Nondestructive detection of brown core in the Chinese pear ‘Yali’ by transmission visible–NIR spectroscopy

Han, Dong; Tu, Runlin; Lü, Chao; Liu, Xinxin; Wen, Zhigang

doi:10.1016/j.foodcont.2005.03.006

Cited by 59 publications

(29 citation statements)

References 10 publications

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“…In the visible region of the spectrum, colorimetry has been used to determine the color of the skin of food and, in the near infrared region, the spectrum of re-emitted light has been studied mainly to estimate the total sugar content [1][2][3][4][5], internal defects [6][7][8][9] and firmness [4,[10][11][12]. Referring to the optical technique, a key limitation is that the total reflected intensity depends on photon loss due to light absorption and on photons scattered into different directions than the one of observation, without separating the effects of these properties.…”

Section: Introductionmentioning

confidence: 99%

Time-resolved reflectance spectroscopy for non-destructive assessment of food quality

Torricelli

Spinelli

Contini

et al. 2008

Sens. & Instrumen. Food Qual.

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In the majority of most food and feed, visible, and near infrared light undergoes multiple scattering events and the overall light distribution is determined more by scattering rather than absorption due to the microscopic spatial changes in the refractive index. Conventional steady state reflectance spectroscopy can provide information on light attenuation, which depends both on light absorption and light scattering, but cannot separate these two effects. In contrast, time-resolved reflectance spectroscopy (TRS) allows more detailed optical characterization of diffusive media in terms of their absorption coefficient and reduced scattering coefficient. From the assessment of the absorption and reduced scattering coefficients, information can then be derived on the composition and internal structure of the medium. The main advantages of the technique are the absolute non-invasiveness, the potentiality for non-contact measurements and the capacity to probe internal properties with no influence from the skin. In this work we review the physical and technical issues related to the use of TRS for non-destructive quality assessment of fruit and vegetable. A laboratory system for broadband TRS, based on tunable mode-locked lasers and fast micro-channel plate photomultiplier and a portable set-up for TRS measurements, based on pulsed diode lasers and compact metal-channel photomultiplier, are described. Results on broadband optical characterization of fruits and applications of TRS to the detection of internal defects in pears and to maturity assessment in nectarines are presented.

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Section: Introductionmentioning

confidence: 99%

Time-resolved reflectance spectroscopy for non-destructive assessment of food quality

Torricelli

Spinelli

Contini

et al. 2008

Sens. & Instrumen. Food Qual.

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show abstract

“…Among these methods, substantial work has focused on using near-infrared spectroscopy (NIRS) to inspect internal defects, like brown core in Chinese pears [7], brown heart in Braeburn apples [8,9] and in Conference pears [10], translucent flesh disorder in mangosteen [11], black air-cavity defects in Japanese radishes [12], and mealiness in apples [13]. Although NIRS is a fast, nondestructive, and effective method for measuring multiple quality attributes simultaneously [14], its measurement only gives an approximate quantification of the total light on a limited area and does not provide spatially resolved information.…”

Section: Introductionmentioning

confidence: 99%

Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm

Song

Sun

et al. 2016

Applied Sciences

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Radishes with black hearts will lose edible value and cause food safety problems, so it is important to detect and remove the defective ones before processing and consumption. A hyperspectral transmittance imaging system with 420 wavelengths was developed to capture images from white radishes. A successive-projections algorithm (SPA) was applied with 10 wavelengths selected to distinguish defective radishes with black hearts from normal samples. Pearson linear correlation coefficients were calculated to further refine the set of wavelengths with 4 wavelengths determined. Four chemometric classifiers were developed for classification of normal and defective radishes, using 420, 10 and 4 wavelengths as input variables. The overall classifying accuracy based on the four classifiers were 95.6%-100%. The highest classification with 100% was obtained with a back propagation artificial neural network (BPANN) for both calibration and prediction using 420 and 10 wavelengths. Overall accuracies of 98.4% and 97.8% were obtained for calibration and prediction, respectively, with Fisher's linear discriminant analysis (FLDA) based on 4 wavelengths, and was better than the other three classifiers. This indicated that the developed hyperspectral transmittance imaging was suitable for black heart detection in white radishes with the optimal wavelengths, which has potential for fast on-line discrimination before food processing or reaching storage shelves.

