Yong He

Zhejiang University, Hang-hsien, Zhejiang Sheng, China

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Publications (293)226.51 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: The intent of present work was to develop a valid method for detection of defective features in loquat fruits based on hyperspectral imaging. A laboratorial hyperspectral imaging device covering the visible and near-infrared region of 380–1,030 nm was utilized to acquire the loquat hyperspectral images. The corresponding spectral data were extracted from the region of interests of loquat hyperspectral images. The dummy grades were assigned to the defective and normal group of loquats, separately. Competitive adaptive reweighted sampling (CARS) was conducted to elect optimal sensitive wavelengths (SWs) which carried the most important spectral information on identifying defective and normal samples. As a result, 12 SWs at 433, 469, 519, 555, 575, 619, 899, 912, 938, 945, 970, and 998 nm were selected, respectively. Then, the partial least squares discriminant analysis (PLS-DA) model was established using the selected SWs. The results demonstrated that the CARS-PLS-DA model with the discrimination accuracy of 98.51 % had a capability of classifying two groups of loquats. Based on the characteristics of image information, minimum noise fraction (MNF) rotation was implemented on the hyperspectral images at SWs. Finally, an effective approach for detecting the defective features was exploited based on the images of MNF bands with “region growing” algorithm. For all investigated loquat samples, the developed program led to an overall detection accuracy of 92.3 %. The research revealed that the hyperspectral imaging technique is a promising tool for detecting defective features in loquat, which could provide a theoretical reference and basis for designing classification system of fruits in further work.
    Food and Bioprocess Technology 11/2014; 7(11). · 4.12 Impact Factor
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    ABSTRACT: Spectral unmixing is a critical issue in multi-spectral data processing, which has the ability to identify the constituent components of a pixel. Most of the hyperspectral unmixing current methods are based on Linear Mixture Model (LMM) and have been widely used in many scenarios. However, both the noise contained in the LMM and the requirement of essential prior knowledge strongly limit their practical applications. In order to address these issues, this paper proposes an iterative approach named CBIGMM. It utilizes infinite Gaussian mixture model to describe the hyperspectral data, which is robust to the noise due to the intrinsic randomness of the Gaussian components; and extracts the endmembers and their corresponding abundance in a fully unsupervised way without prior knowledge. A set of experiment is conducted on both synthetic and real data set from pesticide-contaminated vegetables. The results and analyses show CBIGMM outperforms other methods in addressing hyperspectral unmixing problem.
    Expert Systems with Applications 10/2014; · 1.85 Impact Factor
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    ABSTRACT: The feasibility of protein determination of shiitake mushroom (Lentinus edodes) using mid-infrared spectroscopy (MIR) was studied in the present paper. Wavenumbers 3 581-689 cm(-1) were used for quantitative analysis of protein content after removing of the part of obvious noises. Five points Savitzky-Golay smoothing was applied to pretreat the MIR spectra and partial least squares (PLS) model was built based on the pretreated spectra. The full spectra PLS model obtained poor performance with the ratio of prediction to deviation (RPD) of only 1.77. Successive projections algorithm (SPA) was applied to select 7 sensitive wavenumbers from the full spectra, and PLS model, multiple linear regression (MLR), back-propagation neural network (BPNN) and extreme learning machine (ELM) model were built using the selected sensitive wavenumbers. SPA-PLS model and SPA-MLR model obtained relatively worse results than SPA-BPNN model and SPA-ELM model. SPA-ELM obtained the best results with correlation coefficient of prediction (R(p)) of 0.899 5, root mean square error of prediction (RMSEP) of 1.431 3 and RPD of 2.18. The overall results indicated that MIR combined with chemometrics could be used for protein content determination of shiitake mushroom, and SPA could select sensitive wavenumbers to build more accurate models instead of the full spectra.
    07/2014; 34(7):1844-8.
