Protein content prediction in single wheat kernels using hyperspectral imaging

Abstract

HighlightsHSI was applied for non-destructive prediction of total protein content in wheat kernels.Above 2100 wheat kernels were taken from ~200 batches and individually analysed.PLS regression models had R2 = 0.82 and prediction error lower than 0.93%.Protein distribution had wide range (6–20%) and was visualised by applying the calibration.The performance of HgGcTe was superior to the one built by simulating InGaAs sensors.

Advancing the science of wheat quality evaluation using nuclear magnetic resonance (NMR) and ultrasound‐based techniques

“…The ability of samples to reflect or transmit the energy radiated by the near-infrared energy source depends on the rate at which the component of interest absorbs the near-infrared energy (Wrigley, 2017). NIR spectroscopy has found several uses in wheat quality control at various stages including breeding, production, trading and processing, as well as quality control of the wheat-based products (Akkaya et al, 2016;Caporaso, Whitworth, & Fisk, 2018;Dowell, Maghirang, Graybosch, Berzonsky, & Delwiche, 2009;Jirsa et al, 2008;L€ u et al, 2017;Ramakrishnan, Ridge, Harnly, Mazzola, & Luthria, 2017;Zhao, Guo, Wei, & Zhang, 2013). For instance, the capability of NIR spectroscopy to distinguish differences in European wheat varieties based on the differences in their protein and wet gluten content has been reported by Miralb es (2008).…”

“…With the development of single kernel NIR instruments, more accuracy of the NIR based models has been achieved, which allows for sorting of seeds based on differences in their protein composition (Caporaso, Whitworth, & Fisk, 2017;Caporaso et al, 2018;Wesley, Osborne, Larroque, & Bekes, 2008). Garc ıa-Molina, Garc ıa-Olmo, and Barro (2016) used the entire NIR spectral range (400-2500 nm) to examine the capability of NIR spectroscopy to discriminate between wheat grain and flour samples of different gliadin contents.…”

“…Garc ıa-Molina, Garc ıa-Olmo, and Barro (2016) used the entire NIR spectral range (400-2500 nm) to examine the capability of NIR spectroscopy to discriminate between wheat grain and flour samples of different gliadin contents. In a most recent research in the field by Caporaso et al (2018), hyperspectral imaging (HSI) as a combination of NIR spectroscopy and digital imaging techniques was shown to enable quantification of protein distribution between wheat kernels. Ziegler et al (2016) also demonstrated the ability of NIR reflectance spectroscopy for rapid discrimination of wheat varieties in the form of kernel and flour with the use of partial least square discrimination analysis.…”

“…The authors developed a classification model and demonstrated the ability of their NIR method to distinguish between transgenic low-gliadin and nontransgenic wheat lines, which would be useful in developing food products for patients suffering from celiac disease (Garc ıa-Molina et al, 2016). Moreover, protein distribution in a single kernel of wheat was also demonstrated to be visualized using this technique (Caporaso et al, 2018). In a most recent research in the field by Caporaso et al (2018), hyperspectral imaging (HSI) as a combination of NIR spectroscopy and digital imaging techniques was shown to enable quantification of protein distribution between wheat kernels.…”

Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour

“…The HSI technique has become one of the effective rapid detection methods to predict quality attributes non-destructively, and build chemical images to display the distribution of ingredients of various food products, including monitoring the ripeness of nectarine [10], identifying the browning development of button mushrooms [11], monitoring the total volatile basic nitrogen (TVB-N) values of cured meat [12], and predicting the protein content in single wheat kernels [13]. Furthermore, some studies also demonstrated the successful application of HSI in the quantitative detection of foreign material contamination or adulteration in powdery food products, involving discrimination for milk powders from diverse plants and of different functional qualities [14], detecting melamine adulterated in milk powders [15], the detection of sorghum, oat, and corn flour adulterated in wheat flour [16], and the inspection of common cassava flour, corn flour, and wheat flour adulterated in organic Avatar wheat [17].…”