Article

Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest Biol Technol

Postharvest Biology and Technology (Impact Factor: 2.22). 04/2008; 48(1). DOI: 10.1016/j.postharvbio.2007.09.019
Source: OAI

ABSTRACT

Hyperspectral scattering is a promising technique for nondestructive sensing of multiple quality attributes of apple fruit. This research evaluated and compared different mathematical models for describing the hyperspectral scattering profiles over the spectral region between 450 nm and 1000 nm in order to select an optimal model for predicting fruit firmness and soluble solids content (SSC) of 'Golden Delicious' apples. Ten modified Lorentzian distribution functions of various forms were proposed to fit the spectral scattering profiles at individual wavelengths, each of which gave superior fitting to the data with the average correlation coefficient (r) being greater than 0.995. Mathematical equations were derived to correct the spectral scattering intensity and distance by taking into account the instrument response and individual apples' size. The 10 modified Lorentzian distribution functions were compared for predicting fruit firmness and SSC using multi-linear regression and cross-validation methods. The modified Lorentzian function with three parameters (representing scattering peak value, width and slope) gave good predictions of fruit firmness with r =0.894 and the standard error of prediction (S.E.P.) of 6.14N, and of SSC with r =0.883 and S.E.P.=0.73%. Twenty-one and 23 wavelengths were needed to obtain the best predictions of fruit firmness and SSC, respectively. This new function, coupled with the scattering profile correction methods, improved the hyperspectral scattering technique for measuring fruit quality.

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    • "Similarly, Lieó et al[2]found that the images collected at 800 nm, 675 nm and 450 nm were correlated with the hardness and maturity of peaches upon harvest. The hardness and contents of soluble solids of apple were also previously assessed by Peng et al[3]using the images taken at different wavelengths . Citrus fruits have been extensively studied in this aspect due to their wide distribution and large yield. "
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    ABSTRACT: Visible light imaging of citrus fruit from Mie Prefecture of Japan was performed to determine whether an algorithm could be developed to predict the sugar content. This nondestructive classification showed that the accurate segmentation of different images can be realized by a correlation analysis based on the threshold value of the coefficient of determination. There is an obvious correlation between the sugar content of citrus fruit and certain parameters of the color images. The selected image parameters were connected by addition algorithm. The sugar content of citrus fruit can be predicted by the dummy variable method. The results showed that the small but orange citrus fruits often have a high sugar content. The study shows that it is possible to predict the sugar content of citrus fruit and to perform a classification of the sugar content using light in the visible spectrum and without the need for an additional light source.
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    • "Differing with analytical technology, optical technologies are gaining its importance in recent decade for real time and rapid detection of agricultural products (Dhakal et al., 2011; Peng and Lu, 2008; Qin et al., 2010; Zhang et al., 2006; Liu et al., 2012). In recent few years Raman technology has gained its importance for detection of trace amount of food additives such as melamine (Qin et al., 2010), pesticide content in fruits (Liu et al., 2012; Li et al., 2012; Liu et al., 2013), lycopene changes in tomatoes (Qin et al., 2011). "
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    ABSTRACT: Different chemicals are sprayed in fruits and vegetables before and after harvest for better yield and longer shelf-life of crops. Cases of pesticide poisoning to human health are regularly reported due to excessive application of such chemicals for greater economic benefit. Different analytical technologies exist to detect trace amount of pesticides in fruits and vegetables, but are expensive, sample destructive, and require longer processing time. This study explores the application of Raman spectroscopy for rapid and non-destructive detection of pesticide residue in agricultural products. Raman spectroscopy with laser module of 785 nm was used to collect Raman spectral information from the surface of Gala apples contaminated with different concentrations of commercially available organophosphorous (48% chlorpyrifos) pesticide. Apples within 15 days of harvest from same orchard were used in this study. The Raman spectral signal was processed by Savitzky-Golay (SG) filter for noise removal, Multiplicative Scatter Correction (MSC) for drift removal and finally polynomial fitting was used to eliminate the fluorescence background. The Raman spectral peak at 677 cm-1 was recognized as Raman fingerprint of chlorpyrifos. Presence of Raman peak at 677 cm-1 after fluorescence background removal was used to develop classification model (presence and absence of pesticide). The peak intensity was correlated with actual pesticide concentration obtained using Gas Chromatography and MLR prediction model was developed with correlation coefficient of calibration and validation of 0.86 and 0.81 respectively. Result shows that Raman spectroscopy is a promising tool for rapid, real-time and non-destructive detection of pesticide residue in agro-products.
    No preview · Conference Paper · May 2014
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    • "Differing with analytical technology, optical technologies are gaining its importance in recent decade for real time and rapid detection of agricultural products (Dhakal et al., 2011; Peng and Lu, 2008; Qin et al., 2010; Zhang et al., 2006; Liu et al., 2012). In recent few years Raman technology has gained its importance for detection of trace amount of food additives such as melamine (Qin et al., 2010), pesticide content in fruits (Liu et al., 2012; Li et al., 2012; Liu et al., 2013), lycopene changes in tomatoes (Qin et al., 2011). "
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    ABSTRACT: Apple is one of the highly consumed fruit and also a major source of pesticide carrier to human health. This study explores the application of Raman spectroscopy for detection of commercially available organophosphorus (chlorpyrifos) pesticide in apple surface. Optical instrument prototype equipped with Raman spectroscopy system with 785 nm laser excitation source was developed for non-destructive, rapid and accurate detection of pesticide residue in apple surface, overcoming the loopholes of traditional detection methods. Software was self developed to control the functionality of Raman CCD, acquire and process the Raman spectral data and display result in real time. The samples detected by the developed system were tested in High Performance Liquid Chromatography. The result shows that the developed system can detect chlorpyrifos residue to minimum limit of 6.69 mg/kg in apple surface within less than 4 s. This innovative and promising system can be a breakthrough technology for pesticide detection in fruits and vegetables.
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