About

Kent D. Rausch is associated with the Center for Digital Agriculture at the University of Illinois. The center focuses on advancing digital and smart agriculture through research, education, and industry collaboration. The center's initiatives include developing AI-driven tools, decision-support systems, and benchmarking frameworks to build trust and transparency in digital agricultural technologies. Rausch's work involves supporting projects that integrate AI, robotics, biotechnology, and data analysis to optimize agricultural practices, improve crop management, and enhance sustainability. The center also offers educational programs such as online master's degrees and professional certificates in digital agriculture, fostering interdisciplinary collaboration between engineering, agriculture, and technology sectors.

Research topics

• Chemistry
• Food science
• Computer Science
• Materials science
• Machine Learning
• Pulp and paper industry
• Artificial Intelligence
• Chromatography
• Statistics
• Mathematics
• Biochemistry
• Environmental science
• Biotechnology
• Engineering

Selected publications

• Chemometric-based approach for economically motivated fraud detection in organic spices via NIR spectroscopy

  Journal of Food Composition and Analysis · 2025-03-26 · 14 citations

  articleOpen access

  Organic spices, recognized as high-value products, are at high risk of intentional adulteration (also called economically motivated adulteration). This highlights the importance of developing reliable methods to ensure the quality and authenticity of organic spices. The main aim of this study was to develop and optimize a reliable technique based on near-infrared (NIR) spectroscopy and chemometrics for rapid and accurate adulterant detection in multiple organic spices. Ground cardamon, cinnamon, cloves, coriander, mustard, and nutmeg spices were adulterated with corn starch in the range of 1–10 % (w/w). Principal component analysis (PCA) was initially performed to examine the spectral properties of pure spices and the adulterant (corn), followed by individual PCA analyses for each spice to explore spectral changes across different levels of adulteration. Partial least squares regression (PLSR) was used with different pre-processing techniques, alone and in combination, to improve adulteration prediction. With second derivative (SD) and multiplicative scatter correction (MSC) pre-processing, the best PLSR model showed excellent prediction performance in the external validation set with a coefficient of determination for prediction (R²p) of 0.95, a root mean square error of prediction (RMSEP) of 0.62 %, and a ratio of predictive to deviation (RPD) of 4.21, demonstrating NIR spectroscopy is a fast and accurate technique for adulteration detection in organic spices and could play a significant role in controlling food safety and preventing potential economic losses. • NIR spectroscopy detected adulteration in organic spices. • PCA identified spectral variance to distinguish pure and adulterated spices. • Spectral pre-processing enhanced the accuracy of the PLSR model. • A global prediction model was created to detect adulteration in six organic spices.

  PublisherDOI

• Corrigendum to “Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics” [Curr. Res. Food Sci. (2023), 100483]

  Current Research in Food Science · 2024-01-01

  erratumOpen access

  [This corrects the article DOI: 10.1016/j.crfs.2023.100483.].

  PublisherDOI

• Optimizing feature selection with gradient boosting machines in PLS regression for predicting moisture and protein in multi-country corn kernels via NIR spectroscopy

  Food Chemistry · 2024-06-12 · 73 citations

  articleOpen access

  Differences in moisture and protein content impact both nutritional value and processing efficiency of corn kernels. Near-infrared (NIR) spectroscopy can be used to estimate kernel composition, but models trained on a few environments may underestimate error rates and bias. We assembled corn samples from diverse international environments and used NIR with chemometrics and partial least squares regression (PLSR) to determine moisture and protein. The potential of five feature selection methods to improve prediction accuracy was assessed by extracting sensitive wavelengths. Gradient boosting machines (GBMs), particularly CatBoost and LightGBM, were found to effectively select crucial wavelengths for moisture (1409, 1900, 1908, 1932, 1953, 2174 nm) and protein (887, 1212, 1705, 1891, 2097, 2456 nm). SHAP plots highlighted significant wavelength contributions to model prediction. These results illustrate GBMs' effectiveness in feature engineering for agricultural and food sector applications, including developing multi-country global calibration models for moisture and protein in corn kernels.

  PublisherDOI

• Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics

  Current Research in Food Science · 2023 · 19 citations

  • Computer Science
  • Artificial Intelligence
  • Food science

  p of 0.99, RMSEP of 2.93%, RPD of 9.18, and RER of 26.60. A visualization map was also generated to predict the level of quinoa in the adulterated samples. The results of this study demonstrate the ability of VNIR hyperspectral imaging for adulteration detection in quinoa flour as an alternative to the complicated traditional method.

