The Possibility of near Infrared Spectroscopy for Evaluation of Microbial Nitrogen Content of in Sacco Feed Residues and Duodenal Digesta of Sheep

1998 ◽  
Vol 6 (1) ◽  
pp. 167-174 ◽  
Author(s):  
S. Atanassova ◽  
N. Todorov ◽  
D. Djouvinov ◽  
R. Tsenkova ◽  
K. Toyoda
Keyword(s):  
Nitrogen Content ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Maize Silage ◽  
Total N ◽  
Alfalfa Hay ◽  
Microbial Nitrogen ◽  
Mn Content ◽  
In Sacco

This study aimed to estimate by near infrared (NIR) spectroscopy the microbial nitrogen content (MN) of feed residues from in sacco degradability trails and duodenal digesta of sheep. NIR spectra from 50 samples of duodenal digesta, and from in sacco residues—110 samples of alfalfa hay and 38 samples of maize silage were obtained using an NIRSystems 4250 spectrophotometer. The microbial nitrogen (MN) content of part of the alfalfa hay in sacco residues (78 samples) was calculated from the percentage of 15N enrichment compared to enrichment in the original samples; for the rest of the alfalfa samples and samples of maize silage residues were determined by diaminopimelic acid (DAPA) as a bacterial marker, and MN of duodenal digesta samples by the purine N (RNA equivalent) content as a microbial marker. The calibration equations were developed by modified least squares as the calibration method. The microbial content of all kinds of samples was accurately calibrated and cross-validated. A standard error of cross validation ( SECV) of 0.418 g microbial N kg−1 DM, a coefficient of determination for the cross validation of 0.925 and a ratio of standard deviation of population and the SECV of 3.88 were obtained for the alfalfa 15N labelled hay residues. For maize silage residues, the corresponding values were 0.832, 0.938 and 3.90, and for duodenal digesta samples the values were 1.05, 0.962 and 5.19, respectively. Prediction of MN as percentage of total N of the samples gave approximately the same level of accuracy. For example, the SECV was 2.35% units, cross-validation R2 was 0.953, SD/SECV was 4.60 for alfalfa 15N labelled hay residues. Despite the different origin of the analysed samples (feed residues and duodenal digesta), the NIR spectroscopy determination of MN content of all samples was based on spectral data at very similar wavelengths. The study indicated that NIR spectroscopy has the potential to predict microbial nitrogen content and to distinguish MN from total N content of in sacco feed residues and duodenal digesta.

Predictive Capacity of Visible-Near Infrared Spectroscopy for Quality Parameter Assessment of Compost

2009 ◽  
Vol 17 (5) ◽  
pp. 289-301 ◽  
Author(s):  
Sandra Herrmann ◽  
Jochen Mayer ◽  
Kerstin Michel ◽  
Bernard Ludwig
Keyword(s):  
Quality Assessment ◽  
Near Infrared ◽  
Cross Validation ◽  
Cellulase Activity ◽  
Nir Spectroscopy ◽  
Disease Suppression ◽  
Total N ◽  
Organic C ◽  
Compost Quality ◽  
Fda Hydrolysis

Screening tests are basic procedures commonly used to assess compost quality. Important parameters for quality assessment are the germination capacity and the suppression of plant pathogens which have to be measured by time-consuming laboratory methods. The objective was to test whether visible (vis) and near infrared (NIR) spectroscopy (vis-NIR) is useful to analyse parameters important for compost quality. Ninety seven compost samples from Switzerland were analysed by conventional methods and by vis-NIR. The content of organic (Corg) and inorganic C (Cinorg), total N (Ntot), mineralisable N after 56 days (Nmin_d56), total P (Ptot), K, Ca and salt, the C/N ratio, pH and microbiological characteristics [hydrolysis of fluorescein diacetate (FDA-hydrolysis) as indicator of total enzyme activity and cellulase activity] were determined. Furthermore, plant tolerance and the suppression of pathogens were tested using germination tests with salad, cress, ryegrass and bean or a Rhizoctonia solani bioassay, respectively. The samples were scanned in the range of 400–2500 nm (visible light and NIR) using a Foss NIRSystems spectrometer 6500. A modified partial least squares regression method and the whole spectrum were used to develop cross-validation equations for all constituents. For this, the first to third derivative was calculated. The prediction accuracy was evaluated as excellent for Corg and good for N, and the C/N ratio based on the RSC values (ratio of standard deviation of laboratory results to standard error of cross-validation) and the coefficients of determination ( r2). Approximate quantitative predictions were possible for the contents of Ptot, K, Ca and salt, whereas for the constituents Cinorg, Nmin_d56, FDA-hydrolysis and the germination tests with cress and salad only between high and low values could be discriminated. Unsuccessful predictions as indicated by RSC values lower than 1.5 and r2 values below 0.50 were obtained for pH, cellulase activity, germination tests with ryegrass and bean and the disease suppression test using R. solani. Overall the results of the present study indicate that vis-NIR spectroscopy has the potential to be used for quality assessment of composts and to replace time-consuming methods such as germination tests using salad and cress. However, the use for monitoring purposes requires further research to clarify whether other complex quality parameters such as disease suppression indicators may also be predicted successfully.

