Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of Valencia oranges

Jamshidi, Bahareh; Minaei, Saeid; Mohajerani, Ezeddin; Ghassemian, Hassan

doi:10.1016/j.compag.2012.03.008

Cited by 127 publications

(62 citation statements)

References 22 publications

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“…The main difference is the chlorophyll content absorption hole (around 700 nm), disappearing as the fruit ripens with the position of peak change. Meanwhile, 760 nm was also found which could be due to the third overtone of O-H and the fourth overtone of C-H; the O-H and C-H functional groups are related to the concentration of some inner compositions with these bands such as soluble solid contents [22]. So, 640 nm, 760 nm (red edges), and 670 nm (for chlorophyll content) were considered as the characteristic wavelengths in this study.…”

Section: Spectra and Multispectral Indexes Considerationsmentioning

confidence: 85%

Navel Orange Maturity Classification by Multispectral Indexes Based on Hyperspectral Diffuse Transmittance Imaging

Wei

et al. 2017

Journal of Food Quality

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Maturity grading is important for the quality of fruits. Nondestructive maturity detection can be greatly beneficial to the consumer and fruit industry. In this paper, a hyperspectral image of navel oranges was obtained using a diffuse transmittance imaging based system. Multispectral indexes were built to identify the maturity with the hyperspectral technique. Five indexes were proposed to combine the spectra at wavelengths of 640, 760 nm (red edges), and 670 nm (for chlorophyll content) to grade the navel oranges into three maturity stages. The index of ( 670 + 760 − 640 )/( 670 + 760 + 640 ) seemed to be more appropriate to classify maturity, especially to distinguish immature oranges that can be straightly identified in accordance with the value of this index (( 670 + 760 − 640 )/( 670 + 760 + 640 )). Different indexes were used as the input of linear discriminate analysis (LDA) and of -nearest neighbor ( -NN) algorithm to identify the maturity, and it was found that -NN with ( 670 + 760 − 640 )/( 670 + 760 + 640 ) could reach the highest correct classification rate of 96.0%. The results showed that the built index was feasible and accurate in the nondestructive classification of oranges based on the hyperspectral diffuse transmittance imaging. It will greatly help to develop low-cost and real-time multispectral imaging systems for the nondestructive detection of fruit quality in the industry.

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Section: Spectra and Multispectral Indexes Considerationsmentioning

confidence: 85%

Navel Orange Maturity Classification by Multispectral Indexes Based on Hyperspectral Diffuse Transmittance Imaging

Wei

et al. 2017

Journal of Food Quality

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“…This can be explained by their molecule structures being composed of different chemical bonds such as C−H, O−H and C=O and the number of chemical bonds also effects an absorbance (Burns & Ciurczak, 2001;Osborne et al, 1993). Almost of the peaks were in wavelength range 902-988 nm, which includes water, carbohydrates and acid absorption bands due to their chemical bonds vibrating in various ways, such as OH stretching (second overtone of OH stretch at 960 nm) and CH stretching (third overtone stretch at 910 nm) (Williams & Norris, 2001;Jamshidi et al, 2012;Zude et al, 2006). As up to 20% by weight of each substance was added to the puree, the calibrations were expected to rely heavily on the water content of the mixture and indeed wavelengths between 970 nm to 988 nm showed that water is the main contributor to the PLSR models for glucose, sucrose, cellulose, citric acid and malic acid.…”

Section: Discussionmentioning

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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“…The apparent absorption peaks at approximately 760 nm appeared on the ARS curves. The 700-780 nm region presented one obvious absorption peak particularly, which could be attributed to the O-H third overtones and C-H forth overtones [38,39]. Because too much noise existed in region of 388-400 nm and 1000-1045 nm in the original ARS and corrected images, the remaining region from 400 to 1000 nm was employed for further analysis.…”

Section: Ars Analysis and Preprocessingmentioning

confidence: 99%

Integration of Artificial Neural Network Modeling and Hyperspectral Data Preprocessing for Discrimination of Colla Corii Asini Adulteration

Wang

et al. 2018

Journal of Food Quality

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The study of hyperspectral imaging in tandem with spectral preprocessing and neural network techniques was conducted to realize Colla Corii Asini (CCA, E'jiao) adulteration discrimination. CCA was adulterated with pig skin gelatin (PSG) in the range of 5-95% (w/w) at 5% increments. Three methods were used to pretreat the original spectra, which are multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, and the combination of MSC and SG (MSC-SG). SPA was employed to select the characteristic wavelengths (CWs) to reduce the high dimension. Colour and texture features of CWs were extracted as input of prediction model. Two kinds of artificial neural network (ANN) with three spectral preprocessing methods were applied to establish the prediction models. The prediction model of generalized regression neural network (GRNN) in tandem with the MSC-SG preprocessed method presented satisfactory performance with the correct classification rate value of 92.5%. The results illustrated that the integration of preprocessing methods, hyperspectral imaging features, and ANN modeling had a great potential and feasibility for CCA adulteration discrimination.

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Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of Valencia oranges

Cited by 127 publications

References 22 publications

Navel Orange Maturity Classification by Multispectral Indexes Based on Hyperspectral Diffuse Transmittance Imaging

Navel Orange Maturity Classification by Multispectral Indexes Based on Hyperspectral Diffuse Transmittance Imaging

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

Integration of Artificial Neural Network Modeling and Hyperspectral Data Preprocessing for Discrimination of Colla Corii Asini Adulteration

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