scholarly journals Assessment of pesticide coating on cereal seeds by near infrared hyperspectral imaging

2017 ◽  
Author(s):  
Ph. Vermeulen ◽  
P. Flémal ◽  
O. Pigeon ◽  
P. Dardenne ◽  
J. Fernández Pierna ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Seed Quality ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Ultra Performance Liquid Chromatography

Classical chromatographic methods, such as ultra performance liquid chromatography (UPLC), are used as reference methods to assess seed quality and homogeneous pesticide coating of seeds. These methods have some important drawbacks since they are time consuming, expensive, destructive and require a substantial amount of solvent, among others. Near infrared (NIR) spectroscopy seems to be an interesting alternative technique for the determination of the quality of seed treatment and avoids most of these drawbacks. The objective of this study was to assess the quality of pesticide coating treatment by near infrared hyperspectral imaging (NIR-HSI) by analysing, on a seed-by-seed basis, several seeds simultaneously in comparison to NIR spectroscopy and UPLC as the reference method. To achieve this goal, discrimination—partial least squares discriminant analysis (PLS-DA)—models and regression—partial least squares (PLS)—models were developed. The results obtained by NIR-HSI are compared to the results obtained with NIR spectroscopy and UPLC instruments. This study has shown the potential of NIR hyperspectral imaging to assess the quality/homogeneity of the pesticide coating on seeds.

Near-infrared hyperspectral imaging and partial least squares regression for rapid and reagentless determination of Enterobacteriaceae on chicken fillets

2013 ◽  
Vol 138 (2-3) ◽  
pp. 1829-1836 ◽  
Author(s):  
Yao-Ze Feng ◽  
Gamal ElMasry ◽  
Da-Wen Sun ◽  
Amalia G.M. Scannell ◽  
Des Walsh ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Least Squares Regression

scholarly journals Simultaneous Determination of Several Fiber Contents in Blended Fabrics by Near-Infrared Spectroscopy and Multivariate Calibration

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Nondestructive Method ◽  
Full Spectrum ◽  
Lattice Design ◽  
The Mean

The qualitative and quantitative determination of the components of textile fibers takes an important position in quality control. A fast and nondestructive method of simultaneously analyzing four fiber components in blended fabrics was studied by near-infrared (NIR) spectroscopy combined with multivariate calibration. Two sample sets including 39 and 25 samples were designed by simplex mixture lattice design methods and used for experiment. Four components include wool, polyester, polyacrylonitrile, and nylon and their mixture is one of the most popular formulas of textiles. Uninformative variable elimination-partial least squares (UVEPLS) and the full-spectrum partial least squares (PLS) were used as the tool. On the test set, the mean standard error of prediction (SEP) and the mean ratio of the standard deviation of the response variable and SEP (RPD) of the full-spectrum PLS model and UVEPLS model were 0.38, 0.32 and 7.6, 8.3, respectively. This result reveals that the UVEPLS can construct local models with acceptable and better performance than the full-spectrum PLS. It indicates that this method is valuable for nondestructive analysis in the field of wool content detection since it can avoid time-consuming, costly, and laborious wet chemical analysis.

scholarly journals DETERMINATION OF BRACHIARIA SPP. FORAGE QUALITY BY NEAR-INFRARED SPECTROSCOPY AND PARTIAL LEAST SQUARES REGRESSION

2017 ◽  
Vol 62 (2) ◽  
pp. 3472-3477 ◽  
Author(s):  
MARIEL MONRROY ◽  
DEHYLIS GUTIÉRREZ ◽  
MARISSA MIRANDA ◽  
KARLA HERNÁNDEZ ◽  
JOSÉ RENÁN GARCÍA
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Forage Quality ◽  
Least Squares Regression

NIR Spectroscopy and Partial Least Squares Regression for the Determination of Phosphate Content and Viscosity Behaviour of Potato Starch

2001 ◽  
Vol 9 (2) ◽  
pp. 133-139 ◽  
Author(s):  
L.G. Thygesen ◽  
S.B. Engelsen ◽  
M.H. Madsen ◽  
O.B. Sørensen
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Potato Starch ◽  
Nir Spectroscopy ◽  
Peak Time ◽  
Phosphate Content ◽  
Least Squares Regression ◽  
Data Prediction

A set of 97 potato starch samples with a phosphate content corresponding to a phosphorus content between 0.029 and 0.11 g per 100 g dry matter was analysed using a Rapid Visco Analyzer (RVA) and near infrared (NIR) spectroscopy, (700–2498 nm). NIR-based prediction of phosphate content was possible with a root mean square error of cross-validation ( RMSECV) of 0.006% using PLSR (partial least squares regression). However, the NIR/PLSR model relied on weak spectral signals, and was highly sensitive to sample preparation. The best prediction of phosphate content from the RVA viscograms was a linear regression model based on the RVA variable Breakdown, which gave a RMSECV of 0.008%. NIR/PLSR prediction of the RVA variables Peak viscosity and Breakdown was successful, probably because they were highly related to phosphate content in the present data. Prediction of the other RVA variables from NIR/PLSR was mediocre (Through, Final Viscosity) or not possible (Setback, Peak time, Pasting temperature).

Non-destructive assessment of ascorbic acid in apples using near-infrared (NIR) spectroscopy together with partial least squares (PLS) regression

2018 ◽  
pp. 447-454
Author(s):  
T. Liu ◽  
M. Bassi ◽  
N. Sadar ◽  
G. Lubes ◽  
S. Agnolet ◽  
...  
Keyword(s):  
Ascorbic Acid ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Pls Regression ◽  
Non Destructive

Authentication of organic sugars by NIR spectroscopy and partial least squares with discriminant analysis

2020 ◽  
Vol 12 (5) ◽  
pp. 701-705 ◽  
Author(s):  
Vitória Maria Almeida Teodoro de Oliveira ◽  
Michel Rocha Baqueta ◽  
Paulo Henrique Março ◽  
Patrícia Valderrama
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Nir Spectroscopy

The present study evaluated the potential of near-infrared (NIR) spectroscopy coupled with partial least squares with discriminant analysis (PLS-DA) for the authentication of organic sugars.

scholarly journals A New Plant Indicator (Artemisia lavandulaefolia DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Lu Xu ◽  
Qiong Shi ◽  
Bang-Cheng Tang ◽  
Shunping Xie
Keyword(s):  
Support Vector Machine ◽  
Fourier Transform ◽  
Least Squares ◽  
Near Infrared ◽  
Mercury Level ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Classification Model ◽  
Support Vector ◽  
Reference Analysis

A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0–3 kilometers, 7.52–88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0–0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.

scholarly journals The application of NIR spectroscopy in moisture determining of vegetable seeds

2020 ◽  
Vol 38 (No. 2) ◽  
pp. 131-136
Author(s):  
Wojciech Poćwiardowski ◽  
Joanna Szulc ◽  
Grażyna Gozdecka
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Moisture Analysis ◽  
Non Destructive

The aim of the study was to elaborate a universal calibration for the near infrared (NIR) spectrophotometer to determine the moisture of various kinds of vegetable seeds. The research was conducted on the seeds of 5 types of vegetables – carrot, parsley, lettuce, radish and beetroot. For the spectra correlation with moisture values, the method of partial least squares regression (PLS) was used. The resulting qualitative indicators of a calibration model (R = 0.9968, Q = 0.8904) confirmed an excellent fit of the obtained calibration to the experimental data. As a result of the study, the possibilities of creating a calibration model for NIR spectrophotometer for non-destructive moisture analysis of various kinds of vegetable seeds was confirmed.<br /><br />

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