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As already mentioned, any multivariate analysis should include some validation, that is, formal testing, to extrapolate the model to new but similar data This requires two separate steps in the computation of each model component: calibration, which consists of nding the new components, and validation, which checks how well the computed components describe the new data Each of these two steps needs its own set of samples: calibration samples or training samples, and validation samples or test samples Computation of spectroscopic data PCs is based solely on optic data There is no explicit or formal relationship between PCs and the composition of the samples in the sets from which the spectra were measured In addition, PCs are considered superior to the original spectral data produced directly by the NIR instrument.

Since the rst few PCs are stripped of noise, they represent the real variation of the spectra, presumably caused by physical or chemical phenomena For these reasons PCs are considered as latent variables as opposed to the direct variables actually measured A convenient analogy for understanding latent variables is reconstructing the spectrum of a mixture from the spectra of the pure chemicals contained in the mixture The spectra of these pure chemicals would be the latent variables of the measured spectrum because they are not directly accessible in the spectrum of the mixture However, PCs are not necessarily the spectra of the pure chemicals in the mixtures representing the samples PCs represent whatever independent phenomena affect the spectra of the samples composing the calibration set.

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It is crucial to quickly collect and collate the captured information, and to ensure that legal guidelines and regulations are followed One must remember to be accountable for the details captured and entered on the database, especially since it can include personal information such as contact data, interests, and information about a donor s close circle of family, friends and colleagues The ideal is to capture and track all the information you can find about the nonprofit s donors and prospects in order to get beyond contact and giving details Although it s often tempting to want to capture this deeper level of detail for all of your constituent records, it is not always practical to do so Your nonprofit s needs will better be met by segmenting records and growing information strategically.

If one sample constituent varies entirely independently of everything else, and this constituent has a spectrum of its own, then one of the PCs will indeed represent the spectrum of that constituent It is most unusual for any one constituent to vary in a manner that is exactly independent of any other There is inevitably some correlation between the various constituents in a set of specimens, and any PC will represent the sum of the effects of these correlated constituents Even if full independence is accomplished, there is dependence in that the sum of all constituents must equal 100% Consequently, the PC representing that source of independent variability will look like the difference between the constituent of interest and all the other constituents in the samples The spectrum of the constituent considered could be extracted mathematically, but the PCs will not look exactly like the spectrum of the pure constituent.

Once samples have been collected and the corresponding NIR spectra stored in suitable les, building and using a PCA model involves three steps: selecting the appropriate preprocessing procedure(s), running the PCA algorithm and diagnosing the model, and interpreting the loading and score plots Once the model is built, it is important to assess its quality before using it for interpretation There are two steps in diagnosing a PCA model The variances must be checked to determine how many components the model should include and to estimate how much information the selected components take into account It is important to verify the variances calculated during validation Then it is advisable to look for outliers, that is, samples that do not t into the general pattern Total explained variance measures how much of the original variation in the data is described by the model.

A table is a great way to organize little bits of data into a meaningful picture. For example, you might use a table to show sales results for several salespeople or to contain a multicolumn list of team member names.

It expresses the proportion of structure found in the data by the model Total residual and explained variances show how well the model ts.

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