Non-destructive Inspection Of Pear Fruit Quality Based On Magnetic Resonance Imaging

Fruit contains many kinds of minerals and vitamins which can maintain the body’s normal physiological function. It is one of the basic components of human diet and indispensable in People’s Daily life. China is a big fruit production country. Fruit planting area and yield are among the best in the world. Low commercialization postharvest process is one of the main factors that affect market competition in the international market. Therefore, nondestructive detection and classification of fruit external and internal quality becomes the necessary for fruit industrialization of our country. Fruit nondestructive detection and grading methods mainly include two categories as external quality detection based on visible light and internal quality based on spectral technology. External quality detection technology is relatively popular.It has shortcomings for the external detection in minor damage defects and internal defects, such as detection effect influenced by damaged time and internal defects affected by detecting position and the size of the fruit. Nuclear magnetic resonance (NMR) imaging technology can reflect the change of the internal water content of fruit. It has been adopted due to its advantages: nondestructive testing, visualization, no radiation, safety and not affected by sample size and so on.Pear is selected as the research object whose production ranked third in the domestic. Nondestructive inspection of fruit quality were summarized and contrasted. The existing problems were pointed out. Nuclear magnetic resonance T2weighted images of Chinese pear, Korla Fragrant pear and Huanghua pear were scanned through the medical nuclear magnetic resonance (NMR) equipment. An image processing method was proposed after the image format transformation, which includes image processing, feature extraction and so on to realize pears extrusion injury and dropping damage identification and recognition of pear internal browning defects. In addition, dropping damage stage of the pears and browning severity level were also discussed. Pearson correlation between firmness of pear and nuclear magnetic resonance image texture coefficient was analyzed through statistics. A multiple regression model was established. The purpose of this study was to verify the feasibility detecting mechanical damage of external and internal defects of fruit with nuclear magnetic resonance (NMR) imaging technology. The influence of injury time on test results can be excluded and the severity of the internal Browning can be determined with MRI. It will Provide basis to research and develop fruit quality on-line detection line.The main contents and conclusions were listed as follows:1) The type of nuclear magnetic resonance imaging to detect fruit defect was analysed and determined. The results indicated that:Through the test of nuclear magnetic resonance image acquisition and image processing, the type of T2weighted imaging resolution could satisfy the requirement to detect the defect and internal quality of the study. The image acquisition speed of coronal image was faster than sagittal image and the image processing was easier and more convenient. For different type fruits different slice thickness and sliced spacing were employed to suit the fruit defect and quality detection.2) A new nondestructive inspection method based on corner feature was proposed which can detect the compressing damage of Chinese pears. The difference of nuclear magnetic resonance images for compressing bruised and sound Chinese pears was analyzed. The slight pressure injury simulated by Instron texture instrument could be detected through the Otsu threshold segmentation, expansion operation and boundary extraction and the corner detection of the fruit image. The207effective pear images were selected as the samples to detect the slight damage defect. The research results showed that the detection accuracy of slight bruise pears image was92.1%, while the detection accuracy was100%respectively for normal and misshapen pear images. Tests on real slight bruised pears and Fuji apples indicated that the detection accuracy was96.8%for32real bruised pear images and4real bruised apple images were all identified as bruised ones. The experimental results showed that detecting subtle compressing bruises on fruits with NMR technique was feasible through corner detection for coronal slice image.3) A new image processing method was suggested which can detect the new and old falling damage of Chinese pears based on nuclear magnetic resonance imaging. The difference of normal tissue and bruised tissue was analyzed. It was found that the gray level of new bruised tissue is higher than normal tissue while the gray level of old bruised tissue was lower than the normal tissue in the MR image. For falling injury caused by free fall movement from the shelf40mm above ground, the old bruised fruits were recognized through the Otsu threshold segmentation, to remove the core of the fruit and old bruise feature extraction; Then the new bruised fruits were discriminated through the fixed threshold segmentation, to remove the core of the fruit and new injury feature extraction from the rest and the remaining are all good fruits. Drop damage detection test of100slice images showed that59old bruised slices are recognized of60old bruised ones and the accuracy rate was98.3%;20new injury slices are distinguished as the new injury ones and the accuracy rate was100%;20sound slices were testing for good ones and the accuracy rate was100%. The result showed that it was feasible to not only detect the falling damage of Chinese pears but also to distinguish the new and old damage with MRI.Fruit damage stage judgment was studied through typical discriminant analysis of histogram parameters of fruit slice image. The classification accuracy rate is81.3%; Good results are achieved for the1st and the4st damage stage of fruit images and the classification accuracy comes to100%; but for the2nd and the3rd damage stage of the fruit image is easy to misclassify each other. Six fruit slices of the2nd damage phase were misclassified as the3rd phase damage fruit and one was misclassified as the1st phase one (the fruit damage area is very small). The8fruit slices of the3rd damage stage of the fruit image were misclassified as the2nd damage stage fruit. For the same fruit slice, there was no obvious difference of the2nd stage and the3rd stage image with the naked eye. The slight change could not be distinguished using histogram parameter features. Bruised pears were divided into three stages as early, middle and late stage to classify with discriminant analysis again. The method could distinguish fruits damage stage better and the overall classification accuracy was98.75%. Results indicated that the use of nuclear magnetic resonance imaging (MRI) combined with the histogram characteristic parameters which could realize fruit damage phase discrimination and it was better to divide into three stages (early, middle and late stage).4) A new nondestructive inspection method based on nuclear magnetic resonance imaging was suggested which can detect the internal browning of Korla fragrant pear caused during storage. During six months storage at room temperature for xinjiang korla fragrant pear, nuclear magnetic resonance images were scanned regularly in order to observe the browning process. Browning was recognized through the Otsu threshold segmentation, ratio of core and fruit area pixels, morphological operation, to remove core and browning characteristic extraction. The128valid slices were selected as the test objects from42pears for internal browning inspection. The analysis results showed that the image processing method was suitable for browning identification. The accuracy reached100%and84%for browning and sound slices respectively. The total recognition accuracy rate is98%. We also found that the algorithm was more appropriate for pears stored longer which would get higher accuracy. It may relate to the internal complicated physical and chemical change of fragrant pear during storage at the beginning. The results indicated that it was feasible to detect the internal browning of fragrant pears based on morphology.The area histogram of browning fragrant pear was analyzed. The fragrant pears were divided into four categories as fine, mild Browning of fragrant pear, mild Browning and severe Browning. Combining with Browning recognition and image processing method, the corresponding identification accuracies were84%,95%,94.4%and84%for each kind of fragrant pear respectively. There was one slice image for mild and moderate Browning pears that was misclassified. Results indicated that the qualitative judgment of fragrant pear browning degree can be realized with nuclear magnetic resonance imaging (MRI) based on the browning fragrant pear area histogram technique.5) The relation of magnetic resonance texture coefficients and firmness of Huanghua pear was analyzed during the ripe and storage process. The multiple regression model was built based on the texture coefficients with higher correlation for the ripe and storage stage respectively. Prediction and assessment was made using the model. The stability and repeatability of the model needs to be more experimental research to supplement and complete.