The milling of wheat flour is a process that has existed since ancient times. In the course of history, the techniques have improved, the equipment modernized. The interest of the miller in charge of the mill is still to ensure that a mill is functional and profitable, as well as to provide a consistent quality of flour. The production of organic flour means that methods of adding chemicals and unnatural agents are not possible. In organic flour production, it is necessary to work with the raw material. A grain of wheat is a living material, and its quality varies according to a multitude of factors. Challenges are therefore present at each stage of the value chain. The use of artificial intelligence techniques offers solutions and new perspectives to meet the different objectives of the miller. A literature review of artificial intelligence techniques developed at each stage of the value chain surrounding the issues of quality and yield is conducted. An analysis of a large number of variables, including process factors, process parameters and wheat grain quality from data collected on the value chain enables the development and training of artificial intelligence models. From these models, it is possible to develop decision support tools and optimize the wheat flour milling process. Several major research directions, other than constant quality, are to be studied to optimize the process and move towards a smart mill. This includes energy savings, resource optimization and mill performance.
Citation: Loïc Parrenin, Christophe Danjou, Bruno Agard, Robert Beauchemin. Future trends in organic flour milling: the role of AI[J]. AIMS Agriculture and Food, 2023, 8(1): 48-77. doi: 10.3934/agrfood.2023003
The milling of wheat flour is a process that has existed since ancient times. In the course of history, the techniques have improved, the equipment modernized. The interest of the miller in charge of the mill is still to ensure that a mill is functional and profitable, as well as to provide a consistent quality of flour. The production of organic flour means that methods of adding chemicals and unnatural agents are not possible. In organic flour production, it is necessary to work with the raw material. A grain of wheat is a living material, and its quality varies according to a multitude of factors. Challenges are therefore present at each stage of the value chain. The use of artificial intelligence techniques offers solutions and new perspectives to meet the different objectives of the miller. A literature review of artificial intelligence techniques developed at each stage of the value chain surrounding the issues of quality and yield is conducted. An analysis of a large number of variables, including process factors, process parameters and wheat grain quality from data collected on the value chain enables the development and training of artificial intelligence models. From these models, it is possible to develop decision support tools and optimize the wheat flour milling process. Several major research directions, other than constant quality, are to be studied to optimize the process and move towards a smart mill. This includes energy savings, resource optimization and mill performance.
| [1] | Déragon F (2016) La ferme biologique, un espace d'éducation relative à l'éco-alimentation et de construction du rapport à la terre[Thèse de Maitrise]: Université du Québec à Montréal. |
| [2] |
Hughner RS, McDonagh P, Prothero A, et al. (2007) Who are organic food consumers? A compilation and review of why people purchase organic food. J Consum Behav 6: 94–110. https://doi.org/10.1002/cb.210 doi: 10.1002/cb.210
|
