Simultaneous detection of surface deficiency and soluble solids content for Amygdalus persica by online visible near infrared transmittance spectroscopy

Surface deficiency and soluble solid content (SSC) are important indexes for evaluating the quality of Amygdalus persica.The feasibility was investigated for detecting surface deficiency and SSC of intact Amygdalus persica simultaneously by online visible near infrared(visible NIR) transmittance spectroscopy.Ten tungsten halogen lamps were installed in a sorting line.The power of each lamp was 100 watt.The light sources were illuminated from both sides of the production line, and the detector received light from the bottom of the fruit cup.The spectrum of each sample was recorded automatically by using the hardware trigger mode.The index plate and driving gear were mounted on the same shaft.The location of the index plate′s tooth was matched with the location of the fruit cup.Hall sensor was placed at a height of 2 mm above the tooth of the index plate.When the index plate turned one tooth, a Hall sensor sent a 3.5 V high frequency signal to trigger spectrometer to save one spectrum.The spectra were recorded with the integration time of 100 ms in the wavelength range of 550~900 nm when the samples were conveyed at the speed of five samples per second.The spectra of the same sample before and after damage were analyzed for investigation of the influence of the damage tissue within a peach affected the spectral content of the light transmitted through it.The spectral intensity of the damage was lower than the healthy ones for the damage issue affected the penetration of the light inside the fruit.Three quality discrimination methods of principle component analysis(PCA), least squares support vector machine(LS SVM) and partial least squares discrimination analysis(PLSDA) were used to identify the damage samples.The input vector and parameters of kernel function of LS SVM model were optimized by two step grid search method.The PLSDA model yielded the best results of accuracy rate of 100% compared to PCA or LS SVM methods.Considering the robustness of the partial least squares(PLS) regression model, two groups of healthy samples and the combinations of healthy samples and damage ones.Then the PLS regression model was developed for predicting SSC values.The performance of the PLS regression model was improved with the stand error of prediction(SEP) of 0.71% when the damage samples were removed out.The effective spectral variables were chosen by successive projections algorithm(SPA) method for improving the robustness of the PLS regression model.It was also investigated that the influence of the damage sample to the predictive ability of the PLS regression model.Therefore a new strategy was proposed for detection of surface deficiency and SSC for intact Amygdalus persica simultaneously by online visible NIR transmittance spectroscopy.The new samples, which were not used in the calibration, were used to access the abilities of recognizing the damage samples and predicting SSC of intact Amygdalus persica.The accuracy rate was 100% for identifying surface deficiency samples, and the SEP was 0.71% for predicting SSC.The accuracy of sorting grade was 93% according to the SSC values.The results showed that simultaneous detection of surface deficiency and SSC were feasible by visible NIR transmittance spectroscopy.