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.

Rapid Determination of Heartwood Extractives in Larix sp. by Means of Fourier Transform near Infrared Spectroscopy

2002 ◽  
Vol 10 (3) ◽  
pp. 203-214 ◽  
Author(s):  
N. Gierlinger ◽  
M. Schwanninger ◽  
B. Hinterstoisser ◽  
R. Wimmer
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Solid Wood ◽  
Hot Water ◽  
Extractive Content ◽  
Fast Method ◽  
Wood Powder ◽  
Test Set

The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy to rapidly determine extractive and phenolic content in heartwood of larch trees ( Larix decidua MILL., L. leptolepis (LAMB.) CARR. and the hybrid L. x eurolepis) was investigated. FT-NIR spectra were collected from wood powder and solid wood using a fibre-optic probe. Partial Least Squares (PLS) regression analyses were carried out describing relationships between the data sets of wet laboratory chemical data and the FT-NIR spectra. Besides cross and test set validation the established models were subjected to a further evaluation step by means of additional wood samples with unknown extractive content. Extractive and phenol contents of these additional samples were predicted and outliers detected through Mahalanobis distance calculations. Models based on the whole spectral range and without data pre-processing performed well in cross-validation and test set validation, but failed in the evaluation test, which is based on spectral outlier detection. But selection of data pre-processing methods and manual as well as automatic restriction of wavenumber ranges considerably improved the model predictability. High coefficients of determination ( R2) and low root mean square errors of cross-validation ( RMSECV) were obtained for hot water extractives ( R2 = 0.96, RMSECV = 0.86%, range = 4.9–20.4%), acetone extractives ( R2 = 0.86, RMSECV = 0.32%, range = 0.8–3.6%) and phenolic substances ( R2 = 0.98, RMSECV = 0.21%, range = 0.7–4.9%) from wood powder. The models derived from wood powder spectra were more precise than those obtained from solid wood strips. Overall, NIR spectroscopy has proven to be an easy to facilitate, reliable, accurate and fast method for non-destructive wood extractive determination.

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.

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.

Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy

2006 ◽  
Vol 82 (1) ◽  
pp. 111-116 ◽  
Author(s):  
N. Barlocco ◽  
A. Vadell ◽  
F. Ballesteros ◽  
G. Galietta ◽  
D. Cozzolino
Keyword(s):  
Shear Force ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Intramuscular Fat ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Calibration Models ◽  
Intact Muscle

AbstractPartial least-squares (PLS) models based on visible (Vis) and near infrared reflectance (NIR) spectroscopy data were explored to predict intramuscular fat (IMF), moisture and Warner Bratzler shear force (WBSF) in pork muscles (m. longissimus thoracis) using two sample presentations, namely intact and homogenized. Samples were scanned using a NIR monochromator instrument (NIRSystems 6500, 400 to 2500 nm). Due to the limited number of samples available, calibration models were developed and evaluated using full cross validation. The PLS calibration models developed using homogenized samples and raw spectra yielded a coefficient of determination in calibration (R2) and standard error of cross validation (SECV) for IMF (R2=0·87; SECV=1·8 g/kg), for moisture (R2=0·90; SECV=1·1 g/kg) and for WBSF (R2=0·38; SECV=9·0 N/cm). Intact muscle presentation gave poorer PLS calibration models for IMF and moisture (R2<0·70), however moderate good correlation was found for WBSF (R2=0·64; SECV=8·5 N/cm). Although few samples were used, the results showed the potential of Vis-NIR to predict moisture and IMF using homogenized pork muscles and WBSF in intact samples.

Assessment of contamination of wool by dark and medullated fibres using near infrared spectroscopy

2006 ◽  
Vol 46 (5) ◽  
pp. 605
Author(s):  
M. R. Fleet ◽  
L. Li ◽  
Y. Ru
Keyword(s):  
Infrared Spectroscopy ◽  
Sample Preparation ◽  
Near Infrared Spectroscopy ◽  
Reference Values ◽  
Near Infrared ◽  
Reference Data ◽  
Wool Fibre ◽  
Reflectance Measurement ◽  
Calibration Models ◽  
Calibration Equations

