Online Detection Of Soluble Solids Content And Maturity Of Tomatoes Using Vis/Nir Full Transmittance Spectra

To meet the demand for high-quality tomatoes of different commercial purposes, the optimal models for predicting soluble solids content (SSC)and classifying maturities of tomatoes were developed simultaneously based on the Vis-NIR spectra collected by an online full transmittance spectrum measurement system. The intensity of full transmittance spectra at maturities of green, turning, pink, light red and red increased with their mature stages, indicating that the optical properties of tomatoes correlated significantly with their growth stages. Hence, the full transmittance has the potential to distinguish the maturities of tomatoes. The prediction models of SSC and classification models of maturity were built by partial least squares (PLS) regression and extreme learning machine (ELM) classifier based on the spectral data obtained by different detection orientations and different preprocessing methods, respectively. The results showed that detection orientations significantly affected the performance of SSC prediction models and maturity classification models, and the best model for SSC prediction was developed using the full wavelength spectra preprocessed by multiplicative scatter correction (MSC) with T1 orientation (the stem-top axis is vertical to conveyor belt). For samples in the prediction set, the correlation coefficient (Rp) and root mean square error (RMSEP) and residual predictive deviation (RPD) of the best SSC prediction model were 0.75, 0.27 ?Brix and 1.49, respectively. The best classification model for five classes was built based on the first ten principle components (PC) of spectra preprocessed by MSC with T1 orientation. The success rate of model for classifying the total, green, turning, pink, light red and red stages were 80.08%, 94.62%, 77.31%, 62.69%, 78.85%, 89.62%, respectively. To further investigate the classification ability of ELM classifier and simplify the classification complexity of maturities, the initial five maturities were converted into three maturities, namely immature, intermediate (turning + pink) and mature (light red + red). Compared with the classification model of five classes, model that was established for classifying three types of maturities obtained better results with accuracies of 91.31%, 95.38%, 89.23% and 91.35% for total, immature, intermediate and mature stages, respectively, based on the full wavelength spectra pretreated by MSC with T1 orientation. Overall, it was optimal for detection models of SSC and maturity by combining T1 orientation with MSC preprocessing, and the full transmittance mode used in this study has further potential applications for quality evaluation of agriculture products.