Measurement of Whole Soybean Fatty Acids by Near Infrared Spectroscopy

Igne, Benoît; Rippke, Glen R.; Hurburgh, Charles R.

doi:10.1007/s11746-008-1311-1

Cited by 12 publications

(9 citation statements)

References 26 publications

(21 reference statements)

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“…Compared to PLS and CLS based methods, ANN do not assume linearity between inputs and outputs and can successfully be deployed in situations where the distribution of the residuals is not normal. Numerous publications show cases where ANNs provided more accurate and precise results than linear techniques [11][12][13][14].…”

Section: Multivariate Calibration Algorithmsmentioning

confidence: 99%

Efficient Near-Infrared Spectroscopic Calibration Methods for Pharmaceutical Blend Monitoring

et al. 2011

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Near-infrared (NIR) spectroscopy is an important analytical tool for online process monitoring of pharmaceutical unit operations. Traditionally, the development and maintenance of robust, precise, and accurate quantitative NIR calibrations requires a substantial investment for the creation of sample sets. This study demonstrates the ability to develop efficient NIR calibrations using reduced sample sets. Prediction performance of several multivariate algorithms was compared on two different NIR spectrometers for pharmaceutical blend monitoring. Classical least-squares (CLS)-based algorithms took advantage of pure component scans to produce the most sensitive quantitative calibrations using reduced sample sets when compared to partial least squares (PLS) regression and two nonlinear methods. The PLS algorithm and the nonlinear methods produced models with low error but lacked the sensitivity needed to model subtle blending trends. The CLS-based methods produced models with adequate sensitivity for blend monitoring. The robustness of the CLS-based methods was further demonstrated in the ease of transfer between instruments using only a bias correction of the predictions.

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Section: Multivariate Calibration Algorithmsmentioning

confidence: 99%

Efficient Near-Infrared Spectroscopic Calibration Methods for Pharmaceutical Blend Monitoring

et al. 2011

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“…Significant works have been published on the prediction of seed oil content and fatty acid compositions by near infrared spectroscopy in rapeseed [3], sunflower [4], peanut [5] and soybean [6] by using linear models. However, low correlations between NIRS and fatty acid reference measurements in some cases show the lack of robustness of prediction models set up on the NIR spectroscopy system [7].…”

Section: Introductionmentioning

confidence: 99%

Artificial and Hybrid Fuzzy Linear Neural Network‐Based Estimation of Seed Oil Content of Safflower

Sabzalian¹,

Khashei²,

Ghaderian³

2014

J Americ Oil Chem Soc

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Keywords Safflower · Oil content · Multiple linear regression · Neural networks · Hybrid fuzzy linear neural network methods of quantitative analysis used by the NIR system are multiple linear regression (MLR), principal component regression (PCR), partial least squares (PLS), and recently artificial neural networks (ANN). The first three methods of MLR, PCR and PLS are suitable for linear analysis, while ANN has advantages when non-linearity exists in the data. Rodriguez-Nogales [2], compared PLS, PCR and MLR to find optimal prediction models for the quantification of Abstract Inexpensive and rapid methods for measurement of seed oil content by near infrared reflectance spectroscopy (NIRS) are useful for developing new oil seed cultivars. Adopting default multiple linear regression (MLR), the predictions of safflower oil content were made by 20-140 samples using a Perten Inframatic 8620 NIR spectrometer. Although the obtained interpolation results of MLR had desired accuracy, the extrapolation was extremely poor. The extrapolation determination coefficient (R 2 ) and standard error (SE) of cross validation for MLR models were 0.63-0.78 and 3.71-4.44, respectively. In order to overcome the accuracy limitation of linear MLR models, a common suggestion is to use a nonlinear artificial neural network (ANN); however, it needs a large number of data to yield significant accurate results. We developed a novel robust hybrid fuzzy linear neural (HFLN) network to capture simultaneously linear and nonlinear patterns of data with a limited number of safflower samples. Empirical extrapolation results showed that the HFLN had higher R 2 (=0.85) and lower SE (=1.83) compared to those obtained by MLR and ANN models. It is concluded that hybrid methodologies could be used to construct efficient and appropriate models for estimation of seed oil content set up on NIR system.

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“…Near infrared spectroscopy (NIRS) is a fast, non destructive, and inexpensive analytical tool involving limited or no sample preparation. For the past thirty years, NIRS has been used in numerous applications from the screening and quality control of food and feed products [1,2,3] to pharmaceutical and chemical processes control [4,5]. Near infrared spectra are the results of the absorption of overlapping bands from different chemical families requiring information extraction from complex and highly collinear databases.…”

Section: Introductionmentioning

confidence: 99%

Improving the transfer of near infrared prediction models by orthogonal methods

Igne

Roger²,

Roussel

et al. 2009

Chemometrics and Intelligent Laboratory Systems

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Eight calibration transfer methods based on the removal of orthogonal signal were compared for the standardization of whole soybean protein and oil models. Dynamic orthogonal projection (DOP), transfer by orthogonal projection (TOP), error removal by orthogonal subtraction (EROS), orthogonal signal correction (OSC), and orthogonal projections to latent structures (O-PLS) as well as the modification and extension of some of these methods were compared in the transfer of models in intra and inter brand situations using two Foss Infratecs and two Bruins OmegAnalyzerGs. For each brand, a master was designated and its models transferred onto the second unit of its network and the two units of the second brand. Calibration models were transferable from brand to brand with similar or better

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Measurement of Whole Soybean Fatty Acids by Near Infrared Spectroscopy

Cited by 12 publications

References 26 publications

Efficient Near-Infrared Spectroscopic Calibration Methods for Pharmaceutical Blend Monitoring

Efficient Near-Infrared Spectroscopic Calibration Methods for Pharmaceutical Blend Monitoring

Artificial and Hybrid Fuzzy Linear Neural Network‐Based Estimation of Seed Oil Content of Safflower

Improving the transfer of near infrared prediction models by orthogonal methods

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