Analysis of wheat grain development using NIR spectroscopy

Salgó, András; Gergely, Szilveszter

doi:10.1016/j.jcs.2012.04.011

Cited by 58 publications

(25 citation statements)

References 44 publications

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“…The carbohydrate accumulation observed in both genotypes of triticales increased rapidly at the early stage (7-20 DAA), reaching the maximum at the medium stage (20-22 DAA) and decreasing slightly or remaining relatively constant at the last stage of kernel development (22-40 DAA) ( Table I). The total carbohydrate contents of the six triticales used in this study were similar to those reported by Salgó and Gergely (2012) for winter wheat. The carbohydrate content of all triticales showed no significant differences at the early stage of development.…”

Section: Resultssupporting

confidence: 86%

Starch Debranching Enzyme Activity and Its Effects on Some Starch Physicochemical Characteristics in Developing Substituted and Complete Triticales (X Triticosecale Wittmack)

et al. 2015

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Cereal Chem. 93(1):64-70The present work was carried out to make a comparison between the starch debranching enzyme activity and the synthesis of total carbohydrates, starch, amylose, and amylopectin in developing kernels of two sets of triticales differing in genome composition (complete and substituted triticales). The results showed that the carbohydrate and starch accumulation observed in both genotypes of triticales increased rapidly at the early stage, reaching the maximum at the medium stage and decreasing slightly or remaining relatively constant at the last stage of kernel development. At the end of the development stage, the mature grains of complete and substituted triticales accumulated 62.5 and 54.6% starch, respectively. Both sets of triticales showed the same amylose accumulation profile as well as degree of polymerization; however, at the maturity stage there was a small but significant difference between the two sets. The final polymerization values of amylose chains in mature seeds of complete and substituted triticales were DP 1,549 and 1,313. A and B 1 amylopectin chains of the complete triticales were approximately 19 and 33% shorter than those of the substituted triticales. The complete triticales showed higher isoamylase and pullulanase activities than the substituted triticales, which coincided with the highest starch and amylose contents observed in kernels under development. The results of this study demonstrate that the synthesis of starch differs between the triticale genotypes used in the study, which influences their chemical structure and physicochemical properties. † Corresponding

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Section: Resultssupporting

confidence: 86%

Starch Debranching Enzyme Activity and Its Effects on Some Starch Physicochemical Characteristics in Developing Substituted and Complete Triticales (X Triticosecale Wittmack)

et al. 2015

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“…In health sector, NIRS has been used for glucose monitoring in human serum (Goodarzi & Saeys, 2016), for the development of an early alert system for cerebral hypoxaemia (Cruz et al, 2016) and for the measurement of muscle oxygenation (Olivier et al, 2016). Recently, application of nearinfrared spectroscopy has also been reported in food and grain industry for the quality prediction of pork (Huang et al, 2014), fermented cocoa (Kr€ ahmer et al, 2015), dried egg-pasta (Bevilacqua et al, 2013), potato chips (Ni et al, 2011), fish fillet (Alamprese & Casiraghi, 2015) and wheat flour (Cocchi et al, 2006), to determine quality parameters of food grains such as amylose and protein content of rice (Xie et al, 2014), moisture, carbohydrate and protein content of wheat grain (Salg o & Gergely, 2012), biophysical characteristics of barley starch (Cozzolino et al, 2013) and protein, starch and seed weight of kidney bean (Phaseolus vulgaris) (Hacisalihoglu et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Green method for determination of phenolic compounds in mung bean (Vigna radiata L.) based on near‐infrared spectroscopy and chemometrics

Meenu

Kamboj

Sharma

et al. 2016

Int J of Food Sci Tech

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Summary Mung bean grains exhibit high level of antioxidant activity due the presence of phenolic compounds. Near‐infrared spectroscopy (NIRS), in conjunction with chemometrics, was used to develop a rapid, nondestructive, chemical free and easy‐to‐use method to determine these compounds in sixty genotypes of mung bean. NIRS calibration curve with high‐performance liquid chromatography (HPLC) as reference method was used to determine phenolic compounds (catechin, chlorogenic acid, caffeic acid, p‐coumaric acid, t‐ferulic acid, vitexin, isovitexin, myricetin, quercetin and kaempferol). It was observed that partial least square regression (PLSR) model in the wavelength range of 1600–2500 nm with standard normal variate (SNV) and linear baseline correction (LBC) as preprocessing techniques can measure phenolic compound accurately (R2 > 0.987) with root‐mean‐square error less than 1.82%. This study shows that NIRS along with chemometrics is an accurate method to estimate the phenolic compounds rapidly and nondestructively.

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“…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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Analysis of wheat grain development using NIR spectroscopy

Cited by 58 publications

References 44 publications

Starch Debranching Enzyme Activity and Its Effects on Some Starch Physicochemical Characteristics in Developing Substituted and Complete Triticales (X Triticosecale Wittmack)

Starch Debranching Enzyme Activity and Its Effects on Some Starch Physicochemical Characteristics in Developing Substituted and Complete Triticales (X Triticosecale Wittmack)

Green method for determination of phenolic compounds in mung bean (Vigna radiata L.) based on near‐infrared spectroscopy and chemometrics

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

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