Anwendungen von Food-Scannern im Obst- und Gemüsesektor
DOI:
https://doi.org/10.15150/lt.2021.3264Schlagworte:
NIR-Spektroskopie; Lebensmittel-Scanner; zerstörungsfreie Messung; QualitätskontrolleAbstract
In den letzten Jahren haben mobile und Smartphone-basierte Diagnosetechnologien ihren Weg in die Agrar- und Lebensmittelbranche gefunden. Das Ziel dieser Forschungsarbeit war es, die Leistungsfähigkeit portabler Nah-Infrarot (NIR) Spektrometer, auch Food-Scanner genannt, auf die Vorhersagegenauigkeit wichtiger Qualitätsparameter von Obst und Gemüse hin zu evaluieren. An einer großen Bandbreite an Früchten aus dem Obst- und Gemüsesortiment wurden deshalb zerstörerischen Messungen der entsprechenden Qualitätsparameter (Zuckergehalt, Trockenmasse, relativer Wassergehalt) in Kombination mit Food-Scanner Messungen durchgeführt. In dieser Studie wurde der Trockenmassegehalt von Apfel, Avocado, Heidelbeere, Tafeltraube und Mandarine ausgewertet, was zu Korrelationen der Cross Validierung (r²) von bis zu 0,95, 0,87, 0,94, 0,92 und 0,92 führte. Des Weiteren ergab die Auswertung von Food-Scanner-Spektren zur Vorhersage des Zuckergehalts von Heidelbeere, Kiwi, Mango, Kaki, Tafeltraube, Mandarine und Tomate Cross Validierungs-Korrelationen (r²) von bis zu 0,95, 0,84, 0,80, 0,75, 0,95, 0,93 und 0,87. Außerdem erreichte der relative Wassergehalt von Ingwer eine Korrelation von r² = 0,91. Die Ergebnisse zeigen, dass diese Merkmale mit hoher Genauigkeit unter Verwendung von drei handelsüblichen Food-Scannern SCiO™, F-750 Produce Quality Meter und H-100F zerstörungsfrei vorhergesagt werden können. Food-Scanner können somit als objektive Messgeräte entlang der Wertschöpfungskette von Obst und Gemüse zur schnellen Ermittlung der Fruchtqualität eingesetzt werden. Darüber hinaus wird an einem Praxisbeispiel das Potential dieser Messgeräte für die zerstörungsfreie Qualitätsbewertung in Wareneingangskontrollen des Obst- und Gemüsegroßhandels aufgezeigt. Weiterhin werden mögliche Einsatzgebiete von Food-Scannern entlang der Wertschöpfungskette von Obst- und Gemüse diskutiert und praktische Einsatzmöglichkeiten aufgezeigt.
Literaturhinweise
Ceccato, P.; Flasse, S.; Tarantola, S.; Jacquemoud, S.; Grégoire, J.-M. (2001): Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment 77, 22-33. https://doi.org/10.1016/S0034-4257(01)00191-2
Choi, J.-H.; Chen, P.-A.; Lee, B.; Yim, S.-H.; Kim, M.-S.; Bae, Y.-S.; Lim, D.-C.; Seo, H.-J. (2017): Portable, non-destructive tester integrating VIS/NIR reflectance spectroscopy for the detection of sugar content in Asian pears. Scientia Horticulturae 220, 147-153. https://doi.org/10.1016/j.scienta.2017.03.050
Correa, A.R.; Quicazán, M.C.; Hernandez, C.E. (2015): Modelling the Shelf-life of Apple Products Accoring to their Water Activity. Chemical Engineering Transactions 43, 199-204. https://doi.org/10.3303/CET1543034
Cozzolino, D. (2009): Near infrared spectroscopy in natural products analysis. Planta medica 75, 746-756. https://doi.org/10.1055/s-0028-1112220
Donis-González, I.R.; Valero, C.; Momin, M.A.; Kaur, A.C.; Slaughter, D. (2020): Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes. Agronomy 10, 148. https://doi.org/10.3390/agronomy10010148
Duan, Y.; Liu, J. (2019): Optimal dynamic pricing for perishable foods with quality and quantity deteriorating simultaneously under reference price effects. International Journal of Systems Science: Operations & Logistics 6, 346-355. https://doi.org/10.1080/23302674.2018.1465618
