| [1] |
Allan AC, Hellens RP, Laing WA. 2008. MYB transcription factors that colour our fruit. Trends in Plant Science 13:99−102 doi: 10.1016/j.tplants.2007.11.012 |
| [2] |
Tanaka Y, Sasaki N, Ohmiya A. 2008. Biosynthesis of plant pigments: anthocyanins, betalains and carotenoids. The Plant Journal 54:733−49 doi: 10.1111/j.1365-313X.2008.03447.x |
| [3] |
Zhang Y, Butelli E, Martin C. 2014. Engineering anthocyanin biosynthesis in plants. Current Opinion in Plant Biology 19:81−90 doi: 10.1016/j.pbi.2014.05.011 |
| [4] |
Chagné D, Kui LW, Espley RV, Volz RK, How NM, et al. 2013. An ancient duplication of apple MYB transcription factors is responsible for novel red fruit-flesh phenotypes. Plant Physiology 161:225−39 doi: 10.1104/pp.112.206771 |
| [5] |
Chagné D, Carlisle CM, Blond C, Volz RK, Whitworth CJ, et al. 2007. Mapping a candidate gene (MdMYB10) for red flesh and foliage colour in apple. BMC Genomics 8:212 doi: 10.1186/1471-2164-8-212 |
| [6] |
Espley RV, Brendolise C, Chagné D, Kutty-Amma S, Green S, et al. 2009. Multiple repeats of a promoter segment causes transcription factor autoregulation in red apples. The Plant Cell 21:168−83 doi: 10.1105/tpc.108.059329 |
| [7] |
Lu Y, Lu R. 2017. Non-destructive defect detection of apples by spectroscopic and imaging technologies: a review. Transactions of the ASABE 60:1765−90 doi: 10.13031/trans.12431 |
| [8] |
Jiang Y, Duan X, Qu H, Zheng S. 2016. Browning: enzymatic browning. In Encyclopedia of Food and Health, eds Caballero B, Finglas PM, Toldrá F. Amsterdam: Elsevier. pp. 508−14. doi: 10.1016/b978-0-12-384947-2.00090-8 |
| [9] |
Nicolas JJ, Richard-Forget FC, Goupy PM, Amiot MJ, Aubert SY. 1994. Enzymatic browning reactions in apple and apple products. Critical Reviews in Food Science and Nutrition 34:109−57 doi: 10.1080/10408399409527653 |
| [10] |
Sidhu RS, Bound SA, Swarts ND. 2023. Internal flesh browning in apple and its predisposing factors—a review. Physiologia 3:145−72 doi: 10.3390/physiologia3020012 |
| [11] |
Di Guardo M, Tadiello A, Farneti B, Lorenz G, Masuero D, et al. 2013. A multidisciplinary approach providing new insight into fruit flesh browning physiology in apple (Malus × domestica Borkh.). PLoS One 8:e78004 doi: 10.1371/journal.pone.0078004 |
| [12] |
Daccord N, Celton JM, Linsmith G, Becker C, Choisne N, et al. 2017. High-quality de novo assembly of the apple genome and methylome dynamics of early fruit development. Nature Genetics 49:1099−106 doi: 10.1038/ng.3886 |
| [13] |
Sun R, Li H, Zhang Q, Chen D, Yang F, et al. 2014. Mapping for quantitative trait loci and major genes associated with fresh-cut browning in apple. HortScience 49:25−30 doi: 10.21273/hortsci.49.1.25 |
| [14] |
Tazawa J, Oshino H, Kon T, Kasai S, Kudo T, et al. 2019. Genetic characterization of flesh browning trait in apple using the non-browning cultivar 'Aori 27'. Tree Genetics & Genomes 15:49 doi: 10.1007/s11295-019-1356-3 |
| [15] |
Volz RK, Kumar S, Chagné D, Espley R, McGhie TK, et al. 2013. Genetic relationships between red flesh and fruit quality traits in apple. Acta Horticulturae 976:363−68 doi: 10.17660/actahortic.2013.976.49 |
| [16] |
Kumar S, Garrick DJ, Bink MCAM, Whitworth C, Chagné D, et al. 2013. Novel genomic approaches unravel genetic architecture of complex traits in apple. BMC Genomics 14:393 doi: 10.1186/1471-2164-14-393 |
