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.

scholarly journals Analysis of Goat Milk by Near-Infrared Spectroscopy

2008 ◽  
Vol 77 (3) ◽  
pp. 415-422 ◽  
Author(s):  
M. Dračková ◽  
L. Hadra ◽  
B. Janštová ◽  
P. Navrátilová ◽  
H. Přidalová ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Freezing Point ◽  
Goat Milk ◽  
Titratable Acidity ◽  
Total Solids ◽  
Calibration Models ◽  
Pls Method

The objective of this study was to determine protein, fat, lactose, total solids, non-fatty solids contents, freezing point, titratable acidity and pH using Fourier transform near infrared spectroscopy (FT-NIR). Sixty samples of goat milk were used to calibrate the instrument by the partial least squares (PLS) method. The spectra were measured on the integration sphere in the reflectance mode with the use of a 0.1 mm wide transflectance cell. The following statistical values were obtained: correlation coefficient (R) = 0.920 and standard error of calibration (SEC) = 0.094 for protein, R = 0.951 and SEC = 0.124 for fat, R = 0.997 and SEC = 0.011 for lactose, R = 0.940 and SEC = 0.260 for total solids, R = 0.873 and SEC = 0.159 for non-fatty solids, R = 0.935 and SEC = 0.003 for freezing point, R = 0.952 and SEC = 0.295 for titratable acidity and R = 0.835 and SEC = 0.057 for pH. The calibration models developed were verified by cross validation. The study showed that FT-NIR is a potentially useful technique for evaluating the composition of goat milk.

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.

scholarly journals Pseudo-Univariate Calibration Based on NIR Spectroscopy in the Determination of Anthocyanins and Antioxidant Activity in Grape Juices

2021 ◽  
Author(s):  
Karla Beltrame ◽  
Thays Gonçalves ◽  
Paulo Março ◽  
Sandra Gomes ◽  
Makoto Matsushita ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Grape Juice ◽  
Calibration Models ◽  
Total Anthocyanins ◽  
Alternative Methodology ◽  
Grape Juices

This work shows an alternative methodology based on a portable near-infrared (NIR) spectroscopy coupled to independent components analysis (ICA) in a pseudo-univariate calibration way to determine total anthocyanins (TA) concentration and antioxidant activity (AA) in whole grape juice. To this, the scores proportions more related to TA and AA were plotted against TA and AA obtained by its respective references methodology to build pseudo-univariate calibration models with correlation coefficients of 0.9699 and 0.9814, respectively. From the results, it is possible the suggestion that NIR spectra coupled to ICA enable to overcome interferences using first-order data and work properly when there is enough selectivity for the analyte profile in the sample data.

Near infrared spectroscopy coupled chemometric algorithms for prediction of the antioxidant activity of peanut seed (Arachis hypogaea)

2021 ◽  
pp. 096703352097942
Author(s):  
Muhammad Bilal ◽  
Zou Xiaobo ◽  
Muhmmad Arslan ◽  
Haroon Elrasheid Tahir ◽  
Yue Sun ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Rapid Assessment ◽  
Research Work ◽  
Antioxidant Properties ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Peanut Seed ◽  
Chemometric Algorithms ◽  
Selection Algorithms

In the present research work, near infrared (NIR) spectroscopy coupled with chemometric algorithms such as partial least-squares (PLS) regression and some effective variable selection algorithms (synergy interval-PLS (Si-PLS), Backward interval-PLS (Bi-PLS), and genetic algorithm-PLS (GA-PLS)) were used for the quantification of antioxidant properties of peanut seed samples. The compositional parameters, such as DPPH, ABTS, FRAP, TPC, FCA, TFC, and TAC, were quantified using NIR spectroscopy. The developed models were assessed using correlation coefficients of the calibration (R2) and prediction (r2); root mean standard error of cross-validation, RMSECV; root mean square error of prediction, RMSEP and residual predictive deviation, RPD. The efficiency of the developed model was significantly enhanced with the use of Si-PLS, Bi-PLS, and GA-PLS as compared to the classical PLS model. The results of the R2 and r2 set varied from 0.76 to 0.95 and 0.72 to 0.94, respectively. The obtained results revealed that NIR spectroscopy, coupled with different chemometric algorithms, has the potential to be used for rapid assessment of the antioxidant properties of peanut seed.

scholarly journals Analysis of cow milk by near-infrared spectroscopy

2011 ◽  
Vol 21 (No. 4) ◽  
pp. 123-128 ◽  
Author(s):  
R. Jankovská ◽  
K. Šustová
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Somatic Cells ◽  
Nir Spectroscopy ◽  
Cow Milk ◽  
Urea Nitrogen ◽  
Total Solids ◽  
Average Spectrum ◽  
Calibration Models

In this work, the major components (total solids, fat, protein, casein, urea nitrogen, lactose, and somatic cells) were determined in cow milk by near-infrared spectroscopy. Fifty calibration samples of milk were analysed by reference methods and by FT NIR spectroscopy in reflectance mode at wavelengths ranging from 4000 to 10&nbsp;000&nbsp;cm<sup>&ndash;1 </sup>with 100 scan. Each sample was analysed three times and the average spectrum was used for calibration. Partial least squares (PLS) regression was used to develop calibration models for the milk components examined. Determined were the highest correlation coefficients for total solids (0.928), fat (0.961), protein (0.985), casein (0.932), urea nitrogen (0.906), lactose (0.931), and somatic cells (0.872). The constructed calibration models were validated by full cross validation. The results of this study indicated that NIR spectroscopy is applicable for a rapid analysis of milk composition. &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;

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.

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Elise A. Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnostic Methods ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

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.

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