Article

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

Potato Research, Springer Nature, ISSN 1871-4528

Volume 59, 4, 2016

DOI:10.1007/s11540-017-9335-2, Dimensions: pub.1083410147,

Authors

Kjær, Anders * (1) (2)
Nielsen, Glenn (1) (3)

* Corresponding author

Affiliations

Organisations

  1. (1) Newtec Engineering A/S, Staeremosegaardsvej 18, DK-5230, Odense, Denmark
  2. (2) Aarhus University, grid.7048.b, AU
  3. (3) University of Southern Denmark, grid.10825.3e, SDU

Countries

Denmark

Continents

Europe

Description

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.

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Main Subject Area

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NORA University Profiles

Aarhus University

University of Southern Denmark

Danish Open Access Indicator

2016: Unused

Research area: Science & Technology

Danish Bibliometrics Indicator

2016: Level 1

Research area: Science & Technology

Dimensions Citation Indicators

Times Cited: 9

Field Citation Ratio (FCR): 4.86