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    ABSTRACT: This study proposed a new method using visible and near infrared (Vis/NIR) hyperspectral imaging for the detection and visualization of the chilling storage time for turbot flesh rapid and nondestructively. A total of 160 fish samples with 8 different storage days were collected for hyperspectral image scanning, and mean spectra were extracted from the region of interest (ROD inside each image. Partial least squares regression (PLSR) was applied as calibration method to correlate the spectral data and storage time for the 120 samples in calibration set. Then the PLSR model was used to predict the storage time for the 40 prediction samples, which achieved accurate results with determination coefficient (R2) of 0.966 2 and root mean square error of prediction (RMSEP) of 0.679 9 d. Finally, the storage time of each pixel in the hyperspectral images for all prediction samples was predicted and displayed in different colors for visualization based on pseudo-color images with the aid of an IDL program. The results indicated that hyperspectral imaging technique combined with chemometrics and image processing allows the determination and visualization of the chilling storage time for fish, displaying fish freshness status and distribution vividly and laying a foundation for the automatic processing of aquatic products.
    07/2014; 34(7):1938-42.
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    ABSTRACT: Near-infrared spectroscopy combined with chemometrics was used to investigate the feasibility of identifying different brands of soymilk powder and the counterfeit soymilk powder products. For this purpose, partial least squares-discriminant analysis (PLS-DA), linear discriminant analysis (LDA) and back-propagation neural network (BPNN) were employed as pattern recognition methods to class ify soymilk powder samples. The performances of different pretreatments of raw spectra were also compared by PLS-DA. PLS-DA models based on De-trending and multiplicative scatter correction (MSC)combined with De-trending(MSC+De-trending) spectra obtained best results with 100% prediction accuracy, respectively. Six and seven optimal wavenumbers selected by chi-loading weights of the best two PLS-DA models were used to build LDA and BPNN models. Results showed that BPNN performed best and correctly classified 100% of the soymilk powder samples for both the calibration and the prediction set. The overall results indicated that NIR spectroscopy could accurately identify branded and counterfeit soymilk powder products.
    07/2014; 34(7):1826-30.
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    ABSTRACT: Healthy tea and tea infected by anthracnose were first studied by confocal Raman microscopy to illustrate chemical changes of cell wall in the present paper. Firstly, Raman spectra of both healthy and infected sample tissues were collected with spatial resolution at micron-level, and ultrastructure of healthy and infected tea cells was got from scanning electron microscope. These results showed that there were significant changes in Raman shift and Raman intensity between healthy and infected cell walls, indicating that great differences occurred in chemical compositions of cell walls between healthy and infected samples. In details, intensities at many Raman bands which were closely associated with cellulose, pectin, esters were reduced after infection, revealing that the content of chemical compounds such as cellulose, pectin, esters was decreased after infection. Subsequently, chemical imaging of both healthy and infected tea cell walls were realized based on Raman fingerprint spectra of cellulose and microscopic spatial structure. It was found that not only the content of cellulose was reduced greatly after infection, but also the ordered structure of cellulose was destroyed by anthracnose infection. Thus, confocal Raman microscopy was shown to be a powerful tool to detect the chemical changes in cell wall of tea caused by anthracnose without any chemical treatment or staining. This research firstly applied confocal Raman microscopy in phytopathology for the study of interactive relationship between host and pathogen, and it will also open a new way for intensive study of host-pathogen at cellular level.
    Guang pu xue yu guang pu fen xi = Guang pu 06/2014; 34(6). · 0.29 Impact Factor
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    ABSTRACT: Propyl 4-(2-(4,6-dimethoxypyrimidin-2-yloxy) benzylamino)benzoate (ZJ0273) is a new herbicide which inhibits acetolactate synthase (ALS). The ZJ0273 is considered as safe for the environment and exhibits a satisfactory effect on weed control in the rapeseed field. ALS is the key enzyme of reactions in the biosynthesis of total amino acids (TAAs) especially branched-chain amino acids (BCAAs). This study reports the effect of ZJ0273 on BCAAs and TAAs in rapeseed leaves using near-infrared spectroscopy (NIRS) techniques. A decrease in TAAs and BCAAs contents was observed as the herbicide dosages were increased along with leaf senescence. The wavelengths 2,416 and 1,340 nm were selected to develop the NIRS model for detecting BCAAs and TAAs, and correlation coefficients of model’s prediction set were 0.9823, 0.9764, 0.9831, and 0.9968 for valine, isoleucine, leucine, and TAAs, respectively. The results indicated that 100 mg/L ZJ0273 was a safe dosage for oilseed rape as it did not show a significant effect on the contents of amino acids compared to other higher dosages (500 and 1,000 mg/L).