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.

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.

Comparison of Transflectance and Reflectance to Analyse Hog Manures

2005 ◽  
Vol 13 (2) ◽  
pp. 99-107 ◽  
Author(s):  
W. Saeys ◽  
J. Xing ◽  
J. De Baerdemaeker ◽  
H. Ramon
Keyword(s):  
Near Infrared ◽  
Organic Matter Content ◽  
Nir Spectroscopy ◽  
Nutrient Content ◽  
Ammonium Nitrogen ◽  
Matter Content ◽  
Dry Matter Content ◽  
Least Squares Regression ◽  
Total N ◽  
Sample Presentation

In this study, the reflectance and transflectance sample presentation mode were compared for the analysis of the nutrient content of hog ( Sus domesticus) manure using visible and near infrared (vis-NIR) spectroscopy. A total of 194 hog manures, which were collected in the spring of 2004 from farms in the northern part of Belgium, were assayed by conventional wet chemical analysis and spectroscopy for the following constituents: dry matter content (DM), organic matter content (OM), pH, total Kjeldahl nitrogen (Ntot), ammonium nitrogen (NH4-N), phosphorus (P), potash (K), calcium (Ca), sodium (Na) and magnesium (Mg). Samples were scanned with a Foss NIRSystems Model 6500 scanning monochromator in reflectance and transflectance mode, respectively. A ceramic reference was measured in between the two modes. The monochromator was equipped with a DCFA sample presentation unit and ranges from 400 to 2498 nm. Partial least squares regression was employed to relate the spectral information to the nutrient content. The PLS models were calibrated for both sample presentation modes using leave-one-out cross-validation. The results of this study showed that the transflectance mode performed better than the reflectance mode. From the transflectance measurements, very good quantitative predictions for total N, good quantitative predictions for K, DM and OM, approximate predictions for NH4-N, P and Mg, very approximate predictions for Ca and a discrimination between high and low values for Na were obtained. pH was not predictable. The reflectance measurements were able to provide good quantitative predictions for total N and K, approximate quantitative predictions for NH4-N, very approximate predictions for DM, OM, P and Mg and discrimination between high and low values for Ca. Na was even less predictable and pH might be unpredictable.

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.

Application of near Infrared Reflectance Spectroscopy for the Analysis of Organic C, Total N and pH in Soils of Uruguay

2002 ◽  
Vol 10 (3) ◽  
pp. 215-221 ◽  
Author(s):  
A. Morón ◽  
D. Cozzolino
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Reflectance Spectroscopy ◽  
Calibration Coefficient ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Total N ◽  
Organic C ◽  
Silver Spring ◽  
Validation Set

Near infrared (NIR) reflectance spectroscopy was used to analyse samples ( n = 332) from different soils from Uruguay (South America) for organic carbon (OC), total nitrogen (N) and pH. One set ( n = 200) of samples randomly selected was used to develop the NIR calibrations while the remaining ( n = 132) samples were used as the validation set. The samples were scanned in a small circular cup in reflectance mode (400–2500 nm), using a Foss NIRSystems 6500 (Silver Spring, MD, USA). Modified partial least squares (MPLS) was used to produce the calibration models and cross-validation was used to avoid collinearity effects among variables. Three mathematical treatments and four scatter corrections were also applied. The calibration coefficient of determination ( R2CAL) and the standard error in cross-validation ( SECV) were 0.94 ( SECV: 1.9) for OC; 0.91 ( SECV: 0.19) for total N in g kg−1 and 0.93 ( SECV: 0.18) for pH, respectively. The simple correlation coefficient of validation ( rVAL) and the standard errors of prediction ( SEP) were 0.74 and 5; 0.73 and 0.4; 0.84 and 0.28 for OC, total N and pH, respectively.

