Application of FT-NIR-DR and FT-IR-ATR spectroscopy to estimate the chemical composition of bamboo (Neosinocalamus affinis Keng)

Holzforschung ◽  
2011 ◽  
Vol 65 (5) ◽  
Author(s):  
Bailing Sun ◽  
Junliang Liu ◽  
Shujun Liu ◽  
Qing Yang
Keyword(s):  
Fourier Transform ◽  
Chemical Composition ◽  
Near Infrared ◽  
Western China ◽  
Coefficient Of Determination ◽  
Chemical Components ◽  
Klason Lignin ◽  
Least Squares Regression ◽  
Ft Ir ◽  
Atr Spectroscopy

Abstract Neosinocalamus affinis Keng is widely grown in south-western China for pulp and paper production. Rapid assessment of the chemical properties of N. affinis is necessary for both bamboo breeding and industrial utilization. This study was performed to investigate the abilities of Fourier transform near-infrared spectroscopy in the diffuse reflectance mode (FT-NIR-DR) and Fourier transform infrared attenuated total reflectance (FT-IR-ATR) spectroscopy to predict the contents of holocellulose, α-cellulose, Klason lignin, and NaOH extractives in N. affinis. Partial least squares regression models based on the raw and preprocessed spectra, including multiplicative scatter correction (MSC) and Savitzky-Golay 1st and 2nd derivative spectra, were developed for the chemical components of bamboo. The NIR-based calibrations displayed better performance than those using FT-IR-ATR spectra. The best calibrations developed by both methods for properties all had satisfactory correlations, with coefficient of determination (R2 c) values ranging from 0.81 (Klason lignin by FT-IR and MSC) to 0.98 (α-cellulose by FT-NIR and 2nd derivative), and root mean standard error of calibration between 0.50 and 1.47%. When applied to prediction sets, the correlations were good, with R2 p above 0.68. The results demonstrate that both spectroscopic methods, combined with chemometric strategies, could rapidly predict the chemical composition of bamboo.

Near infrared spectroscopy to evaluate change in color and chemical composition in heat-treated bamboo

2020 ◽  
Vol 28 (4) ◽  
pp. 214-223
Author(s):  
Junqian Mo ◽  
Wenbo Zhang ◽  
Xiaohui Fu ◽  
Wei Lu
Keyword(s):  
Infrared Spectroscopy ◽  
Chemical Composition ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Chemical Components ◽  
Outer Side ◽  
Heating Process ◽  
Least Squares Regression ◽  
Heat Treated

This study investigated the feasibility of using near infrared spectroscopy technology to predict color and chemical composition in the heat-treated bamboo processing industry. The quantitative presentations of the changes in the chemical components were discussed using the difference spectra method of the 2nd derivative NIR spectra of the heat-treated bamboo samples. Then, the relationships between the color changes of the heat-treated bamboo and its near infrared spectra were constructed using the changes in the chemical components of the bamboo samples during the heating process. The prediction of color and chemical composition of both the outer and inner sides of the heat-treated bamboo surface were constructed using partial least squares regression method combined with a leave-one-out cross-validation process. Then, the results were validated by independent sample sets. The proposed prediction models were found to produce high r2P (above 0.93), RPD (above 3.13), and low RMSEP for both the outer and inner sides of the heat-treated bamboo samples. These studies’ results confirmed that the proposed models, especially outer side models, were perfectly suitable for the in-process inspections of the color and chemical content changes of heat-treated bamboo.

