scholarly journals Characteristics of fermented dough predicted by using the NIR technique

2011 ◽  
Vol 23 (No. 5) ◽  
pp. 184-189 ◽  
Author(s):  
O. Jirsa ◽  
M. Hrušková
Keyword(s):  
Correlation Coefficient ◽  
Cross Validation ◽  
Rheological Characteristics ◽  
Pls Regression ◽  
Independent Validation ◽  
Predicted Values ◽  
Accuracy Of Prediction ◽  
Gas Volume ◽  
Calibration Equations

Rheological characteristics of wheat doughs prepared from 179 variety and commercial wheat flour samples (crop years 1999, 2001, 2002, 2003 and 2004) were assessed with SJA fermentograph (Sweden). NIR spectra of the samples were measured on the spectrometer NIRSystem 6500. Calibration equations with cross and independent validations for all rheological characteristics were computed by NIR Software ISI Present WINISI II using mPLS/PLS regression and ANN. The quality of prediction was evaluated by means of coefficients of correlation between the measured and the predicted values from cross and independent validation. A statistically significant dependence (with probability higher than 99%) was determined in all mentioned rheological characteristics in the case of cross-validation. The results suggested the possibility of prediction particularly for the fermentation gas volume parameter, where a high correlation was achieved (r = 0.888). The accuracy of prediction by independent validation was not found on such a satisfactory level. For the sets of ten commercial flours, higher correlation coefficient was calculated for the final dough volume parameter.  

scholarly journals Bread features evaluation by NIR analysis

2008 ◽  
Vol 25 (No. 5) ◽  
pp. 243-248 ◽  
Author(s):  
O. Jirsa ◽  
M. Hrušková ◽  
I. Švec
Keyword(s):  
Cross Validation ◽  
Standard Errors ◽  
Loaf Volume ◽  
Rheological Characteristics ◽  
Average Cell ◽  
Independent Validation ◽  
Predicted Values ◽  
Spectrometer Calibration ◽  
Calibration Equations

Bakery characteristics of wheat dough and the final product and their predictability by NIR technique was investigated using 231 variety and commercial wheat samples (crop years 2003–2005). The behaviour of doughs was assessed with Brabender maturograph and OTG (Germany), the final product quality was evaluated by the baking test and image analysis. NIR spectra of flours were acquired on a NIRSystem 6500 spectrometer. Calibration equations for the selected rheological characteristics were computed by WINISI II using mPLS regression. The quality of prediction was evaluated by means of coefficients of correlation between measured and predicted values from cross and independent validation. A statistically significant dependence (with probability higher than 99%) was found with all rheological characteristics. The standard errors of cross-validation were achieved as follows: dough elasticity 16 BU, bread volume (11 min) 58 BU, specific loaf volume 34 cm<sup>3</sup>/100 g, bread cut area 2.6 cm<sup>2, penetration 4.1 mm, average cell area 0.4 mm<sup>2</sup> and cells per cm<sup>2 7.4.

scholarly journals Wheat flour dough alveograph characteristics predicted by NIRSystems 6500

2011 ◽  
Vol 21 (No. 1) ◽  
pp. 28-33 ◽  
Author(s):  
M. Hrušková ◽  
P. Šmejda
Keyword(s):  
Wheat Flour ◽  
Cross Validation ◽  
Rheological Characteristics ◽  
Energy Characteristics ◽  
Coefficient Of Correlation ◽  
Flour Dough ◽  
Independent Validation ◽  
Predicted Values ◽  
Calibration Equations

Rheological quality of wheat dough prepared from 130 variety, stream, and commercial wheat flour samples (wheat harvest 1999, 2000 and 2001) was assessed with alveograph. Spectra of all samples were measured on spectrograph NIRSystems 6500. Calibration equations with cross and independent validation for all rheological characteristics were computed by NIR Software ISI Present WINISI II using MPLS and PLS methods. The quality of the prediction was evaluated by the coefficient of correlation between the measured and the predicted values from cross and independent validation. A statistically significant dependence between the predicted and the measured values (with probability P &lt; 0.01) was determined in P (elasticity) and W (energy) characteristics in the case of cross validation only in the commercial flour sets. P (with P &lt; 0.01) and W (with P &lt; 0.05) were successfully predicted by independent validation in the set of all samples. &nbsp;

scholarly journals Wheat flour dough rheological characteristics predicted by NIRSystems 6500

