Applications

A-TEEM Analysis of Wine Quality Compounds and Parameters with Machine Learning

We introduce A-TEEM™ spectroscopy as a new and fast way to analyze 45 specific quality parameters in wine (ref. 1). A-TEEM, a fluorescence spectroscopy technique, simultaneously acquires transmission, absorbance, and fluorescence Excitation Emission Matrices, overcoming the inner filter effect on the fly (ref. 2).

No practical method currently exists to analyze these important compounds together for the wine industry. We designed the method to work within a certain range of concentrations, making it easy to measure absorbance and fluorescence . We tested the method on 126 wines and verified its accuracy with a prediction test-set of 13% and another independent validation set of 16 wines. We compared the individual and combined data of these components using two regression algorithms, namely, Partial Least Squares Regression and Extreme Gradient Boost Regression (XGBR). With various reference data measurements, we concluded that XGBR yielded more accurate results. Using a multi-block data approach to coordinate both the fluorescence and absorbance variable and analyzing with XGBR (Eigenvector Inc. Solo v8.9-9.21). we obtained accurate results, with respective average R2 values of 0.941±0.041, 0.920±.040 and 0.971±0.017 for the anthocyanins and phenolic parameters (n=29), basic chemistry parameters (n=8) and the UV-assay parameters (n=8).

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