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.  

scholarly journals The Quality Control of Tea by Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ming-Zhi Zhu ◽  
Beibei Wen ◽  
Hao Wu ◽  
Juan Li ◽  
Haiyan Lin ◽  
...  
Keyword(s):  
Quality Control ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Chemical Compositions ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Control Quality ◽  
Tea Quality ◽  
Instrumental Technique

Tea is known to be one of the most popular beverages enjoyed by two-thirds of the world’s population. Concern of variability in tea quality is increasing among consumers. It is of great significance to control quality for commercialized tea products. As a rapid, noninvasive, and nondestructive instrumental technique with simplicity in sample preparation, near-infrared reflectance (NIR) spectroscopy has been proved to be one of the most advanced and efficient tools for the control quality of tea products in recent years. In this article, we review the most recent advances and applications of NIR spectroscopy and chemometrics for the quality control of tea, including the measurement of chemical compositions, the evaluation of sensory attributes, the identification of categories and varieties, and the discrimination of geographical origins. Besides, challenges and future trends of tea quality control by NIR spectroscopy are also presented.

Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef

2021 ◽  
pp. 096703352110495
Author(s):  
Cassius EO Coombs ◽  
Robert R Liddle ◽  
Luciano A González
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Consumer Confidence ◽  
Spectroscopic Methods ◽  
Marbling Score ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Beef Steak

The present study analysed the ability for portable near infrared reflectance (NIR) and Raman spectroscopy sensors to differentiate between grass-fed and grain-fed beef. Scans were made on lean and fat surfaces of 108 beef steak samples labelled as grass-fed ( n = 54) and grain-fed ( n = 54), with partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) used to develop discrimination models which were tested on independent datasets. Furthermore, PLS-DA was used to predict visual marbling score and days on feed (DOF). The NIR spectra accurately discriminated between grass- and grain-fed beef on both fat (91.7%, n = 92) and lean (88.5%, n = 96), as did Raman (fat 95.2%, n = 82; lean 69.6%, n = 68). Fat scanning using NIR spectroscopy moderately predicted DOF (r2val = 0.53), though Raman and NIR spectroscopy lean prediction models for DOF and marbling were less precise (r2val < 0.50). It can be concluded that portable NIR and Raman spectrometers can be used successfully to differentiate grass-fed from grain-fed beef and therefore aid retail and consumer confidence.

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. &nbsp;

The Use of near Infrared Spectroscopy to Quantify Lignite-Derived Carbon in Humus-Lignite Mixtures

2007 ◽  
Vol 15 (3) ◽  
pp. 195-200 ◽  
Author(s):  
Marcin Chodak ◽  
Maria Niklińska ◽  
Friedrich Beese
Keyword(s):  
Near Infrared ◽  
Regression Coefficient ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Mine Soils ◽  
Organic Horizons ◽  
Predicted Values ◽  
Calibration Equations ◽  
Mixed Samples ◽  
Forest Humus

Assessment of the percentage of lignite-derived C (lign-C%) in mine soils may be achieved only by using time-consuming and expensive methods. The objectives of this study were (1) to compare near infrared (NIR) spectra of forest humus and lignite and (2) to test whether NIR spectroscopy may assess lign-C% in artificial mixtures of humus and lignite. The experiment consisted of three trials (T1, T2 and T3). In T1 the mixed samples ( n = 75) were produced from one humus sample and one lignite sample, in T2 (n = 74) from 74 different humus samples and one lignite sample and in T3 (n = 74) from 74 different humus samples and 15 lignite samples. In each trial, 35 samples were used to develop calibration equations and the remaining samples were used for validation. The humus and the lignite samples used to produce the mixed samples were analysed for C, H, N and S and their NIR spectra were recorded. The lignite samples contained more C, H and S and less N than the humus samples. Principal component analysis revealed significant differences between NIR spectra of the humus and the lignite samples. The prediction of lign-C% in T1 [regression coefficient (b) of linear regression (measured against predicted values) = 0.99, correlation coefficient ( r2) = 1.00, standard error of prediction (SEP) = 1.2%] and T2 ( b = 0.99, r2 = 0.99, SEP = 1.9%) was very good and in T3 satisfactory ( b = 0.83, r2 = 0.92, SEP = 4.0% ). The calibration equations of T2 predicted lign-C% satisfactorily and also in the validation samples of T3 (b = 0.88, r2 = 0.93, SEP = 4.0% ). The results indicate the ability of NIR spectroscopy to predict lign-C% in the mixed humus and lignite samples and suggest usefulness of NIR spectroscopy for the assessment of the percentage of lignite-derived C in the organic horizons of mine soils.

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;

Development of a Sample Set for Soya Bean Calibration of near Infrared Reflectance Spectroscopy

1994 ◽  
Vol 2 (4) ◽  
pp. 223-227 ◽  
Author(s):  
T.L. Hong ◽  
S.-J. Tsai ◽  
S.C.S. Tsou
Keyword(s):  
Near Infrared ◽  
Analytical Data ◽  
Storage Period ◽  
Nir Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Soya Bean ◽  
Chemical Compositions ◽  
Near Infrared Reflectance ◽  
Sample Set ◽  
Calibration Equations

The potential application of near infrared (NIR) spectroscopy is limited since its calibration equations are not always transferable from one instrument to another. Hence, an attempt was made to develop a selected sample set of soya beans with analytical data, which could be distributed to collaborators to calibrate their instruments. Sixty soya bean samples, (1 kg each) were selected and packed (200 g each) in laminated film bags after thorough mixing. During their storage at 4°C, the soya bean samples were periodically evaluated by chemical analysis as well as by NIR spectroscopy. Chemical compositions (i.e. moisture, protein and fat) were determined using conventional methods. Experimental results showed that no significant differences were found in the compositions of interest as well as in the reflectance spectra over a storage period of up to three years, and that the NIR spectroscopy method is independent of the location and model of the instruments. The experiment demonstrated that it is possible practically to use a pre-packed sample set with chemical data for calibration purposes.

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.

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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