Oleic acid content in ground corn by NIR spectroscopy with an indirect calibration method

Wright, Steve; Hagen, Lisa

doi:10.1007/s11746-003-0836-4

Cited by 9 publications

(11 citation statements)

References 16 publications

(15 reference statements)

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“…Since NIR calibration development is based on the Beer-Lambert's Law, i.e., that the absorbance varies linearly with the analyte concentration, it would make sense that better statistical results should be achieved with a calibration based on absolute FA content. These results are similar to those achieved with the oleic acid content in ground corn (17).…”

Section: Resultssupporting

confidence: 89%

Determination of the fatty acid composition of canola, flax, and solin by near‐infrared spectroscopy

Siemens¹,

Daun²

2005

J Americ Oil Chem Soc

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The ability to rapidly measure key FA in oilseed crops would assist in the administration of identity-preserved systems in the grain-handling system or in selection systems in plantbreeding programs. This study shows near-infrared reflectance (NIR) spectroscopy to be a reliable method of determining FA composition in canola, flax, and solin (low-linolenic flax), for oleic acid, linoleic acid, linolenic acid, and iodine value, and to a limited extent for saturated fat. Samples from cultivar trials, harvest surveys, and export shipments were scanned on a NIRSystems 6500 spectrometer (Silver Spring, MD), and calibrations were developed and optimized using modified partial least squares. SE of prediction results for prediction sets of canola, flax, and solin, were, respectively: oleic acid (0.77, 1.03, 0.62%); linoleic acid (0.71, 1.20, 0.37%); linolenic acid (0.42, 0.62, 0.08%); saturated FA (0.23, 0.39, 0.31%); and iodine value (0.63, 0.95, 0.43 units). Paper no. J10892 in JAOCS 82, 153-157 (March 2005). RPD, ratio of the SEP to the SD of the prediction data set. e Sum saturated FA (14:0 + 16:0 + 18:0 + 20:0 + 22:0 + 24:0). Sats, saturated FA. Sum saturated FA (14:0 + 16:0 + 18:0 + 20:0 + 22:0 + 24:0).

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Section: Resultssupporting

confidence: 89%

Determination of the fatty acid composition of canola, flax, and solin by near‐infrared spectroscopy

Siemens¹,

Daun²

2005

J Americ Oil Chem Soc

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“…These results are partially in agreement with Wright et al (2003) showing that the nonlinear relationship between fatty acid content and absorbance could be corrected by using absolute concentrations. However, the correction to relative concentrations by the NIRS-predicted oil content degraded the calibration precision.…”

Section: Discussionsupporting

confidence: 91%

“…The use of absolute fatty acid concentration was suggested by Dr. Steven Wright, Pioneer Hi-Bred International Inc. [34] because of the nature of NIRS to determine content by counting molecules. Soybean oil calibrations were developed on the fatty acid calibration samples.…”

Section: Fatty Acids Expressed In Absolute Concentration and Correctementioning

confidence: 99%

Measurement of Whole Soybean Fatty Acids by Near Infrared Spectroscopy

Igne¹,

Rippke²,

Hurburgh³

2008

J Americ Oil Chem Soc

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Whole soybean fatty acid contents were measured by near infrared spectroscopy. Three calibration algorithms-partial least squares (PLS), artificial neural networks (ANN), and least squares support vector machines (LS-SVM)-were implemented. Three different validation strategies using independent sets and part of calibration samples as validation sets were created. There was a significant improvement of the prediction precision of all fatty acids measured on relative concentration of oil compared with previous literature using PLS (standard error of prediction of 0.85, 0.42, 1.64, 1.67, and 0.90% for palmitic, stearic, oleic, linoleic and linolenic acids respectively). ANN and LS-SVM methods performed significantly better than PLS for palmitic, oleic and linolenic acids. Calibration models developed on relative concentrations (% of oil) were compared to prediction models created on absolute fatty acid concentration (% of weight) and corrected to relative concentration by multiplying by the predicted oil content. While models were easier to develop in absolute concentration (higher coefficients of determination), the multiplication of errors with the total oil content model resulted in no net precision improvement.

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“…In fact, the results obtained in a study estimating oleic acid content on maize flour suggested that whole product computation of oleic acid content had a positive effect on the estimation power of the model (Wright and Hagen 2003). Additionally, computing the oleic acid content on a whole product level (i.e., multiplying oleic acid by oil content) may yield better results.…”

Section: Resultsmentioning

confidence: 99%

“…Reports on general quality traits in maize are available in which the researchers simultaneously evaluated the effects of sample type and statistical method on developing calibration models (Orman and Schumann 1991). The studies on the analysis of maize fatty acids with NIR (Wright and Hagen 2003;Baye et al 2006;Yang et al 2009) are rather limited compared with studies on the general quality traits of maize kernel (Weinstock et al 2006;Jiang et al 2007;Spielbauer et al 2009;Tallada et al 2009;Egesel and Kahrıman 2012;Fassio et al 2015). Most experiments used one type of sample (either intact kernel or flour) and did not attempt any comparison between the models developed with different chemometric methods.…”

mentioning

confidence: 99%

Analysis of Fatty Acids in Kernel, Flour, and Oil Samples of Maize by NIR Spectroscopy Using Conventional Regression Methods

et al. 2016

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Cereal Chem. 93(5):487-492High cost and painstaking procedures associated with fatty acid analyses of maize kernel necessitate the use of alternative methods. NIR spectroscopy offers advantages in this respect for a variety of areas such as plant breeding, food and feed industries, and biofuel production, in which different forms of maize kernel (e.g., intact kernel, flour, or oil) are used as material. We investigated the possibility of estimating maize oil quality traits by using different samples (intact kernel, flour, and oil) and conventional regression methods (multiple linear regression [MLR] and partial least squares regression [PLSR]) applied to their NIR spectra. MLR and PLSR calibration models were developed for oleic acid, linoleic acid, oleic/linoleic acid ratios, total monounsaturated fatty acid, total polyunsaturated fatty acid (PUFA), and total saturated fatty acid by analyzing 120 maize samples. Robustness in terms of prediction accuracy of the models developed here was tested with a reserved set of samples (n = 30). The results suggested that fatty acids could be possibly estimated by calibrations developed from flour and oil samples with a high degree of accuracy, whereas intact samples did not offer satisfactory results. PLSR and MLR methods gave better results in flour and oil samples, respectively. PUFA was the trait that was most successfully estimated from both flour (for the PLSR model, standard error of the estimate [SEP] of 1.78%, relative performance to deviation [RPD] of 3.09, R 2 = 0.93) and oil (for the MLR model, SEP of 0.85%, RPD of 6.52, R 2 = 0.98) samples. We concluded that sample type and chemometric method should be handled as important factors in calibration development, and the effects of these factors may vary depending on the trait being analyzed. † Corresponding author. Phone: +(90) 0286 2180018/1907.

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Oleic acid content in ground corn by NIR spectroscopy with an indirect calibration method

Cited by 9 publications

References 16 publications

Determination of the fatty acid composition of canola, flax, and solin by near‐infrared spectroscopy

Determination of the fatty acid composition of canola, flax, and solin by near‐infrared spectroscopy

Measurement of Whole Soybean Fatty Acids by Near Infrared Spectroscopy

Analysis of Fatty Acids in Kernel, Flour, and Oil Samples of Maize by NIR Spectroscopy Using Conventional Regression Methods

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