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Featured researches published by Renfu Lu.


2006 Portland, Oregon, July 9-12, 2006 | 2006

Visible/Near-Infrared Hyperspectral Transmittance Imaging for Detection of Internal Mechanical Injury in Pickling Cucumbers

Diwan P. Ariana; Renfu Lu

Internal product quality is an important aspect in the quality control and assurance of npickled products. A rapid and non-destructive method for internal defect detection would be of value nto the pickling cucumber industry. A hyperspectral transmittance imaging technique was developed nto detect internal mechanical injury in the form of carpel suture separation or hollow cucumbers. nPartial least squares discriminant analysis (PLS-DA) and hyperspectral image thresholding methods nwere used to classify cucumber samples into defective or normal classes. Transmittance spectra of ndefective and normal cucumbers were similar in shape but higher in magnitude for the defective ncucumbers. Transmittance values for both defective and normal cucumbers were higher in the nearinfrared nrange of 700-950 nm than that of the visible range (450-700nm). The hyperspectral image nthresholding method resulted in higher classification accuracies compared to PLS-DA. An overall nclassification accuracy up to 94.3% percent was achieved. The hyperspectral transmittance imaging ntechnique has the potential for rapid detection of internal mechanical injury in pickling cucumbers.


2009 Reno, Nevada, June 21 - June 24, 2009 | 2009

Optical Properties of Bruised Apple Tissue

Renfu Lu; Haiyan Cen; Min Huang; Diwan P. Ariana

Understanding optical properties of apple tissue, especially bruised tissue, can help us develop an effective method for detecting bruises during sorting and grading. This research was conducted on determining the optical properties of bruised apple tissue over 500-1,000 nm and quantifying their changes with time. Spectral absorption and reduced scattering coefficients were determined from the normal, unbruised tissues of Golden Delicious and Red Delicious apples and then bruised tissues at different time intervals after bruising, using a hyperspectral imaging-based spatially resolved technique. Absorption for normal tissues was generally lower than that for the bruised tissues in the spectral region of less than 600 nm, while an opposite trend was observed in the spectral region of 800-1,000 nm. The reduced scattering coefficient for normal tissues was much higher than that for the bruised tissues; it decreased consistently over time. Bruising had greater impact on scattering than on absorption. Hence an optical system that can enhance scattering features would be better suited for bruise detection.


2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010 | 2010

Integration of Hyperspectral Scattering Characteristics and Image Analysis Techniques for Improved Prediction of Apple Fruit Firmness and Soluble Solids Content

Fernando Mendoza; Renfu Lu; Diwan P. Ariana; Haiyan Cen; Benjamin B. Bailey

Spectral scattering is useful for assessing the firmness and soluble solids content (SSC) of apples. In previous research, mean reflectance extracted from the hyperspectral scattering profiles was used for this purpose since the method is simple and fast and also gives relatively good predictions. The objective of this study was to improve firmness and SSC prediction for Golden Delicious (GD), Jonagold (JG), and Red Delicious (RD) apples by integration of critical spectral and image features extracted from the hyperspectral scattering images over the wavelength region of 500-1,000 nm, using spectral scattering profile and image analysis techniques. Scattering profile analysis was based on mean reflectance method and discrete and continuous wavelet transform decomposition, while image analysis included textural features based on first order statistics, Fourier analysis, co-occurrence matrix and variogram analysis, as well as multiresolution image features obtained from discrete and continuous wavelet analysis. A total of 294 parameters were extracted by these methods from each apple, which were then selected and combined for predicting fruit firmness and SSC using partial least squares (PLS) method. Prediction models integrating spectral scattering and image characteristics have improved firmness and SSC prediction results compared with the mean reflectance method when used alone. The standard errors of prediction (SEP) for GD, JG, and RD apples were reduced by 6.6, 16.1, 13.7% for firmness (R-values of 0.87, 0.95, and 0.84 and the SEPs of 5.9, 7.1, and 8.7 N), and by 11.2, 2.8, and 3.0% for SSC (R-values of 0.88, 0.78, and 0.66 and the SEPs of 0.7, 0.7,and 0.9 °Brix), respectively.


2009 Reno, Nevada, June 21 - June 24, 2009 | 2009

Wavebands selection for a hyperspectral reflectance and transmittance imaging system for quality evaluation of pickling cucumbers

Diwan Prima Ariana; Renfu Lu

Hyperspectral imaging under transmittance mode has shown promising results for detecting internal defect in pickling cucumbers, however, the technique still cannot meet the online speed requirement because it needs to acquire and process a large amount of image data. This study was conducted on selecting important wavebands as a basis for developing an online imaging system to detect internal defect in pickling cucumbers. Journey pickling cucumbers were subjected to mechanical stress to induce damage in the seed cavity. Hyperspectral transmittance/reflectance images were acquired from normal and defective cucumbers using a prototype hyperspectral reflectance (400-740 nm)/ transmittance (740-1,000 nm) imaging system. Optimal wavelengths were determined by correlation analysis on single, ratio, and difference of two pairs of wavelengths. A global image thresholding method was applied to the selected spectral images to identify defective cucumbers. Images at 740 nm were the best for single waveband classification with an overall accuracy of 87%. For ratios of two wavebands, 925 nm and 940 nm resulted in an overall classification accuracy of 85%, and for differences of two wavebands, images at 745 nm and 850 nm were the best with a classification accuracy of 91%. All the selected wavebands were in the near infrared region, which is more effective for internal defect detection compared to the visible region.


