scholarly journals Local Strategy Combined with a Wavelength Selection Method for Multivariate Calibration

Sensors ◽  
2016 ◽  
Vol 16 (6) ◽  
pp. 827
Author(s):  
Haitao Chang ◽  
Lianqing Zhu ◽  
Xiaoping Lou ◽  
Xiaochen Meng ◽  
Yangkuan Guo ◽  
...  
Keyword(s):  
Multivariate Calibration ◽  
Selection Method ◽  
Wavelength Selection ◽  
Local Strategy

Genetic algorithm-based wavelength selection method for spectral calibration

2010 ◽  
Vol 25 (1) ◽  
pp. 10-19 ◽  
Author(s):  
Masamoto Arakawa ◽  
Yosuke Yamashita ◽  
Kimito Funatsu
Keyword(s):  
Genetic Algorithm ◽  
Selection Method ◽  
Wavelength Selection ◽  
Spectral Calibration

Optimizing Rice Near-Infrared Models Using Fractional Order Savitzky–Golay Derivation (FOSGD) Combined with Competitive Adaptive Reweighted Sampling (CARS)

2020 ◽  
Vol 74 (4) ◽  
pp. 417-426 ◽  
Author(s):  
Zhenzhen Xia ◽  
Jie Yang ◽  
Jing Wang ◽  
Shengpeng Wang ◽  
Yan Liu
Keyword(s):  
Fractional Order ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Predictive Ability ◽  
Selection Method ◽  
Wavelength Selection ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Accuracy And Stability

Developing a rapid and stable method for analyzing the quality parameters of rice is important. Near-infrared (NIR) spectroscopy combined with chemometric techniques have been used to predict the critical contents of rice and shown its accuracy and stability. To further improve the predictive ability, we combine the derivative method of fractional order Savitzky–Golay derivation (FOSGD) with the wavelength selection method of competitive adaptive reweighted sampling (CARS). Compared with the traditional integer order Savitzky–Golay derivation (IOSGD), the FOSGD could improve the resolution ratio of the raw spectra more effectively. The wavelength selection method, CARS, could further extract the informative variables from the processed spectra. Four key contents of rice samples, including moisture, amylose, chalkiness degree, and gel consistency, were utilized to validate this method. The prediction results indicated that partial least squares (PLS) models optimized with FOSGD-CARS own higher accuracy and stability with smaller the root mean squared error of cross validations (RMSECVs) and root mean squared error of predictions (RMSEPs). The proposed method is convenient and provides a practical alternative for rice analysis.

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