Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging


Autoři: Mathias Wiegmann aff001;  Andreas Backhaus aff002;  Udo Seiffert aff002;  William T. B. Thomas aff003;  Andrew J. Flavell aff004;  Klaus Pillen aff001;  Andreas Maurer aff001
Působiště autorů: Martin Luther University Halle-Wittenberg (MLU), Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Halle, Germany aff001;  Fraunhofer Institute for Factory Operation and Automation (IFF), Magdeburg, Germany aff002;  The James Hutton Institute (JHI), Invergowrie, Dundee, Scotland, United Kingdom aff003;  University of Dundee at JHI, School of Life Sciences, Invergowrie, Dundee, Scotland, United Kingdom aff004
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224491

Souhrn

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts.

Klíčová slova:

Barley – Cereal crops – Chemical analysis – Near-infrared spectroscopy – Neural networks – Nutrients – Plant breeding


Zdroje

1. Kearney J. Food consumption trends and drivers. Philos Trans R Soc Lond, B, Biol Sci. 2010; 365: 2793–2807. doi: 10.1098/rstb.2010.0149 20713385

2. OECD-FAO Agricultural outlook 2017–2026. Special focus: Southeast Asia. Paris: OECD Publishing; 2017.

3. McKevith B. Nutritional aspects of cereals. Nutr Bull. 2004; 29: 111–142. doi: 10.1111/j.1467-3010.2004.00418.x

4. Elleuch M, Bedigian D, Roiseux O, Besbes S, Blecker C, Attia H. Dietary fibre and fibre-rich by-products of food processing. Characterisation, technological functionality and commercial applications: A review. Food Chem. 2011; 124: 411–421. doi: 10.1016/j.foodchem.2010.06.077

5. Gaudichon CC. Protein quality in human nutrition and contribution of cereals to protein intake. Nantes, France; 2015.

6. Wrigley CW, Miskelly D, Batey IL, editors. Cereal grains. Assessing and managing quality. Oxford: Woodhead Publishing; 2017.

7. Mondal A, Datta AK. Bread baking–A review. Journal of Food Engineering. 2008; 86: 465–474. doi: 10.1016/j.jfoodeng.2007.11.014

8. Zhou MX. Barley Production and Consumption. In: Zhang G, Li C, editors. Genetics and Improvement of Barley Malt Quality. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg; 2010. pp. 1–17.

9. Black JL. Variation in nutritional value of cereal grains across livestock species. Proceedings of the Australian Poultry Science Symposium (2001). Sydney; 2001.

10. Verstegen MWA, van der Poel AFB. Grains in nutrition for farm animals. XXV Curso de Especializacion FEDNA 5–6 Nov 2009. Madrid; 2008.

11. FAOSTAT. FAOSTAT; 2017. Available: http://www.fao.org/faostat/en/#home. Accessed 28 September 2017.

12. Fox GP, Panozzo JF, Li CD, Lance RCM, Inkerman PA, Henry RJ. Molecular basis of barley quality. Aust. J. Agric. Res. 2003; 54: 1081. doi: 10.1071/AR02237

13. Baik B-K, Ullrich SE. Barley for food: Characteristics, improvement, and renewed interest. J. Cereal Sci. 2008; 48: 233–242. doi: 10.1016/j.jcs.2008.02.002

14. Gupta M, Abu-Ghannam N, Gallaghar E. Barley for Brewing. Characteristic Changes during Malting, Brewing and Applications of its By-Products. Comprehensive Reviews in Food Science and Food Safety. 2010; 9: 318–328. doi: 10.1111/j.1541-4337.2010.00112.x

15. Fox GP. Chemical Composition in Barley Grains and Malt Quality. In: Zhang G, Li C, editors. Genetics and Improvement of Barley Malt Quality. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg; 2010. pp. 63–98.

16. White PJ, Broadley MR. Biofortification of crops with seven mineral elements often lacking in human diets—iron, zinc, copper, calcium, magnesium, selenium and iodine. New Phytol. 2009; 182: 49–84. doi: 10.1111/j.1469-8137.2008.02738.x 19192191

17. Carvalho SMP, Vasconcelos MW. Producing more with less. Strategies and novel technologies for plant-based food biofortification. Food Research International. 2013; 54: 961–971. doi: 10.1016/j.foodres.2012.12.021

18. Wu G, Fanzo J, Miller DD, Pingali P, Post M, Steiner JL, et al. Production and supply of high-quality food protein for human consumption. Sustainability, challenges, and innovations. Ann N Y Acad Sci. 2014; 1321: 1–19. doi: 10.1111/nyas.12500 25123207

19. Foley WJ, McIlwee A, Lawler I, Aragones L, Woolnough AP, Berding N. Ecological applications of near infrared reflectance spectroscopy—a tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance. Oecologia. 1998; 116: 293–305. doi: 10.1007/s004420050591 28308060

