Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry

Liú, Dan; Sun, Da‐Wen; Zeng, Xin‐An

doi:10.1007/s11947-013-1193-6

Cited by 320 publications

(144 citation statements)

References 97 publications

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“…One way to overcome this problem is to implement the hyperspectral imaging technique in conjunction with multivariate methods which will preprocess the original obtained spectrum and decrease the amount of data by identifying effective wavelengths for rapid and accurate quantitative or qualitative analysis of food quality (Liu et al, 2014b). Preprocessing of the original spectrum is necessary to make the samples easier to handle and establish a stable and reliable basis for the forecasting model.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the unique potential of the imaging spectroscopy technique, many researchers have been attracted to this powerful analytical technique for detection of many different types of products, including meat (Kamruzzaman et al,, Barbin et al, 2012), fruit Naes, 1988, Rajkumar et al, 2012), and vegetables Slaughter, 2011, Diezma et al, 2013). However, a hyper-spectral imaging system acquires a substantial number of hyper-spectral images, each composed of a large amount of information, which complicates the process of predicting the value of any single dependent variable.One way to overcome this problem is to implement the hyperspectral imaging technique in conjunction with multivariate methods which will preprocess the original obtained spectrum and decrease the amount of data by identifying effective wavelengths for rapid and accurate quantitative or qualitative analysis of food quality (Liu et al, 2014b). Preprocessing of the original spectrum is necessary to make the samples easier to handle and establish a stable and reliable basis for the forecasting model.…”

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confidence: 99%

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Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Hou

Zhou

et al. 2016

FSTR

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This paper presents a study that was performed for rapid and noninvasive detection of waxed chestnuts using hyper-spectral imaging. A visual near-infrared (400 _ 1026 nm) hyper-spectral imaging system was assembled to acquire scattering images from two groups of chestnuts (waxed and non-waxed chestnuts). The spectra of the samples were extracted from the hyper-spectral images using image segmentation process. Then multiplicative scatter correction (MSC) was conducted to preprocess the original spectra. Effective wavelengths were selected to reduce the computational burden of the hyper-spectral data. Using the seven effective wavelengths that were obtained from a successive projections algorithm (SPA), three calibration algorithms were compared: partial least squares regression (PLSR), multiple linear regression (MLR) and linear discriminant analysis (LDA). The best model for discriminating between waxed and non-waxed chestnuts was found to be the MSC-SPA-MLR model.Keywords: hyper-spectral imaging, waxed chestnut, effective wavelength, pear IntroductionThe chestnut (Castanea mollissima Bl.) is an important edible fruit in the northern hemisphere, which has been consumed as extensively as the potato in the past (Ferreira-Cardoso et al., 1999).and has become increasingly important in human nutrition because of its nutrient composition and potential beneficial health effects, for example, in reducing coronary heart disease and cancer rates (Sabaté et al., 2001). Fried chestnut is the most popular preparation method for chestnuts. However, some unscrupulous traders add industrial wax while frying chestnuts to ensure that the chestnuts are brighter and more attractive. Generally, industrial wax is extracted directly from oil, which will also extract polycyclic aromatic hydrocarbons and polycyclic aromatic hydrocarbons which are present in the industrial process, both of which are highly carcinogenic (Bonser et al., 1963). Mixed industrial wax will infiltrate into the pulp in the process of frying chestnuts which seriously endangers human health. Hence, a sorting process is necessary to identify and screen out waxed chestnuts.Traditionally, screening waxed chestnuts is usually dependent on subjective criteria to distinguish between waxed and non-waxed chestnuts, taking into consideration that brighter and more attractive chestnuts are mixed with industrial wax. Although industrial wax will make chestnuts brighter and more attractive, it is not a reliable way to identify waxed chestnuts. Hyper-spectral imaging (also called imaging spectrometry or imaging spectroscopy) is an emerging analytical technology which is receiving growing attention as a rapid, efficient, and nondestructive analytical tool for quantitative determination of the quality and safety of agricultural goods. By combining the advantages of computer vision and spectroscopy technology into one system, hyper-spectral imaging can generate a spatial image of the spectral variation, which provides structural, chemical, physical and functional information abou...

