Search Results (2)

Utilizing coffee by-products in the fermentation process of coffee offers a sustainable strategy by repurposing agricultural waste and enhancing product quality. This study evaluates the effect of applying a starter culture, derived from coffee residues, on the dynamics of reducing and total sugars during coffee fermentation, as well as the composition of aromatic compounds, organic acids, and the sensory profile of coffee inoculated with yeast (Saccharomyces cerevisiae) and lactic acid bacteria (Lactobacillus delbrueckii subsp. bulgaricus and Streptococcus thermophilus), in comparison to a spontaneously fermented sample. Volatile compounds were identified and quantified using dynamic headspace gas chromatography-mass spectrometry (HS/GC-MS), with predominant detection of 2-furancarboxaldehyde, 5-methyl; 2-furanmethanol; and furfural—compounds associated with caramel, nut, and sweet aromas from the roasting process. A reduction in sugars (glucose, fructose, and sucrose) occurred over the 36 h fermentation period. Lactic acid (2.79 g/L) was the predominant organic acid, followed by acetic acid (0.69 g/L). The application of the inoculum improved the sensory quality of the coffee, achieving a score of 86.6 in evaluations by Q-graders, compared to 84 for the control sample. Additionally, descriptors such as red apple, honey, and citrus were prominent, contributing to a uniform and balanced flavor profile. These findings indicate that controlled fermentation with starter cultures derived from coffee by-products enhances sustainability in coffee production. It achieves this by supporting a circular economy, reducing reliance on chemical additives, and improving product quality. This approach aligns with sustainable development goals by promoting environmental stewardship, economic viability, and social well-being within the coffee industry. Full article

Apple grading is an essential part of the apple marketing process to achieve high profits. In this paper, an improved YOLOv5 apple grading method is proposed to address the problems of low grading accuracy and slow grading speed in the apple grading process and is experimentally verified by the designed automatic apple grading machine. Firstly, the Mish activation function is used instead of the original YOLOv5 activation function, which allows the apple feature information to flow in the deep network and improves the generalization ability of the model. Secondly, the distance intersection overUnion loss function (DIoU_Loss) is used to speed up the border regression rate and improve the model convergence speed. In order to refine the model to focus on apple feature information, a channel attention module (Squeeze Excitation) was added to the YOLOv5 backbone network to enhance information propagation between features and improve the model’s ability to extract fruit features. The experimental results show that the improved YOLOv5 algorithm achieves an average accuracy of 90.6% for apple grading under the test set, which is 14.8%, 11.1%, and 3.7% better than the SSD, YOLOv4, and YOLOv5s models, respectively, with a real-time grading frame rate of 59.63 FPS. Finally, the improved YOLOv5 apple grading algorithm is experimentally validated on the developed apple auto-grader. The improved YOLOv5 apple grading algorithm was experimentally validated on the developed apple auto grader. The experimental results showed that the grading accuracy of the automatic apple grader reached 93%, and the grading speed was four apples/sec, indicating that this method has a high grading speed and accuracy for apples, which is of practical significance for advancing the development of automatic apple grading. Full article