Lotus root starch adulteration identification method based on machine learning
A technology of machine learning and lotus root flour, applied in machine learning, pattern recognition in signals, instruments, etc., can solve the problems of not being able to identify atypical small grains of cassava flour, high selection requirements, and insufficient breadth, and achieve simplified lotus root flour The effect of quality identification, improvement of detection efficiency, and broad application prospects
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[0043] (2) Preparation of adulterated lotus root starch samples for use on the machine.
[0044] In this example, the lotus root flour sample from Fujian is selected as the blank group, and there are 3 types of doping: corn flour, sweet potato flour, and cassava flour. A program is written in matlab2019b, and 10 integers between 1 and 30 are randomly generated as the original The adulteration ratio was generated 3 times in total; the samples of each type of adulteration were divided into 10 parts, and the mass of lotus root starch and adulterated powder under each adulteration rate were calculated in turn, and the total mass was 5g. After mixing in the device, it is ready for use.
[0045] (3) Collect spectral data of lotus root starch samples with different doping ratios.
[0046]In this embodiment, the ANTARIS II Fourier transform near-infrared spectrometer is used to collect the near-infrared spectrum of the lotus root powder sample.
[0047] (4) Based on the spectral dat...
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