Analysis of Betaines Using near Infrared Spectroscopy

2005 ◽  
Vol 13 (3) ◽  
pp. 133-138 ◽  
Author(s):  
Emma Ibarra ◽  
Omar Valencia ◽  
Héctor Pérez
Keyword(s):  
Infrared Spectroscopy ◽  
Sodium Chloride ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Accurate Method ◽  
Coefficient Of Determination ◽  
Active Matter ◽  
Surface Active Properties ◽  
Surface Active ◽  
True Values

Cocoamidopropyl betaines are becoming increasingly important in cosmetic formulations because of their mild and effective surface active properties. A simple and accurate method is needed for quality control during the production of betaines. The aim of the present study was to determine if near infrared spectroscopy can replace wet methods for routine analysis of betaine. The calibration curve was obtained by partial least squares. The optimisation of calibration factors was guided by coefficient of determination ( R2) and the root mean square error of evaluation ( RMSEE). R2 was 0.99 or higher and RMSEE 0.025, 0.071, 0.03% and 0.007 units for active matter, sodium chloride, solids and pH, respectively. The method was validated with independent samples in the same manner on a different day and true values were obtained with R2 of 0.99 or higher and root mean standard error of prediction of 0.060, 0.074, 0.075% and 0.035 units for active matter, sodium chloride, solids and pH, respectively.

scholarly journals OTIMIZAÇÃO DE CALIBRAÇÕES BASEADAS EM ESPECTROSCOPIA NO INFRAVERMELHO PRÓXIMO PARA ESTIMATIVA DE PROPRIEDADES DA MADEIRA DE Eucalyptus

FLORESTA ◽  
2010 ◽  
Vol 40 (3) ◽  
Author(s):  
Paulo Ricardo Gherardi Hein ◽  
José Tarcísio Lima ◽  
Gilles Chaix Gilles Chaix
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Laboratory Data ◽  
Wood Properties ◽  
Coefficient Of Determination ◽  
Mathematical Treatment ◽  
Least Squares Regression ◽  
Eucalyptus Wood ◽  
Prediction Density

A espectroscopia no infravermelho próximo (NIRS) é uma técnica não-destrutiva, rápida e utilizada para avaliação, caracterização e classificação de materiais, sobretudo de origem biológica. A obtenção de informações contida nos espectros NIR é complexa e requer a utilização de métodos quimiométricos. Assim, por meio de regressão multivariada, os espectros de absorbância podem ser associados às propriedades da madeira, tornando possível a sua predição em amostras desconhecidas. Existem algumas ferramentas quimiométricas que melhoram o ajuste dos modelos preditivos. Assim, o objetivo deste trabalho foi simular regressões dos mínimos quadrados parciais baseados nas informações espectrais e de laboratório e estudar a influência da aplicação de tratamentos matemáticos, do descarte de amostras anômalas e da seleção de comprimentos de onda no ajuste dos modelos para estimativa da densidade básica e do módulo de elasticidade em ensaio de compressão paralela às fibras da madeira de Eucalyptus. A aplicação da primeira e segunda derivada nos espectros, o descarte de amostras anômalas e a seleção de algumas das variáveis espectrais melhorou significativamente o ajuste do modelo, reduzindo o erro padrão e aumentando o coeficiente de determinação e a relação de desempenho do desvio.Palavras-chave:  Espectroscopia no infravermelho próximo; predição; densidade básica; MOE; madeira; Eucalyptus. AbstractOptimization of calibrations based on near infrared spectroscopy for estimation of Eucalyptus wood properties. Near infrared spectroscopy (NIRS) is a non-destructive technique used for rapid evaluation, characterization and classification of biological materials. The extraction of the information contained in the NIR spectrum is complex and requires the use of chemo metric methods. Thus, by means of multivariate regression, the absorbance spectra are correlated to wood properties, making possible the prediction in unknown samples. There are some chemo metric tools that can improve the adjustment of the predictive models. The aim of this work was to simulate partial least squares regression based on NIR spectra and laboratory data and to study the influence of the application of mathematical treatment, the removal of outliers and the wavelengths selection in the adjustment of models to estimate the density and modulus of elasticity in Eucalyptus wood. The use of the first and second derivative spectra, the disposal of outliers, and the variables selection improved significantly the model fit, reducing the standard error and increasing the coefficient of determination and the ratio of performance to deviation.Keywords: Near infrared; spectroscopy; prediction; density; MOE; wood; Eucalyptus.

