Fermentation plays a critical role in shaping the flavor and quality of coffee beans.The aim of this study was to investigate the effect of mixed apple and yeast fermentation on sensory evaluation,physicochemical comp...Fermentation plays a critical role in shaping the flavor and quality of coffee beans.The aim of this study was to investigate the effect of mixed apple and yeast fermentation on sensory evaluation,physicochemical composition and flavour of coffee beans.Both green and roasted coffee beans can have their flavor and quality changed by the addition of apple and yeast.This not only significantly enhances the sweetness and flavor of coffee but also increases the variety and quantity of volatile compounds in roasted coffee beans,as well as elevates the levels of organic acids in coffee.Mixed fermentation can increase the levels of fat and certain chlorogenic acids in roasted coffee beans and also affects the levels of trigonelline and acrylamide.It can boost the content of specific minerals in coffee beans,although the effect on green beans differs from that on roasted beans.Mixed fermentation can create a significant amount of micropores on the internal surface of coffee beans,without impacting pore size.In correlation analysis results,sensory indicators are strongly associated with non-volatile substances.The results suggest that mixed fermentation of apple and yeast plays an important role in sensory evaluation,physicochemical composition and flavour of coffee beans.In this study,we demonstrated that mixed fermentation with apples and yeast has a positive effect on coffee bean flavour,making it possible to commercialise mixed fermentation of coffee beans with apples and yeast.展开更多
Water quality assessment is currently based on time-consuming and costly laboratory pro-cedures and numerous expensive physicochemical sensors deployment.In response to the trend of device minimization and reduced out...Water quality assessment is currently based on time-consuming and costly laboratory pro-cedures and numerous expensive physicochemical sensors deployment.In response to the trend of device minimization and reduced outlays in sustainable aquaponic water monitoring,the integration of aquaphotomics and computational intelligence is presented in this paper.This study used the combination of temperature,pH,and electrical conductivity sensors in predicting crop growth primary macronutrient concentration(nitrate,phos-phate,and potassium(NPK)),thus,limiting the number of deployed sensors.A total of 220 water samples collected from an outdoor artificial aquaponic pond were temperature perturbed from 16 to 36℃ with 2℃ increments to mimic ambient range,which varies water compositional structure.Aquaphotomics was applied on ultraviolet,visible light,and near-infrared spectral regions,100 to 1000 nm,to determine NPK compounds.Princi-pal component analysis emphasized nutrient dynamics through selecting the highly corre-lated water absorption bands resulting in 250 nm,840 nm,and 765 nm for nitrate,phosphate,and potassium respectively.These activated water bands were used as wave-length protocols to spectrophotometrically measure macronutrient concentrations.Exper-iments have shown that multigene symbolic regression genetic programming(MSRGP)obtained the optimal performance in parameterizing and predicting nitrate,phosphate,and potassium concentrations based on water physical properties with an accuracy of 87.63%,88.73%,and 99.91%,respectively.The results have shown the established 4-dimensional nutrient dynamics map reveals that temperature significantly strengthens nitrate and potassium above 30℃ and phosphate below 25℃ with pH and electrical con-ductivity ranging between 7 and 8 and 0.1 to 0.2 mS cm^(-1) respectively.This novel approach of developing a physicochemical estimation model predicted macronutrient concentra-tions in real-time using physical limnological sensors with a 50%reduction of energy consumption.This same approach can be extended to measure secondary macronutrients and micronutrients.展开更多
基金supported by the postgraduate joint training program of Yunnan Agricultural University and Yunnan Open University,Yunling Scholar Program(YNWR-YLXZ2018-026).
文摘Fermentation plays a critical role in shaping the flavor and quality of coffee beans.The aim of this study was to investigate the effect of mixed apple and yeast fermentation on sensory evaluation,physicochemical composition and flavour of coffee beans.Both green and roasted coffee beans can have their flavor and quality changed by the addition of apple and yeast.This not only significantly enhances the sweetness and flavor of coffee but also increases the variety and quantity of volatile compounds in roasted coffee beans,as well as elevates the levels of organic acids in coffee.Mixed fermentation can increase the levels of fat and certain chlorogenic acids in roasted coffee beans and also affects the levels of trigonelline and acrylamide.It can boost the content of specific minerals in coffee beans,although the effect on green beans differs from that on roasted beans.Mixed fermentation can create a significant amount of micropores on the internal surface of coffee beans,without impacting pore size.In correlation analysis results,sensory indicators are strongly associated with non-volatile substances.The results suggest that mixed fermentation of apple and yeast plays an important role in sensory evaluation,physicochemical composition and flavour of coffee beans.In this study,we demonstrated that mixed fermentation with apples and yeast has a positive effect on coffee bean flavour,making it possible to commercialise mixed fermentation of coffee beans with apples and yeast.
文摘Water quality assessment is currently based on time-consuming and costly laboratory pro-cedures and numerous expensive physicochemical sensors deployment.In response to the trend of device minimization and reduced outlays in sustainable aquaponic water monitoring,the integration of aquaphotomics and computational intelligence is presented in this paper.This study used the combination of temperature,pH,and electrical conductivity sensors in predicting crop growth primary macronutrient concentration(nitrate,phos-phate,and potassium(NPK)),thus,limiting the number of deployed sensors.A total of 220 water samples collected from an outdoor artificial aquaponic pond were temperature perturbed from 16 to 36℃ with 2℃ increments to mimic ambient range,which varies water compositional structure.Aquaphotomics was applied on ultraviolet,visible light,and near-infrared spectral regions,100 to 1000 nm,to determine NPK compounds.Princi-pal component analysis emphasized nutrient dynamics through selecting the highly corre-lated water absorption bands resulting in 250 nm,840 nm,and 765 nm for nitrate,phosphate,and potassium respectively.These activated water bands were used as wave-length protocols to spectrophotometrically measure macronutrient concentrations.Exper-iments have shown that multigene symbolic regression genetic programming(MSRGP)obtained the optimal performance in parameterizing and predicting nitrate,phosphate,and potassium concentrations based on water physical properties with an accuracy of 87.63%,88.73%,and 99.91%,respectively.The results have shown the established 4-dimensional nutrient dynamics map reveals that temperature significantly strengthens nitrate and potassium above 30℃ and phosphate below 25℃ with pH and electrical con-ductivity ranging between 7 and 8 and 0.1 to 0.2 mS cm^(-1) respectively.This novel approach of developing a physicochemical estimation model predicted macronutrient concentra-tions in real-time using physical limnological sensors with a 50%reduction of energy consumption.This same approach can be extended to measure secondary macronutrients and micronutrients.
