FT-near infrared (NIR) spectroscopy - Screening analysis of used frying fats and oils for rapid determination of polar compounds, polymerized triacylglycerols, acid value and anisidine value [DGF C-VI 21a (13)]

2013 ◽  
pp. n/a-n/a ◽  
Author(s):  
Christian Gertz ◽  
Hans-Jochen Fiebig ◽  
John N. S. Hancock
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Acid Value ◽  
Fats And Oils ◽  
Polar Compounds ◽  
Screening Analysis ◽  
Frying Fats ◽  
Polymerized Triacylglycerols

Rapid determination of polar compounds in frying fats and oils using image analysis

LWT ◽  
2004 ◽  
Vol 37 (6) ◽  
pp. 657-661 ◽  
Author(s):  
Bogim Gil ◽  
Yong Jin Cho ◽  
Suk Hoo Yoon
Keyword(s):  
Image Analysis ◽  
Rapid Determination ◽  
Fats And Oils ◽  
Polar Compounds ◽  
Frying Fats

The rapid determination of total polyphenols content and antioxidant activity in Dendrobium officinale using near-infrared spectroscopy

2016 ◽  
Vol 8 (23) ◽  
pp. 4584-4589 ◽  
Author(s):  
Longhui Ma ◽  
Zhimin Zhang ◽  
Xingbing Zhao ◽  
Sufeng Zhang ◽  
Hongmei Lu
Keyword(s):  
Antioxidant Activity ◽  
Infrared Spectroscopy ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Dendrobium Officinale ◽  
Chemometric Methods ◽  
Total Polyphenols ◽  
Rapid Quantification

NIR spectroscopy coupled with chemometric methods for rapid quantification of total polyphenols content and antioxidant activity inDendrobium officinale.

scholarly journals Rapid determination of hyaluronic acid concentration in fermentation broth with near-infrared spectroscopy

2014 ◽  
Vol 07 (06) ◽  
pp. 1450012 ◽  
Author(s):  
Qin Dong ◽  
Hengchang Zang ◽  
Lixuan Zang ◽  
Aihua Liu ◽  
Yanli Shi ◽  
...  
Keyword(s):  
Hyaluronic Acid ◽  
Fermentation Process ◽  
Near Infrared ◽  
Fermentation Broth ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Promising Tool ◽  
Calibration Models ◽  
Order Polynomial

Hyaluronic acid (HA) concentration is an important parameter in fermentation process. Currently, carbazole assay is widely used for HA content determination in routine analysis. However, this method is time-consuming, environment polluting and has the risk of microbial contamination, as well as the results lag behind fermentation process. This paper attempted the feasibility to predict the concentration of HA in fermentation broth by using near infrared (NIR) spectroscopy in transmission mode. In this work, a total of 56 samples of fermentation broth from 7 batches were analyzed, which contained HA in the range of 2.35–9.69 g/L. Different data preprocessing methods were applied to construct calibration models. The final optimal model was obtained with first derivative using Savitzky–Golay smoothing (9 points window, second-order polynomial) and partial least squares (PLS) regression with leave-one-block-out cross validation. The correlation coefficient and Root Mean Square Error of prediction set is 0.98 and 0.43 g/L, respectively, which show the possibility of NIR as a rapid method for microanalysis and to be a promising tool for a rapid assay in HA fermentation.

Near infrared spectroscopy for rapid determination of solids content of amino resins

2020 ◽  
Vol 28 (5-6) ◽  
pp. 344-350
Author(s):  
M Gonçalves ◽  
NT Paiva ◽  
JM Ferra ◽  
J Martins ◽  
F Magalhães ◽  
...  
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Water Addition ◽  
Reliable Technique ◽  
Standard Normal Variate ◽  
And Performance ◽  
Solids Content

Near infrared (NIR) spectroscopy is a fast and reliable technique for assessing properties of amino resins. One important property that defines the cost and performance of these resins is the solids content (SC). This work studied the prediction of SC of amino resins by combining NIR spectroscopy with partial least squares (PLS) regression. A total of 990 industrial NIR spectra of amino resins were obtained and split randomly by a ratio of 2/3 for calibration and 1/3 for validation. The best model achieved a root mean-square error of prediction (RMSEP) of 0.32% (m/m) and a coefficient of determination of prediction ([Formula: see text]) of 81%. standard normal variate (SNV) was found to be the NIR pre-processing that provided the best results for model construction. Addition of water to two amino resins showed that the NIR model does not respond to the water addition, despite water making great contribution to the SC value. An inference that can be obtained from this is that the NIR model of amino resins uses NIR properties of amino resins that relate to the SC and from there predict the most probable SC, instead of looking at all the components that affect the SC of amino resins.

