Sorting of navel orange soluble solids content based on online near infrared spectroscopy

Abstract A rapid, nondestructive, and online detection of the internal quality of navel orange cannot only reduce the labor intensity, but also improve the economic benefits of the navel orange. In this paper, an online detection and sorting equipment is designed for navel orange. The transmission spectrum data of 1697 navel oranges are divided into the calibration, prediction, and validation sets, with a ratio of 14:3:3. Pre-processing methods such as first derivative (FD), second derivative (SD), standard normal variate transform (SNV), and multiplicative scatter correction (MSC) were chosen to process the spectra. Accordingly, the soluble solids content prediction model for navel oranges is established using standard normal variable transformation (SNV) and partial least squares (PLS). The determination coefficients (R 2) of the calibration set, prediction set, and validation set are 0.8476, 0.8326, and 0.8025, respectively. Moreover, the corresponding root mean square errors are 0.5097°Brix, 0.5590°Brix, and 0.6048°Brix, respectively. The residual predictive deviation (RPD) value is 2.4510 (i.e., greater than 2.0), indicating that the model performs accurate predictive simulations, and has high reliability. In addition, two classification methods based on the national standard method and the normal probability graph of the soluble solids content of navel oranges were used to classify navel oranges into three classes for online validation. 185 navel oranges were selected for online validation, in which the classification method based on the normal probability graph of the soluble solids content of navel oranges was more effective and its average sorting accuracy was 81.13 %. Likewise, the mean absolute error (MAE) is 0.4613°Brix. The experimental results show that the online sorting equipment possesses high sorting accuracy and can be practically used for actual postharvest processing.