Construction and Application of Detection Model for Leucine and Tyrosine Content in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy
Source abstract
Abstract
To meet the demand of the breeding and processing industry of Golden Tartary buckwheat, quantitative identification models were established to test the content of leucine (Leu) and tyrosine (Tyr) in Golden Tartary buckwheat leaves by near-infrared reflectance spectroscopy (NIRS) with quantitative partial least squares (PLS). Leu’s modeling results were as follows: first derivative (11) pretreatment, the wavenumber range of 4000–9000 cm−1 was appropriate for modeling (calibration sets: validation set = 6:1), the mean coefficient of determination (R2), standard error of calibration (SEC), and relative standard deviation (RSD) for the calibration set were 0.9229, 0.45, and 3.45%, respectively; for the validation set, the mean R2, SEC, and RSD were 0.9502, 0.47, and 3.65%, respectively. Tyr modeling results were as follows: first derivative (11) pretreatment, the wavenumber range of 4000–10,000 cm−1 was suitable for modeling (calibration sets: validation set = 4:1), the R2, SEC, and RSD for the calibration set was 0.9016, 0.15, and 5.72%, respectively; for the validation set, the mean R2, SEC, and RSD were 0.9012, 0.15, and 5.53%, respectively. It was proved that the Leu and Tyr content of Golden Tartary buckwheat could be quantified using the model structured by near infrared spectroscopy combined with the partial least squares method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Seed and Plant Biochemistry · Spectroscopy and Chemometric Analyses · Advanced Chemical Sensor Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Center of Buckwheat Industry Technology, Guizhou Normal University, Guiyang 550001, China
Department of Biological Sciences, Pwani University, Kilifi 195-80108, Kenya
Funding
earmarked fund for China Agricultural Research system · National Natural Science Foundation of China · Science and Technology Foundation of Guizhou Province (grant number QianKeHeJiChu
References (34)
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