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Effect of Adverse Storage Conditions on Oil Quality and Tocochromanol Content in Yellow‐Seeded Breeding Lines of Brassica napus L.
ABSTRACTThis study evaluated the contents of tocopherols and plastochromanol‐8, as well as the acid values, in oils extracted from yellow‐seeded Brassica napus L. lines stored under adverse post‐harvest conditions. Seeds were stored at temperatures of 25°C and 30°C, with adjusted seed moisture contents of 10.5%, 12.5%, and 15.5%, corresponding to relative humidity levels of 81%, 85%, and 91%, respectively. A statistically significant reduction in total tocopherol content—up to 22% (p < 0.05)—was observed in seeds with the highest moisture content (15.5%) stored at 30°C. In contrast, seeds with 12.5% moisture stored at 25°C exhibited a smaller but still significant decrease of 11%–14% (p < 0.05). The lowest tocopherol degradation (2%–5%) occurred in seeds with 10.5% moisture stored at 25°C. Additionally, degradation rates differed between tocopherol homologues: α‐tocopherol decreased more rapidly than γ‐tocopherol, as evidenced by a significant decline in the α‐T/γ‐T ratio under high‐moisture and high‐temperature conditions. The most pronounced reduction in this ratio was recorded in seeds stored with 15.5% moisture at 30°C. Plastochromanol‐8 was also highly sensitive to storage parameters, exhibiting an even more pronounced reduction than tocopherols under high‐moisture conditions (p < 0.05). A significant increase in acid value was also observed under high temperature and moisture conditions, exceeding the acceptable threshold of 3.0 mg KOH/g in some cases, indicating advanced lipid hydrolysis during storage.
Effect of climate, growing region, country of origin, and post-harvest processing on the of content chlorogenic acids (CGAs) and aromatic compounds in roasted coffee beans
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.