scholarly journals Rapid assessment of oil and moisture content in seeds of oil flax using IR spectrometry

2020 ◽  
Vol 3 (183) ◽  
pp. 63-70
Author(s):  
S.G. Efimenko ◽  
◽  
S.K. Efimenko ◽  
Keyword(s):  
Moisture Content ◽  
High Speed ◽  
Oil Content ◽  
Near Infrared ◽  
Coat Color ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Calibration Models ◽  
Flax Seeds

We used near-infrared reflectance spectroscopy (NIRS) to assess biochemical parameters in whole oil flax seeds, regardless of differences in seed coat color of the samples. At the first stage of work, the set the task to develop calibration models for the MATRIX-I IR analyzer to determine the oil and moisture content in flax seeds. The carried out the research in the laboratory of biochemistry on brown and yellow seed samples of oil flax, grown in 2015-2020 in various agro-ecological conditions of the Russian Federation. We determined the oil content on an AMV 1006M NMR analyzer in accordance with the GOST 8.597- 2010 measurement procedure; we assessed the moisture content by the standard method of GOST 10856- 96. We used the results of determination of the oil and moisture content of the seeds of test lot in accordance with the accuracy indicator of the calibration of GOST 32749-2014 to verify the reliability of the developed models. We received the best indicators of the quality of calibration models (root-mean-square prediction error, coefficient of determination and the value of the residual deviation of prediction for the rank displayed on the graph) by determining the oil content (RMSEP = 0.27 %, R2 = 99.2 and RPD = 11.2) and moisture content (RMSEP = 0.06 %, R2 = 99.9 and RPD = 39). In the OPUS LAB program we developed the “Flax 51” method for mass analysis based on the developed calibration models for the determination of oil and moisture content in whole oil flax seeds (9-20 g) in a sample cell with a diameter of 51 mm. It enables the quick carrying out a preliminary assessment of the breeding material at a high speed – more than 120 samples in 7 hours without seed destruction.

Determination of Optical Parameters and Moisture Content of Wood with Visible–Near Infrared Spectroscopy

2016 ◽  
Vol 24 (6) ◽  
pp. 571-585 ◽  
Author(s):  
Ataollah Haddadi ◽  
Guillaume Hans ◽  
Brigitte Leblon ◽  
Zarin Pirouz ◽  
Satoru Tsuchikawa ◽  
...  
Keyword(s):  
Moisture Content ◽  
Near Infrared ◽  
Black Spruce ◽  
Scattering Coefficient ◽  
Coefficient Of Determination ◽  
Optical Parameters ◽  
Quaking Aspen ◽  
Near Infrared Reflectance ◽  
Scattering Coefficients

We used the Kubelka-Munk theory equations for calculating the absorption coefficient (Kλ), the scattering coefficient ( Sλ), the transport absorption (σλa), the reduced scattering coefficient [σλs(1 – g)] and the penetration depth (δλ) from visible-near infrared reflectance spectra acquired over thin samples of quaking aspen and black spruce conditioned at three different moisture levels. The computed absorption and scattering coefficients varied from 0.1 mm−1 to 4.0 mm−1 and from 5.5 mm−1 to 10.0 mm−1, respectively. The absorption coefficients varied according to the absorption band, but the scattering coefficients decreased slowly towards high wavelengths. The sample moisture content was then estimated using the partial least squares (PLS) regression method from the Kλ and/or Sλ spectra, and the resulting PLS models were compared to those obtained with raw and transformed [multiplicative scatter corrected (MSC), first and second derivative] absorption spectra. The best PLS models for black spruce, quaking aspen and both species were obtained when only the 800–1800 nm range was used with the raw or MSC spectra. They led to a root mean square error of cross validation ( RMSECV) of 1.40%, 1.09% and 1.23%, respectively, and to a coefficient of determination ( R2CV) higher than 0.94. We also found that the Kλ spectra between 800 nm and 1800 nm can provide PLS models having an acceptable accuracy for moisture content estimation ( R2CV = 0.83 and RMSECV = 2.32%), regardless of the species.

scholarly journals Determination of oil content, linolenic and erucic acids contents in false flax seeds using IRspectrometry

2021 ◽  
Vol 186 (2) ◽  
pp. 50-59
Author(s):  
S.G. Efimenko ◽  
◽  
S.K. Efimenko ◽  
Keyword(s):  
Acid Content ◽  
Oil Content ◽  
Near Infrared ◽  
Coefficient Of Determination ◽  
Erucic Acid Content ◽  
Weight Percentage ◽  
False Flax ◽  
Prediction Coefficient ◽  
Flax Seeds

