Short-term UNIMAN datasets of 200 samples from 17 polymeric sensors. The datasets contains two matricies:

`C` |
The concentration matrix of 200 rows and 3 columns encodes the concentration profile for three gases, ammonia, propanoic acid and n-buthanol. The concentration units are given in the percentage volume (% vol.). Ammonia has three concentration levels 0.01, 0.02 and 0.05, propanoic acid - three levels 0.01, 0.02 and 0.05, and n-buthanol - two levels 0.1 and 1. |

`dat` |
The data matrix of 200 rows and 17
columns cotains the steady-state signals of 17 sensors in
response to the concentration profle `C` . |

The reference dataset has been measured at The University of Manchester (UNIMAN). Three analytes ammonia, propanoic acid and n-buthanol, at different concentration levels, were measured for 10 months with an array of seventeen conducting polymer sensors.

In modeling of the array we make the distinction between
short-term and long-term reference data. Two hundred
samples from the first 6 days are used to characterize
the array assuming the absence of drift. The long-term
reference data (not published within the package) counts
for the complete number of samples from 10 months, these
data were used to model the sensor noise and drift, see
`UNIMANsnoise`

and `UNIMANdnoise`

for more details.

A pre-processing procedure on outliers removal was
applied to the reference data. The standard method based
on the squared Mahalanobis distance was used with
quantile equal to `0.975`

%.

data("UNIMANshort", package="chemosensors") str(UNIMANshort)List of 3 $ C : num [1:200, 1:3] 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:3] "Ammonia" "Propanoic" "n-buthanol" $ dat : num [1:200, 1:17] 8.9 8.88 8.87 8.86 8.73 ... $ dat.corrected: num [1:200, 1:17] 8.56 8.6 8.53 8.58 8.53 ... ..- attr(*, "scaled:center")= num [1:17] 8.53 8.31 8.08 5.99 7.77 ... ..- attr(*, "scaled:scale")= num [1:17] 0.729 0.544 0.655 0.387 0.684 ...C <- UNIMANshort$C dat <- UNIMANshort$dat # plot sensors in affinity space of gases #plotAffinitySpace(conc=C, sdata=dat, gases=c(2, 1)) #plotAffinitySpace(conc=C, sdata=dat, gases=c(2, 3)) #plotAffinitySpace(conc=C, sdata=dat, gases=c(3, 1)) # make standar PCA (package 'pls') to see: # - multi-variate class distribution (scoreplot) # - low-dimensionality of data (variance) # - contribution of 17 sensors in terms of linear modeling (loadings) mod <- prcomp(dat, center=TRUE, scale=TRUE) col <- ccol(C) scoreplot(mod, col=col, main="PCA: Scoreplot")barplot(mod$sdev, main="PCA: Sd. Deviation ~ PCs")loadings <- mod$rotation col <- grey.colors(3, start=0.3, end=0.9) matplot(loadings[, 1:3], t='l', col=col, lwd=2, lty=1, xlab="Sensor", ylab="sdev", main="PCA: Loadings PCs 1-3 ~ Sensors")