Increased crossbreeding of Merino sheep in Australia, involving coloured or highly medullated sire breeds, has increased the risk of dark and highly medullated fibres in wool lots. Commercial implementation of the Dark and Medullated Fibre Risk Scheme, based on producer information, is identifying to buyers some of these risks and technology is sought to provide an inexpensive method for routine presale testing of greasy wool lots. One measurement concept assessed the ability of near infrared spectroscopy (NIRS) to predict variation in levels of pigmented fibres or highly medullated fibres in wool. The project used either ‘seeded’ wool samples or naturally contaminated samples with measured reference values as well as different methods of sample preparation of wool fibre (in air or immersed in benzyl alcohol) or the solutions from alkali hydrolysis of wool fibre and NIRS measurement (reflectance v. transmission). NIRS can predict variation in trace levels of pigmented fibre or highly medullated white fibres (kemp) in wool and, among the methods assessed, reflectance analysis of wool fibre in air was generally better than the other options considered. Calibration models for NIRS reflectance measurement of 5 g wool samples ‘seeded’ with 1–50 black-pigmented, tan-pigmented or white kemp fibres gave coefficients of determination (R2) of 0.96, 0.88 and 0.82 with standard errors of cross-validation (SECV) of 4.34, 6.97 and 7.17 fibres per 5 g sample, respectively. However, these calibration equations generally did not predict variations in the reference values for 3 other sets of naturally contaminated samples. New calibration equations determined for each of the sets of naturally contaminated samples also predicted variation in the pigmented fibre reference values, with the extent of agreement depending on the accuracy of the reference data as well as sample preparation and method of measurement. Calibration models for NIRS reflectance measurement of wool fibre from the 3 sets of naturally contaminated samples produced R2 = 0.99, 0.71 and 0.92 with SECV = 0.923, 6.43 and 4.54 pigmented fibres per 5 g sample, respectively. However, these calibrations and those obtained from various combinations of the wool sets also had limited ability to predict variation in pigmented fibre reference values in other independent or excluded samples. Refinement of the technique and development of calibrations with extensive and reliable reference data, representing all of the wool variation likely to be encountered, may allow this NIRS potential to become relevant in the presale testing of wool as an inexpensive measurement procedure for estimating dark and medullated fibre content.

scholarly journals Use of FT-NIR Spectroscopy for Bovine Colostrum Analysis

2006 ◽  
Vol 75 (1) ◽  
pp. 57-63 ◽  
Author(s):  
P. Navrátilová ◽  
L. Hadra ◽  
M. Dračková ◽  
B. Janštová ◽  
L. Vorlová ◽  
...  
Keyword(s):  
Fourier Transformation ◽  
Near Infrared ◽  
Cross Validation ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Bovine Colostrum ◽  
Total Solids ◽  
Calibration Models ◽  
Spectral Ranges ◽  
Pls Method

Fourier transformation near infrared spectroscopy (FT-NIR) in combination with partial least squares (PLS) method were used to determine the content of total solids, fat, non-fatty solids, lactose and proteins in bovine colostrum. Spectra of 90 samples were measured in the reflectance mode with a transflectance cuvette in the 10000-4000 cm-1 spectral ranges with 100 scans. Calibration was performed and statistical values of correlation coefficients (R) and standard error of calibration values (SEC) were computed for total solids (0.986 and 0.919, respectively), fat (0.997 and 0.285, respectively), non-fatty solids (0.995 and 0.451, respectively), lactose (0.934 and 0.285, respectively) and protein (0.999 and 0.149, respectively). The calibration models developed were verified by cross validation. It follows from the study that FT-NIR spectroscopy can be used to determine the components of bovine colostrum.

Hydraulic traits of Norway spruce sapwood estimated by Fourier transform near-infrared spectroscopy (FT-NIR)

2015 ◽  
Vol 45 (6) ◽  
pp. 625-631 ◽  
Author(s):  
Saskia Luss ◽  
Manfred Schwanninger ◽  
Sabine Rosner
Keyword(s):  
Fourier Transform ◽  
Norway Spruce ◽  
Hydraulic Properties ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Air Pressure ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
The Impact

