Reflectance Spectrometry as a Screening Tool for Prediction of Lutein Content in Diverse Wheat Species (Triticum spp.)
- 38 Downloads
Direct analytical methods assessing lutein content (e.g., high-performance liquid chromatography [HPLC]) are expensive and time-consuming. Their utilization in broad mapping of genetic resources or selecting among hundreds of breeding lines is limited. Therefore, the development of reliable predictive models based on reflectance spectrometry for assessing lutein content would be very useful. This study aims at finding a rapid and accurate lutein quantification method for wheat breeding purposes. Reference samples were represented by hexaploid, tetraploid, and diploid wheat species (119 accessions) cultivated in 2 years. The species Triticum aestivum L. predominated, with 81 accessions. Two near-infrared (NIR) spectroscopy devices were used to collect visible (VIS) and/or NIR spectra: the model FOSS 6500 with dispersive element and wavelength range 400–2500 nm and the Nicolet Antaris II Fourier transform spectrophotometer with wavelength range 1000–2500 nm. HPLC was used as the reference method for assessing lutein content in wheat grain. Sample grinding and proper NIR and VIS spectral combinations were essential for obtaining sufficient predictive parameters in calibration models. The FOSS 6500 calibration models were the most promising (standard error of prediction 0.46–0.78; ratio of performance to deviation 2.36–2.83 mg/kg). NIR spectral instability of ground samples during 12 days in storage was detected and evoked a significant relative increase in lutein content of about 35% using the FOSS 6500 calibration model. After excluding five time-instable spectral regions, prediction differences of lutein content were markedly eliminated and did not exceed 7% in relative terms during 12 days of storage.
KeywordsWheat species Lutein content Reflectance spectrometry Wheat breeding
This work was funded by the Ministry of Agriculture of the Czech Republic under Project Nos. MZE RO 0417 and QJ1510163.
Compliance with Ethical Standards
Conflict of Interest
Václav Dvořáček declares that he has no conflict of interest. Lenka Štěrbová declares that she has no conflict of interest. Eva Matějová declares that she has no conflict of interest. Jana Bradová declares that she has no conflict of interest. Jiří Hermuth declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Bonierbale M, Grüneberg W, Amoros W, Burgos G, Salas E, Porras E, Felde T (2009) Total and individual carotenoid profiles in Solanum phureja cultivated potatoes: II. Development and application of near-infrared reflectance spectroscopy (NIRS) calibrations for germplasm characterization. J Food Compos Anal 22:509–516CrossRefGoogle Scholar
- Duckworth J (2004) Mathematical data processing. In: (ed.) L. Al-Amoodi, Near infrared spectroscopy in agriculture, agronomy monograph 44, Medison Wisconsin, USA, pp 125–127Google Scholar
- Geleta N, Grausgruber H, Vollmann J (2014) Near-infrared spectroscopy of yellow endosperm pigments in wheat. Int J Agr Forest 4(6):446–450Google Scholar
- Landrum JT, Bone RA (2004) Dietary lutein and zeaxanthin: reducing the risk for macular degeneration. Agro Food Industry Hi-Tech 15:22–25Google Scholar
- Miedaner T, Han S, Kessel B, Ouzunova M, Schrag T, Utz FH, Melchinger AE (2015) Prediction of deoxynivalenol and zearalenone concentrations in Fusarium graminearum inoculated backcross populations of maize by symptom rating and near-infrared spectroscopy. Plant Breed 134:529–534CrossRefGoogle Scholar
- Wetzel DL, Sweat JA (1997) FT-NIR comparative quantitative performance for complex samples. In: Progress in Fourier transform spectroscopy. Springer, Vienna, pp 325–327Google Scholar
- Weyer L, Lo S-C (2002) Spectra-structure correlations in the near-infrared. In: Griffiths P, Chalmers JM (eds) Handbook of vibrational spectroscopy, vol 4. Wiley, U.K., pp 1817–1837Google Scholar