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

• Type: Article in International Scientific Journal
• Date: 2022

Authors

R. C. Martins (Author) Other; T. G. Barroso (Author) Other; Pedro Alberto Jorge (Author) FCUP; M. Cunha (Author) Other; Filipe Neves dos Santos (Author) FEUP

Journal

Title: Computers and Electronics in Agriculture

Vol. 194

Pages: 1-11

ISSN: 0168-1699

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 16 Citations

Scopus - 19 Citations

Other information

Authenticus ID: P-00W-0W4

DOI: 10.1016/j.compag.2022.106710

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11