Near Infrared Spectroscopy—Advanced Analytical Tool in Wheat Breeding, Trade, and Processing

Pojić, Milica; Mastilović, Jasna

doi:10.1007/s11947-012-0917-3

Cited by 67 publications

(52 citation statements)

References 116 publications

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“…Over the past years, spectroscopy techniques have been proved to be useful for food quality assessment (Abdel-Nour, Ngadi, Prasher, & Karimi, 2011;Alexandrakis, Downey, & Scannell, 2012;Bao et al, 2014;Lu, Al-Qadiri, Lin, & Rasco, 2011;Pojic & Mastilovic, 2013;Shahin, Symons, & Hatcher, 2014;Vongsvivut et al, 2014;Zhang et al, 2014), including fish, crab meat, and salmon flesh (Kimiya, Sivertsen, & Heia, 2013;Qu et al, 2014). In addition, spectroscopic techniques have also been used for authentication and discrimination between frozen-thawed and unfrozen foodstuffs including beef, red sea bream, and swordfish (Fasolato et al, 2012;Uddin et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Discrimination of shelled shrimp (Metapenaeus ensis) among fresh, frozen-thawed and cold-stored by hyperspectral imaging technique

Cheng

Sun

et al. 2015

LWT - Food Science and Technology

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Section: Introductionmentioning

confidence: 99%

Discrimination of shelled shrimp (Metapenaeus ensis) among fresh, frozen-thawed and cold-stored by hyperspectral imaging technique

Cheng

Sun

et al. 2015

LWT - Food Science and Technology

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“…Recently, spectrally based noninvasive techniques were developed for evaluating chemical characteristics of cereals (Cozzolino et al 2013;Pojić and Mastilović 2013;Rasch et al 2010;Roa et al 2014;Shahin et al 2014), especially for its quality screening (Kawamura et al 2003). In particular, two techniques, spectroscopy and digital imaging, were used to provide information about the spectral profile and textural information of cereals.…”

Section: Introductionmentioning

confidence: 99%

“…Spectra generated from rice of different quality or variety was found to be dramatically different (Qingyun et al 2007). Many published papers (Baye et al 2006;Salgó and Gergely 2012;Sirisomboon et al 2009) have reported the feasibility of visible and near-infrared (Vis/NIR) and/or NIR spectroscopy for discriminating the variety and quality of cereals with varying degrees of success, such as quantification of nitrogen status in rice (Shao et al 2012), prediction of maize seed composition (Baye et al 2006), analysis of wheat grain development (Pojić and Mastilović 2013;Salgó and Gergely 2012) and nondestructive evaluation of defect pods for green soybean processing (Sirisomboon et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice

Wang

Liu

et al. 2014

Food Anal. Methods

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This paper investigated the use of hyperspectral imaging (HSI) to discriminate the variety and quality of rice. Hyperspectral images (400-1,000 nm) of paddy rice samples were acquired to extract both spectral and image information. Dimension reduction was carried out on the region of interest (ROI) of the images by principal component analysis (PCA). The first principal components (PCs) explained over 98 % of variances of all spectral bands. Chalkiness degree and shape feature ('MajorAxisLength', 'MinorAxisLength', lengthwidth ratio, 'Perimeter' and 'Eccentricity') were further extracted and used for subsequent rice variety discrimination by PCA and back propagation neural network (BPNN). An integration of spectral and image data was used for BPNN classification. The BPNN model based on spectral data (seven optimal wavelengths) achieved better results than PCA based on spectral data (seven optimal wavelengths) in variety discrimination with classification accuracy of 89.18 and 89.91 % for PCA and BPNN model, respectively. The BPNN model based on data fusion achieved the best results (94.45 %), which was superior to the results based on spectral data (89.91 %) or image data (88.09 %) alone. Finally, the resulting classification maps were able to visualize different rice varieties. The results demonstrated that discrimination of rice variety and quality with HSI technology was feasible and could be utilized for quality control purposes and/or for innovative sorting of rice.

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“…Cork has numerous applications though the most important of these in economic terms is the manufacture of different types of cork stoppers to be used in the bottling of wines (Sánchez-González et al 2016). It is the suitability of the raw cork material for this production that establishes its commercial value (Pereira 2007). Agglomerated, micro-agglomerated and technical stoppers are made from cork granulate which is obtained by grinding the cork that is not suitable for the production of natural cork stoppers and disks, as well as from the waste generated during cork manufacturing (Sánchez-González et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Feasibility study of near infrared spectroscopy to detect yellow stain on cork granulate

Pérez-Terrazas¹,

González-Adrados²,

Sánchez-González³

2018

iForest

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The aim of this study was to evaluate the viability of near infrared spectroscopy (NIRS) to detect the anomaly known as yellow stain on cork granulate. Detecting this anomaly is crucial to the cork granulate stopper industry, since it is associated with the presence of 2,4,6-Trichloroanisole (TCA), this compound having been identified as the main agent responsible for cork offflavours. Samples for the NIRS spectra were prepared by mixing in different proportions cork granulate with high visual quality and cork granulate with yellow stain, obtaining 120 samples with 8 different percentages of yellow stain (0, 5, 10, 15, 25, 35, 50 and 100%). Two spectra per sample were collected using a Bruker MPA spectrophotometer and the partial least squares (PLS) method was used to obtain numerous equations. The best equation was obtained by utilizing the standard normal variate (SNV) spectral preprocessing, making use of only one specific part of the near infrared spectral range: 9400-4250 cm -1 . This equation shows a coefficient of determination (R²) of 99.42%, a root mean square error of cross validation (RMSECV) of 2.34%, and a residual prediction deviation (RPD) of 13.10. The critical level and the limit of detection are 3.8% and 7.6%, respectively. The calculated receiver operating characteristic (ROC) curves show great discrimination capacity and the area under the ROC curve (AUC) is higher than 0.93 in any case. This study demonstrates that NIRS provides a viable technique for detecting yellow stain in cork granulate.

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Near Infrared Spectroscopy—Advanced Analytical Tool in Wheat Breeding, Trade, and Processing

Cited by 67 publications

References 116 publications

Discrimination of shelled shrimp (Metapenaeus ensis) among fresh, frozen-thawed and cold-stored by hyperspectral imaging technique

Discrimination of shelled shrimp (Metapenaeus ensis) among fresh, frozen-thawed and cold-stored by hyperspectral imaging technique

Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice

Feasibility study of near infrared spectroscopy to detect yellow stain on cork granulate

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