20. Stuth J, Jama A, Tolleson D. Direct and indirect means of predicting forage quality through near infrared reflectance spectroscopy. Field Crops Research. 2003; 84: 45–56. doi: 10.1016/S0378-4290(03)00140-0

21. Spielbauer G, Armstrong P, Baier JW, Allen WB, Richardson K, Shen B, et al. High-Throughput Near-Infrared Reflectance Spectroscopy for Predicting Quantitative and Qualitative Composition Phenotypes of Individual Maize Kernels. Cereal Chemistry Journal. 2009; 86: 556–564. doi: 10.1094/CCHEM-86-5-0556

22. Osborne BG. Applications of near Infrared Spectroscopy in Quality Screening of Early-Generation Material in Cereal Breeding Programmes. Journal of Near Infrared Spectroscopy. 2006; 14: 93–101. doi: 10.1255/jnirs.595

23. Diepenbrock CH, Gore MA. Closing the Divide between Human Nutrition and Plant Breeding. Crop Science. 2015; 55: 1437. doi: 10.2135/cropsci2014.08.0555

24. Montes JM, Melchinger AE, Reif JC. Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci. 2007; 12: 433–436. doi: 10.1016/j.tplants.2007.08.006 17719833

25. Pojić MM, Mastilović JS. Near Infrared Spectroscopy—Advanced Analytical Tool in Wheat Breeding, Trade, and Processing. Food Bioprocess Technol. 2013; 6: 330–352. doi: 10.1007/s11947-012-0917-3

26. Cen H, He Y. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food Science & Technology. 2007; 18: 72–83. doi: 10.1016/j.tifs.2006.09.003

27. ElMasry G, Sun D-W. Principles of Hyperspectral Imaging Technology. In: Sun D-W, editor. Hyperspectral imaging for food quality analysis and control. 1st ed. London: Academic; 2010. pp. 3–43.

28. Park B, Lu R. Hyperspectral Imaging Technology in Food and Agriculture. 1st ed. New York, NY: Springer New York; 2015.

29. Chao K, Chen YR, Hruschka WR, Park B. Chicken heart disease characterization by multi-spectral imaging. Applied Engineering in Agriculture. 2001: 99–106.

30. Roberts CA, Workman J, Reeves JB, editors. Near-infrared spectroscopy in agriculture. Madison, Wis.: American Society of Agronomy; Crop Science Society of America; Soil Science Society of America; 2004.

31. Amigo JM, Babamoradi H, Elcoroaristizabal S. Hyperspectral image analysis. A tutorial. Anal Chim Acta. 2015; 896: 34–51. doi: 10.1016/j.aca.2015.09.030 26481986

32. Lombi E, Smith E, Hansen TH, Paterson D, Jonge MD de, Howard DL, et al. Megapixel imaging of (micro)nutrients in mature barley grains. J. Exp. Bot. 2011; 62: 273–282. doi: 10.1093/jxb/erq270 20819790

33. Esteve Agelet L, Hurburgh CR. Limitations and current applications of Near Infrared Spectroscopy for single seed analysis. Talanta. 2014; 121: 288–299. doi: 10.1016/j.talanta.2013.12.038 24607140

34. Caporaso N, Whitworth MB, Fisk ID. Protein content prediction in single wheat kernels using hyperspectral imaging. Food Chem. 2018; 240: 32–42. doi: 10.1016/j.foodchem.2017.07.048 28946278

35. Batten GD. Plant analysis using near infrared reflectance spectroscopy. The potential and the limitations. Aust. J. Exp. Agric. 1998; 38: 697. doi: 10.1071/EA97146

36. Li L, Zhang Q, Huang D. A review of imaging techniques for plant phenotyping. Sensors (Basel). 2014; 14: 20078–20111. doi: 10.3390/s141120078 25347588

37. Cao N. Calibration optimization and efficiency in near infrared spectroscopy. Dissertation, Iowa State University. 2013. Available: https://lib.dr.iastate.edu/etd/13199/?utm_source=lib.dr.iastate.edu%2Fetd%2F13199&utm_medium=PDF&utm_campaign=PDFCoverPages.