scholarly journals Analysis of Sticky Cotton by Near-Infrared Spectroscopy

2005 ◽  
Vol 59 (11) ◽  
pp. 1388-1392 ◽  
Author(s):  
F. E. Barton ◽  
J. D. Bargeron ◽  
G. R. Gamble ◽  
D. L. McAlister ◽  
E. Hequet
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Sugar Content ◽  
Nir Spectroscopy ◽  
Maximum Efficiency ◽  
Successful Model ◽  
A Value ◽  
Cotton Gins ◽  
Before And After ◽  
Opening Line

“Stickiness” in cotton is a major problem affecting throughput in cotton gins and spinning mills alike. Stickiness is thought to be caused by the deposition of sugars by insects, principally aphid and whitefly, on the open boll. Fourier transform near-infrared (FT-NIR) spectroscopy was used to develop models for sugar content from high-pressure liquid chromatography (HPLC), thermodetector, and mini-card data. A total of 457 cotton samples were selected to represent both Upland and Pima varieties and cotton processing before and after ginning. The Unscrambler was used to develop the models. A successful model was made to determine the mini-card value and successfully detect “stickiness”. The standard error of cross-validation (SECv) was 0.26 with an R2 of 0.96. The model was not improved by increasing the range of “stickiness” as measured by the mini-card from the usual 0–3 scale to a scale of 0–8. If a value is determined to be greater than 1 it will be difficult to blend bales at a spinning plant “opening line” to allow for maximum efficiency of spinning.

Freshness Evaluation in Chub Mackerel (Scomber japonicus) Using Near-Infrared Spectroscopy Determination of the Cadaverine Content

2019 ◽  
Vol 82 (5) ◽  
pp. 768-774 ◽  
Author(s):  
KILBO SHIM ◽  
YEONGYEOM JEONG
Keyword(s):  
Nitrogen Content ◽  
Biogenic Amine ◽  
High Performance ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Viable Cell ◽  
High Water Content ◽  
Scomber Japonicus ◽  
Chub Mackerel ◽  
Basic Nitrogen

ABSTRACT We analyzed the volatile basic nitrogen content, pH, total viable cell count, and biogenic amine contents in chub mackerel (Scomber japonicus) stored at 5 and 25°C to examine changes in freshness. Among the various parameters, we found the volatile basic nitrogen content had the highest correlation with cadaverine content (r2 = 0.72 to 0.88). We also tried to measure cadaverine contents at different times during storage by using near-infrared (NIR) spectroscopy. However, because of the high water content in the fish, we could not obtain meaningful results. Next, we prepared samples for NIR spectroscopy by dilution with 0.1 N HCl, ultrafiltration (3 or 10 kDa) with a glass filter, and dehydration. The samples prepared with the 3-kDa filter had peaks in the NIR spectra between 1,379.3 and 1,388.9 nm, and those prepared with the 10-kDa filter had peaks in the spectra between 1,897.3 and 1,898.6 nm. The correlation coefficient (r2) between the NIR spectroscopy and high-performance liquid chromatography with cadaverine results was 0.98 to 0.99. We concluded that the biogenic amine content could be used to evaluate freshness in fish products, and that NIR measurements could be used to rapidly and accurately determine freshness.

Use of NIR Spectroscopy Combined with Recognition Methods for the Identification of Black Teas from Different Regions

2012 ◽  
Vol 503-504 ◽  
pp. 1601-1604 ◽  
Author(s):  
Jing Ming Ning ◽  
Sheng Peng Wang ◽  
Zheng Zhu Zhang ◽  
Xiao Chun Wan
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Black Tea ◽  
Rapid Identification ◽  
K Nearest Neighbor ◽  
Identification Rate

Near-infrared (NIR) spectroscopy, combined with pattern recognition, was applied in this study for the rapid identification of Black tea from different origins.The K-Nearest Neighbor model recognition method was used for the establishment of a tea origin recognition model, which involved optimization of the principal component factors (PCs) and the identification rate using a cross-validation method. The experimental results showed that, after standard normal variant spectral preprocessing, an optimized model was obtained when the PCs were equal to three, with the cross-validation recognition rate and the predicted recognition rate reaching 98.1% and 93.3%, respectively.

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