Potato Research

, Volume 59, Issue 4, pp 357–374 | Cite as

Prediction of Starch, Soluble Sugars and Amino Acids in Potatoes (Solanum tuberosum L.) Using Hyperspectral Imaging, Dielectric and LF-NMR Methodologies

  • Anders KjærEmail author
  • Glenn Nielsen
  • Søren Stærke
  • Morten Rahr Clausen
  • Merete Edelenbos
  • Bjarke Jørgensen


Handling and processing of potatoes is performed in increasingly large and more automated facilities, and the industry calls for more automated machinery for quality assessment and sorting by concentration of starch, soluble sugars, protein, amino acids etc. of the potato tubers. The present study was designed to evaluate five different scanning methods for their potential use in potato assessment and sorting. Two methods were based on hyperspectral imaging, two were based on dielectric/bio-impedance and one was based on low-field nuclear magnetic resonance. A set of 60 potatoes of 10 different cultivars were simultaneously sampled for analyses of content and scanned by the five different scanning methods. The resulting multivariate dataset was used to estimate the prediction ability of the individual scanning methods on starch-related parameters, selected simple sugars, selected amino acids, conductivity of pressed cell sap and cell sizes. Results showed that most types of spectral analyses had relatively high potential for predicting the starch-related parameters and medium potential for predicting the concentration of the reducing sugars fructose and glucose. Most methods showed medium potential for prediction of several amino acids, including asparagine, which showed particularly promising predictions in the hyperspectral analyses of intact potatoes. The presented screening study enabled us to perform robust choices for the further development and optimization of the methods and instruments for industrial implementation.


Amino acids Dielectric function Hyperspectral imaging LF-NMR Starch Sugars 



The present study was funded by the Innovationsfonden, Denmark [129-2013-5], Newtec Engineering and Aarhus University. The authors wish to extend their gratitude to Bjarne Thiesgaard, AKS and Ruth Madsen, Danespo A/S, for supplying the potato samples and to laboratory technicians Jens Madsen, Nina Eggers, Annette Brandsholm, Karin Henriksen and Elmedina Dervisevic for their efforts in the project.

Supplementary material

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ESM 1 (DOCX 2206 kb)
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ESM 2 (DOCX 25 kb)