38. Wiegmann M, Maurer A, Pham A, March TJ, Al-Abdallat A, Thomas WTB, et al. Barley yield formation under abiotic stress depends on the interplay between flowering time genes and environmental cues. Sci Rep. 2019; 9: 6397. doi: 10.1038/s41598-019-42673-1 31024028

39. Maurer A, Draba V, Jiang Y, Schnaithmann F, Sharma R, Schumann E, et al. Modelling the genetic architecture of flowering time control in barley through nested association mapping. BMC Genomics. 2015; 16: 290. doi: 10.1186/s12864-015-1459-7 25887319

40. Wiegmann M, Thomas WTB, Bull HJ, Flavell AJ, Zeyner A, Peiter E, et al. “Wild barley serves as a source for biofortification of barley grains”. Plant Sci. 2019; 283: 83–94. doi: 10.1016/j.plantsci.2018.12.030 31128718

41. Martinetz TM, Berkovich SG, Schulten KJ.;Neural-gas' network for vector quantization and its application to time-series prediction. IEEE Trans Neural Netw. 1993; 4: 558–569. doi: 10.1109/72.238311 18267757

42. Moody J, Darken CJ. Fast Learning in Networks of Locally-Tuned Processing Units. Neural Computation. 1989; 1: 281–294. doi: 10.1162/neco.1989.1.2.281

43. Wold S, Sjöström M, Eriksson L. PLS-regression. A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems. 2001; 58: 109–130. doi: 10.1016/S0169-7439(01)00155-1

44. Menz P, Backhaus A, Seiffert U. Transfer Learning for transferring machine-learning based models among various hyperspectral sensors. ESANN 2019 proceedings—Computational Intelligence and Machine Learning. European Symposium on Artificial Neural Networks. 2019.

45. McClelland JL, Rumelhart DE. An interactive activation model of context effects in letter perception. I. An account of basic findings. Psychological Review. 1981; 88: 375–407. doi: 10.1037/0033-295X.88.5.375

46. SAS. SAS. Cary, North Carolina, USA: SAS Institute inc.; 2013.

47. Tukey JW. Comparing individual means in the analysis of variance. Biometrics. 1949; 5: 99–114. 18151955

48. Fisher RA. Frequency Distribution of the Values of the Correlation Coefficient in Samples from an Indefinitely Large Population. Biometrika. 1915; 10: 507. doi: 10.2307/2331838

49. Fligner MA, Killeen TJ. Distribution-Free Two-Sample Tests for Scale. Journal of the American Statistical Association. 1976; 71: 210. doi: 10.2307/2285771

50. R Development Core Team. R. A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2008.

51. Wickham Hadley. ggplot2. Elegant Graphics for Data Analysis. New York, USA: Springer-Verlag; 2009.

52. Esteve Agelet L, Hurburgh CR. A tutorial on near infrared spectroscopy and Its calibration. Critical Reviews in Analytical Chemistry. 2010; 40: 246–260. doi: 10.1080/10408347.2010.515468

53. Balabin RM, Lomakina EI, Safieva RZ. Neural network (ANN) approach to biodiesel analysis. Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy. Fuel. 2011; 90: 2007–2015. doi: 10.1016/j.fuel.2010.11.038

54. Chen Q, Guo Z, Zhao J, Ouyang Q. Comparisons of different regressions tools in measurement of antioxidant activity in green tea using near infrared spectroscopy. J Pharm Biomed Anal. 2012; 60: 92–97. doi: 10.1016/j.jpba.2011.10.020 22104136

55. Morellos A, Pantazi X-E, Moshou D, Alexandridis T, Whetton R, Tziotzios G, et al. Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosystems Engineering. 2016; 152: 104–116. doi: 10.1016/j.biosystemseng.2016.04.018

56. Mouazen AM, Kuang B, Baerdemaeker J de, Ramon H. Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma. 2010; 158: 23–31. doi: 10.1016/j.geoderma.2010.03.001

57. Rady A, Guyer D, Lu R. Evaluation of Sugar Content of Potatoes using Hyperspectral Imaging. Food Bioprocess Technol. 2015; 8: 995–1010. doi: 10.1007/s11947-014-1461-0

58. Leardi R. Application of genetic algorithm-PLS for feature selection in spectral data sets. J. Chemometrics. 2000; 14: 643–655. doi: 10.1002/1099-128X(200009/12)14:5/6<643::AID-CEM621>3.0.CO;2-E

59. Mehmood T, Liland KH, Snipen L, Sæbø S. A review of variable selection methods in Partial Least Squares Regression. Chemometrics and Intelligent Laboratory Systems. 2012; 118: 62–69. doi: 10.1016/j.chemolab.2012.07.010

60. Hansen PM, Schjoerring JK. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment. 2003; 86: 542–553. doi: 10.1016/S0034-4257(03)00131-7

61. Kamruzzaman M, ElMasry G, Sun D-W, Allen P. Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Anal Chim Acta. 2012; 714: 57–67. doi: 10.1016/j.aca.2011.11.037 22244137

62. Kooistra L, Wehrens R, Leuven RSEW, Buydens LMC. Possibilities of visible–near-infrared spectroscopy for the assessment of soil contamination in river floodplains. Anal Chim Acta. 2001; 446: 97–105. doi: 10.1016/S0003-2670(01)01265-X

