A data-driven approach to improve coffee drying: Combining environmental sensors and chemical analysis.

The study introduces a methodology that utilizes data-driven approaches to optimize coffee drying operations. This is achieved through the integration of ambient sensor data and chemical analysis. This statement underscores the significance of temperature regulation, humidity levels, and light intensity within the context of coffee production. There exists a positive correlation between elevated temperatures and increased rates of drying, but humidity has a role in determining the duration of the drying process and the preservation of aromatic compounds. The significance of light intensity in dry processing is also crucial, since excessive exposure can compromise both the taste and quality of the product. The findings of chemical investigations demonstrate a correlation between environmental factors and the composition of coffee. Specifically, increased temperatures are associated with higher quantities of caffeine, while the concentration of chlorogenic acid is influenced by humidity levels. The research additionally underscores the variations in sensory characteristics among various processing techniques, underscoring the significance of procedure choice in attaining desirable taste profiles. The integration of weather monitoring, chemical analysis, and sensory assessments is a robust approach to augmenting quality control within the coffee sector, thereby facilitating the provision of great coffee products to discerning consumers.

Potential of Arabica Coffee Beans from Northern Thailand: Exploring Antidiabetic Metabolites through Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS) Metabolomic Profiling across Diverse Postharvest Processing Techniques.

Coffee, a widely consumed beverage worldwide, undergoes postharvest methods that influence its physicochemical characteristics, while roasting modulates its composition, affecting sensory attributes. This study investigates the impact of distinct postharvest methods (washed and natural) on the antidiabetic activities, including α-amylase and DPP4, as well as the phytochemical profiling of geological indicator (GI) coffee beans (Coffea arabica L.). The results indicate notable differences in antidiabetic activity and phytochemical profiles between washed and natural processing methods. Coffee beans processed naturally exhibit significant suppression of DPP4 and α-amylase activities (p-value < 0.01) compared to beans processed using the washed technique. TLC profiling using the ratios of the solvent systems of ethyl acetate/dichloromethane (DCM) and acetone/DCM as separation solvents reveals dominant spots for the washed technique. LC-MS/MS-based untargeted metabolomics analysis using principle component analysis (PCA) clearly segregates samples processed by the natural and washed techniques without any overlap region. A total of 1114 phytochemicals, including amino acids and short peptides, are annotated. The natural processing of coffee beans has been shown to yield a slightly higher content of chlorogenic acid (CGA) compared to the washed processing method. Our findings highlight the distinct bioactivities and phytochemical compositions of GI coffee beans processed using different techniques. This information can guide consumers in choosing coffee processing methods that offer potential benefits in terms of alternative treatment for diabetes.

Non-Contact Respiration Monitoring and Body Movements Detection for Sleep Using Thermal Imaging.

Monitoring of respiration and body movements during sleep is a part of screening sleep disorders related to health status. Nowadays, thermal-based methods are presented to monitor the sleeping person without any sensors attached to the body to protect privacy. A non-contact respiration monitoring based on thermal videos requires visible facial landmarks like nostril and mouth. The limitation of these techniques is the failure of face detection while sleeping with a fixed camera position. This study presents the non-contact respiration monitoring approach that does not require facial landmark visibility under the natural sleep environment, which implies an uncontrolled sleep posture, darkness, and subjects covered with a blanket. The automatic region of interest (ROI) extraction by temperature detection and breathing motion detection is based on image processing integrated to obtain the respiration signals. A signal processing technique was used to estimate respiration and body movements information from a sequence of thermal video. The proposed approach has been tested on 16 volunteers, for which video recordings were carried out by themselves. The participants were also asked to wear the Go Direct respiratory belt for capturing reference data. The result revealed that our proposed measuring respiratory rate obtains root mean square error (RMSE) of 1.82±0.75 bpm. The advantage of this approach lies in its simplicity and accessibility to serve users who require monitoring the respiration during sleep without direct contact by themselves.