Hyperspectral Scattering for assessing Peach Fruit Firmness

Lu, Renfu; Peng, Yankun

doi:10.1016/j.biosystemseng.2005.11.004

Cited by 226 publications

(110 citation statements)

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“…Single laser-based techniques, although having shown correlation between scattering features and fruit firmness, have limitations as the selected wavelength may be not optimal or insufficient for predicting firmness and other quality attributes like SSC. Our laboratory first proposed the concept of using spectral scattering imaging, including multispectral and hyperspectral, to measure scattering characteristics to enhance fruit quality assessment [24,43]. Multispectral scattering imaging allows acquiring scattering images at several discrete key wavelengths, while hyperspectral imaging enables acquisition of scattering images over a series of contiguous wavelengths, thus enhancing the detection capability.…”

Section: Spectral Scattering Imagingmentioning

confidence: 99%

“…A smaller beam, on the other hand, albeit desirable for scattering measurement, results in lower intensities of images acquired and a lower signal-to-noise ratio (SNR) as well. A beam size of 1.6 mm in diameter is often used for acquiring scattering images from apple fruit with the maximum scattering area of about 25 mm in diameter [43,48]. …”

Section: Spectral Scattering Imaging (Ssi) Configurationsmentioning

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: Spectral Scattering Imagingmentioning

confidence: 99%

Section: Spectral Scattering Imaging (Ssi) Configurationsmentioning

confidence: 99%

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

Huang

2017

Applied Sciences

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“…Also, these techniques could obviously be applied only to a limited number of samples, and the resulting information was extended to the whole batch of fruits, with inaccuracies due to the high levels of biological variability. The need to assess the quality of each single fruit, in order to improve consumer satisfaction and thus more profitability along the commercial chain, has pushed researchers to develop better non-invasive techniques (Lu and Peng, 2006). Different techniques have been developed in connection with the quality parameter to be evaluated (Cen et al, 2012;Dahm and Dahm, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…In a diffusive medium such as a fruit, in the visible and near infrared spectral region, light scattering is stronger than light absorption, so that light can be scattered many times before being either absorbed or re-emitted from the medium: multiple scattering of light (Cubeddu et al, 2002). During this process photons are also absorbed, so that the scattering profile at the surface of the fruit is influenced by, or related to, both absorption and scattering properties of the fruit (Lu and Peng, 2006). The scattering probability per unit length for non-isotropic propagation of photons is described by the reduced scattering coefficient µ s ' = (1 -g)µ s , where g is the anisotropy factor, that is the mean cosine of scattering angle and µ s =1/l s , l s being the photon mean free path between successive scattering events.…”

Section: Introductionmentioning

confidence: 99%

A Preliminary Approach to Assess Peach Fruit Texture by Time-Resolved Spectroscopy (Trs)

Attanasio¹,

Piagnani²,

Chiozzotto³

et al. 2015

Acta Hortic.

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Most fruits can be modeled for their internal composition as a diffusive medium at visible and near-infrared wavelengths. The low absorption in this spectral range allows exploiting VIS/NIR spectroscopic techniques to probe nondestructively the internal food properties. Time-resolved Reflectance Spectroscopy (TRS), in particular, allows the separate but simultaneous estimation of absorption and scattering coefficients. Absorption and scattering properties give very different information about the medium investigated. The pigments and flesh constituents produce characteristic spectral features, so that absorption spectra determination gives information concerning the chemical composition. Light scattering inside the medium is due to local variation of the refractive index, and thus scattering spectrum provides information concerning the structural characteristics of the fruits. The possibility of applying TRS to assess the texture type of peach fruit was tested. To this purpose, an instrument for TRS developed at Politecnico di MilanoDepartment of Physics, was exploited. At least two cultivars for each peach flesh phenotypes (melting, non melting, stony hard and slow melting) and a total of 30 fruits for each cultivar were analyzed over the spectral range 540-940 nm. The absorption spectra exhibit high values around 550 nm, due to the anthocyanins' absorption features. Furthermore, an absorption peak is visible at 670 nm, linked to the chlorophyll-a content and, then, gives an idea about the fruit ripeness (a higher chlorophyll content corresponds to a less ripe fruit). With the exception of the cultivar 'Iride', particularly rich in anthocyanins, and 'Ghiaccio', a cultivar totally depigmented, the absorption spectra of all the samples are similar. Concerning the scattering properties of peaches, by considering the equivalent density and the scatter power Mie parameters, it is possible to discriminate between three out of four texture types (melting, slow melting and stony hard). Further improvements may lead to a full discrimination in the future.

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“…Hyperspectral imaging technology has been used widely in detection of food defects (Kong et al, 2004), assessment of food quality (Ariana and Renfu, 2010;Fontaine et al, 2002;Lu and Peng, 2006;Martens and Martens, 1986;Okamoto and Lee, 2009), classification of near-isogenic crop genotypes (Nansen et al, 2008), and in detection of surface contaminations (Lefcout and Kim, 2006;Mehl et al, 2004;Park et al, 2006;Vargas et al, 2005). Comprehensive reviews of the use of imaging technology in detection of traits in food products have been published (Gowen et al, 2007;Wang and Paliwal, 2007).…”

Section: Introductionmentioning

confidence: 99%

Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation

Nansen

Zhang

Aryamanesh

et al. 2014

Journal of Food Engineering

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In this study, we acquired 72 (training data) and 30 (independent validation) high-spatial resolution (7 by 7 pixels per mm 2 ) hyperspectral imaging data [240 spectral bands from 392 to 889 nm (spectral resolution = 2.1 nm)] from samples of field peas (Pisum sativum) with and without pea weevil (Bruchus pisorum) infestation. The reflectance data were analyzed with linear discriminant analysis (LDA) or either reflectance values only or of a combination of reflectance values and variogram parameters (derived from variogram analysis) from a single spectral band (782 nm). All examined classification models were assessed based on sensitivity (ability to positively detect infestation), specificity (ability to positively detect noninfestation), and accurate classification of 30 samples of independent validation data. Highest classification performance was obtained with a combination of reflectance values in two spectral bands (641 nm and 868 nm) and variogram parameters derived from 782 nm. This classification model was associated with a sensitivity of 94.7% and a specificity of 100%. In addition, all 30 independent validation data were accurately classified (100%). For comparison, traditional linear discriminant analyses of 108,351 reflectance profiles from individual pixels in the 72 samples or average reflectance profiles from the 72 samples classified the validation data with 84.0% and 83.3% accuracy, respectively. We are unaware of any reflectance-based classification system, which -based on reflectance data acquired in only three channels (spectral bands) -can provide this level of sensitivity and specificity and classification of independent validation data of internal defects in food products. Accurate and reliable classification of food objects based reflectance values in only a few spectral bands would likely imply low computer processing requirements and rapid data analysis. Thus, we believe that the current classification method may be useful for quality control systems of a wide range of food products.

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Hyperspectral Scattering for assessing Peach Fruit Firmness

Cited by 226 publications

References 16 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

A Preliminary Approach to Assess Peach Fruit Texture by Time-Resolved Spectroscopy (Trs)

Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation

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