Prediction of apple fruit firmness and soluble solids content using characteristics of multispectral scattering images

Peng, Yankun; Lu, Renfu

doi:10.1016/j.jfoodeng.2006.12.027

Cited by 107 publications

(50 citation statements)

References 24 publications

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“…Hence there are basically four sensing configurations, as shown in Figure 6, for measuring scattering images. The wavelength dispersive device is used to disperse the incoming broadband light into separate wavelengths, and it can be a line-scan imaging spectrograph using a prism-grating-prism configuration for hyperspectral scattering imaging [43,48], or a common aperture multi-channel imaging spectrograph for multispectral measurement [25,49]. In addition, multispectral measurement can also be achieved using a LCTF [50][51][52], or a mechanical rotating filter wheel containing four to six bandpass filters [24].…”

Section: Spectral Scattering Imaging (Ssi) Configurationsmentioning

confidence: 99%

“…In addition to conventional reflectance imaging mode, we have developed several innovative hyperspectral imaging-based techniques or platforms, including spectral scattering for evaluation of firmness and soluble solids content (SSC) [24,25], full transmittance for internal defect detection [26,27], integrated reflectance and transmittance for detecting surface and internal quality or defect [28,29], and spatially-resolved spectroscopy for measuring the optical absorption and scattering properties of fruits, vegetables and other food products [30,31].…”

Section: Introductionmentioning

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 Imaging (Ssi) Configurationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Huang

2017

Applied Sciences

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“…Another method which also injures the sample either partially visible or invisible is use of mechanical methods comprising dynamic force-deformation, impact and sonic principle. Firmness of apples and other fresh fruits using these principles have been determined (Galili et al 1998, Ozer et al 1998, Stone et al 1998, Sugiyama et al 1998, McGlone et al 1999, Zude et al 2006, Peng and Lu 2007. These mechanical methods differ in the instrumentation setup and the properties measured and their correlation with destructive measurement techniques.…”

Section: Measurement Of Various Quality Parameters Of Mangomentioning

confidence: 99%

Quality parameters of mango and potential of non-destructive techniques for their measurement — a review

et al. 2010

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The king of fruits "Mango" (Mangifera indica L.) is very nutritious and rich in carotenes. India produces about 50% of the total world's mango. Many researchers have reported the maturity indices and quality parameters for determination of harvesting time and eating quality. The methods currently used for determination of quality of mango are mostly based on the biochemical analysis, which leads to destruction of the fruits. Numerous works are being carried out to explore some non-destructive methods such as Near Infrared (NIR), Nuclear Magnetic Resonance (NMR), X-ray and Computed Tomography (CT), electronic nose, machine vision and ultrasound for quality determination of fruits. This paper deals with some recent work reported on quality parameters, harvesting and post-harvest treatments in relation to quality of mango fruits and reviews on some of the potential non-destructive techniques that can be explored for quality determination of mango cultivars.

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“…Therefore, a calibration model is required to establish the relationship between the component of interest and the spectrum. In fact, a combination of near infrared spectroscopy and calibration model has been successfully implemented in non-destructive SSC assessment of various fruits such as apple (Ventura et al, 1998;Peirs et al, 2003;Liu and Ying, 2004;Alamar et al, 2007;Peng and Lu, 2007;Zou et al, 2007;Fan et al, 2009;Zhu et al, 2009), citrus (Lu et al, 2006;Zude et al, 2008), loquat (Fu et al, 2009) mango He, 2009), mangosteen (Teerachaichayut et al, 2007), melon (Long, 2005;Tian et al, 2007), and pineapple (Pathaveerat et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, numerous studies have been published concerning the use of linear models [e.g., multiple linear regression (MLR) (Ventura et al, 1998;Peng and Lu, 2007;Jha and Garg, 2010) and partial least square (PLS) (Peirs et al, 2003;Xing et al, 2005;Alamar et al, 2007;Zou et al, 2007;Fan et al, 2009;Zhao et al, 2009;Zhu et al, 2009;Jha and Garg, 2010)] for the internal quality measurement of apples, using spectroscopic technology. However, the implementation of nonlinear models [e.g., least squares support vector machine (LS-SVM) and least squares support vector regression (LS-SVR) (Liu et al, 2010)] has been reported to be better than that of linear models (e.g., PLS).…”

Section: Introductionmentioning

confidence: 99%

Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison

Chia

Rahim

2012

J. Zhejiang Univ. Sci. B

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Abstract:Visible and near infrared spectroscopy is a non-destructive, green, and rapid technology that can be utilized to estimate the components of interest without conditioning it, as compared with classical analytical methods. The objective of this paper is to compare the performance of artificial neural network (ANN) (a nonlinear model) and principal component regression (PCR) (a linear model) based on visible and shortwave near infrared (VIS-SWNIR) (400-1000 nm) spectra in the non-destructive soluble solids content measurement of an apple. First, we used multiplicative scattering correction to pre-process the spectral data. Second, PCR was applied to estimate the optimal number of input variables. Third, the input variables with an optimal amount were used as the inputs of both multiple linear regression and ANN models. The initial weights and the number of hidden neurons were adjusted to optimize the performance of ANN. Findings suggest that the predictive performance of ANN with two hidden neurons outperforms that of PCR.

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Prediction of apple fruit firmness and soluble solids content using characteristics of multispectral scattering images

Cited by 107 publications

References 24 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

Quality parameters of mango and potential of non-destructive techniques for their measurement — a review

Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison

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