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Dynamic Controlled Atmosphere-Chlorophyll Fluorescence storage (DCA-CF) uses a fluorescence-based measurement method to detect fermentation in apples (Malus × domestica BORKH.) caused by low-oxygen levels at an early stage. In recent years, it has been observed that individual apples of the same variety and origin can exhibit different fermentation behavior when stored under completely identical conditions. The causes of the different fermentation behavior must be found in order to be able to use DCA storage optimally. This study aimed to find the causes of the different fermentation behaviors of individual apples. Our results show that fruit ripeness can affect the lower oxygen limit (LOL), especially immediately after harvest, when the starch degradation in the fruit is not yet complete. A significant increase in the LOL was observed in ‘Elstar’ (2020: 0.3 kPa, 0.6 kPa, 0.9 kPa; 2021: 0.3 kPa, 0.4 kPa, 0.6 kPa). ‘Braeburn’ also exhibited this behavior regarding the LOL at a lower level. The LOL could not be identified for some of the fruit (varying from 12.5% to 41.7% of the examined apples) previously stored in Ultra Low Oxygen (ULO) storage for 4 months. Also, the chlorophyll content in the apple skin influences the fluorescence measurement method. Within 2 weeks, the chlorophyll content in the apple skin was halved. If the chlorophyll content drops, the reliability of the fluorescence measurement also decreases. It turned out that apples with an Fv/Fm < 0.7 were unsuitable for valid LOL identification.
Chlorophyllfluoreszenz als Werkzeug für die DCA-Lagerung von Äpfeln (Malus x domestica BORKH.)
(2024)
Recording of Low-Oxygen Stress Response Using Chlorophyll Fluorescence Kinetics in Apple Fruit
(2023)
Long-term storage of apples (Malus x domestica, Borkh.) is increasingly taking place under Dynamic Controlled Atmosphere (DCA). The oxygen level is lowered to ≤ 1 kPa O2 and the apples are stored just above the Lower Oxygen Limit (LOL). Low oxygen stress during controlled atmosphere storage can lead to fermentation in apples if oxygen levels are too low. Chlorophyll fluorescence can be used to detect low-oxygen stress at an early stage during storage. The currently available non-imaging fluorescence systems often use the minimal fluorescence (Fo) parameter. In contrast, the use of chlorophyll fluorescence kinetics is insufficiently described. Therefore, this study aimed to gain more knowledge about the response of chlorophyll fluorescence kinetics to low oxygen stress in apples using a fluorescence imaging system. The results show that the kinetic fluorescence curves differ under aerobic and fermentation conditions. The fermentative conditions initiated a decrease in fluorescence intensity upon application of the saturation pulses during exposure to actinic light. This result was made at 18 °C and 2 °C ambient temperatures. Interestingly, the kinetic curve changed at 2 °C before fermentation products accumulated in the apples. Non-photochemical quenching (NPQ) decreased under fermentation conditions in the dark phase after relaxation. Upon entering the dark relaxation phase after Kautsky induction, ɸPSII began to increase. Under atmospheric oxygen conditions, ɸPSII reached values of 0.81 to 0.76, while under fermentation, ɸPSII values ranged from 0.57 to 0.44.
Currently, only non-imaging chlorophyll fluorescence measurements are used to identify the Lower Oxygen Limit (LOL) in Dynamic Controlled Atmosphere - Chlorophyll Fluorescence (DCA-CF) storage. The disadvantage of non-imaging fluorescence is that no statement can be made about the spatial heterogeneity of the sample. In contrast, chlorophyll fluorescence imaging can detect spatial heterogeneity of photosynthetic activity and has been established in research for some decades because the information benefit is higher. In this study, the chlorophyll fluorescence (Fo, Fm, Fv, Fv/Fm) of apples (Malus x domestica, BORKH.) was measured with a fluorescence imaging system in situ during storage. Intact apples of ‘Braeburn’ and ‘Golden Delicious’ were stored under low-oxygen stress conditions (< 1 kPa). The metabolic shift from aerobic to fermentative metabolism was made visible with the chlorophyll fluorescence imaging and was spatially localized on the sample. Furthermore, a method was developed to identify the LOL based on the chlorophyll fluorescence imaging combined with the histogram division method. This method considers the heterogeneity of the fluorescence and bundles the measured Fo data as histograms. Our results showed that the fluorescence imaging combined with the histogram division method can be a powerful tool for identifying the LOL.
Hintergrund:
Die Wahl der Versorgungsform, stationär oder ambulant, unterliegt im deutschen Gesundheitswesen derzeit einem tiefgreifenden Wandel. Eine neue Qualität erhält die Öffnung der Krankenhäuser mit der Neufassung des § 116b SGB V. Die Studie untersucht die Frage, welches ambulante Potenzial sich aus bisher vollstationär behandelten Fällen in der Rheumatologie ergibt.
Methode:
Die Auswertung basiert auf einem Datensatz für die Jahre 2004 bis 2008. Dieser enthält anonymisiert die Abrechnungsdaten von rund 23,6 Mio. GKV-Versicherten. Die Auswahl von Patienten mit rheumatologischen Erkrankungen erfolgte anhand der in § 116b SGB V angegebenen ICD-10-Diagnosen.
