Hyperspectral waveband selection for internal defect detection of pickling cucumbers and whole pickles

Ariana, Diwan P.; Lu, Renfu

doi:10.1016/j.compag.2010.07.008

Cited by 64 publications

(29 citation statements)

References 21 publications

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“…Most research on hyperspectral imaging has focused on the reflectance measurement, which is appropriate for evaluating surface or subsurface quality characteristics but not for internal defects in food products. Our group proposed a new concept of integrating reflectance and transmittance measurements in one hyperspectral imaging system for detecting both external quality attributes (e.g., color and size) and internal defects [28,[82][83][84][85][86][87][88]. The concept is based on the fact that the visible light in the region of 400-675 nm, which is relatively poor in penetrating biological tissues, is more suitable for assessing surface features of the product, while the red and NIR light in the region of 675-1000 nm has better penetration capabilities and can thus be used for internal quality assessment in transmittance mode.…”

Section: Integrated Reflectance and Transmittance Imagingmentioning

confidence: 99%

“…Identifying effective wavelengths is critical for implementation in a multispectral imaging mode, which greatly reduces the time for image acquisition and can also improves the detection performance. Ariana and Lu [84] acquired hyperspectral reflectance (500-740 nm) and transmittance (740-1000 nm) images from normal and defective cucumbers and pickles ( Figure 16). A branch and bound algorithm combined with the k-nearest neighbor (K-NN) classifier was utilized for wavelength selection.…”

Section: Quality Evaluation For Pickling Cucumbers and Picklesmentioning

confidence: 99%

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Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Huang

2017

Applied Sciences

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New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of sensing technology for non-destructive food quality and safety evaluation, because it integrates the major features of imaging and spectroscopy, thus enabling the acquisition of both spectral and spatial information from an object simultaneously. This paper first provides a brief overview of hyperspectral imaging configurations and common sensing modes used for food quality and safety evaluation. The paper is, however, focused on the three innovative hyperspectral imaging-based techniques or sensing platforms, i.e., spectral scattering, integrated reflectance and transmittance, and spatially-resolved spectroscopy, which have been developed in our laboratory for property and quality evaluation of fruits, vegetables and other food products. The basic principle and instrumentation of each technique are described, followed by the mathematical methods for processing and extracting critical information from the acquired data. Applications of these techniques for property and quality evaluation of fruits and vegetables are then presented. Finally, concluding remarks are given on future research needs to move forward these hyperspectral imaging techniques.

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Section: Integrated Reflectance and Transmittance Imagingmentioning

confidence: 99%

Section: Quality Evaluation For Pickling Cucumbers and Picklesmentioning

confidence: 99%

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Huang

2017

Applied Sciences

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“…Although NIRS is a fast, nondestructive, and effective method for measuring multiple quality attributes simultaneously [14], its measurement only gives an approximate quantification of the total light on a limited area and does not provide spatially resolved information. Hence, NIRS may not comprehensively evaluate internal defects in fruits and vegetables that may occur locally along the cavity [15].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Cen et al [20] reported internal defect detection of cucumbers during pickling using hyperspectral imaging for real-time grading. Their previous studies also demonstrated the feasibility of detecting both external characteristics and internal defects of pickling cucumbers [15,21]. Pan et al [22] conducted a study to detect cold injury in peaches.…”

Section: Introductionmentioning

confidence: 99%

Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm

Song

Sun

et al. 2016

Applied Sciences

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Radishes with black hearts will lose edible value and cause food safety problems, so it is important to detect and remove the defective ones before processing and consumption. A hyperspectral transmittance imaging system with 420 wavelengths was developed to capture images from white radishes. A successive-projections algorithm (SPA) was applied with 10 wavelengths selected to distinguish defective radishes with black hearts from normal samples. Pearson linear correlation coefficients were calculated to further refine the set of wavelengths with 4 wavelengths determined. Four chemometric classifiers were developed for classification of normal and defective radishes, using 420, 10 and 4 wavelengths as input variables. The overall classifying accuracy based on the four classifiers were 95.6%-100%. The highest classification with 100% was obtained with a back propagation artificial neural network (BPANN) for both calibration and prediction using 420 and 10 wavelengths. Overall accuracies of 98.4% and 97.8% were obtained for calibration and prediction, respectively, with Fisher's linear discriminant analysis (FLDA) based on 4 wavelengths, and was better than the other three classifiers. This indicated that the developed hyperspectral transmittance imaging was suitable for black heart detection in white radishes with the optimal wavelengths, which has potential for fast on-line discrimination before food processing or reaching storage shelves.

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“…Ariana & Lu, [10] used Hyper spectral imaging to detect the defect internal option for pickling, however, the technique still cannot meet the online speed requirement because of the need to acquire and analyze a large amount of image data. Hyper spectral reflectance characteristics was used to discriminate different geographical origins of Jatropha curcas L. seeds to estimate chlorophyll content and to detect macronutrients content in oil seeds.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Spectral Characteristics of Different Physiological Stages of Some Olive cvs and Its Relation with Productivity

El-Wesemy

Abdelghany

Abohadid

et al. 2016

IJARSG

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Remote sensing satellite imagery is the tool to obtain synoptic, multi-temporal, dynamic and timely efficient information about any target on Earth. The main objective of the current study is to use remote sensing satellite data and field spectral reflectance measurements to identify the spectral pattern of the different cultivars of olives and to statistically correlate this spectral reflectance pattern with crop productivity. The study was carried out in El-Beheira governorate (Wadi El Natrun) city during the whole year of 2014. The three observed varieties were Picual, Manzanillo and Kalamata. Measurements were carried out for five growth stages: dormancy stage, flowering stage, fruit sat stage, mature stage and ripening stage. The spectral reflectance pattern for each cultivar through the different growth stages was identified. Then, seven vegetation indices (normalized difference vegetation index (NDVI), modified chlorophyll absorption ration index (MCARI), triangular vegetation index (TVI), modified chlorophyll absorption ration index-1 (MCARI-1), modified chlorophyll absorption ration index-2 (MCARI-2), modified triangular vegetation index-2 (MTVI2) and chlorophyll index (CI)) were calculated through the five growth stages for each cultivar and then were observed as estimators for crop yield modeling. Analysis of the result based on the comparison of the correlation coefficient (r 2 ) for all generated models, the target is to identify the optimal vegetation index and the optimal growth stage to predict yield for each variety. Generally, Manzanillo variety showed the highest reflectance followed by Picual and Kalamata. The result showed that the highest (r 2 ) was with the two cultivars Picual and Kalamata during mature stage, while the highest (r 2 ) was with cultivar Manzanillo during fruit sat stage. While the lowest (r 2 ) was found during dormancy stage for the three cultivars.

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Hyperspectral waveband selection for internal defect detection of pickling cucumbers and whole pickles

Cited by 64 publications

References 21 publications

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm

Assessment of the Spectral Characteristics of Different Physiological Stages of Some Olive cvs and Its Relation with Productivity

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