Feasibility quantitative analysis of NIR spectroscopy coupled with Si-PLS to predict total acidity of Seedless White table grapes

Abstract

Total acidity (TA) is a critical parameter for evaluating the quality of table grapes. Research on rapid detection techniques for table grapes contributes substantially to the comprehensive assessment of grape quality. This study employed visible-near-infrared (Vis-NIR) spectroscopy to rapidly and quantitatively determine TA in Seedless White grapes. Various spectral preprocessing techniques were employed on the spectral data within the 400 to 1100 nm wavelength range. The synergy interval partial least squares (Si-PLS) method was utilized to screen the optimal subintervals from the preprocessed spectral data correlating with the TA content in grapes. Spectral prediction models for total acidity were developed based on full-band spectrum data and optimal subintervals. The impact of various preprocessing methods on the accuracy of the TA prediction models was evaluated, and the performance of the full-band spectrum model was compared with that of the subinterval-based model. Through comparative analysis, the first derivative method combined with the Savitzky-Golay smoothing method emerged as the most effective preprocessing approach. Si-PLS was subsequently employed to select spectral intervals, and a prediction model based on these intervals was established. The optimal model showed a correlation coefficient (Rc) of 0.915 and a root mean square error (RMSEC) of 0.584 g/L for the calibration set, and a correlation coefficient (Rp) of 0.835 with root mean square error (RMSEP) of 0.788 g/L for the prediction set, yielding a residual predictive deviation (RPD) of 1.815. The results demonstrate that integrating NIR spectroscopy and Si-PLS facilitates the rapid and precise quantitative detection of TA in grapes. This study provides a reference for developing rapid detection devices.

Keywords

Table Grape / Total acidity / Non-destructive testing / Si-PLS / Full-band / Subinterval

Author contributions

The authors confirm contribution to the paper as follows: conceptualization, writing - draft manuscript preparation: Xing J, He X; investigation and methodology: Sun X, Liu W; investigation: He K, Li J; supervision, funding, administration: He K, Su Y. All authors reviewed the results and approved the final version of the manuscript

Data availability

The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Project No. 52305281), the Modern Agricultural Engineering Key Laboratory at Universities of Education Depart-ment of Xinjiang Uygur Autonomous Region (Project No. TDNG2024101 and TDNG2023102), the Shaanxi Provincial Youth Fundation (2025JC-YBQN-238), the Key R&D Program of Shaanxi Province (2025NC-YBXM-194) and the Shaanxi Province Youth Talent Support Program (20250604). The authors declare no conflict of interest, and the authors are grateful to anonymous reviewers for their comments.

Conflict of interest

The authors declare that they have no conflict of interest.

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