Peer review

Soluble sugar content is a key factor affecting super sweet corn quality, making the development of a rapid, simple, and environmentally friendly method for measuring soluble sugar content significant for successful breeding. The near-infrared (NIR) spectra of 131 sets of super sweet corn kernels with different soluble sugar contents (5.57?45.35 mg/g) were collected and preprocessed using multiple scattering correction (MSC), standard normal variate (SNV) transformation, and first and second derivatives. The first derivative spectrum, which gave the best preprocessing result, was used to construct the synergy interval partial least squares (Si-PLS) model. The PLS model developed using the 1349?1513 nm, 1842?2005 nm, 2005?2168 nm, and 2337?2500 nm wavebands gave the best result: root mean square error of the prediction set (RMSEP) =6.9199 mg/g and correlation coefficient of the prediction set (RP) = 0.7695. A competitive adaptive reweighted sampling (CARS)-Si-PLS wavelength screening algorithm was used to improve the predictive accuracy of the model further (RMSEP =5.8292 mg/g and RP = 0.8431). Compared to the original spectrum, the optimized model using CARS-SI-PLS is more concise and robust, confirming the ability of NIR spectroscopy to accurately measure soluble sugar content in super sweet corn.

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Xinhao Yang : Not in IRS Staff list but CIMMYT Affiliation

Han Song : Not in IRS Staff list but CIMMYT Affiliation

Furong Huang : Not in IRS Staff list but CIMMYT Affiliation