Fan, S.; Li, J.; Xia, Y.; Tian, X.; Guo, Z.; Huang, W. (2019): Long-term evaluation of soluble solids content of apples with biological variability by using near-infrared spectroscopy and calibration transfer method. Postharvest Biology and Technology 151, 79-87. https://doi.org/10.1016/j.postharvbio.2019.02.001
Felix Instruments (2020): Food science instruments. https://www.felixinstruments.com/food-science-instruments/portable-nir-analyzers/f-750-produce-quality-meter/, accessed on 21 June 2020
Giovenzana, V.; Beghi, R.; Buratti, S.; Civelli, R.; Guidetti, R. (2014): Monitoring of fresh-cut Valerianella locusta Laterr. shelf life by electronic nose and VIS-NIR spectroscopy. Talanta 120, 368-375. https://doi.org/10.1016/j.talanta.2013.12.014
Goisser, S.; Fernandes, M.; Ulrichs, C.; Mempel, H. (2018): Non-destructive measurement method for a fast quality evaluation of fruit and vegetables by using food-scanner. DGG-Proceedings 8, 1-5. https://doi.org/10.5288/DGG-PR-SG-2018
Goisser, S.; Mempel, H.; Bitsch, V. (2020): Food-Scanners as a Radical Innovation in German Fresh Produce Supply Chains. 101 - 116 Pages / International Journal on Food System Dynamics, Vol 11, No 2 (2020) / International Journal on Food System Dynamics, Vol 11, No 2. https://doi.org/10.18461/IJFSD.V11I2.43
Guthrie, J.A.; Walsh, K.B.; Reid, D.J.; Liebenberg, C.J. (2005): Assessment of internal quality attributes of mandarin fruit. 1. NIR calibration model development. Aust. J. Agric. Res. 56, 405. https://doi.org/10.1071/AR04257
Jannok, P.; Kamitani, Y.; Kawano, S. (2014): Development of a Common Calibration Model for Determining the Brix Value of Intact Apple, Pear and Persimmon Fruits by near Infrared Spectroscopy. Journal of Near Infrared Spectroscopy 22, 367-373. https://doi.org/10.1255/jnirs.1130
Kaur, H.; Künnemeyer, R.; McGlone, A. (2017): Comparison of hand-held near infrared spectrophotometers for fruit dry matter assessment. Journal of Near Infrared Spectroscopy 25, 267-277. https://doi.org/10.1177/0967033517725530
Li, J.; Xue, L.; Liu, M.H.; Lv, P.; Yan, L.Y. (2011): Determination of Moisture Content in Ginger Using PSO Combined with Vis/NIR. AMR 320, 563-568. https://doi.org/10.4028/www.scientific.net/AMR.320.563
McGlone, V.A.; Fraser, D.G.; Jordan, R.B.; Künnemeyer, R. (2003a): Internal quality assessment of mandarin fruit by vis/NIR spectroscopy. Journal of Near Infrared Spectroscopy 11, 323-332. https://doi.org/10.1255/jnirs.383
McGlone, V.A.; Jordan, R.B.; Seelye, R.; Clark, C.J. (2003b): Dry-matter—a better predictor of the post-storage soluble solids in apples? Postharvest Biology and Technology 28, 431-435. https://doi.org/10.1016/S0925-5214(02)00207-7
McGlone, V.A.; Kawano, S. (1998): Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy. Postharvest Biology and Technology 13, 131-141. https://doi.org/10.1016/S0925-5214(98)00007-6
Mehinagic, E.; Royer, G.; Bertrand, D.; Symoneaux, R.; Laurens, F.; Jourjon, F. (2003): Relationship between sensory analysis, penetrometry and visible–NIR spectroscopy of apples belonging to different cultivars. Food Quality and Preference 14, 473-484. https://doi.org/10.1016/S0950-3293(03)00012-0
Møller, S.M.; Travers, S.; Bertram, H.C.; Bertelsen, M.G. (2013): Prediction of postharvest dry matter, soluble solids content, firmness and acidity in apples (cv. Elshof) using NMR and NIR spectroscopy: a comparative study. Eur Food Res Technol 237, 1021-1024. https://doi.org/10.1007/s00217-013-2087-6