Spectroscopy of near infrared reflection (NIRS) was used for estimation of biochemical indicators in seeds of false flax. The purpose of our work was to develop calibrating models for IR-analyzer MATRIXI for determination of weight percentage of oil, linolenic and erucic acids contents in oil of seeds in unbroken seeds of false flax (winter and spring forms). The researches were conducted in the biochemistry laboratory on false flax samples cultivated in 2016–2020 in the different environments of the Russian Federation. Oil content was determined with NMR-analyzer АМV 1006М according to the technique described in the State Standard 8.597-2010, percentage contents of linolenic and erucic acids in oil was estimated on the gas chromatograph “Chromatech – Kristal 5000” with an automatic dipper on a capillary column SolGelWax 30 m × 0.25 mm × 0.5 µcm. The best indicators of quality of the calibrating models (root mean square error of prediction, coefficient of determination, and meaning of a residual deflection of prediction for a rank reflected on a figure) were obtained by oil content (RMSEP = 0.20%, R 2 = 99.3, and RPD = 12.3), linolenic acid content (RMSEP = 0.35%, R 2 = 98.8, and RPD = 9.2) and erucic acid content (RMSEP = 0.14%, R 2 = 85.7, and RPD = 2.6). In a program OPUS LAB, we received a method “False flax 51” based on the developed calibrating models for a routine analysis for determination of oil content, linolenic and erucic acids contents in oil in the unbroken seeds of false flax in an average (9–20 g) in a cuvette with diameter of 51 mm. this method allows conducting express-estimation of false flax seeds for breeding traits with performance of more than 100 sample per seven hours.

scholarly journals Rapid determination of the content of digestible energy and metabolizable energy in sorghum fed to growing pigs by near-infrared reflectance spectroscopy1

2019 ◽  
Vol 97 (12) ◽  
pp. 4855-4864 ◽  
Author(s):  
Jie Hu ◽  
Juntao Li ◽  
Long Pan ◽  
Xiangshu Piao ◽  
Li Sui ◽  
...  
Keyword(s):  
Near Infrared ◽  
External Validation ◽  
Growing Pigs ◽  
Metabolizable Energy ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Calibration Models

Abstract The object of this study was to establish a new method to predict the content of DE and ME in sorghum fed to growing pigs by using near-infrared reflectance spectroscopy (NIRS). A total of 33 sorghum samples from all over China were used in this study. The samples were scanned for their spectra in the range of 12,000 to 4,000 cm−1. Based on principal components analysis of the spectra, the samples were split into a calibration set (n = 24) and a validation set (n = 9) according to the ratio of 3:1. With animal experiment values as calibration reference, the calibration models of DE and ME were established using partial least squares regression algorithm. Different spectral pretreatments were applied on the spectra to reduce the noise level. The best wavenumber ranges were also investigated. Results showed that DE and ME content in sorghum fed to growing pigs ranged from 14.57 to 16.70 MJ/kg DM and 14.31 to 16.35 MJ/kg DM, respectively. The optimal spectral preprocessing method for DE and ME was the combination of first derivative and multiplicative scatter correction. The most informative near-infrared spectral regions were 9,403.9 to 6,094.4 cm−1 and 4,605.5 to 4,242.9 cm−1 for both DE and ME. The best performance for DE and ME calibration models was the coefficient of determination of calibration (R2c) of 0.94 and 0.93, coefficient of determination of cross-external validation (R2cv) of 0.88 and 0.86, residual predictive deviation of cross-external validation (RPDcv) of 2.86 and 2.64, coefficient of determination of external validation (R2v) of 0.90 and 0.81, and residual predictive deviation of external validation (RPDv) of 3.15 and 2.35, respectively. There were no significant differences between the measured and NIRS predicted values for DE and ME (P = 0.895 for DE and P = 0.644 for ME). As the number of calibration samples increased from 24 to 33, the calibration performance of DE and ME models was improved, indicated by increased R2c, R2cv, and RPDcv values. In conclusion, NIRS quantitative models of the available energy in sorghum were established in this study. The results demonstrated that the content of DE and ME in sorghum could be predicted with relatively high accuracy based on NIRS and NIRS showed the superiority of speediness and practicality when compared with previous research methods including animal experiments, regression equations, and computer-controlled simulated digestion system.

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.

scholarly journals Nondestructive Prediction of Optimal Harvest Time of Cherry Tomatoes Using VIS-NIR Spectroscopy and PLSR Calibration

2011 ◽  
Vol 1 ◽  
pp. 92-96 ◽  
Author(s):  
Hai Qing Yang
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Harvest Time ◽  
Least Squares Regression ◽  
Calibration Models ◽  
Optimal Harvest ◽  
Leave One Out

In situ determination of optimal harvest time of tomatoes is of value for growers to optimize fruit picking schedule. This study evaluates the feasibility of using visible and near infrared (VIS-NIR) spectroscopy to make an intact estimation of harvest time of tomatoes. A mobile, fibre-type, AgroSpec VIS-NIR spectrophotometer (Tec5, Germany), with a spectral range of 350-2200 nm, was used for spectral acquisition of tomatoes in reflection mode. The harvest time of tomatoes was measured by the days before harvest. After dividing spectra into a calibration set (70%) and an independent prediction set (30%), spectra in the calibration set were subjected to a partial least-squares regression (PLSR) with leave-one-out cross validation to establish calibration models. Validation of calibration models on the independent prediction set indicates that the best model can produce excellent prediction accuracy with coefficient of determination (R2) of 0.90, root-mean-square error of prediction (RMSEP) of 2.5 days and residual prediction deviation (RPD) of 3.01. It is concluded that VIS-NIR spectroscopy coupled with PLSR models can be adopted successfully for in situ determination of optimal harvest time of tomatoes, which allows for automatic fruit harvest by a horticultural robot.

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