A method for calibration and validation subset partitioning

• Published in: Talanta (Oxford) Vol. 67; no. 4; pp. 736 - 740
• Main Authors: Galvão, Roberto Kawakami Harrop, Araujo, Mário César Ugulino, José, Gledson Emídio, Pontes, Marcio José Coelho, Silva, Edvan Cirino, Saldanha, Teresa Cristina Bezerra
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.10.2005; Oxford Elsevier
• Subjects: Analytical chemistry; Chemistry; Diesel analysis; Exact sciences and technology; Kennard–Stone algorithm; NIR spectrometry; PLS regression; Sample subset partitioning; Spectrometric and optical methods; PLS regression; Diesel analysis; Kennard–Stone algorithm; NIR spectrometry; Sample subset partitioning; Performance evaluation; Calibration; Multivariate analysis; Algorithm; Near infrared spectrometry; Instrumental response; Kennard-Stone algorithm; Partitioning; Experimental design; Sampling; Comparative study; NIR spectroinetry
• ISSN: 0039-9140; 1873-3573; 1873-3573
• DOI: 10.1016/j.talanta.2005.03.025

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.