Optimization of Online Determination Model for Sugar in a Whole Apple Using Full Transmittance Spectrum

In Vis/NIR nondestructive detection, the accuracy of the prediction model is affected by many factors such as spectral quality, biological variability, detection system and modeling method. In this study, the multi-point full-transmittance spectra (650 similar to 1 000 nm) of "Fuji" apple were acquired at a speed of 0. 5 m . s(-1) with an integration time of 0. 5 ms using an on-line spectrum measurement system. The spectral intensity changed with the detection orientations significantly, but the spectral curves of different orientations were similar, with an obvious peak at 920 nm and an obvious valley at 850 nm. To establish a reliable, accurate, and stable sugar calibration model of intact apple, three spectra preprocessing methods, including moving average smoothing, standard normal variate, and multiplicative scatter correction (MSC) , were used to reduce the influence of noise from the environment and instrumental fluctuations. In order to analyze the effect of detection orientations on prediction accuracy, a local model based on single orientation and a universal model based on global orientations were built respectively. The result showed that the prediction accuracy was limited by the detection orientation in the local model, while the universal model had better applicability for multiple detection orientation than that of the local model. In order to further improve the prediction ability, a modeling method named efficient spectrum optimization was proposed to select the spectra with a high signal-to-noise ratio and remove the inefficient transmittance spectrum by investigating the interference of transmittance spectral intensity on the accuracy of the prediction model. The result showed that it is beneficial to optimize the prediction model after removing the spectrum collected from the central zone of the apple. The universal intensity optimization model considered the spectral quality of different orientations comprehensively. The prediction model was best with R-p , RMSEP and RPD of 0. 79, 0. 84% and 1. 58 respectively, when the spectral intensity threshold was 5 000. Our result illustrated that the multi-potion spectrum measurement system is promising for on-line detection of apple quality. The modeling method of efficient spectrum optimization could be selective the transmittance spectrum with a high signal-to-noise ratio and optimize the prediction model.