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“…This technique can be used to measure chemical composition such as sucrose content of peach (Kawano et al, 1992), soluble solid content of Satsuma mandarin (Kawano et al, 1993), pineapple (Guthrie et al, 1998), apple (Lovász et al, 1994;McGlone et al, 2003), kiwifruit (McGlone & Kawano, 1998;Schaare & Fraser, 2000), mango (Saranwong et al, 2001), prune (Slaughter et al, 2003), Gannan navel orange (Liu et al, 2010), watermelon , banana (Subedi et al, 2011) and jujube (Wang et al, 2011), and acidity of mango (Schmilovitch et al, 2000) and Satsuma mandarin (Miyamoto 1998;Gomez et al, 2006). Moreover, it can be applied to detect the presence of damaged tissue such as brownheart of Braeburn apple (McGlone et al, 2005), scald, scab tissue and recent bruise of Jonagold apple (Kleynen et al, 2003), and browncore of Chinese pear (Han et al, 2006), and to estimate physiological variables such as maturity of Pawpaw papaya (Greensill & Newman, 1999) and Scarlet apple (Bertone et al, 2012). Mango is a tropical climate fruit with large export markets in Asia, Europe and North America (Litz, 2009).…”

Section: Introductionmentioning

confidence: 99%

Selecting Variables for Near Infrared Spectroscopy (NIRS) Evaluation of Mango Fruit Quality

Theanjumpol

Self

Rittiron

et al. 2013

JAS

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Near infrared spectroscopy (NIRS) can be applied to assess the quality of mango. The purpose of this research is to select the appropriate chemical absorption bands to evaluate two cultivars of mango puree, cv. Keitt and cv. Nam Dok Mai Si Thong. Six main chemical substances found in mango fruit, such as glucose, sucrose, citric acid, malic acid, starch and cellulose, were evaluated in this study and there chemical absorption bands were identified. Mango puree was mixed with the six pure substances at various concentrations; glucose, sucrose, citric acid and malic acid were tested with concentrations of 0, 5, 10, 15 and 20%w/w, starch and cellulose were tested with the concentrations 0, 2.5, 5, 7.5 and 10% w/w. The NIRSystem 6500 was used to scan the spectra in the wavelength range from 400 nm to 1100 nm. The partial least square regression (PLSR) was used to develop a model for each component. The result was a wavelength that corresponds to each component. It was found that the second derivative spectra of glucose, sucrose, citric acid, malic acid, starch and cellulose mixtures showed the best PLSR result. The mango cultivar had no effect on wavelength selection by PLSR model. The coefficient of determination (R 2 ) of all models was 0.99. The standard error of calibration (SEC) and the standard error of prediction (SEP) were less than 0.5%w/w. The regression coefficient plot exhibited more sharp peaks than pure substances. The wavelength selection for NIRS evaluation mango fruit quality could not be done by using only measured spectrum of pure substance. However, the cultivars of mango had no effected on wavelength selection by PLSR model. The most effective wavelength for glucose and sucrose were 900-1000 nm, citric acid and malic acid were 800-1000 nm, starch was 900-1000 nm and cellulose was 800-1000 nm.

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Nondestructive detection of brown core in the Chinese pear ‘Yali’ by transmission visible–NIR spectroscopy

Cited by 59 publications

References 10 publications

Time-resolved reflectance spectroscopy for non-destructive assessment of food quality

Time-resolved reflectance spectroscopy for non-destructive assessment of food quality

Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm

Selecting Variables for Near Infrared Spectroscopy (NIRS) Evaluation of Mango Fruit Quality

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