A method for calibration and validation subset partitioning.
This paper proposes a new method to divide a pool of samples into calibration and validation subsets for multivariate modelling. The proposed method is of value for analytical applications involving complex matrices, in which the composition variability of real samples cannot be easily reproduced by optimized experimental designs. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. The proposed technique is illustrated in a case study involving the prediction of three quality parameters (specific mass and distillation temperatures at which 10 and 90% of the sample has evaporated) of diesel by NIR spectrometry and PLS modelling. For comparison, PLS models are also constructed by full cross-validation, as well as by using the Kennard-Stone and random sampling methods for calibration and validation subset partitioning. The obtained models are compared in terms of prediction performance by employing an independent set of samples not used for calibration or validation. The results of F-tests at 95% confidence level reveal that the proposed technique may be an advantageous alternative to the other three strategies.
Galvão
Roberto Kawakami Harrop
RK
Instituto Tecnológico de Aeronáutica, Divisão de Engenharia Eletrônica, São José dos Campos, São Paulo, Brazil.
Araujo
Mário César Ugulino
MC
José
Gledson Emídio
GE
Pontes
Marcio José Coelho
MJ
Silva
Edvan Cirino
EC
Saldanha
Teresa Cristina Bezerra
TC