Rapid analysis of chemical composition in intact and milled rice cookies using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 330-337 ◽  
Author(s):  
Latthika Wimonsiri ◽  
Pitiporn Ritthiruangdej ◽  
Sumaporn Kasemsumran ◽  
Nantawan Therdthai ◽  
Wasaporn Chanput ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Near Infrared ◽  
Wheat Gluten ◽  
Nir Spectroscopy ◽  
Rice Flour ◽  
Coefficient Of Determination ◽  
Gluten Free ◽  
Least Squares Regression ◽  
Milled Rice ◽  
Control Programs

This study has investigated the potential of near infrared (NIR) spectroscopy to predict the content of moisture, protein, fat and gluten in rice cookies in different sample forms (intact and milled samples). Gluten-free (n = 48) and gluten (n = 48) rice cookies were formulated with brown and white rice flours in which butter was substituted with fat replacer at 0, 15, 30 and 45%. With regard to gluten cookies, rice flour was substituted with wheat gluten at 1, 3 and 5%. Partial least squares regression modeling produced models with coefficient of determination (R2) values greater than 0.88 from NIR spectra of intact samples and greater than 0.92 for milled samples. These models were able to predict the four components with a ratio of prediction to deviation greater than 2.7 and 3.8 in intact and milled samples, respectively. The results suggest that the models obtained from the intact samples can be successfully applied for chemical composition of rice cookies and are reliable enough use for potential quality control programs.

Oil Content Estimation of Individual Kernels of Quercus Ilex Subsp. Rotundifolia [(Lam) O. Schwarz] Acorns by Fourier Transform near Infrared Spectroscopy and Partial Least Squares Regression

2007 ◽  
Vol 15 (4) ◽  
pp. 247-260 ◽  
Author(s):  
Cristina Sousa-Correia ◽  
Ana Alves ◽  
José C. Rodrigues ◽  
Suzana Ferreira-Dias ◽  
José M. Abreu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Partial Least Squares ◽  
Oil Content ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Association Studies ◽  
Extraction Procedure ◽  
Coefficient Of Determination ◽  
Least Squares Regression

The aim of this work was to use Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares regression (PLSR) to estimate the oil content of individual Holm oak ( Quercus sp.) acorn kernels from different trees, sites and years that should be used in the future for molecular marker association studies. Sampling of acorns in two consecutive years (2003 and 2004) and from different sites in Portugal provided independent sample sets. A total of 89 samples (acorn kernels) representative of the natural oil content range were extracted. The results of the analyses performed by three people revealed accuracy of the oil extraction procedure ( n-hexane) and the precision (repeatability) of this method, assessed during a four-day period, gave a standard deviation of 0.1%. Careful wavenumber selection and several steps of validation of the PLSR models led to a final robust model that allowed the precise prediction of the oil content of individual acorns. By using the wavenumber ranges from 5995 to 5323 cm−1 and from 4478 to 4177 cm−1 of the vector normalised spectra, a PLSR model with a coefficient of determination ( r 2) of 0.992 and a root mean square error of cross-validation ( RMSECV) of 0.37% was achieved. The RPD value of about 10 and a bias of almost zero showed that the developed models are good for process control, development, and applied research. Oil content estimation of individual Quercus sp. acorns by FT-NIR and PLSR was shown to be possible. The varying water content detected in the spectra of the milled kernels after drying in similar conditions, within and especially between years, could be handled.

Hydraulic traits of Norway spruce sapwood estimated by Fourier transform near-infrared spectroscopy (FT-NIR)

2015 ◽  
Vol 45 (6) ◽  
pp. 625-631 ◽  
Author(s):  
Saskia Luss ◽  
Manfred Schwanninger ◽  
Sabine Rosner
Keyword(s):  
Fourier Transform ◽  
Norway Spruce ◽  
Hydraulic Properties ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Air Pressure ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
The Impact

The potential of Fourier transform near-infrared (FT-NIR) spectroscopy to predict hydraulic traits in Norway spruce (Picea abies (L.) Karst.) sapwood was evaluated. Hydraulic traits tested were P50 (applied air pressure causing 50% loss of hydraulic conductivity) and RWL50 (applied air pressure causing 50% relative water loss). Samples came from 24-year-old spruce clones. FT-NIR spectra were collected from the axial (transverse) and radial surface of each solid wood sample for the prediction of P50 and RWL50. Partial least squares regression (PLS-R) models with cross validation were used to establish relationships between the FT-NIR spectra and the reference data from hydraulic properties analysis. The impact of the wavenumber range and the pretreatment during the PLS-R model development and the differences between the axial and radial surfaces were shown. Based on the values of the coefficient of determination (r2) and the root mean square error of cross validation, predicted results were evaluated as acceptable. The models from the axial surface gave better results than the models from the radial surface for P50 (r2 = 0.65), as well as for RWL50 (r2 = 0.77). The first approach to predict hydraulic properties such as P50 and RWL50 by FT-NIR spectroscopy can be regarded as successful. We conclude that the method has high potential to be put into practice as a rapid, reliable, and nondestructive method to determine P50 and RWL50.

