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Effect of the roasting level on the content of bioactive and aromatic compounds in Arabica coffee beans
Application of Machine Learning to Assess the Quality of Food Products - Case Study: Coffee Bean
Modern machine learning methods were used to automate and improve the determination of an effective quality index for coffee beans. Machine learning algorithms can effectively recognize various anomalies, among others factors, occurring in a food product. The procedure for preparing the machine learning algorithm depends on the correct preparation and preprocessing of the learning set. The set contained coded information (i.e., selected quality coefficients) based on digital photos (input data) and a specific class of coffee bean (output data). Because of training and data tuning, an adequate convolutional neural network (CNN) was obtained, which was characterized by a high recognition rate of these coffee beans at the level of 0.81 for the test set. Statistical analysis was performed on the color data in the RGB color space model, which made it possible to accurately distinguish three distinct categories of coffee beans. However, using the Lab* color model, it became apparent that distinguishing between the quality categories of under-roasted and properly roasted coffee beans was a major challenge. Nevertheless, the Lab* model successfully distinguished the category of over-roasted coffee beans.
How to Identify Roast Defects in Coffee Beans Based on the Volatile Compound Profile
The aim of this study was to detect and identify the volatile compounds in coffee that was obtained in defect roast processes versus standard roasting and to determine the type and strength of the correlations between the roast defects and the volatile compound profile in roasted coffee beans. In order to achieve this goal, the process of coffee bean roasting was set to produce an underdeveloped coffee defect, an overdeveloped coffee defect, and defectless coffee. The “Typica” variety of Arabica coffee beans was used in this study. The study material originated from a plantation that is located at an altitude of 1400–2000 m a.s.l. in Huehuetenango Department, Guatemala. The analyses were carried out with the use of gas chromatography/mass spectrometry (GC–MS) and an electronic nose. This study revealed a correlation between the identified groups of volatile compounds and the following coffee roasting parameters: the time to the first crack, the drying time, and the mean temperatures of the coffee beans and the heating air. The electronic nose helped to identify the roast defects.
Effects of Drying Conditions on the Content of Biologically Active Compounds in Winter Camelina Sativa Seeds
AbstractThe moisture content of Camelina sativa seeds has to be maintained at 7–12% during storage in order to preserve their quality. If seeds with higher moisture contents are to be stored, they first need to be dried. This study presents the effects of high‐temperature drying (at 40, 60, 80, 100, 120, and 140 °C) of C. sativa seeds on the technological usefulness (expressed as the acid value) and bioactive compound content (as polyenoic fatty acid, vitamin‐E active compounds, and phytosterols). It is shown that drying temperature significantly affects levels of bioactive compounds. Losses of phytosterols reached a maximum of 24% (for temperatures in the 80–140 °C range), while losses of tocopherols range from 2–11%, depending on cultivar. A change in the percentage composition of polyenoic acids is observed upon air drying at 100–140 °C. It is recommended not to exceed 60 °C when drying C. sativa seeds, in order to guarantee that high‐quality cold‐pressed oil with high levels of bioactive compounds is obtained.Practical application: The seeds of Camelina sativa, like other oilseeds, require appropriate storage after harvesting in order to maintain continuity of production. Maintaining the high seed quality during storage requires drying them after harvesting to a moisture content of 7–12%. Drying conditions have a significant effect on seed quality, expressed as acid number, and also affect the levels of bioactive compounds (such as polyene fatty acids, tocopherols, plastochromanol‐8, and phytosterols) in the oil. Information on optimum drying conditions will contribute to the availability of high‐quality camelina oils produced by small local manufacturers.