Rapid assessment of the main economic value indicators in lupine flour samples using infrared spectroscopy

Background. A calibration model has been developed for rapid assessment of economic value indicators (protein, oil, and quinolizidine alkaloid contents) in the seeds of narrowleaf lupine accessions from VIR using near-infrared spectroscopy, with the help of which it is possible to decide on the further use of the accessions.

Materials and methods. Biochemical quality indicators (protein, oil, and quinolizidine alkaloid content) were studied in the seeds of narrowleaf lupine (Lupinus angustifolius L.) grown in 2019 in the northwest of Russia. Calibration models for measuring protein, oil and alkaloids in lupine seeds (62 accessions) were developed using a MATRIX-I IR analyzer (Bruker Optics, Germany). To construct calibration models, we used the values obtained by chemical analysis methods accepted at VIR. The oil content in lupine seeds was assessed by the defatted dry residue technique in Soxhlet extractors, protein by the Kjeldahl method, and quinolizidine alkaloids by gas chromatography coupled with mass spectrometry. All indicators were recalculated on the dry-weight basis.

Results and conclusion. Statistical significance of the developed models was verified according to the results of measuring the content of protein, oil and alkaloids in the seeds of the test batch. The protein and oil content data obtained using a calibration curve did not differ significantly from the results of chemical studies, in contrast to alkaloid indicators. Consequently, the developed calibration model for the MATRIX-I IR analyzer can be used for rapid assessment of protein and oil content in narrowleaf lupine flour samples, thus accelerating the process of obtaining data on the main economic value indicators. The analysis does not require reagents and is safe.

1. Abd Manaf F.Y., Yap A.K.C. Automatic crude oil dilution control with premium oil segregation using near infrared (NIR) on-line system. Journal of Oil Palm Research. 2018;30(2):2- 9. DOI: 10.21894/jopr.2018.0012

2. Ageeva P.A., Pochutina N.А., Pigareva S.A. Comparative characteristics of grain and green mass quality of fodder narrow-leafed lupin varieties. Adaptive Fodder Production. 2018;(1):42-48. [in Russian].

3. Burns D.A., Ciurczak E.W. (eds). Handbook of near-infrared analysis. 3rd ed. Boca Raton, FL: CRC Press; 2007. DOI: 10.1201/9781420007374

4. Efimenko S.G., Efimenko S.K. Determination of oil and moisture contents in mustard seeds using IR spectrometry. Oil Crops. Scientific and Technical Bulletin of VNIIMK. 2019;4(180):36-44. [in Russian]. DOI: 10.25230/2412-608Х2019-4-180-36-44

5. Efimenko S.G., Efimenko S.K., Kucherenko L.A., Nagalevskaya Ya.A. Quick-assay of the content of the main fatty acids in oil of rapeseed seeds by means of IR-spectrometry. Oil Crops. Scientific and Technical Bulletin of VNIIMK. 2015;4(164):35-40. [in Russian].

6. Efimenko S.G., Kucherenko L.A., Efimenko S.K., Nagalevskaya Ya.A. Evaluation of the general qualitative traits of soybean seeds using IR-spectrometry. Oil Crops. Scientific and Technical Bulletin of VNIIMK. 2016;3(167):33-38. [in Russian].

7. Ermakov A.I., Arasimovich V.V., Yarosh N.P., Peruanskiy Yu.V., Lukovnikova G.A., Ikonnikova M.I. Methods of biochemical research in plants (Metody biokhimicheskogo issledovaniya rasteniy). A.I. Ermakov (ed.). 3rd ed. Leningrad: Agropromizdat; 1987. [in Russian].

8. Kushnareva A.V., Shelengа T.V., Perchuk I.N., Egorova G.P., Malyshev L.L., Kerv Yu.A. et al. Selection of an optimal method for screening the collection of narrow-leaved lupine held by the Vavilov Institute for the qualitative and quantitative composition of seed alkaloids. Vavilov Journal of Genetics and Breeding. 2020;24(8):829-835. DOI: 10.18699/VJ20.680

9. Naumkin V.N., Naumkina L.A., Sergeeva V.A, Artgukhov А.I., Lukaschevich M.I. Lupin crops prospects in the Central Black-Soil Zone. Achievements of Science and Technology of AIC. 2009;(1):27-29. [in Russian].

10. Timoshenko E.S., Lukashevich M.I., Yagovenko G.L., Ageeva P.A., Zaitseva N.M. Characteristics of promising varieties of lupine Michurinsky and Belorozovy 144 for food use. Storage and Processing of Farm Products. 2022;(2):188-200. [in Russian]. DOI: 10.36107/10.36107/spfp.2022.310

11. Vaccari G., Mantovani G., Sgualdino G. The development of near-infrared (NIR) technique on-line in the sugar factory. Sugar Journal. 1990;52(10):4-8.

12. Vishnyakova M.A., Seferova I.V., Buravtseva T.V., Burlyaeva M.O., Semenova E.V., Filipenko G.I., Aleksandrova T.G., Egorova G.P., Yankov I.I., Bulyntsev S.V., Gerasimova T.V., Drugova E.V. VIR global collection of grain legume crop genetic resources: replenishment, conservation and studying: (guidelines). 2nd ed. M.A. Vishnyakova (ed.). St. Petersburg: VIR; 2018. [in Russian]. DOI: 10.30901/978-5-905954-79-5

13. Wheeler O.H. Near infrared spectra of organic compounds. Chemical Reviews 1959;59(4):629-666. DOI: 10.1021/cr50028a004