| [17] |
Espley RV, Leif D, Plunkett B, McGhie T, Henry-Kirk R, et al. 2019. Red to brown: an elevated anthocyanic response in apple drives ethylene to advance maturity and fruit flesh browning. Frontiers in Plant Science 10:1248 doi: 10.3389/fpls.2019.01248 |
| [18] |
Holderbaum DF, Kon T, Kudo T, Guerra MP. 2010. Enzymatic browning, polyphenol oxidase activity, and polyphenols in four apple cultivars: dynamics during fruit development. HortScience 45:1150−54 doi: 10.21273/hortsci.45.8.1150 |
| [19] |
Kumar S, Deng CH, Molloy C, Kirk C, Plunkett B, et al. 2022. Extreme-phenotype GWAS unravels a complex nexus between apple (Malus domestica) red-flesh colour and internal flesh browning. Fruit Research 2:12 doi: 10.48130/frures-2022-0012 |
| [20] |
Bouillon P, Fanciullino AL, Belin E, Bréard D, Boisard S, et al. 2024. Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity. Plant Methods 20:71 doi: 10.1186/s13007-024-01196-1 |
| [21] |
Kirchgessner N, Hodel M, Studer B, Patocchi A, Broggini GAL. 2024. FruitPhenoBox – a device for rapid and automated fruit phenotyping of small sample sizes. Plant Methods 20:74 doi: 10.1186/s13007-024-01206-2 |
| [22] |
Shimizu T, Okada K, Moriya S, Komori S, Abe K. 2021. A high-throughput color measurement system for evaluating flesh browning in apples. Journal of the American Society for Horticultural Science 146:241−51 doi: 10.21273/jashs05027-20 |
| [23] |
Subhashree SN, Sunoj S, Xue J, Bora GC. 2017. Quantification of browning in apples using colour and textural features by image analysis. Food Quality and Safety 1:221−26 doi: 10.1093/fqsafe/fyx021 |
| [24] |
Serouart M, Madec S, David E, Velumani K, Lopez Lozano R, et al. 2022. SegVeg: segmenting RGB images into green and senescent vegetation by combining deep and shallow methods. Plant Phenomics 2022:9803570 doi: 10.34133/2022/9803570 |
| [25] |
Segonne SM, Bruneau M, Celton JM, Le Gall S, Francin-Allami M, et al. 2014. Multiscale investigation of mealiness in apple: an atypical role for a pectin methylesterase during fruit maturation. BMC Plant Biology 14:375 doi: 10.1186/s12870-014-0375-3 |
| [26] |
Gonzalez JJ, Valle RC, Bobroff S, Biasi WV, Mitcham EJ, et al. 2001. Detection and monitoring of internal browning development in 'Fuji' apples using MRI. Postharvest Biology and Technology 22:179−88 doi: 10.1016/S0925-5214(00)00183-6 |
| [27] |
Ding C, Feng Z, Wang D, Cui D, Li W. 2021. Acoustic vibration technology: toward a promising fruit quality detection method. Comprehensive Reviews in Food Science and Food Safety 20:1655−80 doi: 10.1111/1541-4337.12722 |
| [28] |
Gabriëls SHEJ, Mishra P, Mensink MGJ, Spoelstra P, Woltering EJ. 2020. Non-destructive measurement of internal browning in mangoes using visible and near-infrared spectroscopy supported by artificial neural network analysis. Postharvest Biology and Technology 166:111206 doi: 10.1016/j.postharvbio.2020.111206 |
| [29] |
Bouillon P, Fanciullino AL, Belin E, Hanteville S, Muranty H, et al. 2024. Tracing the color: quantitative trait loci analysis reveals new insights into red-flesh pigmentation in apple (Malus domestica). Horticulture Research 11:uhae171 doi: 10.1093/hr/uhae171 |
| [30] |
Berg S, Kutra D, Kroeger T, Straehle CN, Kausler BX, et al. 2019. ilastik: interactive machine learning for (bio)image analysis. Nature Methods 16(12):1226−32 doi: 10.1038/s41592-019-0582-9 |
