scholarly journals Determination Residues of Penicillin G and Cloxacillin in Raw Cow Milk Using Fourier Transform Near Infrared Spectroscopy

2009 ◽  
Vol 78 (4) ◽  
pp. 685-690 ◽  
Author(s):  
Michaela Dračková ◽  
Pavlína Navrátilová ◽  
Luboš Hadra ◽  
Lenka Vorlová ◽  
Lenka Hudcová
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Raw Milk ◽  
Partial Least Square ◽  
Penicillin G ◽  
Standard Errors ◽  
Calibration And Validation ◽  
Calibration Models

The objective of this study was to study the use of Fourier transform near infrared spectroscopy (FTNIR) combined with the partial least square (PLS) method for determining the residues of penicillin and cloxacillin in raw milk. The spectra were measured in the reflectance mode with transflectance cell in the spectral range of 10,000 – 4,000 cm-1 with 100 scans. Calibration models were developed. They were assessed statistically based on correlation coefficients (R) and standard errors of calibration (SEC). For penicillin, the following values were established: R = 0.951 and SEC = 0.004. For cloxacillin, they were R = 0.871 and SEC = 0.007. These calibration models were verified later with cross-validation. Better results were obtained in the calibration and validation models that were developed on milk samples coming from one farm. Using FT-NIR, the maximum residue limit (MRL) of cloxacillin in milk can be determined. However, standard errors of calibration and validation for penicillin G exceed the fixed MRL. FT-NIR spectroscopy is not a suitable method for accurate determination of these substances in raw milk. Variability in milk composition has a major influence on detection of substances present at very low concentrations.

scholarly journals Near-Infrared Spectroscopy for Measuring Urea in Hemodialysis Fluids

2001 ◽  
Vol 47 (7) ◽  
pp. 1279-1286 ◽  
Author(s):  
Christopher V Eddy ◽  
Mark A Arnold
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Spectral Range ◽  
Near Infrared ◽  
Factor Model ◽  
Partial Least Square ◽  
Least Square ◽  
Calibration Model ◽  
Calibration Models ◽  
Sensitivity And Selectivity

Abstract Background: Near-infrared spectroscopy is proposed as a method for providing real-time urea concentrations during hemodialysis treatments. The feasibility of such noninvasive urea measurements is evaluated in undiluted dialysate fluid. Methods: Near-infrared spectra were collected from calibration solutions of urea prepared in dialysate fluid. Spectra were collected over three distinct spectral regions, and partial least-squares calibration models were optimized and compared for each. Selectivity for urea was demonstrated with two-component samples composed of urea and glucose in the dialysate matrix. The clinical significance of this approach was assessed by measuring urea in real hemodialysate samples. Results: Urea absorptions within the combination and short-wavelength, near-infrared spectral regions provided sufficient spectral information for sound calibration models in the dialysate matrix. The combination spectral region had SEs of calibration (SEC) and prediction (SEP) of 0.38 mmol/L and 0.26 mmol/L, respectively, over the 4720–4600 cm−1 spectral range with 5 partial least-square factors. A second calibration model was established over the combination region from a series of solutions prepared with independently variable concentrations of urea and glucose. The best calibration model for urea in the presence of variable glucose concentrations had a SEC of 0.6 mmol/L and a SEP of 0.4 mmol/L for a 5-factor model over the 4600–4350 cm−1 spectral range. There was no significant decrease in SEP when the 4720–4600 cm−1 calibration model was used to measure urea in real samples collected during actual hemodialysis. Conclusions: Urea can be determined with sufficient sensitivity and selectivity for clinical measurements within the matrix of the hemodialysis fluid.

scholarly journals Determination of fatty acid content in sheep milk by means of near infrared spectroscopy

2014 ◽  
Vol 83 (10) ◽  
pp. S27-S34 ◽  
Author(s):  
Táňa Lužová ◽  
Květoslava Šustová ◽  
Jan Kuchtík ◽  
Jiří Mlček ◽  
Lenka Vorlová ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Fatty Acid Content ◽  
Correlation Coefficients ◽  
Standard Errors ◽  
Sheep Milk ◽  
Calibration Models ◽  
The Fourier Transform

The study focused on the use of the Fourier transform near infrared spectroscopy in determining the content of selected fatty acids in raw non-homogenized sheep milk. The raw sheep milk sample spectra were scanned in reflectance mode using the FT NIR Antaris spectrophotometer. The reliable functional calibration models were created for estimation of the contents of myristic, oleic, lauric, palmitic, and stearic acids (with calibration correlation coefficients of R = 0.999; 0.999; 0.993; 0.992; 0.858) and with standard errors SEC = 0.056; 0.152; 0.066; 0.367; 1.36%.

scholarly journals Discriminant analysis of Olomouc curd cheese by Fourier transform near infrared spectroscopy

2014 ◽  
Vol 32 (No. 1) ◽  
pp. 31-36 ◽  
Author(s):  
M. Králová ◽  
Z. Procházková ◽  
V. Svobodová ◽  
E. Mařicová ◽  
B. Janštová ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Storage Time ◽  
Near Infrared ◽  
Great Similarity ◽  
Calibration Models

We used the discriminant analysis of curd cheese during storage by Fourier transform near infrared spectroscopy method (FT-NIRs). Olomouc curd cheese samples were stored at 5 and at 20&deg;C during seven weeks. The spectra of samples were measured at the integration sphere in reflectance mode with the use of a compressive cell in the spectral range of 10&nbsp;000&ndash;4000 cm<sup>&ndash;1</sup> with 100 scans. Ten principal components were used for all the calibration models. Great similarity between the samples stored at 5 and 20&deg;C was found. Twelve samples stored at 20&deg;C for 1 week and 2 samples stored at 20&deg;C for 2 weeks were classified as samples stored at 5&deg;C. Different results were found out by comparing the storage time. 100% variability was described between the spectra scanned in different weeks of storage at 5&deg;C and 99.9% variability was obtained for the samples stored at 20&deg;C. Thus, the discriminant analysis of Olomouc curd cheese by FT-NIRs is a suitable method for the determination of ripening time. &nbsp;

