Zerstörungsfreie Qualitätsbewertung von Obst und Gemüse entlang der Supply Chain mit Hilfe von Food-Scannern

Zielsetzung

Ziel des Forschungsprojekts ist die Erarbeitung von Einsatz- und Nutzungsmöglichkeiten von Food-Scannern zur schnellen und nicht zerstörerischen Erfassung von Qualitätsparametern für ausgewählte Obstarten auf den verschiedenen Stufen der Supply Chain. Das Projekt baut auf dem Forschungsprojekt "Zerstörungsfreie Messmethode zur schnellen Qualitätsbewertung und Haltbarkeitsabschätzung von Lebensmitteln mit Hilfe von Food-Scannern" auf und legt den Fokus auf den Praxiseinsatz von Food-Scannern entlang der gärtnerischen Wertschöpfungskette.

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Erfassung der Qualitätsparameter an Weintrauben mit dem Food-Scanner

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Im Rahmen des Projekts werden Foodscanner von verschiedenen Herstellern getestet

Vorgehensweise

Als Grundlage zur Beschreibung relevanter Schlüssel-Qualitätsmerkmale dient eine Beschreibung des O+G-Sortiments im Lebensmitteleinzelhandel. Die Qualitätsparameter werden getrennt für die Stufen Produktion, Großhandel und Endkonsument beschrieben. Im Fokus stehen insbesondere positiv besetzte Qualitätsmerkmale (z. B. Festigkeit, Wasser-, Zucker-, Säuregehalt, TM), welche für die Beurteilung des Reifegrades sowie Geschmacks und Shelflifes von großer Bedeutung sind. Eine besondere Berücksichtigung sollen auch innere, optisch nicht erkennbare Mängel finden (z. B. Verbräunungen im Fruchtfleisch, geringer Saftgehalt an Zitrusfrüchten). Insbesondere im Bereich der Reifebeurteilung, welche bei vielen Produkten äußerlich nicht möglich ist (z. B. Avocado, Kiwi, Mango), sowie der geschmacklichen Bewertung (z. B. Zucker-Säure Verhältnis, Saftgehalt, Festigkeit), können anhand dieser Merkmale Qualitätsmängel erkannt werden. Im Rahmen von Lagerversuchen an der Hochschule soll im Anschluss die Qualitätsveränderung entlang der Supply Chain simuliert sowie die Nachweisbarkeit der vorher definierten Qualitätsparameter mittels Food-Scannern untersucht, bewertet und mögliche Nutzungsmöglichkeiten in der Praxis aufgezeigt werden.

