In-Stream Measurement of Canola (<i>Brassica Napus</i> L.) Seed Oil Concentration Using in-line near Infrared Reflectance Spectroscopy

Long, Dan S.; McCallum, J.; Young, Francis L.; Lenssen, Andrew W.

doi:10.1255/jnirs.993

Cited by 4 publications

(5 citation statements)

References 23 publications

(28 reference statements)

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“…Non-destructive successful NIR applications to measure oil in seed have been published so far for several oilseed crops [11,[28][29][30][31]. Those cultivars are grown primarily for their rich seed oil content, which ranges from about 20% of the seed weight for soybeans to over 40% for sunflower and rapeseed.…”

Section: Discussionmentioning

confidence: 99%

“…The NIR technique has been used already in the early 1960s to detect the moisture content in seeds [10]. The charm of this methodology relies greatly on its uncomplicated sample presentation; it is a fast (a single measurement takes only a few seconds), non-destructive and reagent-free methodology, with levels of precision comparable to the primary reference methods and easily adaptable to industrial measurements [11,12]. Over the years, the NIR applications in agriculture have slowly, but steadily, been spreading and are nowadays well established in the seed sector for determining protein, moisture, and oil content [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Quantification of Oil Content in Intact Sugar Beet Seed by Near-Infrared Spectroscopy

Martínez-Arias¹,

Ronquillo-López²,

Schechert³

2018

Agronomy

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Sugar beet seed oil reserves play an important role in successful germination and seedling development. The purpose of this study was to establish a non-destructive near-infrared (NIR) methodology with good predictive accuracy to quantify stored seed oil in sugar beet seed. Reflectance NIR spectra were acquired from viable monogerm seeds. Calibration equations were developed using partial least squares. The optimized calibration model reached a Pearson correlation of 0.946; an independent prediction test reached a correlation of 0.919 and a Root Mean Square Error of Prediction of 0.388. The possible role of the outer pericarp in the prediction of oil content was additionally considered. The results indicate that the model is suitable for a rapid and accurate determination of the oil content in both polished and unpolished sugar beet seeds. This NIR application might help to understand the role of seed energy reservoirs in sugar beet germination and further plant growth.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantification of Oil Content in Intact Sugar Beet Seed by Near-Infrared Spectroscopy

Martínez-Arias¹,

Ronquillo-López²,

Schechert³

2018

Agronomy

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“…The oil content in the grains varied between hybrids and between sowing dates (Table 3). This behavior was expected because the oil concentration of canola seeds is influenced by the genotype (TOMM et al, 2009b) and by environmental factors (LONG et al, 2012), such as temperature and rainfall (TOMM et al, 2009a).…”

Section: Near-infrared (Nir) Spectroscopymentioning

confidence: 98%

Path analysis and near-infrared spectroscopy in canola crop

et al. 2023

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This study measured the effect of the association between agronomic traits related to the yield of canola grains grown at different sowing dates through path analysis. Another objective was to obtain a method to predict the oil content in the grains, fitting a multivariate model through near-infrared (NIR) spectroscopy analysis. The experiment was conducted in the field using a randomized block design in plots subdivided by time, with four plots (sowing dates), six subplots (canola hybrids), and four replicates. In each hybrid, phenological observations were performed, and the grain yield was determined. The data were subjected to analysis of variance in the R environment using the F test at 5% probability. The oil content in the grains was determined by the traditional chemical method, and based on the NIR spectral signature of the grain samples, partial least squares regression (PLS-R) was established to estimate the oil content in the canola grains. The sowing dates influenced the production components and oil content of the grains of all hybrids. The trait number of grains in five plants (0.6857) and their height (0.4943) had greater estimates of positive correlations with grain yield, as well as higher values of positive direct effects on yield (0.2494 and 0.1595, respectively). The NIR technique combined with PLS-R was able to predict the oil content in the grains, resulting in good predictive models (R2 of 0.86 and root mean square error (RMSE) of 1.56 in external validation).

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“…Additional work has shown the usefulness of NIRS in predicting features of canola seed [27] and/or meal [28]. Long et al [29] found that NIRS could measure the oil content of canola seed prior to crushing. Wittkop et al [30] used NIRS to predict NDF, ADF, and ADL in intact seed of various genotypes of oilseed rape (Brassica napus L.).…”

Section: Correlation Between Laboratory Reference Measurements and Nimentioning

confidence: 99%

Prediction of Canola Residue Characteristics Using Near-Infrared Spectroscopy

Stubbs

Kennedy²

2017

International Journal of Agronomy

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Little work has been done to characterize and quantify the residue traits affecting decomposition of winter and spring canola (Brassica napus L.) residue in dryland farming systems of the Pacific Northwest United States. Traditional methods of characterizing residue fiber and nutrients are time-consuming and expensive and require large quantities of chemical reagents. The goal of this research was to determine whether near-infrared spectroscopy (NIRS) could accurately predict neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), carbon (C), and nitrogen (N) of canola stems, litter, and roots and decomposition of canola stems. Canola residue varied in decomposition, fiber, and nutrients by year, location, and type. NIRS predictions were successful for NDF and ADF in 2011 (standard error of prediction SEP<2.67; R2>0.95) and NDF, ADF, and N in 2012 (SEP<2.38; R2>0.91). Other predictions for residue fiber and nutrient characteristics were considered moderately successful. Prediction of canola residue decomposition with NIRS was useful for screening purposes. Near-infrared spectroscopy shows promise for rapidly and reproducibly predicting some canola residue fiber and nutrient traits and may be useful for estimating residue decomposition potential in dryland conservation cropping systems.

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In-Stream Measurement of Canola (Brassica Napus L.) Seed Oil Concentration Using in-line near Infrared Reflectance Spectroscopy

Cited by 4 publications

References 23 publications

Quantification of Oil Content in Intact Sugar Beet Seed by Near-Infrared Spectroscopy

Quantification of Oil Content in Intact Sugar Beet Seed by Near-Infrared Spectroscopy

Path analysis and near-infrared spectroscopy in canola crop

Prediction of Canola Residue Characteristics Using Near-Infrared Spectroscopy

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