Detection of Solid-acid Value of Tomato during Storage Using NIR Spectroscopy

Abstract

Abstract. The tomato detection system was built base on the near infrared diffuse transmission principle. The soluble solids (SSC), total acid (TA) and solid / acid ratio of tomato were detected by using this system. A partial least squares regression (PLSR) quantitative analysis model was established for the spectral data of the 630-920nm wavelength range after they were pretreated by Savitzky-Golay smooth(SG-Smooth), standard normal variable transformation(SNV), multiplication scattering correction(MSC), first derivative (FD). The results show that the SSC and TA prediction models using Savitzky-Golay smoothing is the best. The correlation coefficient (r) of calibration and prediction of the SSC prediction model are 0.9831 and 0.9727, the root mean square error of calibration and prediction are 0.1286°Brix and 0.2295°Brix. And the correlation coefficient (r) of calibration and prediction of the TA prediction model are 0.9739 and 0.9501, the root mean square error of calibration and prediction are 0.2160% and 0.6321%. The partial least squares regression (PLSR) model with multiplicative scatter correction (MSC) data treatment could exhibit better prediction efficiency for solid / acid ratio of tomato. The correlation coefficient (r) of calibration and prediction are 0.9572 and 0.9253, root mean standard error are 0.0354 and 0.0539.This method is feasible for quantitative analysis of multi component content in tomato, and provides a theoretical basis for the rapid and nondestructive detection of the internal value of tomato.