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PublicationHandheld spectral sensing devices should not mislead consumers as far as non-authentic food is concerned: A case study with adulteration of milk powder( 2022)With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5-10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications. Full article(This article belongs to the Special Issue Rapid Detection Methods for Food Fraud and Food Contaminants Series II).
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PublicationMixture of Experts of Neural Networks and Kalman Filters for Optical Belt Sorting( 2022)In optical sorting of bulk material, the composition of particles may frequently change. State-of-the-art sorting approaches rely on tuning physical models of the particle motion. The aim of this work is to increase the prediction accuracy in complex, fast-changing sorting scenarios with data-driven approaches. We propose two neural network (NN) experts for accurate prediction of a priori known particle types. To handle the large variety of particle types that can occur in real-world sorting scenarios, we introduce a simple but effective mixture of experts approach that combines NNs with hand-crafted motion models. Our new method not only improves the prediction accuracy for bulk material consisting of many particle classes, but also proves to be very adaptive and robust to new particle types.
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PublicationNew active learning algorithms for near-infrared spectroscopy in agricultural applications( 2021)The selection of training data determines the quality of a chemometric calibration model. In order to cover the entire parameter space of known influencing parameters, an experimental design is usually created. Nevertheless, even with a carefully prepared Design of Experiment (DoE), redundant reference analyses are often performed during the analysis of agricultural products. Because the number of possible reference analyses is usually very limited, the presented active learning approaches are intended to provide a tool for better selection of training samples.
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PublicationDetection of pyrrolizidine alkaloid containing herbs using hyperspectral imaging in the short-wave infrared( 2021)Plants containing pyrrolizidine alkaloids (PA) are unwanted contaminants in consumer products such as herbal tea due to their toxicity to humans. The detection of these plants or their components using hyperspectral imaging was investigated, with focus on application in sensor-based sorting. For this, 431hyperspectral images of leafs from three common herbs (peppermint, lemon balm, stinging nettle) and the poisonous common groundsel were acquired. By using a convolutional neural network, a mean F1 score of 0.89 was obtained for the classification of all four plant products based on the individual spectra. To validate the neural network, significant wavelengths were determined and visualized in an attribution map.
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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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PublicationExperimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking( 2021)Sensor-based sorting is a machine vision application that has found industrial application in various fields. An accept-or-reject task is executed by separating a material stream into two fractions. Current systems use line-scanning sensors, which is convenient as the material is perceived during transportation. However, line-scanning sensors yield a single observation of each object and no information about their movement. Due to a delay between localization and separation, assumptions regarding the location and point in time for separation need to be made based on the prior localization. Hence, it is necessary to ensure that all objects are transported at uniform velocities. This is often a complex and costly solution. In this paper, we propose a new method for reliably separating particles at non-uniform velocities. The problem is transferred from a mechanical to an algorithmic level. Our novel advanced image processing approach includes equipping the sorter with an area-scan camera in combination with a real-time multiobject tracking system, which enables predictions of the location of individual objects for separation. For the experimental validation of our approach, we present a modular sorting system, which allows comparing sorting results using a line-scan and area-scan camera. Results show that our approach performs reliable separation and hence increases sorting efficiency.
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PublicationMotion-based visual inspection of optically indiscernible defects on the example of hazelnuts( 2021)Automatic quality control has long been an integral part of the processing of food and agricultural products. Visual inspection offers solutions for many issues in this context and can be employed in the form of sensor-based sorting to automatically remove foreign and low quality entities from a product stream. However, these methods are limited to defects that can be made visible by the employed sensor, which usually restricts the system to defects appearing on the surface. An alternative non-visual solution lies in impact-acoustic methods, which do not suffer from this constraint. However, these are strongly limited in terms of material throughput and consequently not suitable for large scale industrial application. In this paper, we present a novel approach that performs inspection based on optically acquired motion data. A high-speed camera captures image sequences of test objects during a transportation process on a chute with a specific structured surface. The trajectory data is then used to classify test objects based on their motion behavior. The approach is evaluated experimentally on the example of distinguishing defect-free hazelnuts from ones that suffer from insect damage. Results show that by merely utilizing the motion data, a recognition rate of up to for undamaged hazelnuts can be achieved. A major advantage of our approach is that it can be integrated in sensor-based sorting systems and is suitable for high throughput applications.
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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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PublicationAn Extended Modular Processing Pipeline for Event-Based Vision in Automatic Visual Inspection( 2021)Dynamic Vision Sensors differ from conventional cameras in that only intensity changes of individual pixels are perceived and transmitted as an asynchronous stream instead of an entire frame. The technology promises, among other things, high temporal resolution and low latencies and data rates. While such sensors currently enjoy much scientific attention, there are only little publications on practical applications. One field of application that has hardly been considered so far, yet potentially fits well with the sensor principle due to its special properties, is automatic visual inspection. In this paper, we evaluate current state-of-the-art processing algorithms in this new application domain. We further propose an algorithmic approach for the identification of ideal time windows within an event stream for object classification. For the evaluation of our method, we acquire two novel datasets that contain typical visual inspection scenarios, i.e., the inspection of objects on a conveyor belt and during free fall. The success of our algorithmic extension for data processing is demonstrated on the basis of these new datasets by showing that classification accuracy of current algorithms is highly increased. By making our new datasets publicly available, we intend to stimulate further research on application of Dynamic Vision Sensors in machine vision applications.
