Newest publications
Investigation of the spatial and temporal variations of weather conditions in a mesoscale vineyard
(2021) Pfisterer, Philipp; Schock, Steffen; Ramaj, Iris; Müller, Joachim
Climatological conditions and weather variability have a momentous impact on viticulture and vineyard management and can be detrimental for grapevine growth and its yield. Humid weather conditions contribute to the spread of fungal pathogens and diseases, which afterwards degrade the quality of the grapevine and risk the longevity of orchards and vineyards in the tropical and subtropical regions. Therefore, it is critical to monitor the spatial and temporal variations of weather conditions in the vineyards. Despite numerous sensor systems developed in academia and industry to address this problem, a scalable and dense sensor system that guarantees low maintenance, fast and reliable data acquisition is still lacking. Thus, in this study, a low-cost wireless networked system was developed for real-time monitoring of weather parameters namely, temperature, relative humidity and dew point temperature. A capacitive-type sensor SHT31 integrated into an STM32L0 microcontroller was employed as a measuring unit. Data transmittance was empowered via a functional radio network. The sensor housings were designed and manufactured in-house via a 3D printer. The accuracy of data readings was validated by a climatic test chamber CTS-20/1000 under a wide-ranging set of temperatures and relative humidities. As an evasive experimental site, a mesoscale 30-ha vineyard located in Hessigheim, Germany was used to test the monitoring system. A number of 30 sensors were installed irregularly in this area. For the graphical analysis, data collected during the summer and winter periods were compared. From the results, substantial differences in temperature were observed between vineyard sites at p ≤ 0.05. The spatial temperature gradients altered up to 8°C, which was mainly attributed to the heterogeneous and steeply sloping terrain of the vineyard. These gradients increased over the summer and decreased during the winter. This behaviour was accredited to the diurnal solar orientation, shaded conditions as well as wind direction imposed by a bend in the river. Likewise, significant differences were observed for dew point and relative humidity. In conclusion, the developed network system demonstrated a high capability to track the variability of weather conditions and should be used as a tool for the prediction of infection hotpots in vineyards.
Improvement of the drying performance of pre-cooked beans (Phaseolus vulgaris) through ultrasonic-assisted hulling
(2022) Ramaj, Iris; Schock, Steffen; Ayetigbo, Oluwatoyin; Ntwali, Janvier; Müller, Joachim
Beans are among the most versatile and widely consumed staple foods worldwide. They are highly nutritious and contain high levels of dietary fibers, complex carbohydrates, proteins, essential vitamins, and minerals that are indispensable to human wellbeing. Due to their given importance, the development of processing methods for hard-to-cook beans for the preparation of instant end-products is of great interest, especially in developing countries. Thus, this study focused on investigating the influence of ultrasonic-assisted dehulling on the drying behaviour of pre-cooked beans as a viable alternative to the present drying approaches. Red kidney beans (Phaseolus vulgaris), unhulled (UHB) and dehulled via ultrasonication (HB/UT), were used for the experimental analysis. The cooking time of beans was determined based on sensory evaluation, with 50 and 25 min proving to be optimal for UHB and HB/UT, respectively. Afterwards, the pre-cooked samples were dried in a high-precision through-flow laboratory dryer (HPD-TF3+) at 30, 50, and 70° C with an air velocity of 0.20 ms-1 and specific humidity of 10 g kg-1. Results revealed a faster moisture transfer of the HB/UT beans compared to UHB beans at p < 0.05, which was attributed to the lower resistance to moisture diffusion induced by the hull removal. Henceforth, a reduction of drying time up to 73.3% was ascertained experimentally. A generalised semi-empirical model was developed from the analysis of the drying data, which was capable of predicting the drying behaviour of beans with R2 ≥ 0.990 and MAPE ≤ 10.0%. In terms of colour, UHB and HB/UT beans differed significantly at p < 0.05 for redness a*, yellowness b*, hue angle H*, and chroma c* across all drying conditions, while no significant differences were observed for luminosity L*. Microstructural analysis revealed comparable structures after drying at 30 and 50° C, with beans exhibiting an intact cellular structure. Temperatures of 70° C, on the other hand, degraded the cellular integrity of beans by breaking down the cell wall boundaries, especially in HB/UT beans. In conclusion, ultrasonic-assisted hulling has demonstrated a great potential for improving the drying performance of beans, thereby making it a viable alternative for practical applications.