    Acta Physiologiae Plantarum 06/2014; · 1.31 Impact Factor
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    ABSTRACT: Visible/near-infrared (3801 030 nm) hyperspectral imaging technique was used to realize SPAD visualization of pumpkin leaves in the present study. Downy mildew could be diagnosed rapidly according to significant positive correlation between downy mildew epidemic and chlorophyll content. Leaves uninfected and infected with different level downy mildew were used to acquire hyperspectral images and extract spectral information. Competitive adaptive reweighted sampling (CARS) was applied to select optimal wavelengths and finally 10 optimal wavelengths were obtained. Partial least squares regression (PLSR) was employed to establish SPAD prediction model. Results showed that, through the analysis of calibration of 48 samples and prediction of 23 samples, CARS-PLSR could obtain good results with R C=0.918, RMSECV=3.932; R CV=0.846, RMSECV=5.254; R P=0.881, and RMSEP=3.714. Regression model was gained based on the relationship between SPAD and spectral of pumpkin leaves. While SPAD of each pixel was calculated with PLSR regression equation, then SPAD distribution map of pumpkin was visualized using imaging processing technology. Final downy mildew infection could be diagnosed based on SPAD distribution map. This study provided a theoretical reference for effective monitoring plant growth and downy mildew epidemic.
    Guang pu xue yu guang pu fen xi = Guang pu 05/2014; 34(5). · 0.29 Impact Factor
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    ABSTRACT: Hyperspectral imaging technology was developed to identify different brand famous green tea based on PCA information and image information fusion. First 512 spectral images of six brands of famous green tea in the 3801 023 nm wavelength range were collected and principal component analysis (PCA) was performed with the goal of selecting two characteristic bands (545 and 611 nm) that could potentially be used for classification system. Then, 12 gray level co-occurrence matrix (GLCM) features (i.e., mean, covariance, homogeneity, energy, contrast, correlation, entropy, inverse gap, contrast, difference from the second-order and autocorrelation) based on the statistical moment were extracted from each characteristic band image. Finally, integration of the 12 texture features and three PCA spectral characteristics for each green tea sample were extracted as the input of LS-SVM. Experimental results showed that discriminating rate was 100% in the prediction set. The receiver operating characteristic curve (ROC) assessment methods were used to evaluate the LS-SVM classification algorithm. Overall results sufficiently demonstrate that hyperspectral imaging technology can be used to perform classification of green tea.
    Guang pu xue yu guang pu fen xi = Guang pu 05/2014; 34(5). · 0.29 Impact Factor
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    ABSTRACT: Identification of early blight on tomato leaves by using hyperspectral imaging technique based on different effective wavelengths selection methods (successive projections algorithmSPA; x-loading weights, x-LW; gram-schmidt orthogonalizationGSO) was studied in the present paper. Hyperspectral images of seventy healthy and seventy infected tomato leaves were obtained by hyperspectral imaging system across the wavelength range of 3801023 nm. Reflectance of all pixels in region of interest (ROI) was extracted by ENVI 4.7 software. Least squares-support vector machine (LS-SVM) model was established based on the full spectral wavelengths. It obtained an excellent result with the highest identification accuracy (100%) in both calibration and prediction sets. Then, EW-LS-SVM and EW-LDA models were established based on the selected wavelengths suggested by SPA, x-LW and GSO, respectively. The results showed that all of the EW-LS-SVM and EW-LDA models performed well with the identification accuracy of 100% in EW-LS-SVM model and 100%, 100% and 97.83% in EW-LDA model, respectively. Moreover, the number of input wavelengths of SPA-LS-SVMx-LW-LS-SVM and GSO-LS-SVM models were four (492550633 and 680 nm), three (631719 and 747 nm) and two (533 and 657 nm), respectively. Fewer input variables were beneficial for the development of identification instrument. It demonstrated that it is feasible to identify early blight on tomato leaves by using hyperspectral imaging, and SPA, x-LW and GSO were effective wavelengths selection methods.
