Detection of soluble solid content in citrus fruit using near-infrared spectroscopy with machine learning regression: An exploration of the influence of sampling positions

Abstract

Near-infrared (NIR) spectroscopy has been widely used as the non-destructive technique for fruit SSC measurement. This study explored the combination of NIR spectroscopy and machine learning regression to predict SSC in 288 citrus fruits (cv. Ponkan mandarin) considering the influence of sampling positions. This research analyzed spectral variations in different sampling positions, as well as the average spectra. Machine learning algorithms, including support vector regression (SVR) and partial least squares regression (PLSR), were used to establish the prediction models for SSC using the single-position spectra, spectra of all sampling positions and the average spectra. Feature wavelengths were identified by the combination of correlation analysis and regression coefficient of PLSR models from the single-position spectra and the average spectra. Using the full spectra or feature wavelengths, the models based on the average spectra significantly outperformed those based on the sample-position spectra, indicating that the average spectra may be more suitable for SSC prediction of Ponkan mandarin. This study indicated the variations among different sampling positions and samples were one of the key factors affecting the precise and robust models for SSC prediction, and future attempts should be conducted to cover the sample variations improve the model robustness and generalization ability.

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