| [3] | Willer H, Trávníček J, Meier C, et al. (2021) The World of Organic Agriculture Statistics and Emerging Trends 2021 Research Institute of Organic Agriculture FiBL, Frick, and IFOAM—Organics International, Bonn. |
| [4] |
Cappelli A, Oliva N, Cini E (2020) Stone milling versus roller milling: A systematic review of the effects on wheat flour quality, dough rheology, and bread characteristics. Trends Food Sci Technol 97: 147–155. https://doi.org/10.1016/j.tifs.2020.01.008 doi: 10.1016/j.tifs.2020.01.008
|
| [5] |
Doblado-Maldonado AF, Pike OA, Sweley JC, et al. (2012) Key issues and challenges in whole wheat flour milling and storage. J Cereal Sci 56: 119–126. https://doi.org/10.1016/j.jcs.2012.02.015 doi: 10.1016/j.jcs.2012.02.015
|
| [6] | Campbell GM (2007) Chapter 7—Roller milling of wheat. In: Salman AD, Ghadiri M, Hounslow MJ, Handbook of Powder Technology, Elsevier Science B.V., 383–419. https://doi.org/10.1016/S0167-3785(07)12010-8 |
| [7] | Boudreau A, Ménard G (1992) Le Blé: éléments fondamentaux et transformation. |
| [8] |
Mateos-Salvador F, Sadhukhan J, Campbell GM (2011) The normalised Kumaraswamy breakage function: A simple model for wheat roller milling. Powder Technol 208: 144–157. https://doi.org/10.1016/j.powtec.2010.12.013 doi: 10.1016/j.powtec.2010.12.013
|
| [9] | FAO (1985) Standard for wheat flour. |
| [10] | IAOM (2018) Fundamentals of Flour Milling. Lenexa: International Association of Operative Millers, 422. |
| [11] | Steffan P (2012) An optimization model: Minimizing flour millers' costs of production by blending wheat and additives[Thèse de Maitrise]: Kansas State University. |
| [12] |
Vrček Ⅳ, Čepo DV, Rašić D, et al. (2014) A comparison of the nutritional value and food safety of organically and conventionally produced wheat flours. Food Chem 143: 522–529. https://doi.org/10.1016/j.foodchem.2013.08.022 doi: 10.1016/j.foodchem.2013.08.022
|
| [13] |
Borghi B, Giordani G, Corbellini M, et al. (1995) Influence of crop rotation, manure and fertilizers on bread making quality of wheat (Triticum aestivum L.). Eur J Agron 4: 37–45. https://doi.org/10.1016/S1161-0301(14)80015-4 doi: 10.1016/S1161-0301(14)80015-4
|
| [14] |
Johansson E, Svensson G (1998) Variation in bread-making quality: Effects of weather parameters on protein concentration and quality in some Swedish wheat cultivars grown during the period 1975–1996. J Sci Food Agric 78: 109–118. https://doi.org/10.1002/(SICI)1097-0010(199809)78:1%3C109::AID-JSFA92%3E3.0.CO; 2-0 doi: 10.1002/(SICI)1097-0010(199809)78:1<109::AID-JSFA92>3.0.CO;2-0
|
| [15] |
Triboi E, Abad A, Michelena A, et al. (2000) Environmental effects on the quality of two wheat genotypes: 1. quantitative and qualitative variation of storage proteins. Eur J Agron 13: 47–64. https://doi.org/10.1016/S1161-0301(00)00059-9 doi: 10.1016/S1161-0301(00)00059-9
|
| [16] |
Köksal G, Batmaz İ, Testik MC (2011) A review of data mining applications for quality improvement in manufacturing industry. Expert Syst Appl 38: 13448–13467. https://doi.org/10.1016/j.eswa.2011.04.063 doi: 10.1016/j.eswa.2011.04.063
|
| [17] |
Misra NN, Dixit Y, Al-Mallahi A, et al. (2020) IoT, big data and artificial intelligence in agriculture and food industry. IEEE Int Things J 9: 6305–6324. https://doi.org/10.1109/JIOT.2020.2998584 doi: 10.1109/JIOT.2020.2998584
|
| [18] | Kelleher JD (2019) Deep Learning (MIT Press Essential Knowledge series). The MIT Press, Illustrated edition, 296. |
| [19] |
Fisher OJ, Watson NJ, Escrig JE, et al. (2020) Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems. Comput Chem Eng 140: 106881. https://doi.org/10.1016/j.compchemeng.2020.106881 doi: 10.1016/j.compchemeng.2020.106881
|