2013 ◽  
Vol 19 (No. 6) ◽  
pp. 213-218 ◽  
Author(s):  
M. Hrušková ◽  
M. Bednářová ◽  
F. Novotný
Keyword(s):  
Wheat Flour ◽  
Cross Validation ◽  
Rheological Characteristics ◽  
Absorption Time ◽  
Flour Dough ◽  
Independent Validation ◽  
Wheat Flour Dough ◽  
Predicted Values ◽  
Calibration Equations

Rheological quality of wheat dough prepared from 114 wheat flour samples (wheat harvest 1998 and 1999) was assessed by help of farinograph and extensigraph. Spectra of all samples were measured on spectrograph NIRSystems 6500 NIR. Calibration equations with cross and independent validation for all rheological characteristics were computed by NIR Software ISI Present WINISI II using mPLS and PLS methods. The quality of prediction was evaluated by coefficients of correlation between measured and predicted values from cross and independent validation. A statistically significant dependence between predicted and measured values (with probability higher than 99%) was determined in all mentioned rheological characteristics in the case of cross validation. Only farinograph absorption, time of dough development and mixing tolerance were successfully predicted by independent validation. Predictions of extensigraph characteristics were not found out statistically significant probably due to a small number of tested samples.

scholarly journals Prediction of wheat and flour Zeleny sedimentation value using NIR technique.

2011 ◽  
Vol 21 (No. 3) ◽  
pp. 91-96 ◽  
Author(s):  
M. Hrušková ◽  
O. Faměra
Keyword(s):  
Protein Content ◽  
Quality Parameters ◽  
Analytical Quality ◽  
Independent Validation ◽  
Predicted Values ◽  
Accuracy Of Prediction ◽  
Calibration Equations ◽  
Pls Method ◽  
Sedimentation Value

Analytical quality parameters of wheat flour prepared from variety and commercial wheat samples (wheat harvest 1998, 1999, 2000 and 2001) were assessed by means of filter spectrograph Inframatic 8620 ASH (moisture and protein content) and Sedi-tester (Zeleny sedimentation value). The spectra of all samples were measured on spectrograph NIRSystem 6500. Calibration equations with cross and independent validation for all analytical characteristics were computed by NIR Software ISI Present WINISI II using MPLS and PLS method. The quality of prediction was evaluated by SEP and r parameters between the measured and the predicted values from cross and independent validation. In case of Inframatic 8620 ASH, validation was realised by NIRPRG software. A statistically significant dependence between the predicted and the measured values of protein content and Zeleny sedimentation (with probability P &lt; 0.01) was determined in both variety and commercial flour sets in the case of cross and independent validation. Better accuracy of prediction was found with NIRSystem 6500. Both important parameters of wheat were successfully predicted by independent validation with nearly the same accuracy. &nbsp;

scholarly journals Prediction of wheat milling characteristics by near-infrared reflectance spectroscopy

2011 ◽  
Vol 23 (No. 4) ◽  
pp. 145-151 ◽  
Author(s):  
J. Blažek ◽  
O. Jirsa ◽  
M. Hrušková
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Milling Process ◽  
Near Infrared Reflectance ◽  
Independent Validation ◽  
Milling Characteristics ◽  
Predicted Values ◽  
Calibration Equations

The aim of this study was to explore the use of NIR spectroscopy of laboratory milled flour to predict the milling characteristics of wheat. Quantitative traits of the milling process of wheat were predicted by analyses of NIR spectra of six sets consisting of 94 samples. Reference data were obtained by grinding the samples on the laboratory mill Chopin CD1-auto (France), spectral data were measured on spectrograph NIRSystem 6500. Commercial spectral analysis software WINISI II was used to collect spectra, develop calibration equations and evaluate calibration performance. The quality of prediction was evaluated by coefficients of correlation between the measured and the predicted values from cross and independent validation. MPLS/PLS regression and ANN methods were used. A statistically significant dependence (at the probability level of 99%) was determined for all traits studied in the case of cross-validation. Satisfactory accuracy of the prediction models by independent validation was achieved only for semolina extraction rate, models for other characteristics did not show acceptable precision. &nbsp;