2011 Louisville, Kentucky, August 7 - August 10, 2011 | 2011

Data Fusion of Visible/Near-infrared Spectroscopy and Spectral Scattering for Apple Quality Assessment

Fernando Mendoza; Renfu Lu; Haiyan Cen

Visible/near-infrared (VNIR) spectroscopy and spectral scattering are based on different sensing principles, and they have shown different abilities for predicting apple fruit firmness and soluble solids content (SSC). Hence the two techniques could work synergistically to improve the quality prediction of apples. In this research, VNIR spectroscopic and spectral scattering data for the wavelength range of 460–1,100 nm were collected for 6,631 apples of ‘Delicious, Golden Delicious and Jonagold cultivars during the 2009 and 2010 harvest seasons and for three months after the refrigerated air storage. Partial least squares models were developed for each sensor and their combination to predict the fruit firmness and SSC for both single-year and cross-year data sets. The spectral scattering technique generally performed better in predicting firmness, whereas the VNIR technique was superior for prediction of SSC. Overall, the data fusion of the two sensors produced significant improvements (p<0.05) for prediction of the firmness and SSC than individual sensors. Cross-year prediction results for firmness and SSC were lower, compared with prediction results for each year. However, the cross-year prediction model for SSC was more robust and less sensitive to the harvest-year effect, compared to the firmness prediction model. Sensor fusion can provide more robust and accurate firmness and SSC assessment for apples.


Archive | 2015

Basics of Image Analysis

Fernando Mendoza; Renfu Lu

Image analysis is used as a fundamental tool for recognizing, differentiating, and quantifying diverse types of images, including grayscale and color images, multispectral images for a few discrete spectral channels or wavebands (normally less than 10), and hyperspectral images with a sequence of contiguous wavebands covering a specific spectral region (e.g., visible and near-infrared). Earlier works on image analysis were primarily confined to the computer science community, and they mainly dealt with simple images for such applications as defect detection, segmentation and classification. Nowadays, image analysis is becoming increasingly important and widespread because it can be done more conveniently, rapidly and cost effectively (Prats-Montalban et al. 2011). Image analysis relies heavily on machine vision technology (Aguilera and Stanley 1999). The explosive growth in both hardware platforms and software frameworks has led to significant advances in the analysis of digital images.


Food Processing Automation Conference Proceedings, 28-29 June 2008, Providence, Rhode Island | 2008

Development of a Hyperspectral Imaging System for Online Quality Inspection of Pickling Cucumbers

Renfu Lu; Diwan P Ariana

This paper reports on the development of a hyperspectral imaging prototype for evaluation of external and internal quality of pickling cucumbers. The prototype consisted of a two-lane round belt conveyor, two illumination sources (one for reflectance and one for transmittance), and a hyperspectral imaging unit. It had a novel feature of simultaneous imaging under reflectance mode covering the visible region of 400-675 nm and transmittance mode for 675-1000 nm, coupled with real-time, continuous calibration of reflectance and transmittance images for each cucumber using reference standards installed on the conveyor. Reflectance information was used for evaluating the external characteristics of cucumbers (i.e., skin color), transmittance for internal defect detection (hollow center), and the combined reflectance and transmittance for predicting flesh firmness. The prototype was tested on ‘Journey’ pickling cucumbers harvested in 2006 and 2007 for predicting skin and flesh color, flesh firmness, and internal defect. Hyperspectral images were processed to extract mean spectra for individual cucumbers, and partial least squares analysis was performed to predict flesh firmness, skin and flesh color, and the presence of internal defect. The prototype performed relatively well in predicting skin color with the coefficient of determination of 0.76 and 0.75 for chroma and hue respectively; however, it had poor prediction of flesh color and firmness. Transmittance data in the spectral region of 675-1000 nm provided excellent detection of internal defect for the pickling cucumbers, with the detection accuracy greater than 90%. The hyperspectral imaging technique would be useful for online inspection of surface color and internal defect on picking cucumbers.


2008 Providence, Rhode Island, June 29 - July 2, 2008 | 2008

Internal Defect Detection of Whole Pickles Using Hyperspectral Transmittance and Reflectance Imaging

Diwan P Ariana; Renfu Lu

Hyperspectral imaging technique in simultaneous reflectance and transmittance modes was investigated for detection of hollow or bloater damage on whole pickles that was caused by mechanical injury during harvesting and handling or developed during the brining process. Normal and bloated pickle samples were collected from a commercial pickle processing plant. Hyperspectral images were acquired from the pickle samples using a hyperspectral reflectance and transmittance imaging system covering the spectral range of 400-1000 nm. Principal component analysis was applied to the hyperspectral images of the pickle samples, and the second principal component score images were used for defect detection by means of image segmentation method. An overall classification accuracy of 86% was achieved using this method. Transmittance images at 675-1000 nm were much more effective for internal defect detection compared to reflectance images for the visible region of 500-675 nm. With further improvement, the hyperspectral imaging system could meet the need of bloated pickles detection in a commercial plant setting.


2013 Kansas City, Missouri, July 21 - July 24, 2013 | 2013

Assessing the sensitivity and robustness of prediction models for apple firmness using spectral scattering technique

Fernando Mendoza; Renfu Lu; Qibing Zhu


2013 Kansas City, Missouri, July 21 - July 24, 2013 | 2013

Comparison of optimal wavelengths selection methods for visible/near-infrared prediction of apple firmness and soluble solids content

Qibing Zhu; Min Huang; Renfu Lu; Fernando Mendoza

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Haiyan Cen

Michigan State University

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Diwan P. Ariana

Michigan State University

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Diwan Prima Ariana

American Society of Agricultural and Biological Engineers

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