| [31] |
Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, et al. 2012. Fiji: an open-source platform for biological-image analysis. Nature Methods 9:676−82 doi: 10.1038/nmeth.2019 |
| [32] |
Bink MCAM, Jansen J, Madduri M, Voorrips RE, Durel CE, et al. 2014. Bayesian QTL analyses using pedigreed families of an outcrossing species, with application to fruit firmness in apple. Theoretical and Applied Genetics 127:1073−90 doi: 10.1007/s00122-014-2281-3 |
| [33] |
Verma S, Evans K, Guan Y, Luby JJ, Rosyara UR, et al. 2019. Two large-effect QTLs, Ma and Ma3, determine genetic potential for acidity in apple fruit: breeding insights from a multi-family study. Tree Genetics & Genomes 15:18 doi: 10.1007/s11295-019-1324-y |
| [34] |
Powell AA, Kostick SA, Howard NP, Luby JJ. 2022. Elucidation and characterization of QTLs for Russet formation on apple fruit in 'Honeycrisp'-derived breeding germplasm. Tree Genetics & Genomes 19:5 doi: 10.1007/s11295-022-01582-7 |
| [35] |
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, et al. 2011. Scikit-learn: machine learning in Python. Journal of Machine Learning Research 12:2825−30 doi: 10.5555/1953048.2078195 |
| [36] |
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, et al. 2016. TensorFlow: large-scale machine learning on heterogeneous systems. arXiv 00:1603.04467 doi: 10.48550/arXiv.1603.04467 |
| [37] |
Chollet F. 2015. Keras. https://github.com/fchollet/keras |
| [38] |
Jordon J, Szpruch L, Houssiau F, Bottarelli M, Cherubin G, et al. 2022. Synthetic data — what, why and how? arXiv 00:1603.04467 doi: 10.48550/arXiv.2205.03257 |
| [39] |
Murata M, Tsurutani M, Tomita M, Homma S, Kaneko K. 1995. Relationship between apple ripening and browning: changes in polyphenol content and polyphenol oxidase. Journal of Agricultural and Food Chemistry 43:1115−21 doi: 10.1021/jf00053a001 |
| [40] |
Vogg G, Fischer S, Leide J, Emmanuel E, Jetter R, et al. 2004. Tomato fruit cuticular waxes and their effects on transpiration barrier properties: functional characterization of a mutant deficient in a very-long-chain fatty acid beta-ketoacyl-CoA synthase. Journal of Experimental Botany 55:1401−10 doi: 10.1093/jxb/erh149 |
| [41] |
Yang T, Li Y, Liu Y, He L, Liu A, et al. 2021. The 3-ketoacyl-CoA synthase WFL is involved in lateral organ development and cuticular wax synthesis in Medicago truncatula. Plant Molecular Biology 105:193−204 doi: 10.1007/s11103-020-01080-1 |
| [42] |
Celia Marín-Rodríguez M, Orchard J, Seymour GB. 2002. Pectate lyases, cell wall degradation and fruit softening. Journal of Experimental Botany 53:2115−19 doi: 10.1093/jxb/erf089 |
| [43] |
Tiplica T, Verron S, Grémy-Gros C, Vandewalle P, Mehinagic E. 2015. On the quality of acoustical measures when evaluating fruits quality. International Journal of Metrology and Quality Engineering 6:201 doi: 10.1051/ijmqe/2015007 |
| [44] |
Zheng H, Tang W, Yang T, Zhou M, Guo C, et al. 2024. Grain protein content phenotyping in rice via hyperspectral imaging technology and a genome-wide association study. Plant Phenomics 6:200 doi: 10.34133/plantphenomics.0200 |
| [45] |
Yan J, Wang X. 2022. Unsupervised and semi-supervised learning: the next frontier in machine learning for plant systems biology. The Plant Journal 111:1527−38 doi: 10.1111/tpj.15905 |