Assessment of apple soluble sugar content by hyperspectral vegetation indexes

Abstract

Soluble sugar content in apple fruit is a crucial internal quality factor. Rapid and accurate estimation of soluble sugar content in apple fruit is helpful to predicting maturity. However, few studies have used an approach of hyperspectral vegetation indexes for monitoring apple soluble sugar content from ground-based handheld hyperspectral data. This study systematically analyzed the performance of hyperspectral vegetation indexes of apple in estimating the soluble sugar content. The visible and near infrared spectral of apple samples were measured by using a hyperspectral equipment, with the wavelength of 350–2500 nm. Eleven early-ripening apple varieties were collected from orchards located in five provinces of China, and the sugar content of each apple fruit sample was measured using laboratory methods. Two-band combination method was used to identify the effective spectral index. Results showed that the newly developed two-band ratio spectral index R₈₃₃/R₈₃₄ (R_λ is the reflectance value at wavelength λ) was recommended as the optimal index for monitoring early-ripening apple soluble sugar content, generated the coefficients of determination, root mean square error (RMSE) and residual prediction deviation (RPD) values between the measured and predicted values of 0.73%, 0.72%, and 1.38%, respectively. Result indicated that the newly developed two-band index R₈₃₃/R₈₃₄ can be used to nondestructively determine apple fruit soluble sugar content. The findings can provide insight into the relationship between the optical properties of apple fruit at different wavelengths and soluble sugar content.

Keywords:

• Apple
• soluble sugar contents
• hyperspectral
• spectral indexes

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The Project supported by Qinling and Loess Plateau Eco-Environmental Meteorological Key Laboratory Open Research Fund Project (2022Y-18, 2023G-16), the Natural Science Basic Research Plan in Shaanxi Province of China (2024JC-YBMS-208).