Enhancing quantitative 1H NMR model generalizability on honey from different years through partial least squares subspace and optimal transport based unsupervised domain adaptation

Abstract

Honey is a nourishing and natural food product that is widely favored by a diverse group of consumers. Proton Nuclear Magnetic Resonance (¹H NMR) is a powerful tool for quantitative analysis of honey and plays a crucial role in ensuring its quality. The ¹H NMR technique necessitates the utilization of multivariate calibration models to facilitate the quantitative analysis of key compounds present in honey. However, maintaining consistent measurement conditions across different years is scarcely possible, which can significantly impact the distribution of training and test spectra, ultimately leading to reduced performance of predictive models. Unsupervised domain adaptation (UDA) methods have gained considerable attention for their ability to match distribution differences between the labeled source spectra and the unlabeled target spectra without costly annotation. To enhance the quantitative model generalizability on honey from different years, we propose a UDA method known as partial least squares subspace and optimal transport-based UDA (PLSS-OT-UDA). This approach eliminates distribution differences between the source subspace and target subspace via partial least squares (PLS) dimensionality reduction and OT. Firstly, the optimal latent variable weight matrix from the source domain (i.e., labeled ¹H NMR data in 2017) is extracted with PLS. Next, the dimension of both source and target domains (i.e., unlabeled ¹H NMR data in 2018) is reduced and their corresponding subspaces are obtained with weight matrix of the source domain. Finally, OT is then employed to align the distribution of the source and target domains within the subspace. Experimental results on the honey dataset demonstrate that the PLSS-OT-UDA outperforms traditional methods, including transfer component analysis (TCA), optimal transport for domain adaptation (OTDA), domain adaptation based on principal component analysis and optimal transport (PCA-OTDA), and subspace alignment (SA), with respect to generalization performance on three components: baume degree, sugar content, and water content.