Prediction of neutral detergent fiber content in corn stover using near-infrared spectroscopy technique

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Xuyang Pan ◽  
Laijun Sun ◽  
Guobing Sun ◽  
Panxiang Rong ◽  
Yuncai Lu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Prediction Model ◽  
Spectral Data ◽  
Corn Stover ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Fiber Content ◽  
Partial Least Square ◽  
Neutral Detergent Fiber

AbstractNeutral detergent fiber (NDF) content was the critical indicator of fiber in corn stover. This study aimed to develop a prediction model to precisely measure NDF content in corn stover using near-infrared spectroscopy (NIRS) technique. Here, spectral data ranging from 400 to 2500 nm were obtained by scanning 530 samples, and Monte Carlo Cross Validation and the pretreatment were used to preprocess the original spectra. Moreover, the interval partial least square (iPLS) was employed to extract feature wavebands to reduce data computation. The PLSR model was built using two spectral regions, and it was evaluated with the coefficient of determination (R2) and root mean square error of cross validation (RMSECV) obtaining 0.97 and 0.65%, respectively. The overall results proved that the developed prediction model coupled with spectral data analysis provides a set of theoretical foundations for NIRS techniques application on measuring fiber content in corn stover.

Simultaneous Dielectric and Fourier Transform near Infrared Spectroscopy to Monitor a Polyepoxy Curing Process

2006 ◽  
Vol 14 (3) ◽  
pp. 161-166 ◽  
Author(s):  
Alexandra Durand ◽  
Laïla Hassi ◽  
Gilbert Lachenal ◽  
Isabelle Stevenson ◽  
Gérard Seytre ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Curing Process

scholarly journals Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat

2021 ◽  
Vol 13 (6) ◽  
pp. 1128
Author(s):  
Iman Tahmasbian ◽  
Natalie K Morgan ◽  
Shahla Hosseini Bai ◽  
Mark W Dunlop ◽  
Amy F Moss
Keyword(s):  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Benefits ◽  
Partial Least Square ◽  
Least Square ◽  
Statistical Population ◽  
Processing Plants ◽  
C And N

Hyperspectral imaging (HSI) is an emerging rapid and non-destructive technology that has promising application within feed mills and processing plants in poultry and other intensive animal industries. HSI may be advantageous over near infrared spectroscopy (NIRS) as it scans entire samples, which enables compositional gradients and sample heterogenicity to be visualised and analysed. This study was a preliminary investigation to compare the performance of HSI with that of NIRS for quality measurements of ground samples of Australian wheat and to identify the most important spectral regions for predicting carbon (C) and nitrogen (N) concentrations. In total, 69 samples were scanned using an NIRS (400–2500 nm), and two HSI cameras operated in 400–1000 nm (VNIR) and 1000–2500 nm (SWIR) spectral regions. Partial least square regression (PLSR) models were used to correlate C and N concentrations of 63 calibration samples with their spectral reflectance, with 6 additional samples used for testing the models. The accuracy of the HSI predictions (full spectra) were similar or slightly higher than those of NIRS (NIRS Rc2 for C = 0.90 and N = 0.96 vs. HSI Rc2 for C (VNIR) = 0.97 and N (SWIR) = 0.97). The most important spectral region for C prediction identified using HSI reflectance was 400–550 nm with R2 of 0.93 and RMSE of 0.17% in the calibration set and R2 of 0.86, RMSE of 0.21% and ratio of performance to deviation (RPD) of 2.03 in the test set. The most important spectral regions for predicting N concentrations in the feed samples included 1451–1600 nm, 1901–2050 nm and 2051–2200 nm, providing prediction with R2 ranging from 0.91 to 0.93, RMSE ranging from 0.06% to 0.07% in the calibration sets, R2 from 0.96 to 0.99, RMSE of 0.06% and RPD from 3.47 to 3.92 in the test sets. The prediction accuracy of HSI and NIRS were comparable possibly due to the larger statistical population (larger number of pixels) that HSI provided, despite the fact that HSI had smaller spectral range compared with that of NIRS. In addition, HSI enabled visualising the variability of C and N in the samples. Therefore, HSI is advantageous compared to NIRS as it is a multifunctional tool that poses many potential applications in data collection and quality assurance within feed mills and poultry processing plants. The ability to more accurately measure and visualise the properties of feed ingredients has potential economic benefits and therefore additional investigation and development of HSI in this application is warranted.

Linear transformations of multivariate calibration models in near infrared spectroscopy: A comparative study

2017 ◽  
Vol 25 (4) ◽  
pp. 223-230 ◽  
Author(s):  
Joseph Dubrovkin
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Cetane Number ◽  
Total Protein Content ◽  
Prediction Errors ◽  
Calibration Data ◽  
Linear Transformations ◽  
Calibration Models

It was shown that linear transformations are suitable for use in multivariate calibration in near infrared spectroscopy as data compression tools. Partial Least Squares calibration models were built using spectral data transformed by expansion in the series of classical orthogonal polynomials, Fourier and wavelet harmonics. These models allowed effective prediction of the cetane number of diesel fuels, Brix and pol parameters of syrup in sugar production and fat and total protein content in milk. Depending on the compression ratio, prediction errors were no larger than 30% of corresponding errors obtained by the use of the non-transformed models. Although selection of the most suitable transformation depends on the calibration data and on the cross-validation method, in many cases Fourier transform gave satisfactory results.

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