Standard error of prediction (SEP) should not be greater than 1.3 times the standard error of calibration (SEC)

"As recommended by the instrument/software vendor, generally, standard error of prediction (SEP) should not be greater than 1.3 times the standard error of calibration (SEC) and the bias should not be greater than 0.6 times the SEC (50). High values of SEP or bias indicate that the errors are significantly larger for the new
cross-validation samples and that the calibration data may not include all the necessary variability or be over fit."

(P. 265 in: Stuart L. Cantor, Stephen W. Hoag, Christopher D. Ellison, Mansoor A. Khan, and Robbe C. Lyon (2011). NIR Spectroscopy Applications in the Development of a Compacted Multiparticulate System for Modified Release. AAPS PharmSciTech, Vol. 12, No. 1, March 2011)

"A large difference indicates that too many latent variables are used in the model and noise is modeled. "

(P.318

In Li et al., (2007) Nondestructive measurement and fingerprint analysis of soluble solid content of tea soft drink based on Vis/NIR spectroscopy, J. of Food Eng, 82, 316-323.)

Standard error of cross-validation (SECV or SEP)

P.318

In Li et al., (2007) Nondestructive measurement and fingerprint analysis of soluble solid content of tea soft drink based on Vis/NIR spectroscopy, J. of Food Eng, 82, 316-323.

A large difference indicates that too many latent variables are used in the model and noise is modeled.

P.318

In Li et al., (2007) Nondestructive measurement and fingerprint analysis of soluble solid content of tea soft drink based on Vis/NIR spectroscopy, J. of Food Eng, 82, 316-323.

PLS2 regression give better results than PLS1 regression only if Y variables are strongly correlated

"When several dependent data are available for calibration, two approaches can be used in PLS regression: either properties are calibrated for one at a time (PLS1), or properties are calibrated at once (PLS2). In PLS1 model, the Y response consists of a single variable. When there is more than one Y response a separated model must be constructed for each Y response. In PLS2 model, responses are multivariate. PLS1 and PLS2 models provide different prediction set and PLS2 regression give better results than PLS1 regression only if Y variables are strongly correlated."

(Page 134 in: O. Galtier, O. Abbas, Y. Le Dréau, C. Rebufa, J. Kister, J. Artaud, N. Dupuy 2011. Comparison of PLS1-DA, PLS2-DA and SIMCA for classification by origin of crude petroleum oils by MIR and virgin olive oils by NIR for different spectral regions. Vibrational Spectroscopy 55 (2011) 132–140)

Indirect prediction method may need to check for robustness

"While good calibration models were obtained for dry matter, it seems more difficult to predict acidity based on the NIR spectrum. The concentration of acids in most fruit and vegetables is typically considerably smaller than that of sugars, and probably too small to affect the NIR spectrum significantly. The water absorption bands dominate the spectrum of fruit and vegetables, and it is not likely that minor constituents can be measured well. Obviously, when the concentration of such a minor constituent is correlated to, e.g., sugar content, the calibration results may seem reasonable but then the method is indirect and robustness issues are to be expected when applied to a different batch."

(Nicolaï, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I., Lammertyn, J.

Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review (2007) Postharvest Biology and Technology, 46 (2), pp. 99-118. )

Non-linearity can be accounted for by extra latent variables of PLS

"So far there does not seem to be convincing evidence that nonlinear techniques, such as ANNs or kernel- ased methods can really offer advantages with respect to the classical linear algorithms. This is due to the fact that NIR spectroscopy is essentially a very linear technique. Further, Li et al. (1999) stated that both PCR and PLS can provide linear approximations to subtle deviations from ideal linear behaviour by using extra latent variables to account for the nonlinearity."

(Nicolaï, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I., Lammertyn, J.

Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review (2007) Postharvest Biology and Technology, 46 (2), pp. 99-118. )

Definition of Robustness of Calibration Model

"Calibration models are called robust when the prediction accuracy is relatively insensitive towards unknown changes of external factors. The main factors which may affect model performance are (Wang et al., 1991): (i) the calibration model developed on one instrument is transported to another instrument that produces instrumental responses that differ from the responses obtained on the first instrument; (ii) the instrumental responses measured on a single instrument drift because of temperature fluctuations, electronic drift, and changes in wavelength or detector stability over time; and (iii) the samples belong to different batches."

(Nicolaï, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I., Lammertyn, J.

Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review (2007) Postharvest Biology and Technology, 46 (2), pp. 99-118. )