The potential of Fourier transform near-infrared (FT-NIR) spectroscopy to predict hydraulic traits in Norway spruce (Picea abies (L.) Karst.) sapwood was evaluated. Hydraulic traits tested were P50 (applied air pressure causing 50% loss of hydraulic conductivity) and RWL50 (applied air pressure causing 50% relative water loss). Samples came from 24-year-old spruce clones. FT-NIR spectra were collected from the axial (transverse) and radial surface of each solid wood sample for the prediction of P50 and RWL50. Partial least squares regression (PLS-R) models with cross validation were used to establish relationships between the FT-NIR spectra and the reference data from hydraulic properties analysis. The impact of the wavenumber range and the pretreatment during the PLS-R model development and the differences between the axial and radial surfaces were shown. Based on the values of the coefficient of determination (r2) and the root mean square error of cross validation, predicted results were evaluated as acceptable. The models from the axial surface gave better results than the models from the radial surface for P50 (r2 = 0.65), as well as for RWL50 (r2 = 0.77). The first approach to predict hydraulic properties such as P50 and RWL50 by FT-NIR spectroscopy can be regarded as successful. We conclude that the method has high potential to be put into practice as a rapid, reliable, and nondestructive method to determine P50 and RWL50.

scholarly journals Rapid and Reliable Assessment of Fish Physiological Condition for Fisheries Research and Management Using Fourier Transform Near-Infrared Spectroscopy

2021 ◽  
Vol 8 ◽  
Author(s):  
Esther D. Goldstein ◽  
Thomas E. Helser ◽  
Johanna J. Vollenweider ◽  
Ashwin Sreenivasan ◽  
Fletcher F. Sewall
Keyword(s):  
Fourier Transform ◽  
Energy Density ◽  
Body Condition ◽  
Lipid Content ◽  
Near Infrared ◽  
Physiological State ◽  
Nir Spectroscopy ◽  
Predictive Ability ◽  
Hepatosomatic Index ◽  
Calibration Models

Measuring fish population responses to climate change requires timely ecological information, warranting innovative approaches to data collection in fisheries research and management. Fourier transform near-infrared (FT-NIR) spectroscopy is a promising tool to efficiently and cost-effectively obtain multiple types of fisheries data including fish physiological health and energetics that can provide indicators of stock status and environmental change. We tested the applicability of FT-NIR spectroscopy to determine fish physiological state and condition by developing calibration models for morphometric indices of body condition [Fulton’s K and hepatosomatic index (HSI)], biochemical measurements of tissue composition (lipid content and energy density), and a nucleic acid-based index of recent growth (RNA:DNA) of juvenile Pacific cod (Gadus macrocephalus). Calibration models had the best predictive ability for lipid content followed by HSI and energy density, whereas spectral data had weak relationships with Fulton’s K and RNA:DNA. For lipid content, energy density, and HSI, informative spectral regions were primarily associated with carbon-hydrogen bonds in lipid molecules. Additionally, FT-NIR spectroscopy calibration models better predicted lipid content than morphometric measurements that are often used as proxies for measuring energy reserves, indicating that FT-NIR spectroscopy might serve as a more informative index of body condition and energy stores than other rapid methods. Efficient sample analysis by FT-NIR spectroscopy can supplement traditional metrics of body condition and be especially useful for ensuring the availability of monitoring data in support of fisheries research and management.

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;

scholarly journals Application of FT NIR spectroscopy in the determination of basic chemical composition of pork and beef

2011 ◽  
Vol 51 (No. 8) ◽  
pp. 361-368 ◽  
Author(s):  
J. Mlček ◽  
K. Šustová ◽  
J. Simeonovová
Keyword(s):  
Chemical Composition ◽  
Chemical Analysis ◽  
Cross Validation ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Calibration And Validation ◽  
Meat Composition ◽  
Calibration Models ◽  
Predicted Values

The objective of this paper was to determine basic components of pork and beef (fat, protein, water content) using FT NIR spectroscopy. The samples were analysed on an FT NIR Nicolet Antaris device in a reflec-tance regimen. Reference results from classical analyses were used for the calibration of the device. Calibration models were created using PLS algorithm (method of partial least squares) and verified by cross-validation. High correlation coefficients (R) of calibration were calculated (fat 0.998; protein 0.976; water 0.994), and subsequently of validation as well (fat 0.997; protein 0.970; water 0.993) and very low standard deviations of the calibration and validation (SEC, SEP). No statistically significant differences between the reference and predicted values of determination were detected in Z-test. According to the published results, the NIRS method has a high potential to replace an expensive and time demanding chemical analysis of meat composition. &nbsp;

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