Comprehensive analysis of the internal structure and firmness in American cranberry (Vaccinium macrocarpon Ait.) fruit
Autoři:
Luis Diaz-Garcia aff001; Lorraine Rodriguez-Bonilla aff001; Matthew Phillips aff001; Arnoldo Lopez-Hernandez aff003; Edward Grygleski aff004; Amaya Atucha aff001; Juan Zalapa aff001
Působiště autorů:
University of Wisconsin-Madison, Department of Horticulture, Madison, Wisconsin, United States of America
aff001; Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Aguascalientes, México
aff002; University of Wisconsin-Madison, Department of Food Science, Madison, Wisconsin, United States of America
aff003; Valley Corporation, Tomah, Wisconsin, United States of America
aff004; USDA-ARS, Vegetable Crops Research Unit, University of Wisconsin, Madison, Wisconsin, United States of America
aff005
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0222451
Souhrn
Background
Cranberry (Vaccinium macrocarpon L.) fruit quality traits encompass many properties. Although visual appearance and fruit nutritional constitution have usually been the most important attributes, cranberry textural properties such as firmness have recently gained importance in the industry. Fruit firmness has become a quality standard due to the recent demand increase for sweetened and dried cranberries (SDC), which are currently the most profitable cranberry product. Traditionally, this trait has been measured by the cranberry industry using compression tests; however, it is poorly understood how fruit firmness is influenced by other characteristics.
Results
In this study, we developed a high-throughput computer-vision method to measure the internal structure of cranberry fruit, which may in turn influence cranberry fruit firmness. We measured the internal structure of 16 cranberry cultivars measured over a 40-day period, representing more than 3000 individual fruit evaluated for 10 different traits. The internal structure data paired with fruit firmness values at each evaluation period allowed us to explore the correlations between firmness and internal morphological characteristics.
Conclusions
Our study highlights the potential use of internal structure and firmness data as a decision-making tool for cranberry processing, especially to determine optimal harvest times and ensure high quality fruit. In particular, this study introduces novel methods to define key parameters of cranberry fruit that have not been characterized in cranberry yet. This project will aid in the future evaluation of cranberry cultivars for in SDC production.
Klíčová slova:
Principal component analysis – Fruits – Fungal structure – Decision making – Density – Pericarp – Plant breeding – Fruit crops
Zdroje
1. Song G.Q., Hancock J.F., 2011. Vaccinium, in: Kole C. (Ed.), Wild Crop Relatives: Genomic and Breeding Resources: Temperate Fruit. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 197–221.
2. Vorsa N., Johnson-Cicalese J., 2012. American Cranberry. In: Badenes M.L. (Eds.), Fruit breeding. Springer US, Boston, MA, pp. 191–223.
3. Vorsa N., Polashock J., Cunningham D.G., Roderick R., Howell A., 2000. Evaluation of fruit chemistry in cranberry germplasm: potential for breeding varieties with enhanced health constituents. Acta Hortic. 574, 215–219. https://doi.org/10.17660/ActaHortic.2002.574.32
4. Vvedenskaya I.O., Vorsa N., 2004. Flavonoid composition over fruit development and maturation in American cranberry, Vaccinium macrocarpon Ait. Plant Science 167 (5), 1043–1054.
5. Wang Y., Johnson-Cicalese J., Singh A.P., Vorsa N., 2017. Characterization and quantification of flavonoids and organic acids over fruit development in American cranberry (Vaccinium macrocarpon) cultivars using HPLC and APCI-MS/MS. Plant Science 262, 91–102. doi: 10.1016/j.plantsci.2017.06.004 28716425
6. FAOSTAT. 2016. Cranberry statistics for 2016. FAOSTAT Database. Rome, Italy: FAO.
7. United State Department of Agriculture, National Agriculture Statistics Service (USDA NASS). 2017. National statistics for cranberry. 28 August 2018. <https://www.nass.usda.gov/Publications/Todays_Reports/reports/ncit0617.pdf >.
8. Alston J.M., Medellin-Azuara J., Saitone T.L., 2014. Economic impact of the North American cranberry industry. Univ. Calif. Davis, CA, USA.
