Single-grain rice amylose quantitative analysis model constructing and detecting method

Abstract

The invention discloses a single-grain rice amylose quantitative analysis model constructing method. The method comprises the following steps: S1, collecting a plurality of parts of single-grain ricesamples of which the amylose contents are different, drying, and using the samples as a calibration set after balancing moisture; S2, collecting a near infrared spectrum of each single-grain rice sample in the calibration set; S3, respectively processing each single-grain rice sample in the calibration set as rice flour, to obtain an amylose content reference value of each single-grain rice sample, and constructing a calibration set reference value matrix; and S4, constructing a near infrared single-grain amylose model: screening a spectrum range from the near infrared spectrum obtained in thestep S2, to obtain a calibration set spectrum matrix, and performing regression correlation analysis on the spectrum matrix and the reference value matrix in the step S3, to obtain the near infraredquantitative analysis model of the single-grain rice amylose content.

Description

technical field [0001] The invention relates to a detection method for the component content of single-grain crops, in particular to a construction of a single-grain rice amylose quantitative analysis model and a detection method thereof. Background technique [0002] In order to shorten the breeding cycle and speed up the breeding process, rice breeding requires the ability to detect and even sort out seeds with specific traits that meet the needs of breeders in the early generations of seed breeding. In rice quality breeding, amylose is one of the most important quality indicators of rice. The content of amylose determines the eating quality and nutritional quality of rice, so it has attracted the attention of breeders and consumers. Traditional amylose detection methods such as iodine chromogenic photometry and iodine affinity determination (including Abe titration and potentiometric titration), etc., have defects of destroying seeds, consuming reagents, cumbersome operat...

AI Technical Summary

Problems solved by technology

However, this method has great limitations: even for the same species, the chemical value components of seeds of different sizes and growth conditions are not the same

[0004] However, for the former method, the half-grain method detects the half-grain rice without embryos, which does not completely correspond to the collected complete single-grain rice spectrum, thus affecting the analysis results; while for the latter method In this method, since the spectrum finally used for modeling is the spectrum after the arithmetic mean of each grain spectrum, however, the quality and size of each grain are different, resulting in different contributions of each grain to the final calculated chemical value of the mixed sample , which does not exactly correspond to the average spectrum, so it will also affect the prediction results to a certain extent

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