Application of near-Infrared Spectroscopy to Predict Paper Properties of Acacia

2011 ◽  
Vol 236-238 ◽  
pp. 1372-1378 ◽  
Author(s):  
Shu Ping Song ◽  
Hao Zhang ◽  
Qian Lang ◽  
Jun Wen Pu
Keyword(s):  
Mathematical Model ◽  
Infrared Spectroscopy ◽  
Physical Properties ◽  
Mathematical Models ◽  
Spectral Data ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Paper Industry ◽  
Paper Properties ◽  
Kraft Paper

First attempt to predicting physical properties of paper by Near Infrared Spectroscopy (abbreviated as NIRS) mathematical model, Acacia is a kind of fast-growing tree which is a potential resource in pulp and paper industry. The mathematical models of physical properties of Acacia unbleached kraft paper were established by software OPUS6.5 of Near Infrared Spectroscopy. Spectral data of Acacia unbleached kraft paper were acquired by Near-Infrared. Physical properties, which include quality, whiteness, tensile index, burst index and tear index, of the paper were measured by GB methods. NIRS mathematical models between the spectral data and the laboratory reference values were established and optimized by software OPUS6.5 partial least squares (abbreviated as PLS). The NIRS mathematical models were evaluated by its parameters, and used to predict the physical properties of unknown samples rapidly and accurately. Compared with NIRS mathematical model of physical properties of Acacia unbleached kraft pulp, the NIRS mathematical models of paper have a better prediction on unknown samples; Compared with traditional laboratory methods, predicting properties of paper by the NIRS mathematical models of paper is rapid, accurate and non-destructive.

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 Detection of deltamethrin in cabbages using visible shortwave near-infrared spectroscopy

Food Research ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 273-280
Author(s):  
C.D.M. Ishkandar ◽  
N.M. Nawi ◽  
R. Janius ◽  
N. Mazlan ◽  
T.T. Lin
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Spectral Data ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Calibration Model ◽  
Mean Square

Pesticides have long been used in the cabbage industry to control pest infestation. This study investigated the potential application of low-cost and portable visible shortwave near-infrared spectroscopy for the detection of deltamethrin residue in cabbages. A total of sixty organic cabbage samples were used. The sample was divided into four batches, three batches were sprayed with deltamethrin pesticide whereas the remaining batch was not sprayed (control sample). The first three batches of the cabbages were sprayed with the pesticide at three different concentrations, namely low, medium and high with the values of 0.08, 0.11 and 0.14% volume/volume (v/v), respectively. Spectral data of the cabbage samples were collected using visible shortwave near-infrared (VSNIR) spectrometer with wavelengths range between 200 and 1100 nm. Gas chromatography-electron capture detector (GC-ECD) was used to determine the concentration of deltamethrin residues in the cabbages. Partial least square (PLS) regression method was adopted to investigate the relationship between the spectral data and deltamethrin concentration values. The calibration model produced the values of coefficient of determination (R2 ) and the root mean square error of calibration (RMSEC) of 0.98 and 0.02, respectively. For the prediction model, the values of R2 and the root mean square error of prediction (RMSEP) were 0.94 and 0.04, respectively. These results demonstrated that the proposed spectroscopic measurement is a promising technique for the detection of pesticide at different concentrations in cabbage samples.

scholarly journals Use of near-infrared spectroscopy for prediction of chemical composition of Tifton 85 grass

2021 ◽  
Vol 42 (3) ◽  
pp. 1287-1302
Author(s):  
Camila Cano Serafim ◽  
◽  
Geisi Loures Guerra ◽  
Ivone Yurika Mizubuti ◽  
Filipe Alexandre Boscaro de Castro ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Chemical Composition ◽  
Spectral Data ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Neutral Detergent Fiber ◽  
Mineral Matter ◽  
Data Set

The reduction in the quality, consumption, and digestibility of forage can cause a decrease in animal performance, resulting in losses to the rural producer. Thus, it is important to monitor these characteristics in forage plants to devise strategies or practices that optimize production systems. The aim of this study was to develop and validate prediction models using near-infrared spectroscopy (NIRS) to determine the chemical composition of Tifton 85 grass. Samples of green grass, its morphological structures (whole plant, leaf blade, stem + sheath, and senescent material) and hay, totaling 105 samples were used. Conventional chemical analysis was performed to determine the content of oven-dried samples (ODS), mineral matter (MM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), cellulose (CEL), hemicellulose (HEM), and in vitro dry matter digestibility (IVDMD). Subsequently, all the samples were scanned using a Vis-NIR spectrometer to collect spectral data. Principal component analysis (PCA) was applied to the data set, and modified partial least squares was used to correlate reference values to spectral data. The coefficients of determination (R2) were 0.74, 0.85, 0.98, 0.75, 0.85, 0.71, 0.82, 0.77, and 0.93, and the ratio of performance deviations (RPD) obtained were 1.99, 2.71, 6.46, 2.05, 2.58, 3.84, 1.86, 2.35, 2.09, and 3.84 for ODS, MM, CP, NDF, ADF, ADL, CEL, HEM, and IVDMD, respectively. The prediction models obtained, in general, were considered to be of excellent quality, and demonstrated that the determination of the chemical composition of Tifton 85 grass can be performed using NIRS technology, replacing conventional analysis.

scholarly journals Physical properties of Guazuma crinita by conventional methods and near infrared spectroscopy

2019 ◽  
pp. 0-0 ◽  
Author(s):  
Manuel Chavesta ◽  
Rolando Montenegro ◽  
Mario Tomazello-Filho ◽  
Mayara Carnerio ◽  
Silvana Nisgoski
Keyword(s):  
Infrared Spectroscopy ◽  
Physical Properties ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Conventional Methods

scholarly journals PHYSICAL PROPERTIES OF EMISSION-LINE GALAXIES ATz∼ 2 FROM NEAR-INFRARED SPECTROSCOPY WITH MAGELLAN FIRE

2014 ◽  
Vol 785 (2) ◽  
pp. 153 ◽  
Author(s):  
Daniel Masters ◽  
Patrick McCarthy ◽  
Brian Siana ◽  
Mathew Malkan ◽  
Bahram Mobasher ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Physical Properties ◽  
Emission Line ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Emission Line Galaxies
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