Correcting Multivariate Calibration Model for near Infrared Spectral Analysis without Using Standard Samples

2015 ◽  
Vol 23 (5) ◽  
pp. 285-291 ◽  
Author(s):  
Xiaoyong Li ◽  
Wensheng Cai ◽  
Xueguang Shao
Keyword(s):  
Spectral Analysis ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Calibration Model ◽  
Standard Samples ◽  
Infrared Spectral

A dual model strategy to transfer multivariate calibration models for near-infrared spectral analysis

2016 ◽  
Vol 49 (5) ◽  
pp. 348-354 ◽  
Author(s):  
Jiajun Wang ◽  
Zhengfeng Li ◽  
Yi Wang ◽  
Yan Liu ◽  
Wensheng Cai ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Dual Model ◽  
Calibration Models ◽  
Infrared Spectral

scholarly journals Near-Infrared Spectroscopy Combined with Multivariate Calibration to Predict the Yield of Sesame Oil Produced by Traditional Aqueous Extraction Process

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yong-Dong Xu ◽  
Yan-Ping Zhou ◽  
Jing Chen
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Extraction Process ◽  
Sesame Oil ◽  
Aqueous Extraction ◽  
Oil Yield ◽  
Support Vector ◽  
Calibration Model ◽  
Aqueous Extraction Process

Sesame oil produced by the traditional aqueous extraction process (TAEP) has been recognized by its pleasant flavor and high nutrition value. This paper developed a rapid and nondestructive method to predict the sesame oil yield by TAEP using near-infrared (NIR) spectroscopy. A collection of 145 sesame seed samples was measured by NIR spectroscopy and the relationship between the TAEP oil yield and the spectra was modeled by least-squares support vector machine (LS-SVM). Smoothing, taking second derivatives (D2), and standard normal variate (SNV) transformation were performed to remove the unwanted variations in the raw spectra. The results indicated that D2-LS-SVM (4000–9000 cm−1) obtained the most accurate calibration model with root mean square error of prediction (RMSEP) of 1.15 (%, w/w). Moreover, the RMSEP was not significantly influenced by different initial values of LS-SVM parameters. The calibration model could be helpful to search for sesame seeds with higher TAEP oil yields.

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