Process Evaluation of Key Parameters during Plant-Field Composting Using Genetic Algorithms and Near-Infrared Spectroscopy

2012 ◽  
Vol 532-533 ◽  
pp. 202-207
Author(s):  
Guang Qun Huang ◽  
Lu Jia Han ◽  
Xiao Yan Wang
Keyword(s):  
Genetic Algorithms ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Electronic Conductivity ◽  
Chicken Manure ◽  
Total Carbon ◽  
Coefficient Of Determination ◽  
Full Spectrum ◽  
Validation Set

The nondestructive estimation of key parameters during plant-field chicken manure composting is of great importance for quality evaluation. In the process of developing regression models using near-infrared spectroscopy (NIRS), methods used for wavelength selection significantly influence on the efficiency of the calibration. This study explored the method of genetic algorithms (GAs) for selecting highly related wavelengths to improve NIRS models for moisture (Miost), pH and electronic conductivity (EC), total carbon (TC), total nitrogen (TN) and C/N ratio determination in chicken manure during composting. Based on the values of coefficient of determination in the validation set (R2) and root mean square error of prediction (RMSEP), the prediction results were evaluated as excellent for Miost, TC and TN, good for pH and EC, and approximate for C/N ratio. But GAs had better performance than using full spectrum for near-infrared spectroscopy model construction in the process of evaluating key parameters during plant-field chicken manure composting.

scholarly journals Prediction of water content and soluble solids content of ‘manalagi’ apples using near infrared spectroscopy

2021 ◽  
Vol 922 (1) ◽  
pp. 012062
Author(s):  
K Kusumiyati ◽  
Y Hadiwijaya ◽  
D Suhandy ◽  
A A Munawar
Keyword(s):  
Infrared Spectroscopy ◽  
Water Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Quality Attributes ◽  
Soluble Solids ◽  
Coefficient Of Determination ◽  
Soluble Solids Content ◽  
Orthogonal Signal Correction ◽  
Solids Content

Abstract The purpose of the research was to predict quality attributes of ‘manalagi’ apples using near infrared spectroscopy (NIRS). The desired quality attributes were water content and soluble solids content. Spectra data collection was performed at wavelength of 702 to 1065 nm using a Nirvana AG410 spectrometer. The original spectra were enhanced using orthogonal signal correction (OSC). The regression approaches used in the study were partial least squares regression (PLSR) and principal component regression (PCR). The results showed that water content prediction acquired coefficient of determination in calibration set (R2cal) of 0.81, coefficient of determination in prediction set (R2pred) of 0.61, root mean squares error of calibration set (RMSEC) of 0.009, root mean squares of prediction set (RMSEP) of 0.020, and ratio performance to deviation (RPD) of 1.62, while soluble solids content prediction displayed R2cal, R2pred, RMSEC, RMSEP, and RPD of 0.79, 0.85, 0.474, 0.420, and 2.69, respectively. These findings indicated that near infrared spectroscopy could be used as an alternative technique to predict water content and soluble solids content of ‘manalagi’ apples.

Determination of gel time of prepreg in copper clad laminate industry by near infrared spectroscopy

2020 ◽  
pp. 096703352096379
Author(s):  
Qian-Fa Liu ◽  
Dan Li ◽  
Yao-De Zeng ◽  
Wei-Zhuang Huang
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Manufacturing Process ◽  
Near Infrared ◽  
Reference Method ◽  
Coefficient Of Determination ◽  
Resin Matrix ◽  
Calibration Model ◽  
Gel Time ◽  
Significant Difference

Gel time of prepreg is an important quality determinant in the manufacturing process of Copper Clad Laminate (CCL). Prepreg consists of a glass fiber reinforcement impregnated to a predetermined level with a resin matrix. In this work, near infrared spectroscopy associated with partial least squares (PLS) regression has been applied to analyse the gel time of prepreg samples in the manufacturing process. A total of 250 prepreg samples were randomly divided into a calibration set and a validation prediction set with a ratio of 4:1. The values of Root Mean Square Error of leave-one-out Cross-Validation (RMSECV) and the coefficient of determination (R2) of the calibration model was 2.95 s and 0.92 respectively, with eight PLS factors used. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. The analytical result showed that, NIR spectroscopy was a rapid, nondestructive, and accurate method for real-time prediction of prepreg quality in the CCL manufacturing process.

scholarly journals Estimate of the density of Eucalyptus grandis W. Hill ex Maiden using near infrared spectroscopy

