Untargeted food contaminant detection using UHPLC-HRMS combined with multivariate analysis: Feasibility study on tea

2019 ◽  
Vol 277 ◽  
pp. 54-62 ◽  
Author(s):  
Grégoire Delaporte ◽  
Mathieu Cladière ◽  
Delphine Jouan-Rimbaud Bouveresse ◽  
Valérie Camel
2007 ◽  
Vol 88 (1) ◽  
pp. 3-10 ◽  
Author(s):  
E.V. Mikhailov ◽  
O.V. Tupicina ◽  
D.E. Bykov ◽  
K.L. Chertes ◽  
O.Ye. Rodionova ◽  
...  

Author(s):  
Luca Urbinati ◽  
Marco Ricci ◽  
Giovanna Turvani ◽  
Jorge A. Tobon Vasquez ◽  
Francesca Vipiana ◽  
...  

2006 ◽  
Vol 19 (5) ◽  
pp. S280-S283 ◽  
Author(s):  
Saburo Tanaka ◽  
H Fujita ◽  
Y Hatsukade ◽  
T Nagaishi ◽  
K Nishi ◽  
...  

2019 ◽  
pp. 309-340
Author(s):  
Heera Jayan ◽  
L. Bhavani Devi ◽  
C. Anandharamakrishnan

2013 ◽  
Vol 494 ◽  
pp. 199-202 ◽  
Author(s):  
Yoshimi Hatsukade ◽  
Shingo Tsunaki ◽  
Masaaki Yamamoto ◽  
Takayuki Abe ◽  
Junichi Hatta ◽  
...  

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.


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