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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: 2.63). 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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    • "This means mostly information is used from the apple skin or just below the skin. So, the chemical composition of the skin appears to be correlated with the changes in SSC values (Peng and Lu 2008). The starch prediction shows that information further from the illumination spot can be used to build good prediction models. "
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    ABSTRACT: The storage potential of apples highly depends on the maturity at harvest. Optical methods have been proposed to measure maturity in a fast, reliable and non-destructive way. However, the signal is often composed of photons with different penetration depths into the material. An attempt to separate these photons might result in more precise correlations with quality attributes, as these could relate to a specific layer/depth into the sample. Therefore, a Vis/NIR spatially resolved laser reflectance setup was used, combining a supercontinuum laser and a monochromator to illuminate samples with a monochrome focused beam in the 550–1000 nm wavelength range. A panchromatic camera was used to obtain diffuse reflectance profiles at each wavelength. In the period starting 50 days before until 11 days after commercial harvest, 320 Braeburn apples were measured. Partial least squares regression models were developed to relate apple maturity/quality to the diffuse reflectance spectra at different distances from the illumination point. The effect of detector size (spatial bandwidth) was also evaluated. A bandwidth of 0.82 mm in combination with a parameter specific illumination-detection distance, gave the best results. Using an internal test set, an R 2 of prediction of 0.98 and 0.93, and a ratio of prediction to performance (RPD) of 5.84 and 3.42, predicting, respectively, the Streif index and starch conversion values was obtained. The predictions of soluble solids content (SSC) (R 2 of 0.81; RPD of 2.04) and firmness (R 2 of 0.65; RPD of 1.66) were less accurate. Also, worse predictions were obtained using an external test set.
    Food and Bioprocess Technology 07/2015; 8(10). DOI:10.1007/s11947-015-1562-4 · 3.13 Impact Factor
    • "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.
    SPIE Sensing Technology + Applications; 05/2014
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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.
    Journal of Food Engineering 02/2014; 123:94-103. DOI:10.1016/j.jfoodeng.2013.09.025 · 2.58 Impact Factor
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