Class-specific correction and classification of NIR spectra of edible oils

Classification of NIR spectra is a common problem in chemometrics. An ideal classification model should not only classify spectra into the known defined classes but also detect spectra that do not belong to any of the known classes. On top of this, the classification model should also perform well on spectra with unwanted source of variation (batch effects) obtained under different conditions. Classification of spectra into more distinct classes (different edible oils) is relatively easier than classification of spectra into less distinct classes (purities of oils). It is crucial to eliminate the batch effects while keeping the signal that separates the classes in the spectra effectively. Classification of spectra obtained under different batch conditions is often performed by applying a classification model (PCLDA, SIMCA) on spectra adjusted using calibration transfer methods (PDS, CTCCA and PCPDS). However, the simple calibration transfer models are unable to successfully remove the batch effects due to the heterogenous nature of these effects. In this paper, we propose CSCAC, a class-specific correction and classification technique that simultaneously corrects batch effects and performs both multi-class classification and novel-class detection. It provides a simple but an effect way to use calibration transfer models class -specifically when the class of the test spectra is unknown/to-be determined. We built three different versions of CSCAC models with PDS, PCPDS and CTCCA as base transfer models and benchmarked against commonly used classification methods. We show that class-specific framework is worth articulating explicitly and existing methods benefit from being used via such a framework. We illustrate its classification performance on a 14-edible oil dataset obtained across 5 different batches. We further illustrate its ability to be a quality check model on peanut oil - maize oil mixtures obtained across 25 batches.