Publikationen

Goisser, S.; Wittmann, S.; Mempel, H. (2021): Food-scanner applications in the fruit and vegetable sector. Landtechnik 76 (1), S.52-67.
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In the past few years, portable and smartphone-based diagnostic technologies have found their way into the agri-food industry. The aim of this research was to evaluate the perfor-mance of portable near-infrared (NIR) spectrometers, so called food-scanners, with regard to their predictive accuracy of important quality parameters of fruit and vegetables. Food-scan-ner measurements were performed in combination with destructive measurements of the corresponding quality trait (sugar content, dry matter, relative water content) on a wide range of produce from the fruit and vegetable assortment. This study evaluated dry matter content of apple, avocado, blueberry, table grape and tangerine, which yielded cross validation re-sults (r²) of up to 0.95, 0.87, 0.94, 0.92 and 0.92 respectively. Furthermore, the evaluation of food-scanner spectra for the prediction of sugar content of blueberry, kiwi, mango, persim-mon, table grape, tangerine and tomato yielded cross validations (r²) of up to 0.95, 0.84, 0.80, 0.75, 0.95, 0.93, and 0.87. Furthermore, relative water content of ginger obtained a cross val-idation correlation of r² = 0.91. The results show that these traits can be predicted with a high degree of accuracy using non-destructive measurements performed with three commercially available food-scanners SCiOTM, F-750 Produce Quality Meter, and H-100F. Consequently, food-scanners can be used as objective measurement tools along the supply chain of fresh produce to quickly determine fruit quality. In addition, a practical example shows the poten-tial of these instruments for non-destructive quality assessment in incoming goods control at fruit and vegetable wholesalers over a time period of several weeks. Furthermore, possible areas of application of food-scanners along the supply chain of fresh produce are discussed, possibilities for practical applications are presented and time-saving means are highlightedLANDTECHNIK 76(1), 2021, 52–67Food-scanner applications in the fruit and vegetable sectorSimon Goisser, Sabine Wittmann, Heike MempelIn the past few years, portable and smartphone-based diagnostic technologies have found their way into the agri-food industry. The aim of this research was to evaluate the perfor-mance of portable near-infrared (NIR) spectrometers, so called food-scanners, with regard to their predictive accuracy of important quality parameters of fruit and vegetables. Food-scan-ner measurements were performed in combination with destructive measurements of the corresponding quality trait (sugar content, dry matter, relative water content) on a wide range of produce from the fruit and vegetable assortment. This study evaluated dry matter content of apple, avocado, blueberry, table grape and tangerine, which yielded cross validation re-sults (r²) of up to 0.95, 0.87, 0.94, 0.92 and 0.92 respectively. Furthermore, the evaluation of food-scanner spectra for the prediction of sugar content of blueberry, kiwi, mango, persim-mon, table grape, tangerine and tomato yielded cross validations (r²) of up to 0.95, 0.84, 0.80, 0.75, 0.95, 0.93, and 0.87. Furthermore, relative water content of ginger obtained a cross val-idation correlation of r² = 0.91. The results show that these traits can be predicted with a high degree of accuracy using non-destructive measurements performed with three commercially available food-scanners SCiOTM, F-750 Produce Quality Meter, and H-100F. Consequently, food-scanners can be used as objective measurement tools along the supply chain of fresh produce to quickly determine fruit quality. In addition, a practical example shows the poten-tial of these instruments for non-destructive quality assessment in incoming goods control at fruit and vegetable wholesalers over a time period of several weeks. Furthermore, possible areas of application of food-scanners along the supply chain of fresh produce are discussed, possibilities for practical applications are presented and time-saving means are highlighted.

Goisser, S.; Wittmann, S.; Fernandes, M.; Mempel, H.; Ulrichs, C. (2020): Comparison of colorimeter and different portable food-scanners for non-destructive prediction of lycopene content in tomato fruit. Postharvest Biology and Technology 167 (111232), S.1-8.
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Lycopene, the red colored carotenoid in tomatoes, has various health benefits for humans due to its capability of scavenging free radicals. Traditionally, the quantification of lycopene requires an elaborate extraction process combined with HPLC analysis within the laboratory. Recent studies focused simpler methods for determining lycopene and utilized spectroscopic measurement methods. The aim of this study was to compare non-destructive methods for the prediction of lycopene by using color values from colorimeter measurements and Vis/NIR spectra recorded with three commercially available and portable Vis/NIR spectrometers, so called food-scanners. Tomatoes of five different ripening stages (green to red) as well as tomatoes stored up to 22 days after harvest were used for modeling. After measurement of color values and collection of Vis/NIR spectra the corresponding lycopene content was analyzed spectrophotometrically. Applying exponential regression models yielded very good prediction of lycopene for color values L*, a*, a*/b* and the tomato color index of 0.94, 0.90, 0.90 and 0.91, respectively. Color value b* was not a suitable predictor for lycopene content, whereas the (a*/b*)² value had the best linear fit of 0.87. In comparison to color measurements, the cross-validated prediction models developed for all three food-scanners had coefficients of determination (r²CV) ranging from 0.92 to 0.96. Food-scanners also can be used for additional measurements of internal fruit quality, and therefore have great potential for fruit quality assessment by measuring a multitude of important fruit traits in one single scan.


Projektleitung

Prof. Dr. Heike Mempel (Koordination)
T +49 8161 71-5853
heike.mempel [at]hswt.de

Projektbearbeitung

M.Sc. Simon Goisser

Projektdauer

01.09.2019 - 31.08.2020

Projektförderung

Weblinks

Forschungsprojekt Food-Scanner
Video I - Einführung
Video II - Erfassung von Spektren
Video III - Modellerstellung