Crocus sativus identification method based on cloud-interconnection portable near-infrared technology and adulterated product quantitative prediction method thereof
A saffron, portable technology, applied in the field of geological exploration, can solve the problems such as reports on the identification of linear pulp that have not yet been seen, and achieve the effects of simple operation, good accuracy and reliability
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Embodiment 1
[0037] The discrimination of embodiment 1 saffron of the present invention and counterfeit product thereof
[0038] 1. Establish identification model of saffron and its counterfeit products
[0039] (1) Take known saffron, safflower, corn silk, lotus silk, chrysanthemum and pulp samples respectively, and use the PV500R-I portable near-infrared instrument controlled by mobile phone to collect near-infrared spectral data in the wavelength range of 1350-1850nm. Collect 6 times;
[0040] (2) average spectrum to each sample spectral data of step (1), then use Kennard–Stone algorithm to divide each sample into a training sample and a prediction sample;
[0041] (3) Establish a saffron authenticity identification model based on partial least squares discriminant analysis (PLS-DA) with training samples;
[0042] (4) verify the saffron authenticity identification model with the prediction sample;
[0043] 2. Identify the samples to be tested
[0044] (5) Take the sample to be teste...
Embodiment 2
[0047] Embodiment 2 Discrimination of saffron of the present invention and adulterated products thereof
[0048] 1. Establish identification model of saffron and its adulterated products
[0049] (1) Take known pulp samples of saffron, saffron mixed with safflower, saffron mixed with corn silk, saffron mixed with lotus silk, saffron mixed with chrysanthemum and saffron mixed with paper pulp, and controlled by mobile phone The PV500R-I portable near-infrared instrument collects near-infrared spectral data in the wavelength range of 1350-1850nm, and collects 6 times in total;
[0050] (2) average spectrum to each sample spectral data of step (1), then use Kennard–Stone algorithm to divide each sample into a training sample and a prediction sample;
[0051] (3) First use the training samples of saffron, saffron mixed with safflower, saffron mixed with corn silk, saffron mixed with lotus root, saffron mixed with chrysanthemum, and saffron mixed with paper pulp to establish the firs...
Embodiment 3
[0057] Embodiment 3 Discrimination of saffron of the present invention and counterfeit products thereof and adulterated products
[0058] 1. Establish identification model of saffron and its counterfeit and adulterated products
[0059] (1) Take known saffron, safflower, corn silk, lotus silk, chrysanthemum, paper pulp, saffron mixed with safflower, saffron mixed with corn silk, saffron mixed with lotus silk, saffron mixed with chrysanthemum With saffron mixed with pulp samples, use the PV500R-I portable near-infrared instrument controlled by mobile phone to collect near-infrared spectral data in the wavelength range of 1350-1850nm, and collect 6 times in total;
[0060] (2) average spectrum to each sample spectral data of step (1), then use Kennard–Stone algorithm to divide each sample into a training sample and a prediction sample;
[0061] (3) Establish the authenticity identification model of saffron and counterfeit products:
[0062] Using the training samples of saffro...
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