CERNE ◽  
2013 ◽  
Vol 19 (4) ◽  
pp. 647-652 ◽  
Author(s):  
Silviana Rosso ◽  
Graciela Ines Bolzon de Muniz ◽  
Jorge Luis Monteiro de Matos ◽  
Clóvis Roberto Haselein ◽  
Paulo Ricardo Gherardi Hein ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Wood Density ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Eucalyptus Grandis ◽  
Basic Density ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Old Trees

This study aimed to analyze use of near infrared spectroscopy (NIRS) to estimate wood density of Eucalyptus grandis. For that, 66 27-year-old trees were logged and central planks were removed from each log. Test pieces 2.5 x 2.5 x 5.0 cm in size were removed from the base of each plank, in the pith-bark direction, and subjected to determination of bulk and basic density at 12% moisture (dry basis), followed by spectral readings in the radial, tangential and transverse directions using a Bruker Tensor 37 infrared spectrophotometer. The calibration to estimate wood density was developed based on the matrix of spectra obtained from the radial face, containing 216 samples. The partial least squares regression to estimate bulk wood density of Eucalyptus grandis provided a coefficient of determination of validation of 0.74 and a ratio performance deviation of 2.29. Statistics relating to the predictive models had adequate magnitudes for estimating wood density from unknown samples, indicating that the above technique has potential for use in replacement of conventional testing.

Modeling Wood Density of Larch by Near-Infrared Spectrometry and Support Vector Machine

2011 ◽  
Vol 55-57 ◽  
pp. 433-438
Author(s):  
Ya Zhao Zhang ◽  
Yao Xiang Li ◽  
Hong Fu Zhang ◽  
Hui Juan Zhang ◽  
Pai Li
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Wood Density ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Model Building ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Infrared Spectrometry ◽  
Support Vector

Model for predicting wood density of Larch was established using near-infrared spectroscopy (NIR) combined with support vector machine (SVM). A hundred and seventeen Larch samples were used in the study. Wood density of samples was measured according to standard test methods for physical and mechanical properties of wood. Support vector machines for regression (SVR) was used for model building. Radial basis function (RBF) was used as kernel function to establish a model for predicting wood density. For the train set, the coefficient of determination (R2) and the mean square error (MSE) were 0.8504 and 0.6460×10-3, while the R2 and MSE was 0.8520 and 0.4451×10-3, respectively, for the test set. Results showed that using SVM in near-infrared spectroscopy calibration could significantly improve the model performance in order to rapidly and accurately predict wood density.

Rapid evaluation of fat content in curry soup containing coconut milk by using near infrared spectroscopy

2017 ◽  
Vol 26 (1) ◽  
pp. 16-25 ◽  
Author(s):  
Ekkapong Cheevitsopon ◽  
Panmanas Sirisomboon
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Fat Content ◽  
Coconut Milk ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Test Set ◽  
Processing Line

The feasibility of a near infrared spectroscopy to evaluate the fat content in instant curry soup containing coconut milk including green curry, red curry, massaman curry and panang curry was investigated. The soup samples were collected from a processing line and as the finished product. There were also fat content-adjusted samples where the curry was made from the same recipe as in the processing line but increasing by 30, 60 and 90% coconut milk and reducing by 30, 60 and 90% coconut milk from normal. A Fourier transform near infrared spectrometer was used to collect scans. A partial least squares regression model for fat content was established using near infrared spectral data in conjunction with reference data, which was validated using a leave-one-out cross-validation and test set validation. The test set validation, using a set of unknown samples, showed better prediction performance. The best model developed using vector normalization spectral pre-treatment on 9404–7498 and 6102–5446 cm−1 provided coefficient of determination, root mean square error of prediction, bias and ratio of performance to interquartile values of 0.90, 0.9%, −0.1% and 1.2, respectively, for the validation samples. However, the model developed using samples without fat content adjusted samples gave a slightly lower coefficient of determination (0.89), but provided a lower root mean square error of prediction (0.5%) and acceptable ratio of standard error of validation to the standard deviation (3.2). In addition, the vibration bands of CH2 which was in the long chain fatty acid moiety highly influenced the prediction of fat content in the curry soup. The near infrared spectroscopy protocol developed for the determination of fat could be applied in the instant curry soup production line.

A rapid and accurate method for on-line measurement of straw–coal blends using near infrared spectroscopy

2012 ◽  
Vol 110 ◽  
pp. 314-320 ◽  
Author(s):  
Cheng He ◽  
Longjian Chen ◽  
Zengling Yang ◽  
Guangqun Huang ◽  
Na Liao ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Accurate Method ◽  
Line Measurement ◽  
On Line
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