Factors Influencing the Calibration of near Infrared Reflectometry Applied to the Assessment of Total Nitrogen in Potato. II. Operator, Moisture and Maturity Class

1995 ◽  
Vol 3 (3) ◽  
pp. 167-174 ◽  
Author(s):  
Mark W. Young ◽  
Donald K.L. MacKerron ◽  
Howard V. Davies
Keyword(s):  
Field Experiment ◽  
Spectral Data ◽  
Total Nitrogen ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Moisture Contents ◽  
Factors Influencing ◽  
Maturity Class ◽  
The One ◽  
Low Nitrogen

Oven dried samples of leaf stem and tuber material taken from a nitrogen field experiment were analysed by Dumas combustion when fresh and by near infrared (NIR) then, and in the next two years, by a number of operators who made estimates of nitrogen concentration, [N]NIR, with differing degrees of error. The errors differed between years in the case of the one operator who made estimates in two years. Leaf, stem and tuber material of high and low nitrogen concentration were treated to produce samples at various moisture contents. These samples were scanned by NIR and the spectral data were examined. Higher moisture was found to decrease the reflectance at all the wavelengths used and would, therefore, introduce error into [N]NIR estimates. The NIR calibration used was found to be applicable to cultivars in a range of maturity classes. Several recommendations are made that will help to minimise the error introduced into [N]NIR estimates from various sources.

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.

Factors Influencing the Calibration of near Infrared Reflectometry Applied to the Assessment of Total Nitrogen in Potato Tissues. I. Particle Size, Milling Speed and Leaf Senescence

1995 ◽  
Vol 3 (3) ◽  
pp. 155-166 ◽  
Author(s):  
Donald K.L. MacKerron ◽  
Mark W. Young ◽  
Howard V. Davies
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Important Variable ◽  
Calibration Model ◽  
Size Classes ◽  
Factors Influencing ◽  
Large Numbers ◽  
Different Tissues ◽  
Separate Calibration

Several factors thought to modify the calibration between nitrogen concentration, [N]NIR and [N]Dumas, were examined including particle size using size-classes from the distribution produced by milling through a standard screen. The standard milling procedure produced differing distributions of particle size from different tissues, [N] differed between size classes and the pattern of [N] with particle size differed between tissues. In leaf and stem the smallest particles had the highest [N], in tuber material they had the lowest. Samples for analysis are generally milled in batches and analysed later. These findings show that it is important to ensure that samples are well-mixed and that they are not allowed to stratify. Only well-mixed samples should be used to fill the sample cups on a near infrared (NIR) analyser. A divergence in the relations between [N]NIR and [N]Dumas for stem samples implies that the calibration model used for that material might be particularly sensitive to particle size distribution. Within the range examined, milling speed was not an important variable in the preparation of leaf and tuber material. There is little to be gained from treating small quantities of senescent leaves separately from the remainder of a sample. However, if [N] is to be assessed in large numbers of samples of leaves that are mostly senescent then a separate calibration should be derived.

Prediction of Emergent and Floating-Leaved Macrophyte Cover in Central Swedish Lakes

1986 ◽  
Vol 43 (12) ◽  
pp. 2519-2523 ◽  
Author(s):  
Val H. Smith ◽  
Maud Wasllsten
Keyword(s):  
Regression Analysis ◽  
Total Nitrogen ◽  
Nitrogen Concentration ◽  
Percent Cover ◽  
Lake Surface ◽  
Emergent Macrophytes ◽  
Factors Influencing ◽  
Total Nitrogen Concentration ◽  
Lake Surface Area ◽  
Macrophyte Cover

From multiple regression analysis of data from Central Swedish lakes, we suggest that the peak areal cover (hectares) of emergent vegetation is predictable from lake surface area, mean depth, and annual mean total nitrogen concentration in the lake water. The data also suggest that the percent cover of emergent macrophytes is predictable from mean depth and total nitrogen. However, the factors influencing floating-leaved macrophyte cover are not as clear.