  PublisherDOI

• Dry Matter Loss and Lipid Oxidation Evaluation of Soybeans During Storage at Elevated Temperatures and Moisture Content

  Journal of the ASABE · 2022-01-01

  articleSenior author

  Highlights Rates of dry matter loss and lipid oxidation increased as moisture content and temperature increased. Temperature effects were smaller than moisture content effects. Sums of hexanal and 1-hexanol concentrations were used as lipid oxidation indicators during storage. Lipid oxidation products were correlated positively with dry matter loss ( r = 0.88) and dry matter loss rates ( r = 0.84). Abstract. After harvest, soybeans are susceptible to physical, chemical, and biological changes. Prolonged storage under unfavorable conditions, such as elevated temperature and moisture content (m.c.), is responsible for accelerating dry matter loss (DML) rates and lipid oxidation (LO). Knowledge of DML rates (vDML) is useful in developing maximum allowable storage time (MAST) guidelines for soybeans. In addition to DML, monitoring changes in the lipid fraction is essential to assess quality since soybeans are valued for their oil content. The objective of this study was to estimate DML and LO of soybeans during a 30-d period over a wide range of m.c. (14% to 22%wb m.c.) and temperature (25 to 35°C), which were chosen based on climate conditions from low latitude regions where major soybean producing countries are located. A series of respiration tests were conducted using a static grain respiration measurement system with a sensor unit to monitor carbon dioxide (CO2) concentration, which was used to estimate DML. After each respiration test, samples were collected for chemical analysis. Headspace-solid phase microextraction gas chromatography-mass spectrometry was employed to determine volatile compound concentrations, which served as LO indicators. Moreover, volatile compound analysis was also used to evaluate the formation of anaerobic respiration products and other compounds that can affect soybean quality during storage. There was an increase in vDML with increased m.c. and temperature. Across the temperature range chosen, vDML increased 40 to 46 times for soybeans at 18%wb m.c. compared to 14%wb m.c. and 2.7 to 3.7 times for soybeans at 22%wb m.c. compared to 18%wb m.c. Temperature effects on vDML were smaller than moisture effects. vDML increased 1.1 to 3.5 times across the temperature range tested at constant moisture. Changes in storage conditions also affected the formation of volatile compounds. The sum of hexanal and 1-hexanol concentrations (ppm), used as LO indicators, was higher in samples with elevated m.c. Concentrations of these compounds increased 1.3 to 5 times for 22%wb soybeans compared to 18%wb m.c. and 4.6 to 11.8 times for samples at 18%wb compared to 14%wb m.c. at the same temperature. LO products were positively correlated with DML (r = 0.88) and vDML (r = 0.84); this confirmed that lipids are degraded in addition to DML when soybeans are subjected to unfavorable storage conditions. This correlation can be useful to improve MAST guidelines based on qualitative and quantitative deterioration. Keywords: Dry matter loss, Grain storage, Lipid oxidation, Respiration, Soybeans, Volatile compounds.

  PublisherDOI

• Evaluation of grain moisture measurement methods suited for developing countries

  Journal of Stored Products Research · 2022-07-31 · 4 citations

  articleSenior authorCorresponding
  PublisherDOI

• Coprocessing Corn Germ Meal for Oil Recovery and Ethanol Production: A Process Model for Lipid-Producing Energy Crops

  Processes · 2022-03-29 · 5 citations

  articleOpen access

  Efforts to engineer high-productivity crops to accumulate oils in their vegetative tissue present the possibility of expanding biodiesel production. However, processing the new crops for lipid recovery and ethanol production from cell wall saccharides is challenging and expensive. In a previous study using corn germ meal as a model substrate, we reported that liquid hot water (LHW) pretreatment enriched the lipid concentration by 2.2 to 4.2 fold. This study investigated combining oil recovery with ethanol production by extracting oil following LHW and simultaneous saccharification and co-fermentation (SSCF) of the biomass. Corn germ meal was again used to model the oil-bearing energy crops. Pretreated germ meal hydrolysate or solids (160 and 180 °C for 10 min) were fermented, and lipids were extracted from both the spent fermentation whole broth and fermentation solids, which were recovered by centrifugation and convective drying. Lipid contents in spent fermentation solids increased 3.7 to 5.7 fold compared to the beginning germ meal. The highest lipid yield achieved after fermentation was 36.0 mg lipid g−1 raw biomass; the maximum relative amount of triacylglycerol (TAG) was 50.9% of extracted oil. Although the fermentation step increased the lipid concentration of the recovered solids, it did not improve the lipid yields of pretreated biomass and detrimentally affected oil compositions by increasing the relative concentrations of free fatty acids.

