Know more

Our use of cookies

Cookies are a set of data stored on a user’s device when the user browses a web site. The data is in a file containing an ID number, the name of the server which deposited it and, in some cases, an expiry date. We use cookies to record information about your visit, language of preference, and other parameters on the site in order to optimise your next visit and make the site even more useful to you.

To improve your experience, we use cookies to store certain browsing information and provide secure navigation, and to collect statistics with a view to improve the site’s features. For a complete list of the cookies we use, download “Ghostery”, a free plug-in for browsers which can detect, and, in some cases, block cookies.

Ghostery is available here for free: https://www.ghostery.com/fr/products/

You can also visit the CNIL web site for instructions on how to configure your browser to manage cookie storage on your device.

In the case of third-party advertising cookies, you can also visit the following site: http://www.youronlinechoices.com/fr/controler-ses-cookies/, offered by digital advertising professionals within the European Digital Advertising Alliance (EDAA). From the site, you can deny or accept the cookies used by advertising professionals who are members.

It is also possible to block certain third-party cookies directly via publishers:

Cookie type

Means of blocking

Analytical and performance cookies

Realytics
Google Analytics
Spoteffects
Optimizely

Targeted advertising cookies

DoubleClick
Mediarithmics

The following types of cookies may be used on our websites:

Mandatory cookies

Functional cookies

Social media and advertising cookies

These cookies are needed to ensure the proper functioning of the site and cannot be disabled. They help ensure a secure connection and the basic availability of our website.

These cookies allow us to analyse site use in order to measure and optimise performance. They allow us to store your sign-in information and display the different components of our website in a more coherent way.

These cookies are used by advertising agencies such as Google and by social media sites such as LinkedIn and Facebook. Among other things, they allow pages to be shared on social media, the posting of comments, and the publication (on our site or elsewhere) of ads that reflect your centres of interest.

Our EZPublish content management system (CMS) uses CAS and PHP session cookies and the New Relic cookie for monitoring purposes (IP, response times).

These cookies are deleted at the end of the browsing session (when you log off or close your browser window)

Our EZPublish content management system (CMS) uses the XiTi cookie to measure traffic. Our service provider is AT Internet. This company stores data (IPs, date and time of access, length of the visit and pages viewed) for six months.

Our EZPublish content management system (CMS) does not use this type of cookie.

For more information about the cookies we use, contact INRA’s Data Protection Officer by email at cil-dpo@inra.fr or by post at:

INRA
24, chemin de Borde Rouge –Auzeville – CS52627
31326 Castanet Tolosan CEDEX - France

Dernière mise à jour : Mai 2018

Menu Logo Principal chemproject, chemhouse, chemoocs, chemomics, chemflow, chemdata, chemometrics, logo_agropolis,fondation chemproject, chemhouse, chemoocs, chemomics, chemflow, chemdata, chemometrics, logo, muse, montpellier chemproject, chemhouse, chemoocs, chemomics, chemflow, chemdata, chemometrics, logo, cirad

ChemProject

Ressources-Glossary

Acronyms used in CheMoocs

Acronyme

Signification

FR

EN

 

 

 

 

ACP

PCA

principal component analysis

AFD

FDA, LDA

discriminant factor analysis

CAH

 HAC

hierarchical ascending classification

 

CART

classification and regression tree

 

CV

cross validation

kppv

knn

k closest neighbors

 

MLR

multiple linear regression

PIR

NIR

near infrared

SPIR

 

near infrared spectroscopy

 

PCR

Principal component regression

 

PLSR

Partial Least Squares Regression

 

 

 

PRESS

sum of the squares of the prediction errors

 

 

in cross-validation leave-one-out

 

RMSEC

root-square of the calibration error

 

RMSECV

square root of cross validation error

 

RMSEP

square root of the prediction error

 

SVD

decomposition into singular values

 

 

 

Basis of a vector space

A base of a vector space of dimension P is made up of P vectors: fu1; u2; ::: uP g linearly independent, that is to say that none can be written as a linear combination of the others. Similarly, we de ne a base of a vector subspace of RP, of dimension A, by A vectors of nis in RP and linearly independent.
A base is not unique: very many bases (an in nite) can be used to deny the same vector space. An orthonormee base contains only vectors of norm 1 and all orthogonal between them. If the matrix P of dimensions (P A) contains in column the vectors of an orthonormed base, then P P = IA. The loadings of the ACP form an orthonormed base

Factorial map

The factorial map or score plot in English represents the coordinates of the observations on the plane formed by two main axes, generally axes 1 and 2.
In this representation, each point represents an observation. The points are therefore distinct from each other, as are the samples they represent

Correlation circle

The correlation circle is used in PCA. It consists in representing the correlations of each of the initial variables on a plane formed by two main components, often the first two.
Figure 1 {The circle of correlations for 4 variables: Var1, Var2, Var3 and Var4 represented on the plane of principal components 1-2, or axes 1-2.
According to the example in gure 1, Var1 is well explained by axis 1, with a strong positive correlation; Var2 is well explained by axis 2, with a strong negative correlation; Var3 is well explained by axes 1 and 2, due to its proximity to the circle; in n Var4 is not explained at all by the first two components, it must be explained by other components.

Correlation coefficient

The Pearson correlation coefficient allows, like the covariance, to measure how two variables represented here by the vectors x and y vary in the same sense, or not. It is noted r and its value is between 1 (strong positive correlation) and 1 (strong negative correlation). A value of 0 indicates that the variables vary independently of each other. The correlation between a variable and itself is 1.
Let x and y be the means of x and y, xi and yi their values for the index i.
The coe cient of determination R2, between 0 and 1, is the square of the coe cient of correlation.
Rx2; y = r2 (x; y)

Coefficient of determination

 => Voir corrélation

 

Regression coefficients

Let be a matrix of X spectra of dimensions (N P) and a quantitative quantity Y (ex: gluten) whose values predicted from X will give yb. The regression coe cients, or b-coe cients, form a vector of dimension (P 1) note b which verifies:
yb = Xb + E
Being the error. The formula is also written with and instead of b and E:
 yb = X +

Linear combination

Vectors fu1; u2; ::: uP g are connected by a linear combination if the numbers fa1 exist; a2; ::: aP g such as:
a1u1 + a2u2 + ::: + aP uP = 0
! 0 being the null vector. Otherwise, the vectors are said to be independent.

Colinearity of vectors

Two vectors x1 and x2 are linear if we can find a number k such that: x1 = kx2. Two collinear vectors point to the same direction in space, but not necessarily the same direction.

 

Cosinus

The cosine is used to measure the angle between two vectors. It is between 1 (two linear vectors in opposite directions) and 1 (two linear vectors in the same direction). It is worth 0 for two orthogonal vectors.
The measurement of the cosine is illustrated with gure 2. The two vectors u and v are used to give two directions, on which two dimensions of a right triangle ABC, rectangle in B.