A kind of detection method of wheat grain hardness

Abstract

The invention relates to a method for detecting the hardness of wheat grains, which comprises the steps of moistening wheat and milling flour, collecting the color difference value of the reflected light of the calibration set samples, detecting the grain hardness of the calibration set samples, establishing a regression calibration model, and collecting the flour reflection of the prediction set samples. The light color difference value and the final hardness prediction calculation, by using the three parameters of the color difference value as X variables for regression analysis, avoiding that a single index cannot simultaneously represent two or more factors affecting the pink color, and using multivariate regression analysis to screen The optimal variable combination and the establishment of a calibration model, the prediction accuracy of grain hardness is high; the sample composition of the calibration set is required to include horny wheat and flour wheat, to avoid the influence of calibration deviation caused by the hardness of the same type of wheat, the more samples in the prediction set, the better The present invention shows the advantages of rapidity and convenience, and is applicable to the hardness prediction of large batches of wheat grain samples; compared with near-infrared detection instruments, the purchase cost is lower, and compared with other methods, the detection time is greatly shortened.

Description

technical field [0001] The invention belongs to the technical field of wheat hardness detection, and in particular relates to a detection method for wheat grain hardness. Background technique [0002] Grain hardness is an important wheat grading standard, which significantly affects the milling quality of wheat flour such as particle size and flour extraction rate, gluten quality such as gluten content and dough characteristics, and food processing quality such as noodles. [0003] At present, the methods for detecting grain hardness mainly include kernel index method, near-infrared spectroscopy and single-kernel grain characteristic method (SKCS). The particle index method is mainly to grind the grain into flour, and after passing through a specific sieve for a certain period of time, calculate the ratio of the mass after the sieve to the mass before the sieve, which is the PSI value of the sample. The particle index method is a standard reference method and is the most wid...

AI Technical Summary

Problems solved by technology

The particle index method is mainly to grind the grain into flour, and after passing through a specific sieve for a certain period of time, calculate the ratio of the mass after the sieve to the mass before the sieve, which is the PSI value of the sample. The particle index method is a standard reference method and is the most widely used. However, the detection takes the longest time; near-infrared spectroscopy mainly uses two strong absorption points of near-infrared light at wavelengths of 1680 and 2230nm to establish a model for the detection of grain hardness. Although this method is fast and convenient, the purchase cost of near-infrared detection equipment is high ; The Single Kernel Grain Characteristic Analyzer was developed by the American Grain Market Research Office and the Swedish Perten Instrument Company to measure the hardness of the kernel by measuring the force required to crush it

Although the method is simple to operate and the data is reliable, the instrument also has the problem of high price and is easily blocked by impurities and large particles

At present, the latter two methods are mainly used to detect the hardness of wheat kernels, but these three methods have different shortcomings and are difficult to be widely used

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