| [20] |
Branlard G, Dardevet M, Saccomano R, et al. (2001) Genetic diversity of wheat storage proteins and bread wheat quality. Euphytica 119: 59–67. https://doi.org/10.1023/A:1017586220359 doi: 10.1023/A:1017586220359
|
| [21] |
Maphosa L, Langridge P, Taylor H, et al. (2014) Genetic control of grain yield and grain physical characteristics in a bread wheat population grown under a range of environmental conditions. Theor Appl Genet 127: 1607–1624. https://doi.org/10.1007/s00122-014-2322-y doi: 10.1007/s00122-014-2322-y
|
| [22] |
Wrigley CW, Blumenthal C, Gras PW, et al. (1994) Temperature variation during grain filling and changes in wheat-grain quality. Funct Plant Biol 21: 875–885. https://doi.org/10.1071/PP9940875 doi: 10.1071/PP9940875
|
| [23] |
Mefleh M, Conte P, Fadda C, et al. (2020) From seed to bread: Variation in quality in a set of old durum wheat cultivars. J Sci Food Agric 100: 4066–4074. https://doi.org/10.1002/jsfa.9745 doi: 10.1002/jsfa.9745
|
| [24] |
Aydin N, Sayaslan A, Sönmez M, et al. (2020) Wheat flour milling yield estimation based on wheat kernel physical properties using artificial neural networks. Int J Intell Syst Appl Eng 8: 78–83. https://doi.org/10.18201/ijisae.2020261588 doi: 10.18201/ijisae.2020261588
|
| [25] |
Dowell FE, Maghirang EB, Xie F, et al. (2006) Predicting wheat quality characteristics and functionality using near-infrared spectroscopy. Cereal Chem 83: 529–536. https://doi.org/10.1094/CC-83-0529 doi: 10.1094/CC-83-0529
|
| [26] |
Caporaso N, Whitworth MB, Fisk ID (2018) Near-Infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains. Appl Spectrosc Rev 53: 667–687. https://doi.org/10.1080/05704928.2018.1425214 doi: 10.1080/05704928.2018.1425214
|
| [27] |
Assadzadeh S, Walker CK, McDonald LS, et al. (2022) Prediction of milling yield in wheat with the use of spectral, colour, shape, and morphological features. Biosyst Eng 214: 28–41. https://doi.org/10.1016/j.biosystemseng.2021.12.005 doi: 10.1016/j.biosystemseng.2021.12.005
|
| [28] |
Delwiche SR, Souza EJ, Kim MS (2013) Limitations of single kernel near-infrared hyperspectral imaging of soft wheat for milling quality. Biosyst Eng 115: 260–273. https://doi.org/10.1016/j.biosystemseng.2013.03.015 doi: 10.1016/j.biosystemseng.2013.03.015
|
| [29] |
Unlersen MF, Sonmez ME, Aslan MF, et al. (2022) CNN–SVM hybrid model for varietal classification of wheat based on bulk samples. Eur Food Res Technol 248: 2043–2052. https://doi.org/10.1007/s00217-022-04029-4 doi: 10.1007/s00217-022-04029-4
|
| [30] |
Zhu J, Li H, Rao Z, et al. (2023) Identification of slightly sprouted wheat kernels using hyperspectral imaging technology and different deep convolutional neural networks. Food Control 143: 109291. https://doi.org/10.1016/j.foodcont.2022.109291 doi: 10.1016/j.foodcont.2022.109291
|
| [31] |
Gucbilmez CM, Sahin M, Akcacik AG, et al. (2019) Evaluation of GlutoPeak test for prediction of bread wheat flour quality, rheological properties and baking performance. J Cereal Sci 90: 102827. https://doi.org/10.1016/j.jcs.2019.102827 doi: 10.1016/j.jcs.2019.102827
|
| [32] |
Sisman CB, Ergin AS (2011) The effects of different storage buildings on wheat quality. J Appl Sci 11: 2613–2619. https://doi.org/10.3923/jas.2011.2613.2619 doi: 10.3923/jas.2011.2613.2619
|
| [33] |
Jia C, Sun D-W, Cao C (2001) Computer simulation of temperature changes in a wheat storage bin. J Stored Prod Res 37: 165–177. https://doi.org/10.1016/S0022-474X(00)00017-5 doi: 10.1016/S0022-474X(00)00017-5
|
| [34] |
Kibar H (2015) Influence of storage conditions on the quality properties of wheat varieties. J Stored Prod Res 62: 8–15. https://doi.org/10.1016/j.jspr.2015.03.001 doi: 10.1016/j.jspr.2015.03.001
