Buckwheat Identification by Combined UV-VIS-NIR Spectroscopy and Multivariate Analysis

Platov, Yu. T.; Metlenkin, D. A.; Платова, Р. А.; Rassulov, V. A.; Vereshchagin, A. I.; Mar'in, Vasily

doi:10.1007/s10812-021-01231-2

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…GUO et al [31] developed the continuous projection algorithm (SPA), genetic algorithm (GA) and simulated cooling algorithm (SA) using near infrared spectroscopy combined with partial least square number with collaborative interval to build the free amino acid model of matcha with excellent predictive power. Platov et al [32] used the method of UV-visible near-infrared spectroscopy in combination with multivariate analysis to analyze the classification and identification of buckwheat and oats, and also obtained good results. Ren et al performed Savitzky-Golay filtering (SG) and standard normal variable transformation (SNV) to preprocess the collected original spectrum, and then compared the multivariate selection algorithm (IgA-PSO) with the univariate selection algorithm (IGA and PSO) to find the optimal feature wavelength.…”

Section: Discussionmentioning

confidence: 99%

Construction and Application of Detection Model for Leucine and Tyrosine Content in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy

Zhu

Damaris

et al. 2022

Applied Sciences

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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.

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Section: Discussionmentioning

confidence: 99%