Experimental analysis and CFD-based modeling of grain bulk drying dynamics
(2021) Ramaj, Iris; Schock, Steffen; Müller, Joachim
Drying is of great importance in the postharvest processing of agricultural commodities. It refers to the removal of the surplus moisture responsible for biochemical, microbiological, and other moisture-related deteriorative reactions, thereby ensuring quality preservation. However, drying is an intricate process comprising simultaneous heat and moisture transfers, which depends on product and drying air conditions. Therefore, drying practices are oftentimes misused, resulting in serious degradation of product quality. For this reason, modeling can be used to provide a deeper understanding of air-product interactions and to gain insights into drying process. Thus, this study focused on developing a CFD systematic approach to model the drying dynamics of wheat bulk (Pionier A, DSV AG) under controlled conditions. Within the model framework, a porous medium approach with tailored user-defined functions was utilized to represent the grain bulk characteristics. The drying experiments were performed using a high-precision and automated through-flow laboratory dryer. A coherent set of drying air temperatures T = 10 - 50°C, relative humidity RH = 20 - 60% and airflow velocity v = 0.15 - 1.00 ms-1 were employed for model validation. Afterwards, the validated computational model was used to predict the drying performance at T = 40°C, RH = 40% and v = 0.15 ms-1, where the simulated temperature and moisture content agreed very well with the experimental results (R2 ≥ 0.98 and MAPE ≤ 14.93%). The proposed model proved to be an efficient tool capable of simulating temperature and moisture dynamics inside the grain bulk with high spatial and temporal resolution, providing rapid and in-depth information compared to laborious physical experiments. In conclusion, the CFD-based approach has demonstrated a great potential for simulating drying processes. Its capabilities should be further assessed across various drying technologies, operating conditions, and agricultural commodities.
Improved prediction of wheat quality and functionality using near-infrared spectroscopy and novel approaches involving flour fractionation and data fusion
(2025) Ziegler, Denise; Buck, Lukas; Scherf, Katharina Anne; Popper, Lutz; Schaum, Alexander; Hitzmann, Bernd; Ziegler, Denise; Department of Process Analytics, Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany; Buck, Lukas; Department of Bioactive and Functional Food Chemistry, Institute of Applied Biosciences, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Scherf, Katharina Anne; Department of Bioactive and Functional Food Chemistry, Institute of Applied Biosciences, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Popper, Lutz; Mühlenchemie GmbH & Co. KG, Ahrensburg, Germany; Schaum, Alexander; Department of Process Analytics, Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany; Hitzmann, Bernd; Department of Process Analytics, Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany
The accurate and rapid determination of wheat quality is of great importance for the wheat supply chain. Near-infrared (NIR) spectroscopy has become an established method for this purpose. So far, however, predictions for most wheat quality characteristics are not accurate enough to replace reference measurements, with the exception of protein content. This study investigates the potential to improve the prediction of 41 wheat quality parameters (protein- and starch-related parameters, solvent retention capacity, farinograph, extensograph, alveograph) based on a flour fractionation approach (sieve fractionation, dough preparation, gluten washing) and data fusion using the established techniques of NIR spectroscopy and chemometrics. Results show that preprocessing of flour significantly altered the composition of the samples, which reflected in spectral differences of their NIR spectra. This also led to a change in the prediction accuracy for many wheat quality parameters. Compared to the prediction using flour spectra, flour fractionation with or without data fusion improved the RMSECV between 5.6 and 28.6% for 35 out of the 41 quality parameters tested, leading to R2CV between 0.80 and 0.96 for many of them. Gluten, dough, and the 50–75 µm and the 75–100 µm fractions were particularly important for the improved predictions. The best predictions were often based on data fusion of spectra from different sample types, demonstrating the importance of using complementary information from different data sources to improve predictions. The results underline the potential of this novel approach to be established in the industry as an extension of conventional NIR spectroscopy to improve wheat quality prediction.
Assessing the response to genomic selection by simulation
(2022) Buntaran, Harimurti; Bernal-Vasquez, Angela Maria; Gordillo, Andres; Sahr, Morten; Wimmer, Valentin; Piepho, Hans-Peter
The goal of any plant breeding program is to maximize genetic gain for traits of interest. In classical quantitative genetics, the genetic gain can be obtained from what is known as “Breeder’s equation”. In the past, only phenotypic data were used to compute the genetic gain. The advent of genomic prediction (GP) has opened the door to the utilization of dense markers for estimating genomic breeding values or GBV. The salient feature of GP is the possibility to carry out genomic selection with the assistance of the kinship matrix, hence improving the prediction accuracy and accelerating the breeding cycle. However, estimates of GBV as such do not provide the full information on the number of entries to be selected as in the classical response to selection. In this paper, we use simulation, based on a fitted mixed model for GP in a multi-environmental framework, to answer two typical questions of a plant breeder: (1) How many entries need to be selected to have a defined probability of selecting the truly best entry from the population; (2) what is the probability of obtaining the truly best entries when some top-ranked entries are selected.