Determination of the Sum of Dimer and Polymer Triglycerides and of Acid Value of Used Frying Fats and Oils by Near Infrared Reflectance Spectroscopy

1994 ◽  
Vol 77 (5) ◽  
pp. 1184-1189 ◽  
Author(s):  
Andries J Boot ◽  
Andrbes J Speek
Keyword(s):  
Near Infrared ◽  
Acid Value ◽  
Fats And Oils ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Frying Fats ◽  
Value Determination ◽  
Validation Set

Abstract Near infrared reflectance spectroscopy (NIRS) in the transaction mode was applied to determine the sum of dimer and polymer triglycerides (DPTG) contents and acid value of used frying fats and oils. A filter instrument and a calibration sample set were used to determine DPTG content and acid value. For each parameter, a 7-wavelength calibration was developed using multiple linear regression analysis. For a validation set comprising 44 samples for the NIRS-DPTG determination in the range of 2.2 to 32.7% m/m, the correlation coefficient between NIRS and liquid chromatography (LC) was 0.976, with a standard error of prediction (SEP) of 1.8% m/m. For a validation set comprising 36 samples for the NIRS-acid value determination in the range of 0.30 to 18.8 mg potassium hydroxide per gram of sample (mg KOH/g), the correlation coefficient between NIRS and titration was 0.996, with a SEP of 0.33 mg KOH/g. Validation after routine operation for 1 year provided SEPs of 2.3% m/m and 0.44 mg KOH/g for DPTG and acid value determination, respectively. NIRS screening of 1400 samples collected during 1992 precluded the need for 1149 DPTG determinations by LC (82.1%) and 1033 acid value determinations by titration (73.8%), which are methods the judicature in The Netherlands accepted, because those samples appeared to comply with legislation.

Determination of polar compounds, polymerized and oxidized triacylglycerols, and diacylglycerols in oils and fats: results of collaborative studies and the standardized method (Technical report)

2000 ◽  
Vol 72 (8) ◽  
pp. 1563-1575 ◽  
Author(s):  
M. C. Dobarganes ◽  
J. Velasco ◽  
A. Dieffenbacher
Keyword(s):  
High Performance ◽  
Collaborative Study ◽  
Fats And Oils ◽  
Polar Compounds ◽  
Thermal Polymerization ◽  
Size Exclusion ◽  
Frying Fats ◽  
Exclusion Chromatography ◽  
Oxidized Triacylglycerols

A description is given of the development by collaborative study of two standardized methods for the determination of polar compounds in oils and fats by adsorption chromatography using silica minicolumns, and for quantification of polymerized triacylglycerols, oxidized triacylglycerols, and diacylglycerols in polar compounds by high-performance size-exclusion chromatography. The first procedure is sensitive, allowing savings in time, solvents, and reagents as compared to the previous determination (Standard Method 2.507), while the second is very rapid, giving a detailed information on the main groups of compounds in fats and oils associated with hydrolysis, oxidation, and thermal polymerization. Both methods are useful for the analysis of used frying fats as well as for the analysis of virgin or refined oils.

scholarly journals   Near infrared spectroscopy for deoxynivalenol content estimation in intact wheat grain

2012 ◽  
Vol 58 (No. 4) ◽  
pp. 196-203 ◽  
Author(s):  
V. Dvořáček ◽  
A. Prohasková ◽  
J. Chrpová ◽  
L. Štočková
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Resistance Breeding ◽  
Partial Least Square ◽  
Least Square ◽  
Prediction Errors ◽  
Wheat Grain ◽  
Wide Range

Non-invasive determination of deoxynivalenol (DON) still presents a challenging problem. Therefore, the present study was aimed at a rapid determination of DON in whole wheat grain by means of FT-NIR spectroscopy, with a wide range of concentrations for potential applications in breeding programs and common systems of quality management using partial least square calibration (PLS) and discriminant analysis technique (DA). Using a set of artificially infected wheat samples with a known content of DON, four PLS models with different concentration range were created. The broadest model predicting DON in the concentration range of 0&ndash;90 mg/kg possessed the highest correlation coefficients of calibration and cross validation (0.94 and 0.88); but also possessed the highest prediction errors (SEP = 6.23 mg/kg). Thus the subsequent combination of DA as the wide range predictive model and the low-range PLS model was used. This technique gave more precise results in the samples with lower DON concentrations &ndash; below 30 mg/kg (SEP = 2.35 mg/kg), when compared to the most wide-range PLS model (SEP = 5.95 mg/kg).<br />Such technique enables to estimate DON content in collections of artificially infected wheat plants in Fusarium resistance breeding experiments. &nbsp;

Near-Infrared Spectroscopic Measurement of Organic Matter in Soil

2012 ◽  
Vol 499 ◽  
pp. 414-418
Author(s):  
Tao Pan ◽  
Zhen Tao Wu ◽  
Jie Mei Chen
Keyword(s):  
Organic Matter ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Modeling Prediction ◽  
Validation Set ◽  
Selection Of

Near-infrared (NIR) spectroscopy was successfully applied to chemical free and rapid determination of the organic matter in soil, and moving window partial least square (MWPLS) combining with Savitzky-Golay (SG) smoothing was used to the selection of NIR waveband. Thirty-five samples were randomly selected from all 97 collected soil samples as the validation set. The remaining 62 samples were divided into similar modeling calibration set (37 samples) and modeling prediction set (25 samples) based on partial least square cross-validation predictive bias (PLSPB). The selected waveband was 1896 nm to 2138 nm; the SG smoothing parameters and PLS factor OD, DP, NSP and F were 2, 6, 71 and 15, respectively; the modeling effect M-SEP and M-RPwere 0.219% and 0.944, respectively; the validating effect V-SEP and V-RPwere 0.243% and 0.878, respectively. The result provided a reliable NIR model and valuable references for designing specialized NIR instruments.

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