|
| [35] | Posner E, Deyoe C (1986) Changes in milling properties of newly harvested hard wheat during storage. Cereal Chem 63: 451–456. |
| [36] |
Campbell GM, Sharp C, Wall K, et al. (2012) Modelling wheat breakage during roller milling using the Double Normalised Kumaraswamy Breakage Function: Effects of kernel shape and hardness. J Cereal Sci 55: 415–425. https://doi.org/10.1016/j.jcs.2012.02.002 doi: 10.1016/j.jcs.2012.02.002
|
| [37] | Posner ES, Hibbs AN (2005) Wheat flour milling: American Association of Cereal Chemists, Inc. https://doi.org/10.1094/1891127403 |
| [38] | Yoon BS, Brorsen BW, Lyford CP (2002) Value of increasing kernel uniformity. J Agric Resour Econ 27: 481–494. |
| [39] |
González-Torralba J, Arazuri S, Jarén C, et al. (2013) Influence of temperature and r.h. during storage on wheat bread making quality. J Stored Prod Res 55: 134–144. https://doi.org/10.1016/j.jspr.2013.10.002 doi: 10.1016/j.jspr.2013.10.002
|
| [40] | Catterall P (1998) Flour milling. In: Cauvain SP, Young LS, Technology of Breadmaking, Boston, MA: Springer US., 296–329. https://doi.org/10.1007/978-1-4615-2199-0_12 |
| [41] |
Tibola CS, Fernandes JMC, Guarienti EM (2016) Effect of cleaning, sorting and milling processes in wheat mycotoxin content. Food Control 60: 174–179. https://doi.org/10.1016/j.foodcont.2015.07.031 doi: 10.1016/j.foodcont.2015.07.031
|
| [42] |
Magyar Z, Véha A, Pepó P, et al. (2019) Wheat cleaning and milling technologies to reduce DON toxin contamination. Acta Agraria Debreceniensis 0: 89–95. https://doi.org/10.34101/actaagrar/2/3684 doi: 10.34101/actaagrar/2/3684
|
| [43] | Bettge A, Rubenthaler G, Pomeranz Y (1989) Air‐aspirated wheat cleaning in grading and in separation by functional properties. Cereal Chem 66: 15–18. |
| [44] |
Hayta M, Çakmakli Ü (2001) Optimization of wheat blending to produce breadmaking flour. J Food Process Eng 24: 179–192. https://doi.org/10.1111/j.1745-4530.2001.tb00539.x doi: 10.1111/j.1745-4530.2001.tb00539.x
|
| [45] | Elevi B, Öztürk H, Kacir Z (2017) Optimization of wheat and flour blending for cost minimization by using mathematical modelling. Kastamonu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 264–272. |
| [46] |
Kweon M, Martin R, Souza E (2009) Effect of tempering conditions on milling performance and flour functionality. Cereal Chem 86: 12–17. https://doi.org/10.1094/CCHEM-86-1-0012 doi: 10.1094/CCHEM-86-1-0012
|
| [47] |
Warechowska M, Markowska A, Warechowski J, et al. (2016) Effect of tempering moisture of wheat on grinding energy, middlings and flour size distribution, and gluten and dough mixing properties. J Cereal Sci 69: 306–312. https://doi.org/10.1016/j.jcs.2016.04.007 doi: 10.1016/j.jcs.2016.04.007
|
| [48] |
Hook SCW, Bone GT, Fearn T (1982) The conditioning of wheat. The effect of increasing wheat moisture content on the milling performance of uk wheats with reference to wheat texture. J the Sci Food Agric 33: 655–662. https://doi.org/10.1002/jsfa.2740330711 doi: 10.1002/jsfa.2740330711
|
| [49] |
Doblado-Maldonado AF, Flores RA, Rose DJ (2013) Low moisture milling of wheat for quality testing of wholegrain flour. J Cereal Sci 58: 420–423. https://doi.org/10.1016/j.jcs.2013.08.006 doi: 10.1016/j.jcs.2013.08.006
|
| [50] |
Noort MWJ, van Haaster D, Hemery Y, et al. (2010) The effect of particle size of wheat bran fractions on bread quality—Evidence for fibre-protein interactions. J Cereal Sci 52: 59–64. https://doi.org/10.1016/j.jcs.2010.03.003 doi: 10.1016/j.jcs.2010.03.003
|
| [51] |
