assisted flavor evaporation (SAFE) for hot-pressed rapeseed oil was conducted. Besides, methodological validation of these five approaches for 31 aroma standards found in rapeseed oil was conducted to compare their stability, reliability, and robustness. For the qualification of the odorants in hot-pressed rapeseed oil, SAFE gave the best performance, mainly due to the high sample volumes, but it performed worse than other methods regarding linearity, recovery, and repeatability. SPME-Arrow gave good performances in not only odorant extraction but also quantification, which is considered most suitable for quantifying odorants in hot-pressed rapeseed oil. Taking cost/performance ratio into account, SPME is still an efficient flavor extraction method. Multi-method combination of flavor capturing techniques might also be an option of aroma analysis for oil matrix. Afterwards, by application of the Sensomics approach the key odorants in representative commercial FRO samples were decoded. On the basis of the aroma blueprint, changes of overall aroma profiles of oils and their key odorants were studied and compared in different roasting conditions. To better simulate industrial conditions, high temperatures (150-200 ºC) were used in our roasting study, which was rarely studied before. Identification and quantitation of the key odorants in FRO were well performed by means of the Sensomics concept. Glucosinolate degradation products were a special kind of key odorants existing in rapeseed oil. Most of the odorants showed first rising and then decline trends as the roasting process progressed. Aroma profile results showed that high-temperature-short time and low-temperature-long time conditions could have similar effects on the aroma profiles of roasted rapeseed oils, which could provide a reference for the time cost savings in industrial production. To gain the fundamental knowledge of the aroma formation in FRO, the thermal degradation behavior of progoitrin (the main glucosinolate of rapeseed) and the corresponding generated volatile products were investigated in liquid (phosphate buffer at a pH value of 5.0, 7.0, or 9.0) and solid phase systems (sea sand and rapeseed powder). The highest thermal degradation rate of progoitrin at high temperatures (150-200 ºC) was observed at a pH value of 9.0, followed by sea sand and then rapeseed powder. It could be inferred that bimolecular nucleophilic substitution reaction (SN2) was mainly taken place under basic conditions. The highest degradation rate under basic conditions might result from the high nucleophilicity of present hydroxide ions. Under the applied conditions in this study, 2,4-pentadienenitrile was the major nitrile formed from progoitrin during thermal degradation at high temperature compared to l-cyano-2-hydroxy-3-butene, which might be less stable. The possible formation pathways of major S-containing (thiophenes) and N-containing (nitriles) volatile (flavor) compounds were proposed. Hydrogen sulfide, as a degradation product of glucosinolates, could act as a sulfur source to react further with glucose to generate thiophenes. Overall, the present work comprehensively documented the effects of thermal conditions and matrices on the aroma characteristics, aroma profiles, and key odorants of hot-pressed rapeseed oil, which could provide data and theoretical basis for the flavor control of FRO under thermal treatment at actual production temperatures (150-200 °C).
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    Using a machine learning regression approach to predict the aroma partitioning in dairy matrices
    (2024) Anker, Marvin; Borsum, Christine; Zhang, Youfeng; Zhang, Yanyan; Krupitzer, Christian
    Aroma partitioning in food is a challenging area of research due to the contribution of several physical and chemical factors that affect the binding and release of aroma in food matrices. The partition coefficient measured by the Kmg value refers to the partition coefficient that describes how aroma compounds distribute themselves between matrices and a gas phase, such as between different components of a food matrix and air. This study introduces a regression approach to predict the Kmg value of aroma compounds of a wide range of physicochemical properties in dairy matrices representing products of different compositions and/or processing. The approach consists of data cleaning, grouping based on the temperature of Kmg analysis, pre-processing (log transformation and normalization), and, finally, the development and evaluation of prediction models with regression methods. We compared regression analysis with linear regression (LR) to five machine-learning-based regression algorithms: Random Forest Regressor (RFR), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost, XGB), Support Vector Regression (SVR), and Artificial Neural Network Regression (NNR). Explainable AI (XAI) was used to calculate feature importance and therefore identify the features that mainly contribute to the prediction. The top three features that were identified are log P, specific gravity, and molecular weight. For the prediction of the Kmg in dairy matrices, R2 scores of up to 0.99 were reached. For 37.0 °C, which resembles the temperature of the mouth, RFR delivered the best results, and, at lower temperatures of 7.0 °C, typical for a household fridge, XGB performed best. The results from the models work as a proof of concept and show the applicability of a data-driven approach with machine learning to predict the Kmg value of aroma compounds in different dairy matrices.