scholarly journals Effect of Abscisic Acid on Nitrogen Mobilization, Dormancy, and Cold Acclimation in Apple Trees

HortScience ◽  
1997 ◽  
Vol 32 (3) ◽  
pp. 449A-449
Author(s):  
Sunghee Guak ◽  
Leslie H. Fuchigami
Keyword(s):  
Abscisic Acid ◽  
Cold Acclimation ◽  
Leaf Senescence ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Nir Spectroscopy ◽  
Similar Degree ◽  
Near Infrared Reflectance ◽  
Bud Set ◽  
Terminal Bud

Spring-grafted potted `Fuji'/M26 apple (Malus domestica Borkh.) trees were fertigated with Plantex (20N–10P–20K) weekly until 28 Aug., and sprayed with 1000 ppm abscisic Acid (ABA) two times at 5-day intervals in early September. Nitrogen concentrations of leaves, bark, wood, and root tissues were analyzed using near-infrared reflectance (NIR) spectroscopy at 20to 30-day intervals beginning in August. In general, during leaf senescence, the content of leaf nitrogen decreased and stem nitrogen increased. ABA enhanced leaf senescence and the mobilization of nitrogen from the leaves to the stem tissues. ABA significantly enhanced terminal bud set, endodormancy induction, and cold acclimation. Eventually, the controls attained the similar degree of nitrogen concentration in the stem, terminal bud set, endodormancy, and hardiness.

Prediction of Fatty Acid Content in Beef Neck Lean by near Infrared Reflectance Analysis

1998 ◽  
Vol 6 (1) ◽  
pp. 229-234 ◽  
Author(s):  
William R. Windham ◽  
W.H. Morrison
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Near Infrared ◽  
Fatty Acid Content ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Longissimus Dorsi ◽  
Near Infrared Reflectance ◽  
Total Fatty Acids

Near infrared (NIR) spectroscopy in the prediction of individual and total fatty acids of bovine M. Longissimus dorsi neck muscles has been studied. Beef neck lean was collected from meat processing establishments using advanced meat recovery systems and hand-deboning. Samples ( n = 302) were analysed to determine fatty acid (FA) composition and scanned from 400 to 2498 nm. Total saturated and unsaturated FA values ranged from 43.2 to 62.0% and 38.3 to 56.2%, respectively. Results of partial least squares (PLS) modeling shown reasonably accurate models were attained for total saturate content [standard error of performance ( SEP = 1.10%); coefficient of determination on the validation set ( r2 = 0.77)], palmitic ( SEP = 0.94%; r2 = 0.69), unsaturate ( SEP = 1.13%; r2 = 0.77), and oleic ( SEP = 0.97; r2 = 0.78). Prediction of other individual saturated and unsaturated FAs was less accurate with an r2 range of 0.10 to 0.53. However, the sum of individual predicted saturated and unsaturated FA was acceptable compared with the reference method ( SEP = 1.10 and 1.12%, respectively). This study shows that NIR can be used to predict accurately total fatty acids in M. Longissimus dorsi muscle.

Monitoring the performance of a broad-based calibration for measuring the nutritive value of two independent populations of pasture using near infrared reflectance (NIR) spectroscopy

1991 ◽  
Vol 31 (2) ◽  
pp. 205 ◽  
Author(s):  
KF Smith ◽  
PC Flinn
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Linear Regression Analysis ◽  
Spectral Characteristics ◽  
Nir Spectroscopy ◽  
Cost Effective ◽  
Multiple Linear Regression Analysis ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance

Near infrared reflectance (NIR) spectroscopy is a rapid and cost-effective method for the measurement of organic constituents of agricultural products. NIR is widely used to measure feed quality around the world and is gaining acceptance in Australia. This study describes the development of an NIR calibration to measure crude protein (CP), predicted in vivo dry matter digestibility (IVDMD) and neutral detergent fibre (NDF) in temperate pasture species grown in south-western Victoria. A subset of 116 samples was selected on the basis of spectral characteristics from 461 pasture samples grown in 1987-89. Several grass and legume species were present in the population. Stepwise multiple linear regression analysis was used on the 116 samples to develop calibration equations with standard errors of 0.8,2.3 and 2.2% for CP, NDF and IVDMD, respectively. When these equations were tested on 2 independent pasture populations, a significant bias existed between NIR and reference values for 2 constituents in each population, indicating that the calibration samples did not adequately represent the new populations for these constituents. The results also showed that the H statistic alone was inadequate as an indicator of equation performance. It was confirmed that it was possible to develop a broad-based calibration to measure accurately the nutritive value of closed populations of temperate pasture species. For the resulting equations to be used for analysis of other populations, however, they must be monitored by comparing reference and NIR analyses on a small number of samples to check for the presence of bias or a significant increase in unexplained error.

