Preview

Agrarian science

Advanced search

Application of near infrared spectroscopy for identification and quantitative determination of amino acids in their crystalline and salt forms in the preparation of animal feed

https://doi.org/10.32634/0869-8155-2023-377-12-90-94

Abstract

Methods. For the first time, a comprehensive calibration model has been developed and presented for the rapid determination of basic near-infrared spectroscopy (IR) methods in feed amino acids, with application in the production of animal feed. The research principle is based on the Fourier equation for spectroscopy. In this work, Fourier methods in the Italian region (FTIR, FT-NIRS) were applied. The data obtained from the calibration models were confirmed using high-performance liquid chromatography. FT-NIR predictions agreed well with the chromatography data and had predictive deviation (RPD) values >1.3 in all cases.
Results. The results indicate that FT-NIR spectroscopy can be used as a simple and rapid tool for monitoring amino acids. In the course of the work, experimental confirmation of previously known facts was obtained — the possibility of visual separation of the spectra of not only counterfeit amino acids, but also the possibility of separation by L- and DL-optical isomers. The work shows that the discrepancies between the values obtained by the classical “wet chemistry” method and the values obtained from the constructed calibration models do not exceed the reproducibility limits of arbitration methods. A predictive model was used based on information flow elements using the OPUS/QUANT2 software package for multivariate calibration and construction of calibration models for amino acids. This chemometric analysis proved the fundamental possibility of determining amino acids in the product. It has been shown that the use of information channels opens up opportunities for the use of many chemometrics algorithms, including data preprocessing and the construction of predictive models.

About the Authors

N. P. Buryakov
Russian State Agrarian University — K.A. Timiryazev Agricultural Academy
Russian Federation

 Nilolay Petrovich Buryakov, Doctor of Biological Sciences, Professor

 54 Timiryazevskaya Str., Moscow, 127434



M. A. Buryakova
Russian State Agrarian University — K.A. Timiryazev Agricultural Academy
Russian Federation

 Maria Alekseevna Buryakova, Candidate of Agricultural Sciences, Associate Professor

 54 Timiryazevskaya Str., Moscow, 127434



S. O. Shapovalov
Russian State Agrarian University — K.A. Timiryazev Agricultural Academy; LLC Research and Testing Center «Cherkizovo»
Russian Federation

 Sergei Olegovych Shapovalov, Doctor of Biological Sciences, Professor; Director

54 Timiryazevskaya Str., Moscow, 127434

 Cherkizovo, 14 Dorozhnaya Str., Yakovlevskoye village, Moscow, 143340



E. V. Kornilova
LLC Research and Testing Center «Cherkizovo»
Russian Federation

 Elena Vyacheslavovna Kornilova, Candidate of Agricultural Sciences

Cherkizovo, 14 Dorozhnaya Str., Yakovlevskoye village, Moscow, 143340



D. V. Palamarchuk
LLC Research and Testing Center «Cherkizovo»
Russian Federation

 Dmitry Valerievich Palamarchuk, Specialist in Instrumental Test Methods

Cherkizovo, 14 Dorozhnaya Str., Yakovlevskoye village, Moscow, 143340



A.  E. Zhuravlev
Russian State Agrarian University — K.A. Timiryazev Agricultural Academy
Russian Federation

 Aleksandr Evgenievich Zhuravlev, Graduate Student

54 Timiryazevskaya Str., Moscow, 127434



S.  Hatem
Russian State Agrarian University — K.A. Timiryazev Agricultural Academy
Russian Federation

 Saleh Hatem, Graduate Student

54 Timiryazevskaya Str., Moscow, 127434



R. A. Donets
Russian State Agrarian University — K.A. Timiryazev Agricultural Academy
Russian Federation

 Roman Aleksandrovich Donets, Graduate Student

54 Timiryazevskaya Str., Moscow, 127434



T. D. Altukhov
Russian State Agrarian University — K.A. Timiryazev Agricultural Academy
Russian Federation

Tristan Dmitrievich Altukhov, Magister

54 Timiryazevskaya Str., Moscow, 127434



References

1. Selle P.H., Macelline S.P., Chrystal P.V., Liu S.Yu. The Impact of Digestive Dynamics on the Bioequivalence of Amino Acids in Broiler Chickens. Frontiers in Bioscience-Landmark. 2022; 27(4): 126. https://doi.org/10.31083/j.fbl2704126

2. Ji Yu., Hou Yu., Blachier F., Wu Z. Editorial: Amino acids in intestinal growth and health. Frontiers in Nutrition. 2023; 10: 1172548. https://doi.org/10.3389/fnut.2023.1172548

3. Wu G. Dietary requirements of synthesizable amino acids by animals: a paradigm shift in protein nutrition. Journal of Animal Science and Biotechnology. 2014; 5: 34. https://doi.org/10.1186/2049-1891-5-34

4. de Lange C.F.M., Pluske J., Gong J., Nyachoti C.M. Strategic use of feed ingredients and feed additives to stimulate gut health and development in young pigs. Livestock Science. 2010; 134(1‒3): 124‒134. https://doi.org/10.1016/j.livsci.2010.06.117

5. Hermann T. Industrial production of amino acids by coryneform bacteria. Journal of Biotechnology. 2003; 104(1‒3): 155‒172. https://doi.org/10.1016/s0168-1656(03)00149-4

6. Rosales A., Galicia L., Oviedo E., Islas C., Palacios-Rojas N. Near-Infrared Reflectance Spectroscopy (NIRS) for Protein, Tryptophan, and Lysine Evaluation in Quality Protein Maize (QPM) Breeding Programs. Journal of Agricultural and Food Chemistry. 2011; 59(20): 10781‒10786. https://doi.org/10.1021/jf201468x

7. Fontaine J., Schirmer B., Hörr J. Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. The results for wheat, barley, corn, triticale, wheat bran, middlings, rice bran, and sorghum. Journal of Agricultural and Food Chemistry. 2002; 50(14): 3902‒3911. https://doi.org/10.1021/jf011637k

8. Fontaine J., Hörr J., Schirmer B. Near-Infrared Reflectance Spectroscopy Enables the Fast and Accurate Prediction of the Essential Amino Acid Contents in Soy, Rapeseed Meal, Sunflower Meal, Peas, Fishmeal, Meat Meal Products, and Poultry Meal. Journal of Agricultural and Food Chemistry. 2001; 49(1): 57‒66. https://doi.org/10.1021/jf000946s

9. Rännar S., Lindgren F., Geladi P., Wold S. A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: Theory and algorithm. Journal of Chemometrics. 1994; 8(2): 111‒125. https://doi.org/10.1002/CEM.1180080204

10. Kumaravelu C., Gopal A. A review on the applications of Near-Infrared spectrometer and Chemometrics for the agro-food processing industries. 2015 IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR). 2015; 8–12. https://doi.org/10.1109/TIAR.2015.7358523

11. Westad F., Marini F. Validation of chemometric models — A tutorial. Analytica Chimica Acta. 2015; 893: 14–24. https://doi.org/10.1016/j.aca.2015.06.056


Review

For citations:


Buryakov N.P., Buryakova M.A., Shapovalov S.O., Kornilova E.V., Palamarchuk D.V., Zhuravlev A.E., Hatem S., Donets R.A., Altukhov T.D. Application of near infrared spectroscopy for identification and quantitative determination of amino acids in their crystalline and salt forms in the preparation of animal feed. Agrarian science. 2023;(12):90-94. https://doi.org/10.32634/0869-8155-2023-377-12-90-94

Views: 384


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 0869-8155 (Print)
ISSN 2686-701X (Online)
X