@article{ErlerRiebeBeitzetal.2020,
  author    = {Erler, Alexander and Riebe, Daniel and Beitz, Toralf and L{\"o}hmannsr{\"o}ben, Hans-Gerd and Gebbers, Robin},
  title     = {Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR)},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {2},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s20020418},
  pages     = {17},
  year      = {2020},
  abstract  = {Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.},
  language  = {en}
}
@article{ErlerRiebeBeitzetal.2020,
  author    = {Erler, Alexander and Riebe, Daniel and Beitz, Toralf and L{\"o}hmannsr{\"o}ben, Hans-Gerd and Grothusheitkamp, Daniela and Kunz, Thomas and Methner, Frank-J{\"u}rgen},
  title     = {Characterization of volatile metabolites formed by molds on barley by mass and ion mobility spectrometry},
  series = {Journal of mass spectrometr},
  volume    = {55},
  journal   = {Journal of mass spectrometr},
  number    = {5},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {1076-5174},
  doi       = {10.1002/jms.4501},
  pages     = {1 -- 10},
  year      = {2020},
  abstract  = {The contamination of barley by molds on the field or in storage leads to the spoilage of grain and the production of mycotoxins, which causes major economic losses in malting facilities and breweries. Therefore, on-site detection of hidden fungus contaminations in grain storages based on the detection of volatile marker compounds is of high interest. In this work, the volatile metabolites of 10 different fungus species are identified by gas chromatography (GC) combined with two complementary mass spectrometric methods, namely, electron impact (EI) and chemical ionization at atmospheric pressure (APCI)-mass spectrometry (MS). The APCI source utilizes soft X-radiation, which enables the selective protonation of the volatile metabolites largely without side reactions. Nearly 80 volatile or semivolatile compounds from different substance classes, namely, alcohols, aldehydes, ketones, carboxylic acids, esters, substituted aromatic compounds, alkenes, terpenes, oxidized terpenes, sesquiterpenes, and oxidized sesquiterpenes, could be identified. The profiles of volatile and semivolatile metabolites of the different fungus species are characteristic of them and allow their safe differentiation. The application of the same GC parameters and APCI source allows a simple method transfer from MS to ion mobility spectrometry (IMS), which permits on-site analyses of grain stores. Characterization of IMS yields limits of detection very similar to those of APCI-MS. Accordingly, more than 90\% of the volatile metabolites found by APCI-MS were also detected in IMS. In addition to different fungus genera, different species of one fungus genus could also be differentiated by GC-IMS.},
  language  = {en}
}