Determination of Iodine Value of Palm Oils Using Partial Least Squares Regression-Fourier Transform Infrared Data

2014 ◽  
Vol 70 (5) ◽  
Author(s):  
Nor Fazila Rasaruddin ◽  
Mas Ezatul Nadia Mohd Ruah ◽  
Mohamed Noor Hasan ◽  
Mohd Zuli Jaafar
Keyword(s):  
Fourier Transform ◽  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Correlation Coefficient ◽  
Iodine Value ◽  
Cross Validation ◽  
Pls Regression ◽  
Pure Sample

This paper shows the determination of iodine value (IV) of pure and frying palm oils using Partial Least Squares (PLS) regression with application of variable selection. A total of 28 samples consisting of pure and frying palm oils which acquired from markets. Seven of them were considered as high-priced palm oils while the remaining was low-priced. PLS regression models were developed for the determination of IV using Fourier Transform Infrared (FTIR) spectra data in absorbance mode in the range from 650 cm-1 to 4000 cm-1. Savitzky Golay derivative was applied before developing the prediction models. The models were constructed using wavelength selected in the FTIR region by adopting selectivity ratio (SR) plot and correlation coefficient to the IV parameter. Each model was validated through Root Mean Square Error Cross Validation, RMSECV and cross validation correlation coefficient, R2cv. The best model using SR plot was the model with mean centring for pure sample and model with a combination of row scaling and standardization of frying sample. The best model with the application of the correlation coefficient variable selection was the model with a combination of row scaling and standardization of pure sample and model with mean centering data pre-processing for frying sample. It is not necessary to row scaled the variables to develop the model since the effect of row scaling on model quality is insignificant.

True Accuracy of near Infrared Spectroscopy and its Dependence on Precision of Reference Data

2002 ◽  
Vol 10 (1) ◽  
pp. 15-25 ◽  
Author(s):  
L.K. Sørensen
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Reference Data ◽  
Nir Spectroscopy ◽  
Limited Resources ◽  
Standard Errors ◽  
Calibration Data ◽  
Pls Regression ◽  
Calibration Equations ◽  
Very High

A more precise estimate of the accuracy of near infrared (NIR) spectroscopy is obtained when the measured standard errors of cross validation ( SECV) and prediction ( SEP) are corrected for imprecision of the reference data. The significance of correction increases with increasing imprecision of reference data. Very high precision of reference data obtained through replicate analyses under reproducibility conditions may not be the optimal goal for the development of calibration equations. In a situation of limited resources, the precision of the reference data should be related to the obtainable accuracy of the spectroscopic system. Investigation of several routine applications based on the partial least-squares (PLS) regression technique showed that increased precision of calibration data only resulted in marginal improvements in true accuracy if the total standard error of reference results from the beginning was less than the estimated true accuracy of the corresponding NIR calibration.

scholarly journals Application of FTIR Spectrometry Using Multivariate Analysis For Prediction Fuel in Engine Oil

2017 ◽  
Vol 65 (3) ◽  
pp. 933-938 ◽  
Author(s):  
Marie Sejkorová
Keyword(s):  
Diesel Fuel ◽  
Best Practice ◽  
Cross Validation ◽  
Data File ◽  
Engine Oil ◽  
Pls Regression ◽  
Mean Square ◽  
Ftir Spectrometry ◽  
Ftir Spectrometer

This work presents the potentiality of partial least squares (PLS) regression associated with Fourier transform infrared spectroscopy (FTIR spectrometry) for detecting penetration of diesel fuel into the mineral engine oil SAE 15W‑40 in the concentration range from 0 % to 9.5 % (w/w). As a best practice has proven FTIR‑PLS model, which uses the data file in the spectral range 835 – 688 cm−1.The quality of the model was evaluated using the root mean square error of calibration (RMSEC) and cross validation (RMSECV). A correlation coefficient R = 0.999 and values of RMSEC, RMSECV were obtained 0.11 % and 0.38 % respectively. After the calibration of the FTIR spectrometer, the contamination engine oil with diesel fuel could be obtained in 1 – 2 min per sample.

