Plastic identification apparatus and method based on near-infrared spectroscopy analysis
A near-infrared spectrum and identification device technology, which is applied in the field of plastic identification devices based on near-infrared spectrum analysis, can solve the problems of low recognition accuracy, inability to identify plastics with similar densities, and inability to identify and separate plastics, so as to achieve fast and simple analysis, Easy to distinguish, the effect of not polluting the environment
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Embodiment 1
[0047] Embodiment 1 of the present invention provides a plastic identification device based on near-infrared spectral analysis, see figure 1 As shown, it includes a light source unit, a sample collection unit, a detection unit and a circuit control unit.
[0048] The light source unit is used to emit a light source for illuminating the sample to be identified;
[0049] The sample collection unit is used to collect the reflected light reflected by the sample to be identified under the light source and transmit it to the detection unit;
[0050] The detection unit is used for splitting the reflected light and converting it into a photocurrent signal and sending it to the circuit control unit.
[0051] Preferably, see figure 2 As shown, the light source unit includes white light source 1 , optical fiber 2 / 4, monochromator 3 and lens 5 ; the sample collection unit includes sample holder 6 , mirror 7 and integrating sphere 8 .
[0052] The white light source 1 and the monochrom...
Embodiment 2
[0067] Embodiment 2 of the present invention provides a plastic identification method based on near-infrared spectral analysis, see image 3 shown, including steps:
[0068] Step S210, pre-training a recognition model based on the artificial neural network model.
[0069] Step S211, collecting the near-infrared reflection spectrum of the sample to be identified.
[0070] Step S212: Identify the near-infrared reflection spectrum of the sample to be identified according to the identification model, and obtain the identification result of the sample to be identified.
[0071] Preferably, pre-training the recognition model based on the artificial neural network model includes steps:
[0072] Set up the sample training set, detect the reflectance spectra of various plastics in the sample training set in the near-infrared band; extract the emission luminous rate spectral data at each wavelength of the reflective spectrogram; perform principal component analysis on the reflective l...
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