Ncama, K.; Magwaza, L.S.; Poblete-Echeverría, C.A.; Nieuwoudt, H.H.; Tesfay, S.Z.; Mditshwa, A. (2018): On-tree indexing of ‘Hass’ avocado fruit by non-destructive assessment of pulp dry matter and oil content. Biosystems Engineering 174, 41-49. https://doi.org/10.1016/j.biosystemseng.2018.06.011
OECD (2018): OECD fruit and vegetable scheme. Guidelines on objective tests to determine quality of fruit and vegetables, dry and dried produce
Olarewaju, O.O.; Bertling, I.; Magwaza, L.S. (2016): Non-destructive evaluation of avocado fruit maturity using near infrared spectroscopy and PLS regression models. Scientia Horticulturae 199, 229-236. https://doi.org/10.1016/j.scienta.2015.12.047
Parpinello, G.P.; Nunziatini, G.; Rombolà, A.D.; Gottardi, F.; Versari, A. (2013): Relationship between sensory and NIR spectroscopy in consumer preference of table grape (cv Italia). Postharvest Biology and Technology 83, 47-53. https://doi.org/10.1016/j.postharvbio.2013.03.013
Pasquini, C. (2003): Near Infrared Spectroscopy: Fundamentals, Practical Aspects and Analytical Applications. Journal of the Brazilian Chemical Society 14, 198-219. https://doi.org/10.1590/S0103-50532003000200006
Pöpping, B.; Bourdichon, F. (2018): Consumer food testing devices: threat or opportunity? New Food 21, 30-33
Rateni, G.; Dario, P.; Cavallo, F. (2017): Smartphone-Based Food Diagnostic Technologies: A Review. Sensors (Basel, Switzerland) 17. https://doi.org/10.3390/s17061453
Sánchez, M.-T.; Pérez-Marín, D.; Flores-Rojas, K.; Guerrero, J.-E.; Garrido-Varo, A. (2009): Use of near-infrared reflectance spectroscopy for shelf-life discrimination of green asparagus stored in a cool room under controlled atmosphere. Talanta 78, 530-536. https://doi.org/10.1016/j.talanta.2008.12.004
Santos Neto, J.P. dos; Assis, M.W.D. de; Casagrande, I.P.; Cunha Júnior, L.C.; Almeida Teixeira, G.H. de (2017): Determination of ‘Palmer’ mango maturity indices using portable near infrared (VIS-NIR) spectrometer. Postharvest Biology and Technology, 130, 75-80. https://doi.org/10.1016/j.postharvbio.2017.03.009
Schmilovitch, Z.; Mizrach, A.; Hoffman, A.; Egozi, H.; Fuchs, Y. (2000): Determination of mango physiological indices by near-infrared spectrometry. Postharvest Biology and Technology 19, 245-252. https://doi.org/10.1016/s0925-5214(00)00102-2.
Spectral Engines (2020): Food Scanner. The world’s smartest, fastest and easiest way to measure food content. https://www.spectralengines.com/products/nirone-scanner/foodscanner, accessed on 21 June 2020.
Sunforest (2020): Portable Nondestructive Fruit Quality Meter. http://sunforest.kr/index.php?sm_idx=eng, accessed on 3 August 2020
Tellspec (2020): Empowering a Healthier World. with Real-Time Analysis Using Portable Low-Cost Sensors. https://tellspec.com/, accessed on 21 June 2020
UNECE, 2019: Fresh Fruit and Vegetables - Standards. http://www.unece.org/trade/agr/standard/fresh/ffv-standardse.html, accessed on 21 June 2020
Wang, H.; Peng, J.; Xie, C.; Bao, Y.; He, Y. (2015): Fruit Quality Evaluation Using Spectroscopy Technology: A Review. Sensors 15, 11889-11927. https://doi.org/10.3390/s150511889
Wedding, B.B.; Wright, C.; Grauf, S.; White, R.D.; Tilse, B.; Gadek, P. (2013): Effects of seasonal variability on FT-NIR prediction of dry matter content for whole Hass avocado fruit. Postharvest Biology and Technology 75, 9-16. https://doi.org/10.1016/j.postharvbio.2012.04.016