Ergebnisse:
Im untersuchten Zeitraum wurde ein Anstieg der rheumatologischen Fälle um 13,9% beobachtet bei einem Rückgang der durchschnittlichen Verweildauer von 9,46 Tagen auf 8,08 Tage und der behandelnden Krankenhäuser um 3,1%. Der Anteil der rheumatologischen Fälle mit kurzer Verweildauer (≤2 Tage) nahm um 32,3% zu. Als „ambulantes Potenzial“ definieren wir den Anteil dieser Kurzlieger an der Gesamtzahl der vollstationären rheumatologischen Fälle, er stieg von 25,7% auf 29,9%
Diskussion:
Nicht alle Kurzlieger können problemlos in eine ambulante Versorgung überführt werden; diese erfordert spezialisierte Strukturen und Personal. Eine Zentrenbildung findet bisher nicht statt. Die Studie erlaubt keine Aussagen zur Qualität der Versorgung in den betrachteten Krankenhäusern. Eine Verknüpfung von Versorgungsdaten mit Qualitätsdaten wäre sinnvoll.
Hintergrund:
Das zentrale Instrument in der Gesundheitsversorgung zur Messung von Qualität sind Qualitätsindikatoren. Die Qualität der Messung von Versorgungsqualität hängt von der Güte der Indikatoren ab. Mit dem QUALIFY Instrument kann diese Güte auf wissenschaftlicher Grundlage bestimmt werden. Die vorliegende Arbeit evaluiert QUALIFY und gibt Hinweise für eine Weiterentwicklung.
Methode:
Die Evaluation des QUALIFY Instruments erfolgte in Form von strukturierten qualitativen Interviews (Fokusgruppen-Interviews). Die Teilnehmer der ersten Fokusgruppe waren sowohl an der Entwicklung des Instruments beteiligt als auch in der Anwendung erfahren. In der zweiten Fokusgruppe Beteiligte waren ausschließlich QUALIFY Anwender.
Ergebnisse:
Zwischen beiden Gruppen traten keine wesentlichen Unterschiede in der Beurteilung auf. QUALIFY wurde bislang für die Bestimmung von Qualitätsindikatoren des Qualitätsberichts, für eine Vorauswahl von Indikatoren für die nationalen Versorgungsleitlinien und für ein Projekt Arzneimittelsicherheit des Aktionsbündnisses Patientensicherheit angewendet. Als Hemmschwelle für eine weitere Verbreitung wurde gesehen, dass das Instrument in seiner derzeitigen Anwendungsform eine nicht unerhebliche Komplexität aufweist und damit in der Anwendung auch entsprechend ressourcenaufwendig ist. Dennoch wurde die Kosten-Effektivität als angemessen eingestuft, da sich aus dem Einsatz nicht angemessener Qualitätsindikatoren erhebliche Folgekosten ableiten können. Ziel könnte es daher sein, QUALIFY für verschiedene Einsatzzwecke in der Komplexität abzustufen und zudem auch international stärker zu verankern.
Diskussion:
Mit QUALIFY und damit der Bewertung von Qualitätsindikatoren wurde weitgehend Neuland betreten. Die zukünftige Bedeutung von Qualitätsmessung macht die weitere Evaluation und Entwicklung entsprechender Instrumente notwendig. Fokusgruppeninterviews können hierbei ein geeigneter Ansatz sein, um die Akzeptanz und Umsetzungsprobleme in der Praxis zu evaluieren.
Computer-image processing becomes more and more important in the analysis of data in biological and agricultural research and practice. However, robust image processing is highly de pendent on the histogram analysis algorithms used and the quality of the data being processed. The algorithm presented here aims to improve the accuracy of the classification of image data generated under complex boundary situations and inconsistent lighting conditions. Using the example of the determination of nitrogen content of tomato leaves and the qualitative determination of starch con tent of apples on the basis of color image processing, we showed that the developed algorithm is able to perform a robust classification and represents an improvement to simple histogram analysis.
Background
The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable.
Results
An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed.
Conclusion
The technical realization of “Phenomenon” allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.
Within the consortium “Experimentation Field Agro-Nordwest”, a practical concept for knowledge and technology transfer of digital competence in agriculture was created. For this purpose, the web-based e-learning system “SensX” was set up, consisting of videos, presentations and instructions. In addition, the classical e-learning concept was extended by data sets, student experiments and sensor data of plants acquired by a remote phenotyping robot. This resulted in a massive open online course (MOOC), which was tested with agricultural and biotechnology students in higher education at the University of Applied Sciences Osnabrück over two years. The evaluation process of “SensX” included an empirical survey, qualitative interviews of the participating students by an external institution and an evaluation of the concept by the lecturers.
Hyperhydricity (HH) is one of the most important physiological disorders that negatively affects various plant tissue culture techniques. The objective of this study was to characterize optical features to allow an automated detection of HH. For this purpose, HH was induced in two plant species, apple and Arabidopsis thaliana, and the severity was quantified based on visual scoring and determination of apoplastic liquid volume. The comparison between the HH score and the apoplastic liquid volume revealed a significant correlation, but different response dynamics. Corresponding leaf reflectance spectra were collected and different approaches of spectral analyses were evaluated for their ability to identify HH-specific wavelengths. Statistical analysis of raw spectra showed significantly lower reflection of hyperhydric leaves in the VIS, NIR and SWIR region. Application of the continuum removal hull method to raw spectra identified HH-specific absorption features over time and major absorption peaks at 980 nm, 1150 nm, 1400 nm, 1520 nm, 1780 nm and 1930 nm for the various conducted experiments. Machine learning (ML) model spot checking specified the support vector machine to be most suited for classification of hyperhydric explants, with a test accuracy of 85% outperforming traditional classification via vegetation index with 63% test accuracy and the other ML models tested. Investigations on the predictor importance revealed 1950 nm, 1445 nm in SWIR region and 415 nm in the VIS region to be most important for classification. The validity of the developed spectral classifier was tested on an available hyperspectral image acquisition in the SWIR-region.