Apple Origin Classification and Sugar Content Prediction of 'Fuji' Apples Using Near-Infrared Spectroscopy and Deep Learning.

Abstract

Accurate apple origin identification and non-destructive internal quality evaluation are important for fruit traceability, quality grading, and post-harvest management. Unlike previous studies mainly focusing on origin classification, this study established a dual-task near-infrared spectroscopy framework integrating geographical origin classification and soluble solid content (SSC, °Brix) prediction for Fuji apples. Samples were collected from three representative production regions in China: Alar in Xinjiang, Yantai in Shandong, and Luochuan in Shaanxi. Near-infrared diffuse reflectance spectra were acquired from 375 apples, generating 3000 spectral samples for origin classification and 750 SSC-calibrated samples for sugar content prediction. For classification, six deep learning models were evaluated using standardized full-spectrum input without chemometric spectral preprocessing, and the Transformer achieved the best performance, with a test accuracy of 96.22%. For SSC regression, spectra were preprocessed using standard normal variate and Savitzky-Golay filtering. The DNN model achieved the best prediction performance, with MAE = 0.5958 °Brix, RMSE = 0.7333 °Brix, R 2 = 0.8646, and Pearson r = 0.9338. These results indicate that near-infrared spectroscopy combined with deep learning can support both Fuji apple origin authentication and non-destructive local tissue SSC assessment.

Plant phenotyping relevance

リンゴ果実のSSC（糖度）という植物器官形質を、近赤外分光と深層学習で非破壊推定する方法を構築・評価しており、表現型取得手法が研究の中心である。

Code and data availability

The supplied blocks describe NIR spectral data collection (375 Fuji apples, 3000 spectra, 750 SSC samples) and deep learning modeling, but contain no data availability statement, public dataset deposit, or author code repository with a public URL. The only allowed URL is the CC BY license notice, which is not a paper-­

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