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Hou

Zhou

et al. 2016

FSTR

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“…In many situations, dimensionality reduction can improve model performance and model characteristics by identifying and removing useless, noisy and redundant variables. 42 Therefore, in this research, the possibility of dimensionality reduction was evaluated using the Bartlett test and Kaiser-Meyer-Olkin (KMO) test. Subsequently, following the reports of Refs.…”

Section: Manual Selection Of Regions Of Interest (Rois)mentioning

confidence: 99%

Evaluation of Expert Systems Techniques for Classifying Different Stages of Coffee Rust Infection in Hyperspectral Images

Castro¹,

Oblitas²,

Maicelo³

et al. 2018

IJCIS

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In this work, the use of expert systems and hyperspectral imaging in the determination of coffee rust infection was evaluated. Three classifiers were trained using spectral profiles from different stages of infection, and the classifier based on a support vector machine provided the best performance. When this classifier was compared to visual analysis, statistically significant differences were observed, and the highest sensitivity of the selected classifier was found at early stages of infection.

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“…The second phase consists of evaluating candidate subsets of variables according to the root-mean-square error of validation (RMSEV) value obtained by applying the resulting multilinear regression (MLR) model. Then, variable elimination procedures can be used to remove uninformative variables without significant loss of prediction capability (Galvao et al 2008;Liu et al 2014). Many successful applications have proven SPA to be an outstanding variable selection approach Fan et al 2014;Wu et al 2012;Feng and Sun 2012;Kamruzzaman et al 2013).…”

Section: Spectral Analysismentioning

confidence: 99%

“…In order to remove the redundancy of hyperspectral image data, simplify the complexity of computation, and select the candidate optimal wavelength that carry the most important information, SPA was carried out on the full spectral data collected from the training set. More details about SPA method can be found in studies of Galvao et al (2008) and Liu et al (2014).…”

Section: Spectral Analysismentioning

confidence: 99%

Detection of Early Rottenness on Apples by Using Hyperspectral Imaging Combined with Spectral Analysis and Image Processing

Zhang

Fan²,

Li³

et al. 2015

Food Anal. Methods

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Detection of early rottenness on apples is still a challenging task for the automatic grading system due to the highly similarity between the rotten and sound tissues both in spectral and spatial domains. This research was conducted to develop an algorithm for detecting the early rottenness on apples by using hyperspectral reflectance imaging system combined with spectral analysis and image processing. In spectral domain, chemometric and pattern recognition methods were conducted for spectral analysis. In order to select the candidate optimal wavelengths that carry the most important information for distinguishing the rottenness from the sound tissues, successive projections algorithm (SPA) was conducted on the full range of average spectra extracted from the (regions of interest (ROIs) of sound and rotten tissues. The efficiency of the selected candidate optimal wavelengths for rottenness detection was also testified by using a binary particle least square discriminant analysis (PLS-DA) classifier in the spectral domain. In spatial domain, combined image processing methods were conducted for spatial analysis. In order to verify that the images at the optimal wavelengths were efficient and develop a robust detection algorithm, both principal component analysis (PCA) and minimum noise fraction (MNF) combined with conventional image processing methods were conducted on the images at the optimal wavelengths for image processing. Finally, the whole detection algorithm based on SPA-PLS-DA-MNF by using hyperspectral imaging combined with spectral analysis and image processing was designed and testified by the 120 testing apples. The results with 98 % overall detection accuracy indicated that the proposed algorithm was efficient and suitable for the rottenness apple detection. This research provides a foundational basis to develop the fast online inspection or real-time monitoring system for rottenness detection on apples both in the postharvest processing line or storage shelf.

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Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry

Cited by 320 publications

References 97 publications

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Evaluation of Expert Systems Techniques for Classifying Different Stages of Coffee Rust Infection in Hyperspectral Images

Detection of Early Rottenness on Apples by Using Hyperspectral Imaging Combined with Spectral Analysis and Image Processing

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