Cappelli A, Guerrini L, Parenti A, et al. (2020) Effects of wheat tempering and stone rotational speed on particle size, dough rheology and bread characteristics for a stone-milled weak flour. J Cereal Sci 91: 102879. https://doi.org/10.1016/j.jcs.2019.102879 doi: 10.1016/j.jcs.2019.102879
|
| [52] |
Parrenin L, Danjou C, Agard B, et al. (2022) Predicting the moisture content of organic wheat in the first stage of tempering. IFAC-PapersOnLine 55: 678–683. https://doi.org/10.1016/j.ifacol.2022.09.484 doi: 10.1016/j.ifacol.2022.09.484
|
| [53] |
Fang C, Campbell GM (2003) On predicting roller milling performance Ⅴ: Effect of moisture content on the particle size distribution from first break milling of wheat. J Cereal Sci 37: 31–41. https://doi.org/10.1006/jcrs.2002.0476 doi: 10.1006/jcrs.2002.0476
|
| [54] |
Lin S, Gao J, Jin X, et al. (2020) Whole-wheat flour particle size influences dough properties, bread structure and in vitro starch digestibility. Food Funct 11: 3610–3620. https://doi.org/10.1039/C9FO02587A doi: 10.1039/C9FO02587A
|
| [55] |
Tóth Á, Prokisch J, Sipos P, et al. (2006) Effects of particle size on the quality of winter wheat flour, with a special focus on macro‐ and microelement concentration. Commun Soil Sci Plant Anal 37: 2659–2672. https://doi.org/10.1080/00103620600823117 doi: 10.1080/00103620600823117
|
| [56] | Pagani MA, Marti A, Bottega G (2014) Chapter 2 : Wheat milling and flour quality evaluation. In: Zhou W, Hui YH, De Leyn I, et al., Bakery Products Science and Technology, John Wiley & Sons, Ltd. 20–49. https://doi.org/10.1002/9781118792001.ch2 |
| [57] |
Campbell GM, Webb C (2001) On predicting roller milling performance: Part Ⅰ. The breakage equation. Powder Technol 115: 234–242. https://doi.org/10.1016/S0032-5910(00)00348-X doi: 10.1016/S0032-5910(00)00348-X
|
| [58] |
Campbell GM, Bunn PJ, Webb C, et al. (2001) On predicting roller milling performance: Part Ⅱ. The breakage function. Powder Technol 115: 243–255. https://doi.org/10.1016/S0032-5910(00)00349-1 doi: 10.1016/S0032-5910(00)00349-1
|
| [59] |
Cappelli A, Mugnaini M, Cini E (2020) Improving roller milling technology using the break, sizing, and reduction systems for flour differentiation. Lwt-Food Sci Technol 133: 110067. https://doi.org/10.1016/j.lwt.2020.110067 doi: 10.1016/j.lwt.2020.110067
|
| [60] |
Dal-Pastro F, Facco P, Bezzo F, et al. (2015) Data-based multivariate modeling of a grain comminution process. Comput Aided Chem Eng 37: 2219–2224. https://doi.org/10.1016/B978-0-444-63576-1.50064-9 doi: 10.1016/B978-0-444-63576-1.50064-9
|
| [61] |
Dal-Pastro F, Facco P, Bezzo F, et al. (2016) Data-driven modeling of milling and sieving operations in a wheat milling process. Food Bioprod Process 99: 99–108. https://doi.org/10.1016/j.fbp.2016.04.007 doi: 10.1016/j.fbp.2016.04.007
|
| [62] |
Fang Q, Biby G, Haque E, et al. (1998) Neural network modeling of physical properties of ground wheat. Cereal Chem 75: 251–253. https://doi.org/10.1094/CCHEM.1998.75.2.251 doi: 10.1094/CCHEM.1998.75.2.251
|
| [63] | Stefan E-M, Voicu G, Constantin G, et al. (2018) Effects of wheat seeds characteristics on roller milling process—a review. |
| [64] |
Fang C, Campbell GM (2003) On predicting roller milling performance Ⅳ: Effect of roll disposition on the particle size distribution from first break milling of wheat. J Cereal Sci 37: 21–29. https://doi.org/10.1006/jcrs.2002.0475 doi: 10.1006/jcrs.2002.0475
|
| [65] |
Campbell GM, Fang C, Muhamad Ⅱ (2007) On predicting roller milling performance Ⅵ: Effect of kernel hardness and shape on the particle size distribution from first break milling of wheat. Food Bioprod Process 85: 7–23. https://doi.org/10.1205/fbp06005 doi: 10.1205/fbp06005
|
| [66] |
