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PublicationPhenoliner 2.0: RGB and near-infrared (NIR) image acquisition for an efficient phenotyping in grapevine research( 2021)
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PublicationDeveloping a handheld NIR sensor for the detection of ripening in grapevine( 2021)It has already been proven that near infrared (NIR) reflectance spectroscopy can be used to measure the ripeness of grapes by the determination of reducing sugar and acid contents. Until now, winegrowers need to collect a random one hundred berries sample per plot, to measure these parameters destructively for the estimation of the ideal harvest time of the gained product. Meanwhile, inexpensive sensors are available, to build convenient instruments for the non-destructive, low-priced and fast control of ripening parameters in the vineyard. For this, a small device including a NIR sensor (900 nm - 1700 nm / 1300 nm - 2600 nm) was built from a Raspberry Pi 3 and a NIR sensor. Spectra of individual berries, sampled from six different Vitis vinifera (L.) cultivars (Riesling, Chardonnay, Pinot Noir, Dornfelder, Pinot Gris and Dakapo) were collected. Corresponding reference data were determined with high performance liquid chromatography (HPLC). Samples were taken from different fruit-, as well as cluster zones and from the beginning of veraison until after harvest, to ensure a broad range of ingredients and the ripening properties of different berries from the vine. White, as well as red varieties were used to establish the built sensor as a viable tool for ripening prediction for mainly cultivated vines. Spectra of teinturier berries with strongly coloured flesh or skin were collected to verify its accuracy for these cultivars, too. This study is the first that systematically investigates the ripening parameters of a whole vineyard with a handheld sensor. The sensor can be used in viticulture practice to detect the ripening process and ideal harvest time due to effectiveness and simplicity.
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PublicationSmartSpectrometer - Embedded Optical Spectroscopy for Applications in Agriculture and Industry( 2021)The ongoing digitization of industry and agriculture can benefit significantly from optical spectroscopy. In many cases, optical spectroscopy enables the estimation of properties such as substance concentrations and compositions. Spectral data can be acquired and evaluated in real time, and the results can be integrated directly into process and automation units, saving resources and costs. Multivariate data analysis is needed to integrate optical spectrometers as sensors. Therefore, a spectrometer with integrated artificial intelligence (AI) called SmartSpectrometer and its interface is presented. The advantages of the SmartSpectrometer are exemplified by its integration into a harvesting vehicle, where quality is determined by predicting sugar and acid in grapes in the field.
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PublicationFrom Visual Spectrum to Millimeter Wave: A Broad Spectrum of Solutions for Food Inspection( 2020)The consequences of food adulteration can be far reaching. In the past, inexpensive adulterants were used to inflate different products, leading to severe health issues. Contamination of food has many causes and can be physical(plant stems in tea), chemical (melamine in infant formula), or biological (bacterial contamination). Employing suitable sensor systems along the production process is a requirement for food safety. In this article, different approaches to food inspection are illustrated, and exemplary scenarios outline the potential of different sensor systems along the spectrum.
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PatentIdentifizierung eines oder mehrerer spektraler Merkmale in einem Spektrum einer Probe für eine Inhaltsstoffanalyse( 2019)Die Erfindung betrifft ein Verfahren zum Identifizieren eines oder mehrerer spektraler Merkmale in einem Spektrum (4, 5) einer Probe für eine Inhaltsstoffanalyse der Probe, mit einem Bereitstellen des Spektrums (4, 5), einem Vorgeben einer Approximationsfunktion (6), welche eine stetig differenzierbare mathematische Funktion ist, einem jeweiliges Bilden einer Ableitung (n-1)-ten Grades (7, 8, 9) des Spektrums (4, 5) und der Approximationsfunktion (6), wobei die Zahl n>1 ist, einem Erzeugen einer Korrelationsmatrix (10) aus den beiden Ableitungen (n-1)-ten Grades (7, 8, 9), und einem jeweiligen Identifizieren des spektralen Merkmals oder eines der spektralen Merkmale in Abhängigkeit jeweils eines lokalen Extremums (i) der Korrelationsmatrix (10) für zumindest ein Extremum (i) der Korrelationsmatrix (10), um die Inhaltsstoffanalyse der Probe zu vereinfachen.