63. Velasco L, Möllers C. Nondestructive assessment of protein content in single seeds of rapeseed (Brassica napus L.) by near-infrared reflectance spectroscopy. Euphytica. 2002; 123: 89–93. doi: 10.1023/A:1014452700465

64. Lin C, Chen X, Jian L, Shi C, Jin X, Zhang G. Determination of grain protein content by near-infrared spectrometry and multivariate calibration in barley. Food Chem. 2014; 162: 10–15. doi: 10.1016/j.foodchem.2014.04.056 24874350

65. Lorber A, Kowalski BR. The effect of interferences and calbiration design on accuracy. Implications for sensor and sample selection. J. Chemometrics. 1988; 2: 67–79. doi: 10.1002/cem.1180020108

66. Isaksson T, Næs T. Selection of Samples for Calibration in Near-Infrared Spectroscopy. Part II. Selection Based on Spectral Measurements. Appl Spectrosc. 1990; 44: 1152–1158. doi: 10.1366/0003702904086533

67. Ferré J, Rius FX. Selection of the best calibration sample subset for multivariate regression. Anal Chem. 1996; 68: 1565–1571. doi: 10.1021/ac950482a 21619122

68. Shetty N, Rinnan Å, Gislum R. Selection of representative calibration sample sets for near-infrared reflectance spectroscopy to predict nitrogen concentration in grasses. Chemometrics and Intelligent Laboratory Systems. 2012; 111: 59–65. doi: 10.1016/j.chemolab.2011.11.013

69. Falconer DS, Mackay TFC. Introduction to quantitative genetics. 4th ed. Harlow: Pearson, Prentice Hall; 2009.

70. Lehermeier C, Schön C-C, Los Campos G de. Assessment of Genetic Heterogeneity in Structured Plant Populations Using Multivariate Whole-Genome Regression Models. Genetics. 2015; 201: 323–337. doi: 10.1534/genetics.115.177394 26122758

71. Fu Y-B. Understanding crop genetic diversity under modern plant breeding. Theor. Appl. Genet. 2015; 128: 2131–2142. doi: 10.1007/s00122-015-2585-y 26246331

72. Schmid B. Phenotypic variation in plants. Evolutionary Trends in Plants. 1992: 46–60.

73. Bernardo R. Breeding for quantitative traits in plants. 2nd ed. Woddbury Minn.: Stemma Press; 2010.

74. León L, Garrido-Varo A, Downey G. Parent and harvest year effects on near-infrared reflectance spectroscopic analysis of olive (Olea europaea L.) fruit traits. J. Agric. Food Chem. 2004; 52: 4957–4962. doi: 10.1021/jf0496853 15291458

75. Roger J-M, Chauchard F, Williams P. Removing the block effects in calibration by means of dynamic orthogonal projection. application to the year effect correction for wheat protein prediction. Journal of Near Infrared Spectroscopy. 2008; 16: 311–315. doi: 10.1255/jnirs.793

76. Shetty N, Gislum R, Jensen AMD, Boelt B. Development of NIR calibration models to assess year-to-year variation in total non-structural carbohydrates in grasses using PLSR. Chemometrics and Intelligent Laboratory Systems. 2012; 111: 34–38. doi: 10.1016/j.chemolab.2011.11.004

77. Sileoni V, van den Berg F, Marconi O, Perretti G, Fantozzi P. Internal and external validation strategies for the evaluation of long-term effects in NIR calibration models. J. Agric. Food Chem. 2011; 59: 1541–1547. doi: 10.1021/jf104439x 21314179

78. Sileoni V, Marconi O, Perretti G, Fantozzi P. Evaluation of different validation strategies and long term effects in NIR calibration models. Food Chem. 2013; 141: 2639–2648. doi: 10.1016/j.foodchem.2013.04.110 23871006

79. Feudale RN, Woody NA, Tan H, Myles AJ, Brown SD, Ferré J. Transfer of multivariate calibration models. A review. Chemometrics and Intelligent Laboratory Systems. 2002; 64: 181–192. doi: 10.1016/S0169-7439(02)00085-0

80. Liu Y, Jiang Q, Fei T, Wang J, Shi T, Guo K, et al. Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes. Remote Sensing. 2014; 6: 4305–4322. doi: 10.3390/rs6054305

81. Verrelst J, Camps-Valls G, Muñoz-Marí J, Rivera JP, Veroustraete F, Clevers JGPW, et al. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties–A review. ISPRS Journal of Photogrammetry and Remote Sensing. 2015; 108: 273–290. doi: 10.1016/j.isprsjprs.2015.05.005

82. Lagacherie P, Baret F, Feret J-B, Madeira Netto J, Robbez-Masson JM. Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements. Remote Sensing of Environment. 2008; 112: 825–835. doi: 10.1016/j.rse.2007.06.014


Článek vyšel v časopise

PLOS One


2019 Číslo 11