    Guang pu xue yu guang pu fen xi = Guang pu 05/2014; 34(5). · 0.29 Impact Factor
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    ABSTRACT: Visible and short wave infrared spectroscopy (Vis/SW-NIRS) was investigated in the present study for measurement of soil organic matter (OM) and available potassium (K). Four types of pretreatments including smoothing, SNV, MSC and SG smoothing+first derivative were adopted to eliminate the system noises and external disturbances. Then partial least squares regression (PLSR) and least squares-support vector machine (LS-SVM) models were implemented for calibration models. The LS-SVM model was built by using characteristic wavelength based on successive projections algorithm (SPA). Simultaneously, the performance of LS-SVM models was compared with PLSR models. The results indicated that LS-SVM models using characteristic wavelength as inputs based on SPA outperformed PLSR models. The optimal SPA-LS-SVM models were achieved, and the correlation coefficient (r), and RMSEP were 0.860 2 and 2.98 for OM and 0.730 5 and 15.78 for K, respectively. The results indicated that visible and short wave near infrared spectroscopy (Vis/SW-NIRS) (3251 075 nm) combined with LS-SVM based on SPA could be utilized as a precision method for the determination of soil properties.
    Guang pu xue yu guang pu fen xi = Guang pu 05/2014; 34(5). · 0.29 Impact Factor
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    ABSTRACT: A nondestructive and rapid method using near-infrared (NIR) hyperspectral imaging was investigated to determine the spatial distribution of fat and moisture in Atlantic salmon fillets. Altogether, 100 samples were studied, cutting out from different parts of five whole fillets. For each sample, the hyperspectral image was collected with a pushbroom NIR (899–1,694 nm) hyperspectral imaging system followed by chemical analysis to measure its reference fat and moisture contents. Mean spectrum were extracted from the region of interest inside each hyperspectral image. The quantitative relationships between spectral data and the reference chemical values were successfully developed based on partial least squares (PLS) regression with correlation coefficient of prediction of 0.93 and 0.94, and root mean square error of prediction of 1.24 and 1.06 for fat and moisture, respectively. Then the PLS models were applied pixel-wise to the hyperspectral images of the prediction samples to produce chemical images for visualizing fat and moisture distribution. The results were promising and demonstrated the potential of this technique to predict constituent distribution in salmon fillets.
    Food and Bioprocess Technology 04/2014; 7(4). · 4.12 Impact Factor
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    ABSTRACT: In the present study, Mid-infrared spectroscopy was used to identify the producing area of Letinus edodes, and relevance vector machine (RVM) was put forward to build classification models as a novel classification technique, and they obtained good performances. The head and the tail of the acquired mid-infrared spectra with the absolute noise were cut off, and the remaining spectra in the range of 3,581-689 cm(-1) (full spectra) of Letinus edodes were preprocessed by multiplicative scatter correction (MSC). Five classification techniques, including partial least Squares-discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbor algorithm (KNN), support vector machine (SVM) and RVM, were applied to build classification models based on the preprocessed full spectra. All classification models obtained classification accuracy over 80%, KNN, SVM and RVM models based on full spectra obtained similar and good performances with classification accuracy over 90% in both the calibration set and the prediction set. The weighted regression coefficients (Bw) were used to select effective wave numbers of mid-infrared spectra and 6 effective wave numbers in total were selected on the basis of the weighted regression coefficients of PLS-DA model based on full spectra. PLS-DA, KNN, SVM and RVM models were built using these effective wave numbers. Compared with the classification models based on full spectra, PLS-DA models based on effective wave numbers obtained relatively worse results with classification accuracy less than 80%, and KNN, SVM and RVM obtained similar results in both calibration set and prediction set with classification accuracy over 90%. RVM performed well with classification rate over 90% based on full spectra and effective wave numbers. The overall results indicated that producing area of Letinus edodes could be identified by mid-infrared spectroscopy, while wave number selection and the RVM algorithm could be effectively used in mid-infrared spectroscopy analysis. In this study, mid-infrared spectroscopy was successfully applied to identify the producing area of Letinus edodes, which could provide a new concept for quality analysis of Letinus edodes and other agricultural products, and the application of mid-infrared spectroscopy had practical significance.
    03/2014; 34(3):664-7.
