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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.
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.
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.
Machine and Deep Learning in the Evaluation of Selected Qualitative Characteristics of Sweet Potatoes Obtained under Different Convective Drying Conditions
This paper discusses the use of various methods to distinguish between slices of sweet potato dried in different conditions. The drying conditions varied in terms of temperature, the values were: 60 °C, 70 °C, 80 °C, and 90 °C. Examination methods included instrumental texture analysis using a texturometer and digital texture analysis based on macroscopic images. Classification of acquired data involved the use of machine learning techniques using various types of artificial neural networks, such as convolutional neural networks (CNNs) and multi-layer perceptron (MLP). As a result, in the convective drying, changes in color darkening were found in products with the following temperature values: 60 °C (L = 83.41), 70 °C (L = 81.11), 80 °C (L = 79.02), and 90 °C (L = 75.53). The best-generated model achieved an overall classification efficiency of 77%. Sweet potato dried at 90 °C proved to be completely distinguishable from other classes, among which classification efficiency varied between 61–83% depending on the class. This means that image analysis using deep convolutional artificial neural networks is a valuable tool in the context of assessing the quality of convective-dried sweet potato slices.
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.
Effect of the roasting level on the content of bioactive and aromatic compounds in Arabica coffee beans
Predictive Models of Phytosterol Degradation in Rapeseeds Stored in Bulk Based on Artificial Neural Networks and Response Surface Regression
The need to maintain the highest possible levels of bioactive components contained in raw materials requires the elaboration of tools supporting their processing operations, starting from the first stages of the food production chain. In this study, artificial neural networks (ANNs) and response surface regression (RSR) were used to develop models of phytosterol degradation in bulks of rapeseed stored under various temperatures and water activity conditions (T = 12–30 °C and aw = 0.75–0.90). Among ANNs, networks based on a multilayer perceptron (MLP) and a radial basis function (RBF) were tested. The model input constituted aw, temperature and storage time, whilst the model output was the phytosterol level in seeds. The ANN-based modeling turned out to be more effective in estimating phytosterol levels than the RSR, while MLP-ANNs proved to be more satisfactory than RBF-ANNs. The approximation quality of the ANNs models depended on the number of neurons and the type of activation functions in the hidden layer. The best model was provided by the MLP-ANN containing nine neurons in the hidden layer equipped with the logistic activation function. The model performance evaluation showed its high prediction accuracy and generalization capability (R2 = 0.978; RMSE = 0.140). Its accuracy was also confirmed by the elliptical joint confidence region (EJCR) test. The results show the high usefulness of ANNs in predictive modeling of phytosterol degradation in rapeseeds. The elaborated MLP-ANN model may be used as a support tool in modern postharvest management systems.