Unscrambling spectral interference and matrix effects in Vitis vinifera Vis-NIR spectroscopy: Towards analytical grade ‘in vivo’ sugars and acids quantification

Abstract

• Unscrambling interference and matrix effects in spectroscopy for ’in situ’ non-dominant absorbance constituents quantification. • Covariance mode search methodology - finding optimal covariance eigenvector with equivalence to the Beer–Lambert law. • State-of-the-art figures of merit review and benchmark against covariance mode search. • Spectral bands interpretation and relationship with tissue maturation. • Enabling analytical-grade ’in situ’ quantifications of sugars and acids in grapes. Analytical grade ’in vivo’ plant metabolic quantification using spectroscopy is a key enabling technology for precision agriculture.Current methods such as PLS, ANN and LS-SVM are non-optimal for resolving spectral interference and matrix effects to provide similar results to the analytical chemistry laboratory. This research presents a new self-learning artificial intelligence (SL-AI) method based on the search of covariance modes. These isolate the different modes of interference present in spectral data, allowing the consistent quantification of constituents. A review of the state-of-the-art methods with the figures of merit mean absolute standard error percentage (MASEP) and Pearson correlation coefficient (R) is presented for comparison and discussion. 707 grapes were quantified for glucose, fructose, malic and tartaric acids in five wine-making and one table grape varieties, and used to benchmark the new method against the state-of-the-art methodologies: partial least squares, local partial least squares, artificial neural networks and least squares support vector machines. SL-AI provides consistent quantifications, whereas previous methods exhibit data-driven performance dependence. Pearson correlations of 0.93 to 0.99 and MASEP of 3.70% to 7.33% were obtained with the new methodology. Local partial least squares, the method with the best benchmarks from literature, achieved correlations of 0.81 to 0.94 and MASEP of 8.00% to 13.4%. The covariance mode isolates a particular interference, providing a direct relationship between spectral inference and constituent concentrations, consistent with the Beer–Lambert law. Such quantifies non-dominant absorbance constituents (e.g. sugars and acids), which is a significant step towards ’in vivo’ plant physiology-based precision agriculture.