[Multi-Target Recognition of Internal and External Defects of Potato by Semi-Transmission Hyperspectral Imaging and Manifold Learning Algorithm].

The present paper put forward a non-destructive detection method which combines semi-transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM) to recognize internal and external defects in potatoes simultaneously. Three hundred fifteen potatoes were bought in farmers market as research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes. In order to conform to the actual production, defect part is randomly put right, side and back to the acquisition probe when the hyperspectral images of external defects potatoes are acquired. The average spectrums (390-1,040 nm) were extracted from the region of interests for spectral preprocessing. Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data, including supervised locally linear embedding (SLLE), locally linear embedding (LLE) and isometric mapping (ISOMAP), the low-dimensional data gotten by manifold learning algorithms is used as model input, Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi-target classification model. By comparing and analyzing results of the three models, we concluded that SLLE is the optimal manifold learning dimension reduction algorithm, and the SLLE-LSSVM model is determined to get the best recognition rate for recognizing internal and external defects potatoes. For test set data, the single recognition rate of normal, bud, green rind and hollow heart potato reached 96.83%, 86.96%, 86.96% and 95% respectively, and he hybrid recognition rate was 93.02%. The results indicate that combining the semi-transmission hyperspectral imaging technology with SLLE-LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and external defects potatoes and also provide technical reference for rapid on-line non-destructive detecting of the internal and external defects potatoes.

[Non-destructive detection research for hollow heart of potato based on semi-transmission hyperspectral imaging and SVM].

The quality of potato is directly related to their edible value and industrial value. Hollow heart of potato, as a physiological disease occurred inside the tuber, is difficult to be detected. This paper put forward a non-destructive detection method by using semi-transmission hyperspectral imaging with support vector machine (SVM) to detect hollow heart of potato. Compared to reflection and transmission hyperspectral image, semi-transmission hyperspectral image can get clearer image which contains the internal quality information of agricultural products. In this study, 224 potato samples (149 normal samples and 75 hollow samples) were selected as the research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images (390-1 040 nn) of the potato samples, and then the average spectrum of region of interest were extracted for spectral characteristics analysis. Normalize was used to preprocess the original spectrum, and prediction model were developed based on SVM using all wave bands, the accurate recognition rate of test set is only 87. 5%. In order to simplify the model competitive.adaptive reweighed sampling algorithm (CARS) and successive projection algorithm (SPA) were utilized to select important variables from the all 520 spectral variables and 8 variables were selected (454, 601, 639, 664, 748, 827, 874 and 936 nm). 94. 64% of the accurate recognition rate of test set was obtained by using the 8 variables to develop SVM model. Parameter optimization algorithms, including artificial fish swarm algorithm (AFSA), genetic algorithm (GA) and grid search algorithm, were used to optimize the SVM model parameters: penalty parameter c and kernel parameter g. After comparative analysis, AFSA, a new bionic optimization algorithm based on the foraging behavior of fish swarm, was proved to get the optimal model parameter (c=10. 659 1, g=0. 349 7), and the recognition accuracy of 10% were obtained for the AFSA-SVM model. The results indicate that combining the semi-transmission hyperspectral imaging technology with CARS-SPA and AFSA-SVM can accurately detect hollow heart of potato, and also provide technical support for rapid non-destructive detecting of hollow heart of potato.

[Extending Hyperspectral Detecting Model of pH in Fresh Pork to New Breeds].

Calibration transfer is an effective approach to solve model invalidation problems caused by the change of instruments or the prediction samples. However, most studies on calibration transfer were based on different instruments, and models were established by Near Infrared Spectroscopy. In this study, hyperspectral detecting model of pork pH value was established, and in order to enhance the applicability of model to different breeds of pork samples; a new transfer algorithm based on spectra Mahalanobis distance, sync correction of spectrum and prediction value (CSPV), has been proposed, and was compared with model updating method. Equations with correlation coefficient of prediction (rp) > or = 0.837 and residual prediction deviation (RPD) > or = 1.9 were considered as applicable to predict pork quality. In this paper, three breeds, duchangda, maojia and linghao pork were researched, and a pH detecting model of duchangda (the primary breed) was established using partial least squares (PLS) regression method with r(c) of 0.922, r(p) of 0.904, root mean squared error of cross validation (RMSECV) of 0.045, root mean squared error of prediction (RMSEP) of 0.046 and RPD of 2.380. However, the prediction of the model to samples from maojia and linghao breeds (the secondary breeds) was very poor with rp of 0. 770 and 0.731 respectively, RMSEP of 0.111 and 0.209, RPD reached only 1.533 and 1.234 separately. Obviously, the PLS model of duchangda was unable to achieve the prediction to maojia and linghao samples. With the transformation of CSPV algorithm to duchangda model, only 9 and 10 standard samples from maojia and linghao breeds were used respectively, the prediction ability was improved with r(c) of 0.889 and 0.900, RPD grew to 2.071 and 2.231, which met the requirement of r(p) 0.837 and RPD > or = 1.9. While with model updating method, when 11 and 9 representative samples fromitaojia and linghao breeds were added to calibration set of duchangda model, r(c) increased to 0.869 and 0.845, but RPD only raised to 1.934 and 1.804 exclusively, even though tally r(p) > or = 0.837, it didn't meet that RPD > or = 1.9. The results demonstrate that CSPV transfer algorithm could realize the pH value prediction of duchangda model to maojia and linghao samples, while model updating method was only applicable for maojia samples instead of linghao samples, and the performance of CSPV transfer algorithm was better than model updating.

[Comparative study of transmission and reflection hyperspectral imaging technology for potato damage detection].

The randomly placed damage parts of potato will affect the detection accuracy, this paper used transmission and reflection hyperspectral imaging technology to acquire potato images of three directions(the damage part facing to the camera, back to the camera, side to the camera), and then processed the comparative study for damage detection. Independent component (IC) analysis was used to analyze the transmission and reflection hyperspectral images and to extract the features, the resulting char acteristics were used for the secondary IC analysis of the reflected images and the variable selection of the transmittance and re flectance spectroscopy. Finally, the potato injury qualitative recognition model was established based on the reflection images, the reflectance spectral and the transmittance spectral; Further optimization was done for high recognition accuracy of model, and secondary variable selection was carried out for the transmission spectrum by the Sub-window Permutation Analysis(SPA) and the optimal model for damage identification of potato randomly placed was established. The results of experiments show that the accuracy of the identification model based on the reflection image and the reflection spectrum is low, wherein the potato bruise based on the reflection images falls into the lowest recognition accuracy of 43. 10% when it is side to the camera; The accuracy of the model for identification based on the transmittance spectroscopy information is the highest, the recognition accuracy with the damage part facing and back to the camera is 100%t, and 99. 53% when it is side to the camera. The accuracy of the optimal model for identification based on the 3 kinds of transmittance spectroscopy information of randomly placed potato is 97. 39%. Then the application of transmission hyperspectral imaging technology could detect potato injury in any orientation, and the research can provide technical support for the online detection of potato quality.