Method for extracting characteristics of infrared spectrum and identifying samples
An infrared spectrum and feature extraction technology, applied in the field of infrared spectrum analysis, can solve the problems of large amount of data and high time cost, and achieve the effects of fast speed, stable features, and high identification accuracy
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example 1
[0070] Example 1, identification of fake wine
[0071] The sample is Yingjia Samsung, with 50 real wines and 50 fake wines from different batches. The identification steps are as follows:
[0072] 1. The selected instrument is Nexus670 from Nicholas Instrument Company of the United States, and the Nexus smart APK is attached.
[0073] 2. Infrared spectrometer parameter setting: wave number: 4000-650cm -1 ; Resolution: 8cm -1 ; Repeat the scan times 16 times.
[0074] 3. Take a small amount of sample with a disposable straw, spread it evenly on the zinc germanide crystal, and place the experimental plate on the designated position of the infrared spectrometer for detection. After a sample is tested, take the experimental plate out of the instrument, repeat the steps of rinsing with distilled water-wiping with absolute ethanol-drying-smearing the sample-testing, and a total of 50 infrared spectra corresponding to the three-star samples are collected, and Infrared spectra co...
example 2
[0081] Example 2: Identification of vintage wine
[0082] The samples are the 15-year Yellow Crane Tower and the 12-year Yellow Crane Tower. The identification steps are as follows:
[0083] 1. The infrared spectrum signal acquisition process is the same as in Example 1, and the infrared spectra of 50 samples of wines of two years are respectively collected. see Figure 5 , which shows the infrared spectra of the 15-year-old Yellow Crane Tower and 12-year-old Yellow Crane Tower wine samples.
[0084] 2. Extract features: use the sym4 wavelet filter to decompose the signal, and the filters are:
[0085] h 0 ={-0.0758 -0.0296 0.4976 0.8037 0.2979 -0.0992 -0.0126 0.0322}
[0086] h 1 ={-0.0322 -0.0126 0.0992 0.2979 -0.8037 0.4976 0.0296 -0.0758}
[0087] Decompose 4 layers, characterized by:
[0088] ( H i ( 272 ) ...
example 3
[0090] Example 3: Identification of milk powder
[0091] The samples were whole milk powder and skim milk powder. The identification steps are as follows:
[0092] 1. Select the instrument Nexus670 from Nicholas Instrument Company of the United States, and the tabletting accessories.
[0093] 2. Infrared spectrometer parameter setting: wave number: 4000-650cm -1 ; Resolution: 8cm -1 ; Repeat the scan times 16 times.
[0094] 3. Test the infrared spectra of 50 samples of whole milk powder and skimmed milk powder by tablet method. see Figure 7 , which shows the infrared spectra of whole milk powder and skim milk powder samples.
[0095] 4. Extract features: use the db4 wavelet filter to decompose the signal, and the filters are:
[0096] h 0 ={-0.0106 0.0329 0.0308 -0.1870 -0.0280 0.6309 0.7148 0.2304}
[0097] h 1 ={-0.2304 0.7148 -0.6309 -0.0280 0.1870 0.0308 -0.0329 -0.0106}
[0098] Decompose 4 layers, characterized by:
[0099] ( ...
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