Abstract
Near-infrared (800–2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses. This review describes the advancement of NIR to NIR-HSI in agricultural applications with a focus on seed quality features for agronomically important seeds. NIR-HSI seed phenotyping, describing sample sizes used for building high-accuracy calibration and prediction models for full or selected wavelengths of the NIR region, is explored. The molecular interpretation of absorbance bands in the NIR region is difficult; hence, this review offers important NIR absorbance band assignments that have been reported in literature. Opportunities for NIR-HSI seed phenotyping in forage grass seed are described and a step-by-step data-acquisition and analysis pipeline for the determination of seed quality in perennial ryegrass seeds is also presented.
Highlights
Detrending to remove the mean offset from each sample, extended multiplicative scatter correction/signal correction (EMSC), Orthogonal signal correction (OSC) filter, Savitzky–Golay smoothing and derivatives and mean centre to remove mean offset from each variable
The recommended pre-processing method for NIR-hyperspectral imaging (NIR-HSI) perennial ryegrass average absorbance data includes detrending to remove the mean offset from each sample, extended multiplicative scatter correction/signal correction (EMSC), OSC filter, Savitzky–Golay smoothing and derivatives and mean centre to remove mean offset from each variable
There is an extraordinary number of reports on the capabilities of NIR-HSI technology and its real-time applications, for the assessment of food quality
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. NIRS platforms provide spectral information, usually an average of a few selected points or the mean spectrum of a larger area This is not so useful when objects are non-uniform and the features of interest are restricted to a relatively small but unknown part of the object [38]. The non-destructive nature of NIRS imaging allows quality control in agriculturally important fruit (e.g., apples, peaches and apricots) to be obtained, [44–48] for detecting external defects. It has become increasingly evident that hyperspectral imaging can be applied to the non-destructive qualitative and quantitative determination of the desired features of selected samples, without contact [58] It is very much suited for routine diagnostics such as food quality assessments and safety analyses [59]. We provide a detailed summarization of the Sensors 2022, 22, 1981 challenges and requirements of acquiring Lolium species, such as perennial ryegrass seed hyperspectral data for determining key seed quality attributes
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