Hook SCW, Bone GT, Fearn T (1982) The conditioning of wheat. The influence of roll temperature in the bühler laboratory mill on milling parameters. J Sci Food Agric 33: 639–644. https://doi.org/10.1002/jsfa.2740330709 doi: 10.1002/jsfa.2740330709
|
| [67] |
Fang Q, Haque E, Spillman CK, et al. (1998) Energy requirements for size reduction of wheat using a roller mill. Trans ASAE 41: 1713–1720. https://doi.org/10.13031/2013.17314 doi: 10.13031/2013.17314
|
| [68] |
Kalitsis J, Minasny B, Quail K, et al. (2021) Application of response surface methodology for optimization of wheat flour milling process. Cereal Chem 98: 1215–1226. https://doi.org/10.1002/cche.10474 doi: 10.1002/cche.10474
|
| [69] |
Oliver J, Blakeney A, Allen H (1993) The colour of flour streams as related to ash and pigment contents. J Cereal Sci 17: 169–182. https://doi.org/10.1006/jcrs.1993.1017 doi: 10.1006/jcrs.1993.1017
|
| [70] |
Banu I, Stoenescu G, Ionescu V, et al. (2011) Estimation of the baking quality of wheat flours based on rheological parameters of the Mixolab Curve. Czech J Food Sci 29: 35–44. https://doi.org/10.17221/40/2009-CJFS doi: 10.17221/40/2009-CJFS
|
| [71] |
Dowell FE, Maghirang EB, Pierce RO, et al. (2008) Relationship of bread quality to kernel, flour, and dough properties. Cereal Chem 85: 82–91. https://doi.org/10.1094/CCHEM-85-1-0082 doi: 10.1094/CCHEM-85-1-0082
|
| [72] |
Parenti O, Guerrini L, Zanoni B (2020) Techniques and technologies for the breadmaking process with unrefined wheat flours. Trends Food Sci Technol 99: 152–166. https://doi.org/10.1016/j.tifs.2020.02.034 doi: 10.1016/j.tifs.2020.02.034
|
| [73] |
Parenti O, Guerrini L, Cavallini B, et al. (2020) Breadmaking with an old wholewheat flour: Optimization of ingredients to improve bread quality. Lwt-Food Sci Technol 121: 108980. https://doi.org/10.1016/j.lwt.2019.108980 doi: 10.1016/j.lwt.2019.108980
|
| [74] |
Torbica A, Blazek KM, Belovic M, et al. (2019) Quality prediction of bread made from composite flours using different parameters of empirical rheology. J Cereal Sci 89: 102812. https://doi.org/10.1016/j.jcs.2019.102812 doi: 10.1016/j.jcs.2019.102812
|
| [75] |
Cappelli A, Guerrini L, Cini E, et al. (2019) Improving whole wheat dough tenacity and extensibility: A new kneading process. J Cereal Sci 90: 102852. https://doi.org/10.1016/j.jcs.2019.102852 doi: 10.1016/j.jcs.2019.102852
|
| [76] |
Ktenioudaki A, Butler F, Gallagher E (2010) Rheological properties and baking quality of wheat varieties from various geographical regions. J Cereal Sci 51: 402–408. https://doi.org/10.1016/j.jcs.2010.02.009 doi: 10.1016/j.jcs.2010.02.009
|
| [77] |
Oliver JR, Allen HM (1992) The prediction of bread baking performance using the farinograph and extensograph. J Cereal Sci 15: 79–89. https://doi.org/10.1016/S0733-5210(09)80058-1 doi: 10.1016/S0733-5210(09)80058-1
|
| [78] | Abbasi H, Emam-Djomeh Z, Seyedin S (2011) Application of artificial neural network and genetic algorithm for predicting three important parameters in bakery industries. Int J Agric Sci Res 2: 51–64. |
| [79] |
Różyło R, Laskowski J (2011) Predicting bread quality (bread loaf volume and crumb texture). Pol J Food Nutr Sci 61: 61–67. https://doi.org/10.2478/v10222-011-0006-8 doi: 10.2478/v10222-011-0006-8
|
| [80] | Gómez Sarduy J, Viego P, Diaz Torres Y, et al. (2018) A new energy performance indicator for energy management system of a wheat mill plant. Int J Energy Econ Policy 8: 324–330. |
Figures(5) / Tables(6)
Loïc Parrenin, Christophe Danjou, Bruno Agard, Robert Beauchemin. Future trends in organic flour milling: the role of AI[J]. AIMS Agriculture and Food, 2023, 8(1): 48-77. doi: 10.3934/agrfood.2023003
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