  1. Andersen CM, Bro R (2010) Variable selection in regression—a tutorial. J Chemom 24:728–737. doi: 10.1002/cem.1360 CrossRefGoogle Scholar
  2. Ayvaz H, Rodriguez-Saona LE (2015) Application of handheld and portable spectrometers for screening acrylamide content in commercial potato chips. Food Chem 174:154–162. doi: 10.1016/j.foodchem.2014.11.001 CrossRefPubMedGoogle Scholar
  3. Bao JZ, Davis CC, Swicord ML (1994) Microwave dielectric measurements of erythrocyte suspensions. Biophys J 66:2173–2180. doi: 10.1016/S0006-3495(94)81013-6 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Birth GS (1960) A nondestructive technique for detecting internal discolorations in potatoes. Am Potato J 37:53–60CrossRefGoogle Scholar
  5. Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. The Wadsworth and Brooks-Cole statistics-probability series. Taylor & Francis, New YorkGoogle Scholar
  6. Brierley ER, Bonner PLR, Cobb AH (1997) Aspects of amino acid metabolism in stored potato tubers (cv. Pentland dell). Plant Sci 127:17–24. doi: 10.1016/S0168-9452(97)00109-X CrossRefGoogle Scholar
  7. Chen J, Miao Y, Zhanga H, Matsunaga R (2004) Non-destructive determination of carbohydrate content in potatoes using near infrared spectroscopy. J Near Infrared Spectrosc 12:311. doi: 10.1255/jnirs.439 CrossRefGoogle Scholar
  8. Chong IG, Jun CH (2005) Performance of some variable selection methods when multicollinearity is present. Chemom Intell Lab Syst 78:103–112. doi: 10.1016/j.chemolab.2004.12.011 CrossRefGoogle Scholar
  9. Dacal-nieto A, Formella A, Carrion P, Vazquez-Fernandez E, Fernandez-Delgado M (2011) Non-destructive detection of hollow heart in potatoes using hyperspectral imaging. Comp Sci Theory Methods 6855:180–187Google Scholar
  10. Dunlap WC, Makower B (1945) Radio-frequency dielectric properties of dehydrated carrots. Application to moisture determination by electrical methods. J Phys Chem 49:601–622CrossRefPubMedGoogle Scholar
  11. Elbatawi IE (2008) An acoustic impact method to detect hollow heart of potato tubers. Biosyst Eng 100:206–213. doi: 10.1016/j.biosystemseng.2008.02.009 CrossRefGoogle Scholar
  12. Faber K, Kowalski BR (1997) Propagation of measurement errors for the validation of predictions obtained by principal component regression and partial least squares. J Chemom 11:181–238CrossRefGoogle Scholar
  13. Finney EE, Norris KH (1978) X-ray scans for detecting hollow heart in potatoes. Am Potato J 55:95–105CrossRefGoogle Scholar
  14. Gamer M, Lemon J, Fellows I (2012) The irr package: various coefficients of interrater reliability and agreement. R package version 0.84Google Scholar
  15. Guo WC, Nelson SO, Trabelsi S, Kays SJ (2007) 10–1800-MHz dielectric properties of fresh apples during storage. J Food Eng 83:562–569. doi: 10.1016/j.jfoodeng.2007.04.009 CrossRefGoogle Scholar
  16. Guo L-Y, Shao J-H, Liu D-Y et al (2014) The distribution of water in pork meat during wet-curing as studied by low-field NMR. Food Sci Technol Res 20:393–399. doi: 10.3136/fstr.20.393 CrossRefGoogle Scholar
  17. Hansen CL, Thybo AK, Bertram HC et al (2010) Determination of dry matter content in potato tubers by low-field nuclear magnetic resonance (LF-NMR). J Agric Food Chem 58:10300–10304. doi: 10.1021/jf101319q CrossRefPubMedGoogle Scholar
  18. Hartmann R, Büning-Pfaue H (1998) NIR determination of potato constituents. Potato Res 41:327–334CrossRefGoogle Scholar
  19. Hayden RI, Moyse CA, Calder FW et al (1969) Electrical impedance studies on potato and alfalfa tissue. J Exp Bot 20:177–200. doi: 10.1093/jxb/20.2.177 CrossRefGoogle Scholar
  20. Kent M, Meyer W (1982) A density-independent microwave moisture meter for heterogeneous foodstuffs. J Food Eng 1:31–42. doi: 10.1016/0260-8774(82)90011-5 CrossRefGoogle Scholar
