Classification of Pumpkin Seed Oils According to Their Species and Genetic Variety by Attenuated Total Reflection Fourier-Transform Infrared Spectroscopy

2011 ◽  
Vol 59 (8) ◽  
pp. 4125-4129 ◽  
Author(s):  
Yanelis Saucedo-Hernández ◽  
María Jesús Lerma-García ◽  
José Manuel Herrero-Martínez ◽  
Guillermo Ramis-Ramos ◽  
Elisa Jorge-Rodríguez ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Fourier Transform Infrared ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Seed Oils ◽  
Pumpkin Seed

scholarly journals Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Combined with Chemometrics Methods for the Classification of Lingzhi Species

Molecules ◽  
2019 ◽  
Vol 24 (12) ◽  
pp. 2210 ◽  
Author(s):  
Yuan-Yuan Wang ◽  
Jie-Qing Li ◽  
Hong-Gao Liu ◽  
Yuan-Zhong Wang
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Fourier Transform Infrared ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Support Vector ◽  
Test Set ◽  
Medicinal Mushrooms

Due to the existence of Lingzhi adulteration, there is a growing demand for species classification of medicinal mushrooms by various techniques. The objective of this study was to explore a rapid and reliable way to distinguish between different Lingzhi species and compare the influence of data pretreatment methods on the recognition results. To this end, 120 fresh fruiting bodies of Lingzhi were collected, and all of them were analyzed by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). Random forest (RF), support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) classification models were established for raw and pretreated second derivative (SD) spectral matrices to authenticate different Lingzhi species. The results of multivariate statistical analysis indicated that the SD preprocessing method displayed a higher classification ability, which may be attributed to the analysis of powder samples that requires removal of overlapping peaks and baseline shifts. Compared with RF, the results of the SVM and PLS-DA methods were more satisfying, and their accuracies for the test set were both 100%. Among SVM and PLS-DA, the training set and test set accuracy of PLS-DA were both 100%. In conclusion, ATR-FTIR spectroscopy data pretreated by SD combined with PLS-DA is a simple, rapid, non-destructive and relatively inexpensive method to discriminate between mushroom species and provide a good reference to quality assessment.

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