On-line application of visible and near infrared reflectance spectroscopy to predict physical and sensory characteristics of beef quality

2009 ◽  
Vol 2009 ◽  
pp. 135-135
Author(s):  
N Prieto ◽  
D W Ross ◽  
E A Navajas ◽  
G Nute ◽  
R I Richardson ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Sensory Characteristics ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Beef Quality ◽  
Near Infrared Reflectance ◽  
On Line ◽  
Meat Samples

Visible and near infrared reflectance spectroscopy (Vis-NIR) has been widely used by the industry research-base for large-scale meat quality evaluation to predict the chemical composition of meat quickly and accurately. Meat tenderness is measured by means of slow and destructive methods (e.g. Warner-Bratzler shear force). Similarly, sensory analysis, using trained panellists, requires large meat samples and is a complex, expensive and time-consuming technique. Nevertheless, these characteristics are important criteria that affect consumers’ evaluation of beef quality. Vis-NIR technique provides information about the molecular bonds (chemical constituents) and tissue ultra-structure in a scanned sample and thus can indirectly predict physical or sensory parameters of meat samples. Applications of Vis-NIR spectroscopy in an abattoir for prediction of physical and sensory characteristics have been less developed than in other fields. Therefore, the aim of this study was to test the on-line Vis-NIR spectroscopy for the prediction of beef quality characteristics such as colour, instrumental texture, water holding capacity (WHC) and sensory traits, by direct application of a fibre-optic probe to the M. longissimus thoracis with no prior sample treatment.

Determination of Fat, Protein, and Total Solids in Cheese by Near-Infrared Reflectance Spectroscopy

1995 ◽  
Vol 78 (3) ◽  
pp. 802-806 ◽  
Author(s):  
José Louis Rodriguez-Otero ◽  
Maria Hermida ◽  
Alberto Cepeda
Keyword(s):  
Near Infrared ◽  
Independent Set ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sample Treatment ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Total Solids

Abstract Near-infrared reflectance (NIR) spectroscopy was used to analyze fat, protein, and total solids in cheese without any sample treatment. A set of 92 samples of cow’s milk cheese was used for instrument calibration by principal components analysis and modified partial least-square regression. The following statistical values were obtained: standard error of calibration (SEC) = 0.388 and squared correlation coefficient (R2) = 0.99 for fat, SEC = 0.397 and R2 = 0.98 for protein, and SEC = 0.412 and R2 = 0.99 for total solids. To validate the calibration, an independent set of 25 cheese samples of the same type was used. Standard errors of validation were 0.47,0.50, and 0.61 for fat, protein, and total solids, respectively, and hf for the regression of measurements by reference methods versus measurements by NIR spectroscopy was 0.98 for the 3 components.

Spectrometric prediction of secondary metabolites and nitrogen in fresh Eucalyptus foliage: towards remote sensing of the nutritional quality of foliage for leaf-eating marsupials

2002 ◽  
Vol 50 (6) ◽  
pp. 761 ◽  
Author(s):  
M. J. H. Ebbers ◽  
I. R. Wallis ◽  
S. Dury ◽  
R. Floyd ◽  
W. J. Foley
Keyword(s):  
Total Nitrogen ◽  
Near Infrared ◽  
Leaf Surface ◽  
Nitrogen Concentration ◽  
Near Infrared Reflectance ◽  
Ecological Attributes ◽  
Total Nitrogen Concentration ◽  
Volatile Terpene ◽  
Water Absorbance

Near-infrared reflectance spectroscopy provides an excellent means of assessing the chemical composition of Eucalyptus foliage but the standard methods of drying and grinding the samples limit the speed at which spectra can be collected and thus are unsuitable for measurements in the field. We investigated whether reliable spectra could be collected from whole fresh and dry leaves of E. melliodora and E. globulus and whether we could predict the concentration of total nitrogen, the volatile terpene, 1,8 cineole and the phenolic antifeedant compound, sideroxylonal A, from these spectra. Water absorbance peaks did not obscure the absorption spectrum of 1,8 cineole and so cineole concentration was readily predicted from spectra of whole, fresh E. melliodora leaves. Similarly, both total nitrogen and sideroxylonal A could be predicted from spectra of fresh leaf in E. melliodora even though water absorption obscured some spectral features. The predictions of cineole and total nitrogen concentration in E. globulus were not as good as those in E. melliodora, possibly due to interference from waxes on the leaf surface of E. globulus juvenile foliage. Overall, these results suggest that certain important ecological attributes of Eucalyptus foliage can be predicted from spectra of whole fresh leaves. Thus, it is feasible to investigate the collection of spectra by portable or airborne spectrophotometry.

scholarly journals Use of Visible and Near-Infrared Reflectance Spectroscopy Models to Determine Soil Erodibility Factor (K) in an Ecologically Restored Watershed

2020 ◽  
Vol 12 (18) ◽  
pp. 3103
Author(s):  
Qinghu Jiang ◽  
Yiyun Chen ◽  
Jialiang Hu ◽  
Feng Liu
Keyword(s):  
Land Use ◽  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soil Erodibility ◽  
Infrared Reflectance ◽  
Cultivated Land ◽  
Near Infrared Reflectance ◽  
Natural Land ◽  
Soil Erodibility Factor