NIR model transfer of alkali-soluble polysaccharides in Poria cocos with piecewise direct standardization

NIR news ◽  
2019 ◽  
Vol 30 (5-6) ◽  
pp. 6-14
Author(s):  
Weihao Wang ◽  
Christian W Huck ◽  
Bin Yang
Keyword(s):  
Correlation Coefficient ◽  
Cross Validation ◽  
Model Building ◽  
Window Size ◽  
Poria Cocos ◽  
Signal Correction ◽  
Model Transfer ◽  
Positive Effect ◽  
Direct Standardization

Objective To investigate the feasibility for establishing near-infrared spectra model transfer with traditional Chinese medicine. Method Poria cocos, a kind of fungi (called “ Fuling” in Chinese), was used as an investigative object. NIR quantification model transfer of alkali-soluble polysaccharides in Poria cocos was studied. Spectra were obtained from Thermo Antaris II (master device) and Buchi NIRFlex N-500 (slave device), respectively. Procedures and factors that may have effects on the performance of spectral standardization have been investigated. Spline interpolation was applied to align spectra from two different instruments, and piecewise direct standardization was employed to standardize spectra from slave instrument. Full cross-validation was used to evaluate the result of model transfer. Results Spectral pre-processing including multiplicative signal correction and wavelength selection played an important role in operating the model transfer. Spectra within the region of 9000–6500 cm−1 provided relatively good transfer result. In addition, increasing window size had positive effect on improving the spectral transfer. In this study, a window size of 13 provided the best spectra standardization result. Test validation with five samples produced the best full cross-validation result with the values of correlation coefficient of calibration ([Formula: see text]), correlation coefficient of prediction ([Formula: see text]) and root mean square error of prediction as 0.86, 0.73 and 2.3, respectively. Conclusion In this study, many factors influenced the quality of the transferred slave spectra. And it is hard to provide a pretty good quantitative model transfer result about alkali-soluble polysaccharide in Poria cocos. The following factors have great effects on the quality of model transfer: (i) multiplicative signal correction which is necessary for model building; (ii) increasing the window size which has a positive effect on slave spectra standardization; (iii) a proper wavelength region benefits for model building; (iv) a proper scale of standardization group including proper samples is necessary.

Fourier Transform near Infrared Spectroscopy Applied to Analysis of Chocolate

2000 ◽  
Vol 8 (4) ◽  
pp. 251-257 ◽  
Author(s):  
Jolana Tarkošová ◽  
Jana Čopíková
Keyword(s):  
Fourier Transform ◽  
Reference Values ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Sufficient Accuracy ◽  
Standard Methods ◽  
Calibration Models ◽  
Predicted Values ◽  
Calibration Equations

Fourier transform near infrared (FT-NIR) spectroscopy was used to establish calibration equations with the aim of determining sucrose, lactose, fat and moisture in chocolate. The possibility of using FT-NIR spectroscopy for evaluating rheological properties (viscosity and yield) of chocolate was also investigated. The concentrations of individual components and the values of viscosity and yield obtained by standard methods were used as reference values. The spectra of 96 chocolate samples were recorded in reflectance mode in the range of 910–2500 nm using an FT-NIR Nicolet Avatar 360N spectrometer equipped with the UpDRIFT accessory. The first or second derivative transformation of the original NIR spectra gave the best accuracy. A partial least squares (PLS) algorithm was used to create calibration models relating reference values to spectral data. The models were validated using cross-validation. The validation results proved that fat, sucrose and lactose can be predicted with sufficient accuracy, while predicted values for moisture, viscosity and yield are less reliable.

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