scholarly journals A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy

2021 ◽  
Author(s):  
Jiayi Li ◽  
Mei Yu ◽  
Shangke Li ◽  
Liwen Jiang ◽  
Yu Zheng ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Poria Cocos ◽  
Nondestructive Identification ◽  
Novel Strategy

Fast Nondestructive Identification of Endothelium Corneum Gigeriae Galli Using Visible/Near-Infrared Spectroscopy

2011 ◽  
Vol 28 (12) ◽  
pp. 120701 ◽  
Author(s):  
Xiao-Yan Zhang ◽  
Yao-Yong Meng ◽  
Hao Zhang ◽  
Wen-Juan Ou ◽  
Song-Hao Liu
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nondestructive Identification

scholarly journals Use of Near-Infrared Spectroscopy and Chemometrics for the Nondestructive Identification of Concealed Damage in Raw Almonds (Prunus dulcis)

2016 ◽  
Vol 64 (29) ◽  
pp. 5958-5962 ◽  
Author(s):  
Cristian Rogel-Castillo ◽  
Roger Boulton ◽  
Arunwong Opastpongkarn ◽  
Guangwei Huang ◽  
Alyson E. Mitchell
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prunus Dulcis ◽  
Nondestructive Identification

Rapid and Nondestructive Identification of Different Pure Yarn Fabrics Based on near Infrared Spectroscopy

2015 ◽  
Vol 671 ◽  
pp. 363-368 ◽  
Author(s):  
Cai Hong Wang ◽  
Hua Yong Liu ◽  
Xiong Ying Wu ◽  
Xue Mei Ding
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Rejection Rate ◽  
Combustion Method ◽  
Rapid Testing ◽  
Testing Methods ◽  
Mean Centering ◽  
Data Points ◽  
Nondestructive Identification

Near infrared spectroscopy combined with chemometrics analysis were investigated as an emerging method for the identification of textiles, this method allowed straightforward and rapid testing of textiles without destroying their integrity compared to traditional testing methods such as dissolution method, combustion method and microscope observation. In this work, different pretreating algorithms coupled with the Soft Independent Modelling by Class Analogy (SIMCA) have been studied to achieve the best recognition model to identify different pure yarn fabrics (cotton, linen, silk, wool and polyester). Results showed that little difference between different data points smoothing. Percentages of recognition and rejection of 100% were obtained of silk, wool and polyester by pretreating with Savitzky-Golay 7 data points smoothing, Savitzky-Golay second derivative and mean centering. The percentages of recognition were 93.75% and 96.25% for cotton and linen respectively by pretreating with Savitzky-Golay 7 data points smoothing, Savitzky-Golay first derivative and mean centering, nevertheless, the percentages of rejection was low for linen with 83.75%. The results from this paper suggested that near infrared spectroscopy in combination with SIMCA could be applied to the identification of pure yarn fabrics, whereas further study should be made to improve the rejection rate of linen.

Sign in / Sign up

Export Citation Format

Share Document