scholarly journals Application of Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopy Coupled with Wavelength Selection for Fast Discrimination of Similar Color of Tuber Flours

2020 ◽  
Vol 20 (3) ◽  
pp. 680 ◽  
Author(s):  
Rudiati Evi Masithoh ◽  
Hanim Zuhrotul Amanah ◽  
Byoung Kwan Cho
Keyword(s):  
Fourier Transform ◽  
Ir Spectroscopy ◽  
Near Infrared ◽  
Fourier Transform Infrared ◽  
Accurate Method ◽  
Coefficient Of Determination ◽  
Wavelength Selection ◽  
Derivative Method ◽  
Ft Ir ◽  
Ft Ir Spectroscopy

This research aimed at providing a fast and accurate method in discriminating tuber flours having similar color by using Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) spectroscopy in order to minimize misclassification if using human eye or avoid adulteration. Reflectance spectra of three types of tubers (consisted of Canna edulis, modified cassava, and white sweet potato) were collected to develop a multivariate model of partial least-squares discriminant analysis (PLS-DA). Several spectra preprocessing methods were applied to obtain the best calibration and prediction model, while variable importance in the projection (VIP) wavelength selection method was used to reduce variables in developing the model. The PLS-DA model achieved 100% accuracy in predicting all types of flours, both for FT-NIR and FT-IR. The model was also able to discriminate all flours with coefficient of determination (R2) of 0.99 and a standard error of prediction (SEP) of 0.03% by using 1st Savitzky Golay (SG) derivative method for the FT-NIR data, as well as R2 of 0.99 and SEP of 0.08% by using 1st Savitzky Golay (SG) derivative method for the FT-IR data. By applying the VIP method, the variables were reduced from 1738 to 608 variables with R2 of 0.99 and SEP of 0.09% for FT IR and from 1557 to 385 variables with R2 of 0.99 and SEP of 0.05% for FT NIR.

scholarly journals Investigations into the Performance of a Novel Pocket-Sized Near-Infrared Spectrometer for Cheese Analysis

Molecules ◽  
2019 ◽  
Vol 24 (3) ◽  
pp. 428 ◽  
Author(s):  
Verena Wiedemair ◽  
Dominik Langore ◽  
Roman Garsleitner ◽  
Klaus Dillinger ◽  
Christian Huck
Keyword(s):  
Data Analysis ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Analysis Software ◽  
Infrared Spectrometer ◽  
Automated Tools ◽  
Near Infrared Spectrometer ◽  
Data Analysis Software

The performance of a newly developed pocket-sized near-infrared (NIR) spectrometer was investigated by analysing 46 cheese samples for their water and fat content, and comparing results with a benchtop NIR device. Additionally, the automated data analysis of the pocket-sized spectrometer and its cloud-based data analysis software, designed for laypeople, was put to the test by comparing performances to a highly sophisticated multivariate data analysis software. All developed partial least squares regression (PLS-R) models yield a coefficient of determination (R2) of over 0.9, indicating high correlation between spectra and reference data for both spectrometers and all data analysis routes taken. In general, the analysis of grated cheese yields better results than whole pieces of cheese. Additionally, the ratios of performance to deviation (RPDs) and standard errors of prediction (SEPs) suggest that the performance of the pocket-sized spectrometer is comparable to the benchtop device. Small improvements are observable, when using sophisticated data analysis software, instead of automated tools.