This study aimed to assess the ability of using visible and near-infrared reflectance (Vis–NIR) spectroscopy to quantify soil erodibility factor (K) rapidly in an ecologically restored watershed. To achieve this goal, we explored the performance and transferability of the developed spectral models in multiple land-use types: woodland, shrubland, terrace, and slope farmland (the first two types are natural land and the latter two are cultivated land). Subsequently, we developed an improved approach by combining spectral data with related topographic variables (i.e., elevation, watershed location, slope height, and normalized height) to estimate K. The results indicate that the calibrated spectral model using total samples could estimate K factor effectively (R2CV = 0.71, RMSECV = 0.0030 Mg h Mj−1 mm−1, and RPDCV = 1.84). When predicting K in the new samples, models performed well in natural land soils (R2P = 0.74, RPDP = 1.93) but failed in cultivated land soils (R2P = 0.24, RPDP = 0.99). Furthermore, the developed models showed low transferability between the natural and cultivated land datasets. The results also indicate that the combination of spectral data with topographic variables could slightly increase the accuracies of K estimation in total and natural land datasets but did not work for cultivated land samples. This study demonstrated that the Vis–NIR spectroscopy could be used as an effective method in predicting K. However, the predictability and transferability of the calibrated models were land-use type dependent. Our study also revealed that the coupling of spectrum and environmental variable is an effective improvement of K estimation in natural landscape region.

Use of residual maximum likelihood to evaluate accuracy of two NIRS calibration procedures, relative to Kjeldahl, for determining nitrogen concentration of soybeans

1991 ◽  
Vol 71 (2) ◽  
pp. 385-392 ◽  
Author(s):  
G. B. Schaalje ◽  
H. -H. Mündel
Keyword(s):  
Maximum Likelihood ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Field Trials ◽  
Near Infrared Reflectance ◽  
Residual Maximum Likelihood ◽  
Maximum Likelihood Procedure ◽  
Calibration Methods

The accuracy of estimates of plant properties based on near-infrared reflectance spectroscopy (NIRS) varies with many factors including the biological material in question and the method used to calibrate the NIRS instrument. This study investigated the accuracy, relative to Kjeldahl analysis, of NIRS analysis based on two calibration methods in estimating nitrogen concentration of four stages and/or parts of soybean (Glycine max (L.) Merr.) plants. Samples of whole top growth at anthesis, whole top growth at maturity, whole top growth at maturity excluding seeds, and seeds were obtained from two field trials and one phytotron experiment. Two Kjeldahl determinations of nitrogen concentration were obtained for each sample, as well as reflectance values at each of 19 infrared wavelengths, using a Technicon InfraAlyser 400R. Different subsets of the sample data were used for calibration and assessment of accuracy. The instrument was calibrated using stepwise multiple linear regression (SMLR) and principal component regression (PCR). The residual maximum likelihood procedure was useful in showing that NIRS estimates based on either SMLR or PCR were at least as accurate as Kjeldahl estimates for all stages and/or parts except whole top growth at maturity excluding seeds. Key words: Calibration, principal component regression, stepwise regression

Determination of Soil Separates with near Infrared Reflectance Spectroscopy

1996 ◽  
Vol 4 (1) ◽  
pp. 201-212 ◽  
Author(s):  
A. Couillard ◽  
A.J. Turgeon ◽  
M.O. Westerhaus ◽  
J.S. Shenk
Keyword(s):  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Transformation Model ◽  
Natural State ◽  
Peat Moss ◽  
Particle Sizes ◽  
Near Infrared Reflectance ◽  
Laboratory Values

The use of near infrared (NIR) reflectance spectroscopy to evaluate soil properties has started to receive more attention in recent years. The technology is evolving and research on NIR spectroscopic analysis using natural state samples is increasing. There is no method available today, besides NIR spectroscopy, that could simultaneously evaluate physical and chemical properties of a soil sample without processing the sample and affecting the visual quality of the site. More samples can be scanned in their natural undisturbed form resulting in a variety of particle sizes. Research on the effect of scanning products with different particle sizes is essential. The differences in the particle size of the soil separates may lower the prediction accuracy of NIR spectroscopy. In this study, we evaluated the ability of NIR spectroscopy to predict soil separates from artificial soil samples. Feldspar and silica sands and silts, kaolinite and montmorillonite clays, and reed sedge and Canadian sphagnum peat moss organic matters were used as separates. They were scanned alone, and in different mixture percentages, from 400 to 2500 nm with a total of 116 samples. The absence of linearity in the binary mixtures, preventing accurate calibration, was noticed and required the development of a transformation model to generate new laboratory values from a laboratory weight scaling factor generated for each soil separate. The adjustment of the laboratory values improved the prediction accuracy of the mixtures. The coefficient of determination ranged from 0.95 to 0.99. The standard error of cross-validation ranged from 2.09 to 5.82%.

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