  PublisherOA PDFDOI

• Process design and techno-economic analysis of 2′-fucosyllactose enriched distiller’s dried grains with solubles production in dry grind ethanol process using genetically engineered Saccharomyces cerevisiae

  Bioresource Technology · 2021-09-08 · 7 citations

  article
  PublisherDOI

• Comparison of Dry Matter Loss Rates from Static and Dynamic Grain Respiration Measurement Systems for Soybeans at 18% Moisture Content and 30°C

  Transactions of the ASABE · 2021-01-01

  article

  Highlights Design, description, and comparison of static (S) and dynamic (D) grain respiration measurement systems (GRMS). No differences were detected between dry matter loss rates ( v DML ) from S-GRMS and D-GRMS for soybeans at 18% moisture content and 30°C stored for 20 d. Literature reports variable v DML estimates for soybeans stored in S-GRMS and D-GRMS; more studies should be conducted with a wider range of storage conditions before developing maximum allowable safe storage time guidelines. Abstract. Time to reach 0.5% dry matter loss (DML) is the estimated maximum allowable storage time (MAST) for shelled corn and has been suggested for use with other grains. Respiration studies have reported various estimates of this threshold depending on the type of grain respiration measurement system (GRMS) and storage conditions tested. The objectives of this study were (1) to design and evaluate two GRMS in which oxygen needed for respiration was limited in a static system (S-GRMS) or continuously supplied in a dynamic system (D-GRMS) during storage and (2) to compare the effects of GRMS on DML rates (vDML) for 18% moisture content soybeans stored at 30°C for 20 d. In this study, S-GRMS and D-GRMS units were designed to conduct respiration tests. Respired CO2 (mg CO2) was measured over time and used to calculate the specific mass of respired CO2 (mg CO2 kg-1 d.b. beans) and subsequent DML (%) using stoichiometric ratios from the respiration chemical reaction. DML rates, vDML (% d-1), were estimated by least squares linear regression of DML and time data. Four replications of respiration tests were conducted in each GRMS. Average estimates of vDML were 0.0157% d-1 and 0.0189% d-1 for S-GRMS and D-GRMS, respectively. Mean vDML from D-GRMS tests was 1.2 times greater than mean vDML from S-GRMS but not statistically different (p = 0.09). However, the coefficient of variation was 8 times greater for D-GRMS than for S-GRMS. More studies with a wider range of storage conditions should be conducted for development of a safety factor between both systems prior to using data from respiration of soybeans in the literature to estimate MAST. Keywords: Dry matter loss, Grain storage, Respiration, Soybeans.

  PublisherDOI

• Characterization of Amylose Lipid Complexes and Their Effect on the Dry Grind Ethanol Process

  Starch - Stärke · 2021-05-13 · 2 citations

  article

  Abstract Amylose lipid complexes (AMLs) are likely to form during liquefaction of ground corn in the dry grind process. AML will form under high temperature (≥ 85 °C) and excess water conditions, due to interaction of gelatinized starch with corn lipids. AMLs are resistant to α‐amylase action, resulting in a decrease in starch available for enzymatic hydrolysis. This affects sugar available for fermentation and the final ethanol yield. In this study, the effects of liquefaction temperature, corn particle size, slurry solids content, and different commercial α‐amylases on AML formation are evaluated. AML content in post liquefaction solids (liquefact) is found to decrease from 3.46 to 1.00% as corn grind size is increased from 0.5 to 2.5 mm. Across all slurry solids contents tested (25, 32, and 34%), the mean difference in AML content for all three solids contents is 0.61% when liquefaction temperature is increased to 105 from 85 °C. At 85 °C, liquefact from all three α‐amylases used, had similar AML content. However, when liquefaction temperature is increased to 105 °C, enzyme AA2 had lower AML production compared to other amylases. Overall, increasing liquefaction temperature to above 100 °C had the most predominant effect on reducing AML formation. Optimizing liquefaction parameters can help reduce AML formation and may improve profitability of the dry grind ethanol process.

  PublisherDOI

Frequent coauthors

• Vijay Singh

  University of Illinois Urbana-Champaign

  107 shared

• M. E. Tumbleson

  University of Illinois System

  97 shared

• David B. Johnston

  Agricultural Research Service

  40 shared

• R.L. Belyea

  University of Missouri

  36 shared

• M. E. Tumbleson

  31 shared

• Bruce S. Dien

  United States Department of Agriculture

  28 shared

• Lawrence A. Johnson

  27 shared

• John D. McKinney

  École Polytechnique Fédérale de Lausanne

  26 shared