  21. Kent M, Knöchel R, Daschner F, Berger U (2000) Composition of foods using microwave dielectric spectra. Eur Food Res Technol 210:359–366. doi: 10.1007/s002170050564 CrossRefGoogle Scholar
  22. Kjaer KH, Clausen MR, Sundekilde UK et al (2014) Photoperiodic variations induce shifts in the leaf metabolic profile of Chrysanthemum morifolium. Funct Plant Biol 12:1310–1322CrossRefGoogle Scholar
  23. Krstajic D, Buturovic LJ, Leahy DE, Thomas S (2014) Cross-validation pitfalls when selecting and assessing regression and classification models. J Cheminform 6:1–15. doi: 10.1186/1758-2946-6-10 CrossRefGoogle Scholar
  24. Kuhn M, Wing J, Weston S et al (2015) The caret package: classification and regression training. R package version 6.0-47.
  25. Li K, Kang Z-L, Zou Y-F et al (2014) Effect of ultrasound treatment on functional properties of reduced-salt chicken breast meat batter. J Food Sci Technol 52:2622–2633. doi: 10.1007/s13197-014-1356-0 CrossRefPubMedPubMedCentralGoogle Scholar
  26. López A, Arazuri S, Garc I, Jare C (2013) A review of the application of near-infrared spectroscopy for the analysis of potatoes. J Agric Food Chem 61:5413–5424CrossRefPubMedGoogle Scholar
  27. Mashimo S, Miura N, Umehara T (1992) The structure of water determined by microwave dielectric study on water mixtures with glucose, polysaccharides, and L-ascorbic-acid. J Chem Phys 97:6759–6765CrossRefGoogle Scholar
  28. Mevik B-H, Wehrens R, Liland KH (2013) The pls package: principal component and partial least squares regression in R. PLS Partial Least Squares Princ. Compon. regression. R Package. version 2.4-3.
  29. Meyer W, Schilz W (1980) A microwave method for density independent determination of the moisture content of solids. J Phys D Appl Phys 13:1823–1830. doi: 10.1088/0022-3727/13/10/010 CrossRefGoogle Scholar
  30. Mizukami Y, Yamada K, Sawai Y, Yamaguchi Y (2007) Measurements of fresh tea leaf growth using electrical impedance spectroscopy. Agric Journal, Medwell 2:134–139. doi:aj.2007.134.139Google Scholar
  31. Nigmatullin RR, Arbuzov AA, Nelson SO, Trabelsi S (2006) Dielectric relaxation in complex systems: quality sensing and dielectric properties of honeydew melons from 10 MHz to 1.8 GHz. J Instrum 1:1–19. doi: 10.1088/1748-0221/1/10/P10002 CrossRefGoogle Scholar
  32. Povlsen VT, Rinnan Å, van den Berg F et al (2003) Direct decomposition of NMR relaxation profiles and prediction of sensory attributes of potato samples. LWT - Food Sci Technol 36:423–432. doi: 10.1016/S0023-6438(03)00023-9 CrossRefGoogle Scholar
  33. R Development Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  34. Rady AM, Guyer DE, Kirk W, Donis-González IR (2014) The potential use of visible/near infrared spectroscopy and hyperspectral imaging to predict processing-related constituents of potatoes. J Food Eng 135:11–25. doi: 10.1016/j.jfoodeng.2014.02.021 CrossRefGoogle Scholar
  35. Rady A, Guyer D, Lu R (2015) Evaluation of sugar content of potatoes using hyperspectral imaging. Food Bioprocess Technol 995–1010. doi: 10.1007/s11947-014-1461-0
  36. Reeve RM, Weaver ML, Timm H (1971) Anatomy and compositional variations within potatoes IV. Total solids distribution in different cultivars. Am Potato J 48:269–277CrossRefGoogle Scholar
  37. Repo T, Paine DH, Taylor AG (2002) Electrical impedance spectroscopy in relation to seed viability and moisture content in snap bean (Phaseolus vulgaris L.). Seed Sci Res 12:17–29. doi: 10.1079/SSR200194 CrossRefGoogle Scholar
  38. Scanlon MG, Pritchard MK, Adam LR (1999) Quality evaluation of processing potatoes by near infrared reflectance. J Sci Food Agric 771:763–771CrossRefGoogle Scholar
  39. Schwan HP, Foster KR (1980) RF-field interactions with biological systems: electrical properties and biophysical mechanisms. Proc IEEE 68:104–113CrossRefGoogle Scholar
