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.

scholarly journals Quantitative Estimation of Organic Matter Content in Arid Soil Using Vis-NIR Spectroscopy Preprocessed by Fractional Derivative

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jingzhe Wang ◽  
Tashpolat Tiyip ◽  
Jianli Ding ◽  
Dong Zhang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Organic Matter ◽  
Fractional Derivative ◽  
Near Infrared ◽  
Quantitative Estimation ◽  
Organic Matter Content ◽  
Nir Spectroscopy ◽  
Predictive Performance ◽  
Matter Content ◽  
Lake Basin ◽  
Least Squares Regression

Soil organic matter (SOM) content is an important index to measure the level of soil function and soil quality. However, conventional studies on estimation of SOM content concerned about the classic integer derivative of spectral data, while the fractional derivative information was ignored. In this research, a total of 103 soil samples were collected in the Ebinur Lake basin, Xinjiang Uighur Autonomous Region, China. After measuring the Vis-NIR (visible and near-infrared) spectroscopy and SOM content indoor, the raw reflectance and absorbance were treated by fractional derivative from 0 to 2nd order (order interval 0.2). Partial least squares regression (PLSR) was applied for model calibration, and five commonly used precision indices were used to assess the performance of these 22 models. The results showed that with the rise of order, these parameters showed the increasing or decreasing trends with vibration and reached the optimal values at the fractional order. A most robust model was calibrated based on 1.8 order derivative of R, with the lowest RMSEC (3.35 g kg−1) and RMSEP (2.70 g kg−1) and highest Rc2 (0.92), Rp2 (0.91), and RPD (3.42 > 3.0). This model had excellent predictive performance of estimating SOM content in the study area.

scholarly journals Optimization of Instrument Design for In-Line Monitoring of Dry Matter Content in Single Potatoes by NIR Interaction Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 828
Author(s):  
Jens Petter Wold ◽  
Marion O’Farrell ◽  
Petter Vejle Andersen ◽  
Jon Tschudi
Keyword(s):  
Dry Matter ◽  
Near Infrared ◽  
Optical Measurement ◽  
Matter Content ◽  
Instrument Design ◽  
Calibration Model ◽  
Dry Matter Content ◽  
Least Squares Regression ◽  
Process Plant ◽  
Important Quality

Dry matter (DM) content is one of the most important quality features of potatoes. It defines the physical properties of the potatoes and determines what kind of product the potatoes can be used for. This paper presents the results obtained by a novel prototype NIR (near-infrared) instrument designed to measure DM content in single potatoes in process. The instrument is based on interaction measurements to measure deeper into the potatoes. It measures rapidly, up to 50 measurements per second, allowing several moving potatoes to be measured per second. The instrument also enables several interactance distances to be recorded for each measurement. The instrument was calibrated based on three different potato varieties and the calibration measurements were done in a process plant, making the calibration model suitable for in-line use. A good calibration for DM was obtained by partial least squares regression (RMSECV = 0.78% DM, R2 = 0.91). The instrument was tested in-line in the process plant and several batches of potatoes were monitored for the estimation of the DM distribution per batch. Accuracy of DM determination as function of measurement position on the potato was studied, and results indicate that NIR scans along the center part of the potatoes give slightly better results compared to scans taken on either side of the center. Small differences in optical measurement geometry influence the accuracy of the calibration models, underlining the importance of optimizing instrument design for successful measurements.

scholarly journals Mapping nutrient concentration in pasture using hyperspectral imaging

2015 ◽  
Vol 77 ◽  
pp. 47-50 ◽  
Author(s):  
I. Yule ◽  
R. Pullanagari ◽  
M. Irwin ◽  
P. Mcveagh ◽  
G. Kereszturi ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Precision Agriculture ◽  
Nutrient Concentration ◽  
Large Scale ◽  
Near Infrared ◽  
Nutrient Content ◽  
Matter Content ◽  
Electromagnetic Spectrum ◽  
Dry Matter Content ◽  
Hyperspectral Sensing