9. Jesse E., Saupe W., Deller S., Lohr R., Roper T., Stang E., 1993. The economic impact of the Wisconsin cranberry industry. Cranberry Inst. 2538.
10. United State Department of Agriculture, Economic Research Service (USDA-ERS). 2017. Fruit and Tree Nuts Outlook. September 29, 2017. 28 August 2018. <https://www.ers.usda.gov/webdocs/publications/85287/fts-365.pdf>.
11. U.S. Food and Drug Administration (U.S. FDA). 2016. Food Labeling: Revision of the Nutrition and Supplement Facts Labels—Rules and Regulations. 28 August 2018. <https://www.gpo.gov/fdsys/pkg/FR-2016-05-27/pdf/2016-11867.pdf>.
12. Well-Hansen L., 2018. personal communication
13. Gallardo R. K., Klingthong P., Zhang Q., Polashock J., Atucha A., Zalapa J., et al., 2018. Breeding Trait Priorities of the Cranberry Industry in the United States and Canada. HortScience 53 (10), 1467–1474.
14. Ozgen M., Smith J.D., Palta J.D., 2002. Ripening stage has a dramatic influence on cranberry fruit postharvest shelf life: physiological and anatomical explanations. Postharvest Biology and Technology 24, 291–299.
15. Hadfield K.A. and Bennett A.B., 1998. Polygalacturonases: many genes in search of a function. Plant Physiology. 117 (2), 337–343. doi: 10.1104/pp.117.2.337 9625687
16. Gunes G., Liu R.H., Watkins C.B., 2002. Controlled-atmosphere effects on postharvest quality and antioxidant activity of cranberry fruit. Journal of Agricultural and Food Chemistry 50 (21), 5932–5938. doi: 10.1021/jf025572c 12358462
17. Forney C.F., 2010. Maintaining Cranberry Fruit Quality during Storage and Marketing. Fresh Produce Global Science Books 4, 67–75.
18. Forney C.F., 2003. Postharvest Handling and Storage of Fresh Cranberries. Horttechnology 13, 267–272.
19. Meli V.S., Ghosh S., Prabha T.N., Chakraborty N., Chakraborty S., Datta A., 2010. Enhancement of fruit shelf life by suppressing N-glycan processing enzymes. Proceedings of the National Academy of Sciences 107 (6), 2413–2418.
20. Saladié M., Matas A.J., Isaacson T., Jenks M.A., Goodwin S.M., Niklas K.J., et al., 2007. A reevaluation of the key factors that influence tomato fruit softening and integrity. Plant Physiology 144(2), 1012–1028. doi: 10.1104/pp.107.097477 17449643
21. Chaïb J., Devaux M.F., Grotte M.G., Robini K., Causse M., Lahaye M., et al., 2007. Physiological relationships among physical, sensory, and morphological attributes of texture in tomato fruits. Journal of Experimental Botany 58 (8), 1915–1925 doi: 10.1093/jxb/erm046 17452757
22. Chapman N.H., Bonnet J., Grivet L., Lynn J., Graham N., Smith R., et al., 2012. High-resolution mapping of a fruit firmness-related quantitative trait locus in tomato reveals epistatic interactions associated with a complex combinatorial locus. Plant Physiology 159 (4), 1644–1657. doi: 10.1104/pp.112.200634 22685170
23. Figàs M.R., Prohens J., Raigón M.D., Fernández-de-Córdova P., Fita A., Soler S., 2015. Characterization of a collection of local varieties of tomato (Solanum lycopersicum L.) using conventional descriptors and the high-throughput phenomics tool Tomato Analyzer. Genetic Resources and Crop Evolution 62 (2), 189–204.
24. Hurtado M., Vilanova S., Plazas M., Gramazio P., Herraiz F.J., Andújar I., et al., 2013. Phenomics of fruit shape in eggplant (Solanum melongena L.) using Tomato Analyzer software. Scientia Horticulturae. 164, 625–632.
25. Yoshioka Y., Fukino N., 2009. Image-based phenotyping: use of colour signature in evaluation of melon fruit colour. Euphytica 171, 409.