  40. Shao X, Li Y (2012) Classification and prediction by LF NMR. Food Bioprocess Technol 5:1817–1823. doi: 10.1007/s11947-010-0455-9 CrossRefGoogle Scholar
  41. Shao X, Li Y (2013) Application of low-field NMR to analyze water characteristics and predict unfrozen water in blanched sweet corn. Food Bioprocess Technol 6:1593–1599. doi: 10.1007/s11947-011-0727-z CrossRefGoogle Scholar
  42. Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc 36:111–147Google Scholar
  43. Subramanian NK, White PJ, Broadley MR, Ramsay G (2011) The three-dimensional distribution of minerals in potato tubers. Ann Bot 107:681–691. doi: 10.1093/aob/mcr009 CrossRefPubMedPubMedCentralGoogle Scholar
  44. Tareke E, Rydberg P, Karlsson P et al (2002) Analysis of acrylamide, a carcinogen formed in heated foodstuffs. J Agric Food Chem 50:4998–5006. doi: 10.1021/jf020302f CrossRefPubMedGoogle Scholar
  45. Thybo A, Bechmann I, Martens M, Engelsen S (2000) Prediction of sensory texture of cooked potatoes using uniaxial compression, near infrared spectroscopy and low Field 1H NMR spectroscopy. LWT - Food Sci Technol 33:103–111. doi: 10.1006/fstl.1999.0623 CrossRefGoogle Scholar
  46. Thybo A, Andersen H, Karlsson A et al (2003) Low-field NMR relaxation and NMR-imaging as tools in differentiation between potato sample and determination of dry matter content in potatoes. LWT - Food Sci Technol 36:315–322. doi: 10.1016/S0023-6438(02)00210-4 CrossRefGoogle Scholar
  47. Thybo AK, Szczypiński PM, Karlsson AH et al (2004) Prediction of sensory texture quality attributes of cooked potatoes by NMR-imaging (MRI) of raw potatoes in combination with different image analysis methods. J Food Eng 61:91–100. doi: 10.1016/S0260-8774(03)00190-0 CrossRefGoogle Scholar
  48. Thygesen LG, Thybo AK, Engelsen SB (2001) Prediction of sensory texture quality of boiled potatoes from low-field 1H NMR of raw potatoes. The role of chemical constituents. LWT - Food Sci Technol 34:469–477. doi: 10.1006/fstl.2001.0788 CrossRefGoogle Scholar
  49. Trabelsi S, Nelson SO (2003) Free-space measurement of dielectric properties of cereal grain and oilseed at microwave frequencies. Meas Sci Technol 14:589–600. doi: 10.1088/0957-0233/14/5/308 CrossRefGoogle Scholar
  50. Trabelsi S, Nelson SO (2006) Nondestructive sensing of bulk density and moisture content in shelled peanuts from microwave permittivity measurements. Food Control 17:304–311. doi: 10.1016/j.foodcont.2004.11.004 CrossRefGoogle Scholar
  51. Trygg J, Wold S (2002) Orthogonal projections to latent structures (O-PLS). J Chemom 16:119–128. doi: 10.1002/cem.695 CrossRefGoogle Scholar
  52. Yee LK, Abbas Z, Jusoh MA et al (2011) Determination of moisture content in oil palm fruits using a five-port reflectometer. Sensors 11:4073–4085. doi: 10.3390/s110404073 CrossRefPubMedPubMedCentralGoogle Scholar
  53. Zhang MIN, Willison JHM (1992) Electrical impedance analysis in plant tissues: the effect of freeze-thaw injury on the electrical properties of potato tuber and carrot root tissues. Can J Plant Sci 72:545–553. doi: 10.4141/cjps92-068 CrossRefGoogle Scholar
  54. Zhang B, Huang W, Li J et al (2014) Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: a review. Food Res Int 62:326–343. doi: 10.1016/j.foodres.2014.03.012 CrossRefGoogle Scholar

Copyright information

© European Association for Potato Research 2017

Authors and Affiliations

  • Anders Kjær
    • 1
    • 2
    Email author
  • Glenn Nielsen
    • 2
    • 3
  • Søren Stærke
    • 2
  • Morten Rahr Clausen
    • 1
  • Merete Edelenbos
    • 1
  • Bjarke Jørgensen
    • 2
  1. 1.Department of FoodUniversity of AarhusAarslevDenmark
  2. 2.Newtec Engineering A/SOdenseDenmark
  3. 3.Department of Memphys, Centre for Biomembrane PhysicsUniversity of Southern DenmarkDK-5230 OdenseDenmark

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