  • Yu Zhang, Li-Hong Tan, Yong He
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    ABSTRACT: Visible and near-infrared (Vis-NIR) spectroscopy was applied to identify brands of car wax. A total of 104 samples were obtained for the analysis, in which 40 samples (calibration set) were used for model calibration, and the remaining 64 samples (prediction set) were used to validate the calibrated model independently. Linear discriminant analysis (LDA) and least square-support vector machine (LS-SVM) were respectively used to establish identification models for car wax with five brands based on their Vis-NIR spectra. Correct rates for prediction sample set were 84% and 97% for LDA and LS-SVM models, respectively. Spectral variable selection was further conducted by successive projections algorithm, (SPA), resulting in seven feature variables (351, 365, 401, 441, 605, 926, and 980 nm) selected from full range spectra that had 751 variables. The new LS-SVM model established using the feature variables selected by SPA also had the correct rate of 97%, showing that the selected variables had the most important information for brand identification, while other variables with no useful information were eliminated efficiently. The use of SPA and LS-SVM could not only obtain a high correct identification rate, but also simplify the model calibration and calculation. SPA-LS-SVM model could extract the useful information from the Vis-NIR spectra of car wax rapidly and accurately for the non-destructive brand identification of car wax.
    Guang pu xue yu guang pu fen xi = Guang pu 02/2014; 34(2):381-4. · 0.29 Impact Factor
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    ABSTRACT: Crack is one of the most important indicators to evaluate the quality of fresh jujube. Crack not only accelerates the decay of fresh jujube, but also diminishes the shelf life and reduces the economic value severely. In this study, the potential of hyperspectral imaging covered the range of 380 - 1030 nm was evaluated for discrimination crack feature (location and area) of fresh jujube. Regression coefficients of partial least squares regression (PLSR), successive projection analysis (SPA) and principal component analysis (PCA) based full-bands image were adopted to extract sensitive bands of crack of fresh jujube. Then least-squares support vector machine (LS-SVM) discriminant models using the selected sensitive bands for calibration set (132 samples)" were established for identification the prediction set (44 samples). ROC curve was used to judge the discriminant models of PLSR-LS-SVM, SPA-LS-SVM and PCA-LS-SVM which are established by sensitive bands of crack of fresh jujube. The results demonstrated that PLSR-LS-SVM model had an optimal effect (area=1, std=0) to discriminate crack feature of fresh jujube. Next, images corresponding to five sensitive bands (467, 544, 639, 673 and 682 nm) selected by PLSR were executed to PCA. Finally, the image of PC4 was employed to identify the location and area of crack feature through imaging processing. The results revealed that hyperspectral imaging technique combined with image processing could achieve the qualitative discrimination and quantitative identification of crack feature of fresh jujube, which provided a theoretical reference and basis for develop instrument of discrimination of crack of jujube in further work.
    Guang pu xue yu guang pu fen xi = Guang pu 02/2014; 34(2):532-7. · 0.29 Impact Factor
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    ABSTRACT: This study investigated the feasibility of using near infrared hyperspectral imaging (NIR-HSI) technique for non-destructive identification of sesame oil. Hyperspectral images of four varieties of sesame oil were obtained in the spectral region of 874-1734 nm. Reflectance values were extracted from each region of interest (ROI) of each sample. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and x-loading weights (x-LW) were carried out to identify the most significant wavelengths. Based on the sixty-four, seven and five wavelengths suggested by CARS, SPA and x-LW, respectively, two classified models (least squares-support vector machine, LS-SVM and linear discriminant analysis,LDA) were established. Among the established models, CARS-LS-SVM and CARS-LDA models performed well with the highest classification rate (100%) in both calibration and prediction sets. SPA-LS-SVM and SPA-LDA models obtained better results (95.59% and 98.53% of classification rate in prediction set) with only seven wavelengths (938, 1160, 1214, 1406, 1656, 1659 and 1663 nm). The x-LW-LS-SVM and x-LW-LDA models also obtained satisfactory results (>80% of classification rate in prediction set) with the only five wavelengths (921, 925, 995, 1453 and 1663 nm). The results showed that NIR-HSI technique could be used to identify the varieties of sesame oil rapidly and non-destructively, and CARS, SPA and x-LW were effective wavelengths selection methods.
