Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging: Part I. Development of a prototype

Ariana, Diwan P.; Lu, Renfu

doi:10.1007/s11694-008-9057-x

Cited by 45 publications

(29 citation statements)

References 20 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%

Section: Instrumentationmentioning

confidence: 99%

“…It can operate in three different imaging modes, including single reflectance (400-1000 nm) and transmittance (400-1000 nm) modes and the integrated reflectance (400-675 nm) and transmittance (675-1000 nm) mode. As illustrated schematically in Figure 12, the system consists of three basic units, i.e., illumination, imaging and conveying [82]. The hyperspectral imaging unit is composed of a high-performance 12-bit CCD camera, a line-scan imaging spectrograph attached to the camera and a focusing lens connected to the front side of the spectrograph.…”

Section: Instrumentationmentioning

confidence: 99%

“…Higher speed is desirable in practical applications, but it will sacrifice spatial resolution, possibly leaving undetected some small defects; while higher spatial resolution, on the other hand, can increase the workload of image acquisition and also result in an unmanageable data volume. For instance, in experiments for quality inspection of pickling cucumbers, the conveyor belt speed was set to 110 mm/s, and the camera was operated at an exposure time of 50 ms and a delay time of 50 ms [82]. As such, the prototype ran at a speed of 1-2 product items per second per lane, scanning a portion of each sample (about half of the surface area of the sample with the given exposure and delay times).…”

Section: Image Acquistion and Pre-processingmentioning

confidence: 99%

“…If the split images is 'blank', indicating no sample present in the scene, it will be discarded from analysis; otherwise, the image is 'non-blank', containing sample information, and is kept. Consecutive 'non-blank' images between 'blank' images are identifying as coming from the same sample [82]. The split images are then processed for in-line spectral correction by dividing the spectra of references.…”

Section: Image Acquistion and Pre-processingmentioning

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: Instrumentationmentioning

confidence: 99%

Section: Instrumentationmentioning

confidence: 99%

Section: Image Acquistion and Pre-processingmentioning

confidence: 99%

Section: Image Acquistion and Pre-processingmentioning

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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Near‐Infrared Hyperspectral Imaging in Food Research

Geladi

Manley

2010

Raman, Infrared, and Near‐Infrared Chemical Imaging

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It has been known for a very long time that food products can be studied by mid-infrared (MIR) (ca. 2500-15,000 nm) and near-infrared (NIR) (780-2500 nm) spectroscopy, as they contain the C-H, O-H, and N-H bonds that have high absor-banceintheNIRandMIRwavelengthregions.Thesebondsare present in the major constituents of all biological materials. Already during the 1960s and 1970s the pioneers of NIR spectroscopy were mainly interested in food applications, for example, soybeans [1], meat [2], oilseeds [3], and cereals [3][4][5][6][7]. It was found very early that water, fat, protein, and different carbohydrates could be quantified by NIR calibrationsforawidevariety ofagriculturalproducts,half-fabricates, and finished consumer products as presented by various authors [8][9][10][11][12]. Later, constituents such as inorganic salts [13], alcohol [14], fatty acids [15], antioxidants [16,17], and phenolic compounds [17,18] were also quantified, as were physical parameters such as kernel hardness [19][20][21][22][23], maturity [24], and sensory quality [24][25][26]. These analyses are usually done on bulk materials, from which a single NIR spectrum is obtained, as the goal is to integrate over an area that is as large as possible in order to avoid sampling errors. Most food products are inhomogeneous by nature, thus requiring integration or homogenization before bulk NIR measurements. NIR Hyperspectral ImagingThe earliest scientific imaging applications were simple black and white or color images in the visual spectroscopic range, but inspired by satellite imaging, multivariate image analysis [27] was soon becoming useful in the laboratory. Already the first satellite images included wavelengths in the NIR, in addition to visual and MIR wavelengths. Nowadays, hyperspectral imaging [28] is becoming more commonly available, providing complete spectra extending into the long-wave NIR (1100-2500 nm) for each pixel in an image (refer to Chapter 2). The simplest of these images have x and y spatial coordinates and a wavelength coordinate lambda, making a three-way array called a hypercube. This hyperspectral image or hypercube thus allows the description of differences and gradients in the sample under study. Because of this, the samples can be heterogeneous. The spectra in these hyperspectral images show localized spectral features that can be used for exploration and classification. With external information (reference data) also localized, quantitative and qualitative calibrations and predictions are feasible. NIR hyperspectral imaging (NHI) also means that sample preparation different from bulk NIR spectroscopy is needed. Grinding and other homogenization methods are useful for bulk NIR spectroscopic analysis, whereas sampling and sample preparation for NIR imaging have their own peculiarities.Raman, Infrared, and Near-Infrared Chemical Imaging Edited by Slobodan Š ašić and Yukihiro Ozaki

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The Use of Non‐destructive Techniques to Assess the Nutritional Content of Fruits and Vegetables

Amodio

Chaudhry

Colelli

2017

Fruit and Vegetable Phytochemicals

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Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging: Part I. Development of a prototype

Cited by 45 publications

References 20 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

Near‐Infrared Hyperspectral Imaging in Food Research

The Use of Non‐destructive Techniques to Assess the Nutritional Content of Fruits and Vegetables

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