Comparison of Si‐GA‐PLS and Si‐CARS‐PLS build algorithms for quantitation of total polyphenols in black tea using the spectral analytical system

Abstract

BACKGROUNDThe objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si‐PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables.RESULTSThe collected spectral data were divided into 19 sub‐interval selections totaling 246 variables, yielding the lowest root mean square error of cross‐validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (RC), prediction (RP), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si‐GA‐PLS model produced the following results: PCs = 9; RC = 0.915; RMSECV = 1.39; RP = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si‐CARS‐PLS model was noted to be best at PCs = 10, while RC = 0.9723, RMSECV = 0.81, RP = 0.9114, RMSEP = 1.45 and RPD = 2.59.CONCLUSIONThe build model's prediction ability was amended in the order PLS < Si‐PLS < CARS‐PLS when full spectroscopic data were used and Si‐PLS < Si‐GA‐PLS < Si‐CARS‐PLS when interval selection was performed with the Si‐PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry.

Prediction and visualization of moisture content in Tencha drying processes by computer vision and deep learning

“…A lower RMSE and a higher R (closer to 1) indicate a good calibration model with reliable and accurate predictive performance. A higher residual predictive deviation ( RPD ) suggests a more accurate prediction model, as it signifies that the prediction errors are relatively small compared to the data's variation 33 …”