Single kernel wheat hardness estimation using near infrared hyperspectral imaging

By C. Erkinbaev, Kieran Derksen, Jitendra Paliwal. Infrared physics & technology, 2019

52 citations recorded by the source corpus.

Abstract Wheat is a major component of human diets across the world; hence, it is important to produce superior quality wheat for consumption. Hardness of wheat is one of its major quality and end-use determinants and it is desirable that this parameter could be rapidly and non-destructively measured along with its other functional attributes. Recent research has established that a number of quality parameters could be accurately measured using near infrared (NIR) hyperspectral imaging. In order to fully characterize wheat kernels using NIR hyperspectral imaging, this study aims to provide an optimal methodology to predict hardness of individual kernels. The predicted hardness of wheat kernels using this non-destructive imaging technique was referenced against the industry adopted hardness index (HI). Principal component analysis (PCA) was used as an exploratory method for classification of wheat kernels based on their hardness index into three categories of soft, medium, and hard. Various spectral pre-processing techniques were used and the best combination (Savitzky–Golay 1st derivative with 11 smoothing points followed by orthogonal signal correction and mean centering) was selected for partial least squares (PLS) regression and artificial neural network (ANN) models. The PLS model yielded moderate prediction of wheat hardness for calibration (R2 = 0.81 and RMSECV = 10.85) and prediction (R2 = 0.80 and RMSEP = 12.90). The ANN superseded the PLS model for calibration (R2 = 0.94 and RMSECV = 5.38) and prediction (R2 = 0.90 and RMSEP = 6.59). Although, these preliminary results have clearly established the potential of hyperspectral imaging for non-destructive and on-line pre-screening of individual wheat kernels for hardness determination, further studies on larger datasets are needed to scale-up the technique to commercial throughput levels.

Subfields: Analytical Chemistry, Animal Science and Zoology, Biophysics

View Paper: https://www.semanticscholar.org/paper/5e55bb0e2c4d300bdf0445489e1cf9cede0e0a6c

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In Analytical Chemistry

In Animal Science and Zoology

In Biophysics

Citations in the corpus

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review (2021, methodology): “…in detection of mechanical damage in mushrooms, single kernel wheat hardness estimation, cold injury in peaches, honey adulteration, prediction of ﬁ rmness in kiwi fruit (Rojas-Moraleda et al., 2017; Erkinbaev et al., 2019; Pan et al., 2015; Sha ﬁ ee et al., 2016; Siripatrawan et al., 2011).”