Hyperspectral sensing using ground-based equipment has been demonstrated to be successful in determining pasture nutrient content (N, P, K, S) and parameters such as dry matter content and metabolisable energy. This technology needed to be up-scaled so that large areas could be rapidly covered with adequate spatial resolution. This paper describes work which demonstrates a progression from hyperspectral sensing to hyperspectral imaging which utilises the visible, near infrared and short wave infrared parts of the electromagnetic spectrum. Large scale calibration and validation field trials were conducted at the same time as hyperspectral imaging was completed. These trials demonstrate the feasibility of producing information, in detailed map form, on pasture nutrient concentration and other parameters to inform fertiliser placement decisions as well as other farm management tasks. The technique effectively produces a forage analysis for every square meter of any farm. Keywords: hyperspectral imaging, precision agriculture.

scholarly journals Low-cost, handheld near-infrared spectroscopy for root dry matter content prediction in cassava

2021 ◽  
Author(s):  
Jenna Hershberger ◽  
Edwige Gaby Nkouaya Mbanjo ◽  
Prasad Peteti ◽  
Andrew Smith Ikpan ◽  
Kayode Ogunpaimo ◽  
...  
Keyword(s):  
Dry Matter ◽  
Near Infrared ◽  
Low Cost ◽  
Model Performance ◽  
Matter Content ◽  
Dry Matter Content ◽  
Least Squares Regression ◽  
Breeding Programs ◽  
Test Sets ◽  
Root Dry Matter Content

Over 800 million people across the tropics rely on cassava as a major source of calories. While the root dry matter content (RDMC) of this starchy root crop is important for both producers and consumers, characterization of RDMC by traditional methods is time-consuming and laborious for breeding programs. Alternate phenotyping methods have been proposed but lack the accuracy, cost, or speed ultimately needed for cassava breeding programs. For this reason, we investigated the use of a low-cost, handheld NIR spectrometer for field-based RDMC prediction in cassava. Oven-dried measurements of RDMC were paired with 21,044 scans of roots of 376 diverse clones from 10 field trials in Nigeria and grouped into training and test sets based on cross-validation schemes relevant to plant breeding programs. Mean partial least squares regression model performance ranged from R2p = 0.62 - 0.89 for within-trial predictions, which is within the range achieved with laboratory-grade spectrometers in previous studies. Relative to other factors, model performance was highly impacted by the inclusion of samples from the same environment in both the training and test sets. Random forest variable importance analysis of root spectra revealed increased importance in a region previously identified as predictive of water content in plants (~950 - 990 nm). With appropriate model calibration, the tested spectrometer will allow for field-based collection of spectral data with a smartphone for accurate RDMC prediction and potentially other quality traits, a step that could be easily integrated into existing harvesting workflows of cassava breeding programs.

scholarly journals BIOMASSA HIJAUAN Mucuna bracteata DAN PENGARUHNYA TERHADAP KADAR N TANAH DI PERKEBUNAN KELAPA SAWIT

2019 ◽  
Vol 3 (2) ◽  
pp. 54-62
Author(s):  
Ir. Mardiana Wahyuni, MP
Keyword(s):  
Oil Palm ◽  
Cover Crop ◽  
Organic Matter Content ◽  
Nutrient Content ◽  
Growth Conditions ◽  
Biological Properties ◽  
Matter Content ◽  
Total N ◽  
Legume Cover Crop ◽  
Growth Character

Mucuna bracteata is one of the legume cover crop that is usually used in oil palm and rubber plantations. In general, legume cover crop are planted during the preparation / after land clearing when the oil palm are planted, Mucuna bracteata has covered the planting area. Mucuna bracteata growth conditions will affect soil biological properties, namely organic nutrient content, C/N balance, microbial population and macro-fauna activity. This study aimed to describe the soil and Mucuna bracteata plants in the phase of oil palm plants. The study was conducted in April - June 2019 in the area of ​​TBM 3 oil palm plantations in 2 locations, namely Pabatu Plantation and Tinjowan Plantation. Soil types in Pabatu Plantation are typic dystrudepts (Reddish Brown Podsolik) and in Tinjowan Plantation the type of soil is typic hapludults (Yellowish Red Podsolik) and some typic paleudults (Yellow Podsolik). This study used a descriptive design that describes the growth character / biomass of Mucuna bracteata on TBM 3 oil palm plantations. The results of this study are the fresh biomass of Mucuna bracteata in Pabatu Plantation is 43,260 kg / ha and Tinjowan Plantation is 34,146 kg / ha (79%) and dry biomass is 13,943 kg / ha in Pabatu Plantation and 12,705 kg / ha in Tinjowan Plantation. The levels of N leaves of Mucuna bracteata are in the high category. Soil organic matter content and total N in Pabatu and Tinjowan Plantation are in the low category.