Assessing the effect of ingredients variability on the composition of the final complete feed for swine

2019 ◽  
Vol 99 (1) ◽  
pp. 7-14 ◽  
Author(s):  
Lluís Fabà ◽  
David Solà-Oriol ◽  
Aitor Balfagon ◽  
Jaume Coma ◽  
Josep Gasa
Keyword(s):  
Chemical Composition ◽  
Crude Protein ◽  
Near Infrared ◽  
Chemical Components ◽  
Small Scale ◽  
Diet Formulation ◽  
Three Samples ◽  
Ingredient Composition ◽  
Fattening Pigs ◽  
Theoretical Procedure

To characterize the variability of 11 feed ingredients and their impact on the final feed, 728 ingredient samples were collected during 5 months in a feed-plant and were analyzed by near-infrared spectrophotometry (NIRS). Six diets for fattening pigs and gestating sows were formulated using regional information of ingredient chemical composition (reference): LIM, limited; EU, common European; and MULT, multi-ingredient; respectively, including 5, 7, and 10 ingredients. The formulas were replicated 15 times using actual chemical composition (NIRS) from three samples per ingredient and month. This theoretical procedure was validated through small-scale manufacturing 30 LIM-diets, which samples were proximal (PA) and NIRS analyzed for dry matter and crude protein (CP) contents. Those mixtures were also PA analyzed. The ingredients showed coefficient of variation (CV %) higher for crude fiber (CF) (2.6%–18.3%) than CP (2.0%–9.3%). Comparing all diets for all chemical components, variability was reduced when including more ingredients from 0.5%–5.5% to 0.3%–2.6% CV. In most cases, the actual chemical composition of the diets underestimated their reference formula (1.3%–10.8%, CP and CF). A deviation from the targeted diet occurs if variability is not regarded. Therefore, a proper method to predict ingredient composition and nutritional value before use may increase the accuracy of diet formulation between 2% and 10%.

scholarly journals Rapid Quantitative Analysis of Forest Biomass Using Fourier Transform Infrared Spectroscopy and Partial Least Squares Regression

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Gifty E. Acquah ◽  
Brian K. Via ◽  
Oladiran O. Fasina ◽  
Lori G. Eckhardt
Keyword(s):  
Fourier Transform ◽  
Chemical Composition ◽  
Ftir Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Fourier Transform Infrared ◽  
Least Squares Regression ◽  
Forest Logging ◽  
Logging Residue

Fourier transform infrared reflectance (FTIR) spectroscopy has been used to predict properties of forest logging residue, a very heterogeneous feedstock material. Properties studied included the chemical composition, thermal reactivity, and energy content. The ability to rapidly determine these properties is vital in the optimization of conversion technologies for the successful commercialization of biobased products. Partial least squares regression of first derivative treated FTIR spectra had good correlations with the conventionally measured properties. For the chemical composition, constructed models generally did a better job of predicting the extractives and lignin content than the carbohydrates. In predicting the thermochemical properties, models for volatile matter and fixed carbon performed very well (i.e.,R2> 0.80, RPD > 2.0). The effect of reducing the wavenumber range to the fingerprint region for PLS modeling and the relationship between the chemical composition and higher heating value of logging residue were also explored. This study is new and different in that it is the first to use FTIR spectroscopy to quantitatively analyze forest logging residue, an abundant resource that can be used as a feedstock in the emerging low carbon economy. Furthermore, it provides a complete and systematic characterization of this heterogeneous raw material.

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.

scholarly journals Estimation for Different Genotypes of Plants based on DNA Analysis using Near-infrared (NIR) and Fourier-transform Infrared (FT-IR) Spectroscopy

2006 ◽  
Vol 56 (4) ◽  
pp. 399-403 ◽  
Author(s):  
Koji Emura ◽  
Shinsuke Yamanaka ◽  
Hiroko Isoda ◽  
Kazuo N. Watanabe
Keyword(s):  
Fourier Transform ◽  
Ir Spectroscopy ◽  
Near Infrared ◽  
Dna Analysis ◽  
Fourier Transform Infrared ◽  
Ft Ir ◽  
Ft Ir Spectroscopy
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