26. Diaz-Garcia L. Covarrubias-Pazaran G., Schlautman B., Grygleski E., Zalapa J., 2018a. Image-based phenotyping for identification of QTL determining fruit shape and size in American cranberry (Vaccinium macrocarpon L.). PeerJ 6:e5461. doi: 10.7717/peerj.5461 30128209
27. Gorny J.R., Cifuentes R.A., Hess-Pierce B., Kader A.A., 2008. Quality Changes in Fresh-cut Pear Slices as Affected by Cultivar, Ripeness Stage, Fruit Size, and Storage Regime. Journal of Food Science 65 (3), 541–544.
28. Kumar S., Mishra B.B., Saxena S., Bandyopadhyay N., More V., Wadhawan S., et al., 2012. Inhibition of pericarp browning and shelf life extension of litchi by combination dip treatment and radiation processing. Food Chemisrty 131 (4), 1223–1232.
29. Zude M., Herold B., Roger J.-M., Bellon-Maurel V., Landahl S., 2006. Non-destructive tests on the prediction of apple fruit flesh firmness and soluble solids content on tree and in shelf life. Journal of Food Engineering 77 (2), 254–260.
30. Valley Corp. Cranberry variety Information. Retrieved 2017 from http://www.cranberryvine.com/cranberry-varieties
31. R Core Team, 2013. R: A language and environment for statistical computing.
32. Gonzalo M.J. and Van Der Knaap E., 2008. A comparative analysis into the genetic bases of morphology in tomato varieties exhibiting elongated fruit shape. Theoretical and Applied Genetics, 116 (5), 647–656. doi: 10.1007/s00122-007-0698-7 18185917
33. Depypere L., Chaerle P., Breyne P., Vander Mijnsbrugge K., Goetghebeur P., 2009. A combined morphometric and AFLP based diversity study challenges the taxonomy of the European members of the complex Prunus L. section Prunus. Plant systematics and evolution 279 (1–4), 219–231.
34. Prohens J., Plazas M., Raigón M.D., Seguí-Simarro J.M., Stommel J.R., Vilanova S., 2012. Characterization of interspecific hybrids and first backcross generations from crosses between two cultivated eggplants (Solanum melongena and S. aethiopicum Kumba group) and implications for eggplant breeding. Euphytica 186 (2), 517–538.
35. Zhang B., Huang W., Li J., Zhao C., Fan S., Wu J., et al., 2014. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Research International 62, 326–343.
36. Walter A., Liebisch F., Hund A., 2015. Plant phenotyping: from bean weighing to image analysis. Plant Methods. 11 (1), 14.
37. Diaz-Garcia L., Schlautman B., Covarrubias-Pazaran G., Maule A., Johnson-Cicalese J., Grygleski E., et al., 2018b. Massive phenotyping of multiple cranberry populations reveals novel QTLs for fruit anthocyanin content and other important chemical traits. Molecular Genetics and Genomics https://doi.org/10.1007/s00438-018-1464-z.
38. Konopacka D. and Plocharski W.J., 2004. Effect of storage conditions on the relationship between apple firmness and texture acceptability. Postharvest Biology and Technology 32 (2), 205–211.
39. Brewer M.T., Lang L., Fujimura K., Dujmovic N., Gray S., van der Knaap E., 2006. Development of a controlled vocabulary and software application to analyze fruit shape variation in tomato and other plant species. Plant physiology 141 (1), 15–25. doi: 10.1104/pp.106.077867 16684933
40. Magwaza L.S. and Opara U.L., 2014. Investigating non-destructive quantification and characterization of pomegranate fruit internal structure using X-ray computed tomography. Postharvest Biology and Technology 95, 1–6.
41. Jarolmasjed S., Espinoza C.Z., Sankaran S., Khot L.R., 2016. Postharvest bitter pit detection and progression evaluation in ‘Honeycrisp’apples using computed tomography images. Postharvest Biology and Technology 118, 35–42.
42. Çelik H., Özgen M., Serçe S., Kaya C., 2008. Phytochemical accumulation and antioxidant capacity at four maturity stages of cranberry fruit. Scientia Horticulturae 117 (4), 345–348.
Článok vyšiel v časopise
PLOS One
2019 Číslo 9
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Těžké menstruační krvácení může značit poruchu krevní srážlivosti. Jaký management vyšetření a léčby je v takovém případě vhodný?
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
Najčítanejšie v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania