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Food Analytical Methods

, Volume 10, Issue 7, pp 2436–2443 | Cite as

Rapid Determination of Amino Acids in Chinese Wolfberry (Lycium bararum L.) Fruit by Using Fourier Transform Infrared Spectroscopy and Partial Least Square Regression

  • Xiuzhu Yu
  • Yuan Gao
  • Zhong Zhao
  • Jin-Ming Gao
Article

Abstract

The content and amino acid composition of Chinese wolfberry (Lycium bararum L.) produced from various regions was analyzed. Wolfberry contains high amounts of aspartic acid (1.445 ± 0.290 g/100 g), glutamine (1.380 ± 0.252 g/100 g), and lysine (0.254 ± 0.048 g/100 g) and is ideally grown in Zhongning. This study presents a new method for predicting three amino acids (aspartic acid, glutamine, and lysine) in Chinese wolfberry. The robustness and strength of the prediction model for each parameter vary with spectral pretreatment and wavelengths. An optimal prediction model was also established. High relative percent deviation (RPD) values (4.95 for aspartic, 4.89 for glutamine, and 4.28 for lysine) for validation and high R values (0.9948 for aspartic, 0.986 for glutamine, and 0.9836 for lysine) for blind validation were obtained. The results showed that FT-IR spectroscopy can be an effective method for evaluation of amino acids in Chinese wolfberry.

Keywords

Chinese wolfberry Amino acid Quantitative analysis FT-IR Partial least square regression 

Notes

Acknowledgements

The authors would like thank the support from the Fundamental Research Funds for the Central Universities (QN 2013057).

Compliance with Ethical Standards

Funding

The Fundamental Research Funds for the Central Universities (QN 2013057), China.

Conflict of Interest

Xiuzhu Yu declares that he has no conflict of interest. Yuan Gao declares that he has no conflict of interest. Zhong Zhao declares that he has no conflict of interest. Jin-Ming Gao declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human or animal subjects.

Informed Consent

Not applicable

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.College of Food Science and EngineeringNorthwest A&F UniversityYanglingChina
  2. 2.College of ForestryNorthwest A&F UniversityYanglingChina
  3. 3.School of Chemistry and PharmacyNorthwest A&F UniversityYanglingChina

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