Correlation between near Infrared Spectra and Texture Profiling of Steam Cooked Potatoes

1998 ◽  
Vol 6 (A) ◽  
pp. A291-A297 ◽  
Author(s):  
C.G. Boeriu ◽  
D. Yuksel ◽  
R. van der Vuurst de Vries ◽  
T. Stolle-Smits ◽  
C. van Dijk
Keyword(s):  
Near Infrared ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Quantitative Model ◽  
Matter Content ◽  
Dry Matter Content ◽  
Least Squares Regression ◽  
Sensory Data ◽  
Positive Side ◽  
Sensory Parameters

This study evaluated the ability of near infrared (NIR) spectrosocopy for the assessment of sensory texture properties of steam-cooked potatoes. The texture of steam-cooked potatoes from three cultivars, classified according to tuber size and dry matter content, was sensory evaluated after one, three and six months of storage. The sensory data were analysed using principal component analysis (PCA). PCA revealed that the first two principal components explained 95% or more of the variance between the data. The first principal component was dominated by the descriptors mealy and crumbly on the positive side and the descriptor waxy on the negative side. The descriptor firm had a high positive loading on the second principal component. For the same potato samples, NIR spectra were measured. A quantitative model based on partial least squares regression (PLS), relating NIR spectral data of fresh potato samples with sensory perceived texture attributes of same samples, has been developed. Among the sensory parameters, moist, waxy, firm and mealy were best predicted, with standard errors of calibration ( SEC) ranging from 7.4 to 10.5 and correlation coefficients (Rc) between 0.89 and 0.94.

scholarly journals Optimized Prediction of Reducing Sugars and Dry Matter of Potato Frying by FT-NIR Spectroscopy on Peeled Tubers

Molecules ◽  
2019 ◽  
Vol 24 (5) ◽  
pp. 967 ◽  
Author(s):  
Cédric Camps ◽  
Zo-Norosoa Camps
Keyword(s):  
Dry Matter ◽  
Near Infrared ◽  
Reducing Sugars ◽  
Prediction Models ◽  
Storage Temperature ◽  
Sugar Content ◽  
Nir Spectroscopy ◽  
Matter Content ◽  
Infrared Spectrometry ◽  
Dry Matter Content

Dry matter content (DMC) and reducing sugars (glucose, fructose) contents of three potato varieties for frying (Innovator, Lady Claire, and Markies) were determined by applying Fourier-transform near-infrared spectrometry (FT-NIR), with paying particular attention to tubers preparation (unpeeled, peeled, and transversally cut tubers) before spectral acquisitions. Potatoes were subjected to normal storage temperature as it is processed in the industry (8 °C) and lower temperature inducing sugar accumulations (5 °C) for 195 and 48 days, respectively. Prediction of DMC has been successfully modeled for all varieties. A common model to the three varieties reached R2, root mean square error (RMSEP), and ratio performance to deviation (RPD) values of 0.84, 1.2, and 2.49. Prediction accuracy of reducing sugars was variety dependent. Reducing sugars were accurately predicted for Innovator (R2 = 0.84, RMSEP = 0.097, and RPD = 2.86) and Markies (R2 = 0.78, RMSEP = 0.033, and RPD = 2.15) and slightly less accurate for Lady Claire (R2 = 0.63, RMSEP = 0.036, and RPD = 1.64). The lack of accuracy obtained with the Lady Claire variety is mainly due to the tight variability in sugar content measured over the storage. Finally, the best preparation of the tuber from the point of view of the accuracy of the prediction models was to use the whole peeled potato. Such preparation allowed for the improvement in RPD values by 15% to 38% the RPD values depending on reducing sugars and 35% for DMC.

scholarly journals EXPLORE THE CHARACTER OF SOIL SPECTRAL REFLECTANCE RELATE TO THE SOIL ORGANIC MATTER CONTENT

2019 ◽  
Vol 8 (3) ◽  
pp. 214
Author(s):  
Sari Virgawati ◽  
Muhjidin Mawardi ◽  
Lilik Sutiarso ◽  
Sakae Shibusawa ◽  
Hendrik Segah ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Low Cost ◽  
Organic Matter Content ◽  
Nir Spectroscopy ◽  
Matter Content ◽  
Strong Negative Correlation

