Abstract
An alternative method to biological testing on animals has been studied. The method is based on pattern recognition by combination of near-infrared spectroscopy and multivariate classification. The pharmaceutical product under study is based on a high-molecular-weight carbohydrate, Hyaluronan, used as a medical device for eye surgery. The effect of data preprocessing on classification performance of approved and rejected samples was evaluated by use of principal components models for each class. With the use of multiplicative scatter correction techniques, the class discrimination was enhanced 10 times in comparison to uncorrected data. From a leave-one-out procedure including a total of 29 samples, 80% of the samples were classified in agreement with the biological reference data and the remaining 20% were classified as non-members. With the use of partial least-squares discriminant analysis, 90% of the predicted samples were in accordance with the biological assay.
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