Infrared spectral feature based chemical rapid discrimination method and device
A chemical and fast technology, applied in the direction of color/spectral characteristic measurement, measuring device, material analysis by optical means, etc., can solve the problems of weak absorption signal, broadened band, overlapped spectral peak, etc., and achieve strong stability , The device is small, light, and easy to carry
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Embodiment ( 1
[0046] The principal component analysis (PCA) algorithm was used to model the six chemicals and perform qualitative analysis of unknown samples.
[0047] 1. Training set sample preprocessing: select ethyl acetate; diethyl phthalate; diallyl phthalate; ethyl benzoate; methyl benzoate; Processing to obtain the pure products of the above medicines, and five groups of samples were taken for each medicine as the training set;
[0048] 2. Spectrum collection of training set samples: place the training set samples in the sample box 5, cover the sample box cover 4, and collect the absorbance spectrum of the training set samples;
[0049] 3. Use PCA to train the model: The central purpose of PCA is to reduce the dimensionality of the data and convert the original variables so that a few new variables are linear combinations of the original variables. At the same time, these variables should express as much as possible of the original variables. data characteristics without loss of inf...
Embodiment ( 2
[0054] Using Fisher's linear discriminant algorithm to establish models of six categories of chemicals and perform qualitative analysis of unknown samples
[0055] 1. Sample preprocessing of the training set: select more than 20 chemicals, a total of six categories (acids, aldehydes, ketones, esters, alcohols, plasticizers) for enrichment processing to obtain the pure products of the above drugs as the training set;
[0056] 2. Spectrum collection of training set samples: same as in example (1) step 2;
[0057] 3. Use Fisher's linear discriminant training model: the basic idea of linear discriminant analysis is to project high-dimensional pattern samples into the best discriminant vector space to achieve the effect of extracting classification information and compressing the dimension of feature space. The new subspace has the largest inter-class distance and the smallest intra-class distance, that is, the samples have the best separability in this space, and the distance in...
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