ABSTRACTThe visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy has emerged as a rapid and low-cost tool for extensive investigation of soil properties. The objective of this research was to explore how significant the relationship between the soil spectral reflectance and soil organic matter (SOM) content. Some soil samples in Yogyakarta were taken for SOM content and spectroscopy measurement. The SOM was analyzed using Walkley and Black method, while the spectral reflectance was determined using ASD Field-spectrophotometer by scanned the sample with Vis-NIR spectrum. Pearson’s coefficient showed that there was a strong negative correlation between SOM and soil spectral of certain wavelengths. Soil with less organic matter content performed high reflectance. Keywords: Soil organic matter; Vis-NIR spectroscopy; soil reflectance; Pearson’s correlation coefficient.

scholarly journals Use of NIR spectroscopy on fresh apples to determine the phenolic compounds and dry matter content in peel and flesh

2017 ◽  
pp. 3-12
Author(s):  
Audrey Pissard ◽  
Vincent Baeten ◽  
Pierre Dardenne ◽  
Pascal Dupont ◽  
Marc Lateur
Keyword(s):  
Phenolic Compounds ◽  
Dry Matter ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Matter Content ◽  
Total Phenolic ◽  
Dry Matter Content ◽  
Calibration And Validation ◽  
Apple Quality ◽  
Non Destructive

Description of the subject. The article deals with the use of near-infrared spectroscopy (NIR) on fresh apples to determine the phenolic compounds and dry matter content in peel and flesh powders. Objectives. The aim is the rapid and non-destructive determination of these nutritional parameters. Method. Two hundred twenty-nine fruits from 20 varieties were analyzed with NIR spectroscopy and reference methods. Results. Great variability of total phenolic compounds (TPC) in peel and flesh powders was observed among varieties. The dry matter (DM) content also differed greatly between peel and flesh. Calibration and validation models showed high coefficients of determination for the TPC content, which were slightly higher for the peel than for the flesh (R² val = 0.91 and 0.84 respectively). For the DM content, high coefficients of determination and ratios of prediction to deviation (RPDs) were also observed (R² val = 0.94, RPD = 4.8 and R² val = 0.94, RPD = 4.9 for the peel and flesh respectively). Conclusions. Calibration and validation models allow quantitative predictions to be made for TPC and DM content. They confirm the potential of NIRS for predicting the polyphenol content and highlight its potential for determining the DM content, a parameter often neglected in research into apple quality.

scholarly journals (213) Effect of Shading on Yield and Composition of Fruit and Leaves of Greenhouse Tomato

HortScience ◽  
2005 ◽  
Vol 40 (4) ◽  
pp. 1057B-1057
Author(s):  
Martin P. N. Gent
Keyword(s):  
Fruit Size ◽  
Weight Fraction ◽  
Dry Weight ◽  
Nutrient Content ◽  
Matter Content ◽  
Dry Matter Content ◽  
Fresh Weight ◽  
Total N ◽  
Marketable Fruit ◽  
Summer Fruit

Tomatoes were grown in spring and summer in Connecticut in greenhouses covered with a double layer of 4-mL clear polyethylene film. Some sections were covered with reflective aluminized shadecloth that provided 85%, 70%, or 50% transmittance of direct radiation, respectively. This shading was applied in mid-June, after fruit began to ripen, and remained for the rest of the summer. Fruit was picked through August. A similar experimental protocol was used in 2003 and 2004. The maximum shading only decreased daily integrated solar radiation to 69% of that without shade, as measured by PARsensors set at a 2-m height in each greenhouse. Shading reduced yield of ripe fruit from 16.6 and 13.1 kg·m-2, proportional to the measured decrease in radiation. Neither fruit size nor weight fraction of marketable fruit was affected by shading in 2004. Nutrient content was analyzed in tissues of ripe fruits, and uppermost expanded leaves harvested in early August. As shading decreased transmittance, it increased the concentration of most elements in leaves. Total N, P, and K concentrations followed this trend; however, Ca was not affected by shading. Fruit dry matter content declined slightly, from 5.9% to 5.7% of fresh weight, for plants grown with no shade or shade with 50% transmittance, respectively. However, there was no significant effect of shading on K, Ca, Mg, or on minor elements in fruit tissue, whether expressed on a fresh weight or dry weight basis. Thus, shading a greenhouse to improve fruit quality had no effect on the value of ripe tomatoes as a dietary source of mineral nutrients.

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