scholarly journals A non-destructive NIR spectroscopic method combined with chemometry for simultaneous assay of paracetamol and caffeine in tablets

2021 ◽  
Vol 57 (2) ◽  
pp. 68-75
Author(s):  
Dana MUNTEAN ◽  
◽  
Alina PORFIRE ◽  
Cristian ALECU ◽  
Sonia IURIAN ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Hplc Method ◽  
Fixed Dose ◽  
Calibration Model ◽  
Dose Combination ◽  
The One

The use of near infrared (NIR) spectroscopy to predict the concentration of two active pharmaceutical ingredients (APIs), paracetamol and caffeine, in intact tablets, has been evaluated in this study. A partial least squares (PLS) regression model was developed using spectral data obtained on a calibration set consisting of 28 formulations containing 80, 90, 100, 110 and 120% of each API. Regression models were developed for each API, both using un-processed spectral data as well as after applying various spectra pre-processing methods. Cross-validation was used to select best calibration model. The selected model was validated in terms of precision, trueness, accuracy and linearity in a concentration ranging from 90 to 110% of the targeted APIs concentration. The applicability of the method was tested on tablets containing 300 mg paracetamol and 30 mg caffeine as targeted composition, and the API content predicted by the proposed NIR-chemometric method was not statistically different from the one obtained by HPLC method, used as a reference method. Thus, the method presented in the current paper is a step forward towards the implementation NIR as useful tool for monitoring the manufacturing process of fixed-dose combination tablets with paracetamol and caffeine.

scholarly journals Biomechanical, biochemical, and near infrared spectral data of bovine knee ligaments and patellar tendon

Data in Brief ◽  
2021 ◽  
Vol 36 ◽  
pp. 106976
Author(s):  
Aapo Ristaniemi ◽  
Jari Torniainen ◽  
Tommi Paakkonen ◽  
Lauri Stenroth ◽  
Mikko A.J. Finnilä ◽  
...  
Keyword(s):  
Spectral Data ◽  
Patellar Tendon ◽  
Near Infrared ◽  
Knee Ligaments ◽  
Infrared Spectral

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.

scholarly journals Prediction Model of the Key Components for Lodging Resistance in Rapeseed Stalk Using Near-Infrared Reflectance Spectroscopy (NIRS)

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Jie Kuai ◽  
Shengyong Xu ◽  
Cheng Guo ◽  
Kun Lu ◽  
Yaoze Feng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Spectral Data ◽  
Near Infrared ◽  
Soluble Sugar ◽  
Computing Time ◽  
Colorimetric Method ◽  
Mature Stage ◽  
Near Infrared Reflectance ◽  
Lodging Resistance ◽  
Physiological Basis

The chemical composition of rape stalk is the physiological basis for its lodging resistance. By taking the advantage of NIRS, we developed a rapid method to determine the content of six key composition without crushing the stalk. Rapeseed stalks in the mature stage of growth were collected from three cultivation modes over the course of 2 years. First, we used the near-infrared spectroscope to scan seven positions on the stalk samples and took their average to form the spectral data. The stalks were then crushed and sieved; then the ratio of carbon and nitrogen, ratio of acid-insoluble lignin and lignin, and the content of soluble sugar and cellulose were determined using the combustion method, weighing method, and colorimetric method, respectively. The partial least squares regression (PLSR) method was used to establish a prediction model between the spectral data and the chemical measurements, and all models were evaluated by an internal interaction verification and an external independent test set sample. To improve the accuracy of the model and reduce the computing time, some optimization methods have been applied. Some outliers were removed, and then the data were preprocessed to determine the best spectral information band and the optimal principal component number. The results showed that elimination of outliers effectively improved the precision of the prediction model and that no spectral pretreatment method exhibited the highest prediction accuracy. In summary, the NIRS-based prediction model could facilitate the rapid nondestructive detection in the key components of rapeseed stalk.

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