    PLoS ONE 01/2014; 9(5):e98522. · 3.53 Impact Factor
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    ABSTRACT: The present study presents prediction models for determining the N content in citrus leaves by using hyperspectral imaging technology combined with several chemometrics methods. The steps followed in this study are: hyperspectral image scanning, extracting average spectra curves, pretreatment of raw spectra data, extracting characteristic wavelengths with successive projection algorithm and developing prediction models for determining N content in citrus leaves. The authors obtained three optimal pretreatment methods through comparing eleven different pretreatment methods including Savitzky-Golay (SG) smoothing, standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (1-Der) and so on. These selected pretreatment methods are SG smoothing, detrending and SG smoothing-detrending. Based on these three pretreatment methods, the authros first extracted the characteristic wavelengths respectively with successive projection algorithm, and then used the spectral reflectance of the extracted characteristic wavelengths as input variables of partial least squares regression (PLS), multiple linear regression (MLR) and back propagation neural network (BPNN) modeling. Hence, the authors developed three prediction models with each pretreatment method, and obtained nine models in total. Among all the nine prediction models, the two models based on the methods of SG smoothing-detrending-SPA-BPNN (R(p): 0.8513, RMSEP: 0.1881) and detrending-SPABPNN (R(p): 0.8609, RMSEP: 0.1595) were found to have achieved the best prediction results. The final results show that using hyperspectra data to determine N content in citrus leaves is feasible. This would provide a theoretical basis for real-time and accurate monitoring of N content in citrus leaves as well as rational N fertilizer application during the plant's growth.
    Guang pu xue yu guang pu fen xi = Guang pu 01/2014; 34(1):212-6. · 0.29 Impact Factor
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    ABSTRACT: As an emerging biometric technology, palm print recognition technology has been researched widely and developed rapidly, which is well known for its high verification accuracy, low-price capture device, high reliability and user acceptance. Feature extraction as a core part of palm print recognition, can effectively extract the characteristics or not, largely determines the system recognition rate. Reviewing current feature extraction research, we roughly group the palm print feature extraction algorithms into four categories: structure-based, statistics-based, subspace-based and texture & transform domain feature based methods. The purpose of this paper is to provide an updated survey of palm print feature extraction. After analyzing the state-of-the-art methods, the performance of various methods was compared and the future tendency was also discussed.
    Proceedings of the 2013 Fourth International Conference on Intelligent Systems Design and Engineering Applications; 11/2013
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    ABSTRACT: Quality and safety of foods is one of the world’s top topics. Using high-precision spectral devices is a main technology trends by its high accuracy and nondestructive of food inspection, but the common obstacle is how to extract informative variables from raw data without losing significant information. This article proposes a novel feature selection algorithm named Support Vector Machine-Multiclass Forward Feature Selection (SVM-MFFS). SVM-MFFS adopts the wrapper and forward feature selection strategy, explores the stability of spectral variables, and uses classical SVM as classification and regression model to select the most relevant wavelengths from hundreds of spectral data. We compare SVM-MFFS with Successive Projection Analysis and Uninformative Variable Elimination in the experiment of identifying different brands of sesame oil. The results show that SVM-MFFS outperforms in accuracy, Receiver Operating Characteristic curve, Prediction and Cumulative Stability, and it will provide a reliable and rapid method in food quality inspection.
    Journal of Food Engineering 11/2013; 119(1):159–166. · 2.28 Impact Factor
  • Jia-Jia Yu, Yong He
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    ABSTRACT: The present paper put forward the technology route for feature images extraction of grey mold sick on tomato leaves based on SIMCA--combination image extraction based on MLR-grey mold sick information extraction based on minimum distance method. Firstly, through the 680-740 nm band's variance image and the discrimination power parameter, the feature band images was found, then the feature bands information was used as the input of MLR analysis, and under the 0.5 accuracy threshold value, 99% accuracy was obtained, which showed the discrimination power of the features bands for grey mold sick tomato leaf detection, and using the MLR regression coefficient to extract a band combination image, and through the minimum distance method, tomato grey mold sick information was found. The result shows that the proposed method has a very good prediction ability and greatly reduces the hyperspectral data processing time.
    Guang pu xue yu guang pu fen xi = Guang pu 08/2013; 33(8):2168-71. · 0.29 Impact Factor

Publication Stats

868 Citations
226.51 Total Impact Points

Institutions

  • 2005–2014
    • Zhejiang University
      • School of Biosystems Engineering and Food Science
      Hang-hsien, Zhejiang Sheng, China
  • 2013
    • Hangzhou University
      Hang-hsien, Zhejiang Sheng, China
  • 2009
    • Zhejiang University of Technology
      Hang-hsien, Zhejiang Sheng, China
  • 2008
    • Xiamen University
      • Department of Physics
      Xiamen, Fujian, China
    • Zhejiang Technical Institute of Economics
      Hang-hsien, Zhejiang Sheng, China
  • 2007
    • Clemson University
      Clemson, South Carolina, United States