Research on Non-destructive On-line Detection System for Internal Quality of Qilin Watermelon
|Course||Agricultural Mechanization Engineering|
|Keywords||Non-destructive detection Qilin watermelon Near infrared spectroscopy (NIR) Soluble solids content (SSC) Maturity Monte Carlo-uninformation variable removalalgorithm (MC-UVE) Partial Least squares regression (PLSR)|
China has large yield and consumption of fruit, with the highest yield of watermelon in the world. However the export is very low due to the relatively low level of fruit’s commercialization. The detection technology of fruit in our country is relatively far behind from many other countries. For watermelon, it is necessary to grade according to the internal quality before exportation. At present, the reports about non-destructive on-line detection of watermelon at home and abroad are limited. The existing non-destructive on-line detective technology needs to be improved. There are only a few of foreign countries own the grading equipment for the large and thick-skinned fruit such as watermelon. It is still lack of comprehensive scientific research. Besides, the domestic manufacturers can’t meet the needs of fruit production companies with relatively week abilities in grading equipment production. Thus, the domestic fruit production companies spend a lot of money every year on buying foreign detection equipment. To date, there is no commercial watermelon internal quality detection equipment in China.The final goal of this research is to develop the on-line watermelon internal quality detection system. We hope to enhance the competition of the Chinese equipment manufacture industry for the fruit quality detection, and improve the benefit of domestic fruit production and processing enterprises.In this study, we chose the Qilin watermelon that is a variety with thick skin as the object. We determined the soluble solids content (SSC) and total acid (TA), which are indicators of maturity; the traditional chemical analysis methods were adopted. We discussed the key technical issues during the on-line detection of internal quality of watermelon. We designed and finished a watermelon internal quality on-line testing system. The detection system combined the optical, mechanical, electrical, biological and chemical materials metrology technology, it is a system integrated the light-controlled electromechanical operator. It mainly involves spectral analysis, information fusion, automatic control and more related discipline. This work focuses on the key technologies of on-line internal quality detection system of watermelon. The study contains following aspects:(1) According to the characteristics of watermelon, we designed the overall frame of on-line detection system for the internal quality of watermelon, including system integration, control unit and spectral model algorithms optimization. To meet the needs of fast, on-line nondestructive detection requirements; We designed the on-line detection system based on near-infrared spectroscopy (NIRS) technology, solved the problem that it is hard to determine the internal quality because of the big size and thick skin of watermelon.(2) It is important to create an accurate rapid predictive detection model for SSC and TA of watermelon. This is the key issue in achieving the non-destructive on-line detection for internal quality of watermelons. In this study, different pretreatment methods were used to optimize the predictive model. We also applied the genetic algorithm (GA), Monte Carlo-uninformation variable elimination (MC-UVE) and Monte Carlo-uninformation variable elimination and genetic algorithm (MC-UVE-GA) to extract feature wavelength. We reduced the variables and established partial least squares regression (PLSR) predictive models to adapt our system;(3) In order to study the detection portion and convey speed on detection accuracy, four different positions (stem, cylax, equator and grounded position) and four speed (0.1m/s,0.3m/s,0.5m/s and0.8m/s) were investigated. In this study, we used QE65000Ocean Optics spectrometer, diffuse transmission mode and the incident angle was set as120°(the angle between watermelon and light source), twelve150W halogen light source power are variable. The results showed that after the model optimization by spectral pretreatment and feature wavelength selection, among the four speeds applied in this study, the better predictive results of SSC obtained at0.8m/s and detected at stem position. Under this condition,14wavelengths were picked out; they were800.8,801.5,802.3,806.7,806.0,800.0,807.5,788.8,829.1,775.3,788.1,820.9,781.3and787.3nm, respectively. The correlation coefficient of calibration (rcal) was0.847, root mean square error of calibration (RMSEC) was0.550°Brix, correlation coefficient of prediction (rpre) was0.836, root mean square error of prediction (RMSEP)0.500°Brix. In TA predictive model, modeling with the spectra acquired at0.5m/s detection speed and at the equator position was the best,8wavelengths (792.5,757.3,791.8,793.3,774.6,758.1,775.3and756.6nm) were picked out, rcal was0.785, RMSEC to0.01076%, rpre was0.763, RMSEP was0.01106%. The results proved that different convey speeds and different positions would make an effect the detection accuracy. The detective precision still needs to be improvd to achieve the final purpose.(4) To carry on the qualitative watermelon maturity discriminant analysis:we used the Ocean Optics QE65000miniature fiber optic spectrometers to acquire transmittance spectra and establish qualitative discriminant model. We firstly proposed using the ratio of spectral peaki to peak2(RPP) to classify the maturity. We compared this effect of this method with other common chemometric discriminant method, including linear discriminant analysis (LDA), soft independent modeling of class analogy (SIMCA) and least squares support vector machines (LS-SVM). This method used the ratio of spectral peak at720-740nm and spectral peak at803-805nm to identify the watermelon maturity. We successfully classified them into four groups:immature, mid-mature, mature and over-mature. The specific division method is that when RPP is in0.4to0.69, the watermelon has over-mature; if the RPP is0.7to0.96, watermelon is mature; when the RPP is located between0.97and1.23, the watermelon is mid-ripen; when the RPP greater than1.24, the samples would be classified to immature. The classification accurate was82.1%by this method.(5) We designed and built an on-line detection system of watermelon, the speed range was0.1-0.8m/s. This system can be used for on-line detection of Qilin watermelon. We verified the function of this system through experiment, but the detection accuracy is not high enough. The stability and reliability of the system are still need to further improvement.