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Related Experiment Video

Updated: Aug 26, 2025

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

Machine learning for classifying and predicting grape maturity indices using absorbance and fluorescence spectra.

Claire E J Armstrong¹, Adam M Gilmore², Paul K Boss³

¹Australian Research Council Training Centre for Innovative Wine Production, The University of Adelaide, PMB 1, Glen Osmond, South Australia 5064, Australia; School of Agriculture, Food and Wine, and Waite Research Institute, The University of Adelaide, PMB 1, Glen Osmond, South Australia 5064, Australia.

|October 3, 2022

Summary

Rapid A-TEEM spectroscopy with machine learning accurately predicts Cabernet Sauvignon grape maturity. This method effectively forecasts key indices like IBMP, pH, and TSS, offering a faster alternative to traditional analysis.

Keywords:

A-TEEM Chemometrics Data fusion Discriminant analysis Regression XGBoost

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Area of Science:

Agricultural Science
Analytical Chemistry
Spectroscopy

Background:

Grape maturity assessment is crucial for wine quality.
Traditional methods for determining grape maturity indices are time-consuming.
Need for rapid, accurate analytical techniques in viticulture.

Purpose of the Study:

To evaluate Absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) spectroscopy for predicting Cabernet Sauvignon grape maturity.
To develop and validate machine learning models for predicting key maturity indices.
To assess the potential of spectral data fusion for grape quality analysis.

Main Methods:

A-TEEM spectroscopy was employed to collect spectral data from Cabernet Sauvignon grapes.
Machine learning algorithms, including Extreme Gradient Boosting (XGB) regression and Partial Least Squares Regression (PLSR), were utilized.
Fused spectral data were used to predict 3-isobutyl-2-methoxypyrazine (IBMP), pH, total tannins (Tannin), total soluble solids (TSS), malic acid, and tartaric acid.

Main Results:

XGB regression achieved high prediction accuracy (R ² = 0.92-0.96) for IBMP, malic acid, pH, and TSS.
PLSR showed superior performance for TSS prediction (R ² = 0.97).
Moderate prediction accuracies (R ² = 0.64-0.81) were obtained for tartaric acid and Tannin.
XGB discriminant analysis correctly classified an average of 78% of samples for grape maturity classification.

Conclusions:

A-TEEM spectroscopy combined with machine learning offers a rapid and effective method for predicting grape maturity indices.
The developed models demonstrate potential for real-time quality control in the wine industry.
Spectral analysis provides a promising alternative to conventional methods for viticultural analysis.