Near infrared (NIR) spectroscopy for estimating the chemical composition of (Acacia mangium Willd.) wood

Eucalyptus pellita F. Muell. has become an important tree species in the forest plantations of SE Asia, and in Malaysian Borneo in particular, to replace thousands of hectares of Acacia mangium Willd. which has suffered significant loss caused by Ceratocystis manginecans infection in Sabah, Malaysia. Since its first introduction at a commercial scale in 2012, E. pellita has been planted in many areas in the region. The species replacement requires new silvicultural practices to induce the adaptability of E. pellita to grow in the region and this includes relevant research to optimise such regimes as planting distance, pruning, weeding practices and nutrition regimes. In this present study, the nutritional status of the foliage was investigated with the aim to develop near infrared spectroscopic calibrations that can be used to monitor and quantify nutrient status, particularly total foliar nitrogen (N) and phosphorus (P) in the field. Spectra acquired on fresh foliage in situ on the tree could be used to predict N and P with accuracy suitable for operational decision-making regards fertiliser application. If greater accuracy is required, spectra acquired on dry, milled foliage could be used to predict N and P within a relative error of 10% (R2c, r2CV, RMSEP, RPD = 0.77, 0.71, 0.02 g 100/g, 1.9 for foliar P and = 0.90, 0.88, 0.21 g 100/g, 3.0 for foliar N on dry, milled foliage). The ultimate application of this is in situ nutrient monitoring, particularly to aid longitudinal studies in fertiliser trial plots and forest operations, as the non-destructive nature of NIR spectroscopy would enable regular monitoring of individual leaves over time without the need to destructively sample them. This would aid the temporal and spatial analysis of field data.

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Use of Near Infrared Reflectance (NIR) Spectroscopy to Predict Chemical Composition of Forages in Broad-Based Calibration Models

Agricultura Técnica ◽

10.4067/s0365-28072006000100005 ◽

2006 ◽

Vol 66 (1) ◽

Cited By ~ 6

Author(s):

Jaime Garcia ◽

Daniel Cozzolino

Keyword(s):

Chemical Composition ◽

Near Infrared ◽

Nir Spectroscopy ◽

Infrared Reflectance ◽

Near Infrared Reflectance ◽

Calibration Models

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Ability of NIR spectroscopy to predict meat chemical composition and quality – a review

Czech Journal of Animal Science ◽

10.17221/4337-cjas ◽

2011 ◽

Vol 49 (No. 11) ◽

pp. 500-510 ◽

Cited By ~ 69

Author(s):

M. Prevolnik ◽

M. Čandek-Potokar ◽

D. Škorjanc

Keyword(s):

Chemical Composition ◽

Meat Quality ◽

Quality Evaluation ◽

Near Infrared ◽

Nir Spectroscopy ◽

The Other ◽

Meat Composition ◽

Feeding Regimes ◽

Simultaneous Assessment

In contrast to conventional methods for the determination of meat chemical composition and quality, near infrared spectroscopy (NIRS) enables rapid, simple and simultaneous assessment of numerous meat properties. The present article is a review of published studies that examined the ability of NIRS to predict different meat properties. According to the published results, NIRS shows a great potential to replace the expensive and time-consuming chemical analysis of meat composition. On the other hand, NIRS is less accurate for predicting different attributes of meat quality. In view of meat quality evaluation, the use of NIRS appears more promising when categorizing meat into quality classes on the basis of meat quality traits for example discriminating between feeding regimes, discriminating fresh from frozen-thawed meat, discriminating strains, etc. The performance of NIRS to predict meat properties seems limited by the reliability of the method to which it is calibrated. Moreover, the use of NIRS may also be limited by the fact that it needs a laborious calibration for every purpose. In spite of that, NIRS is considered to be a very promising method for rapid meat evaluation.    

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From the Laboratory to The Vineyard—Evolution of The Measurement of Grape Composition using NIR Spectroscopy towards High-Throughput Analysis

High-Throughput ◽

10.3390/ht8040021 ◽

2019 ◽

Vol 8 (4) ◽

pp. 21

Author(s):

Aoife Power ◽

Vi Khanh Truong ◽

James Chapman ◽

Daniel Cozzolino

Keyword(s):

Chemical Composition ◽

Hyperspectral Imaging ◽

High Throughput ◽

Analytical Methods ◽

Near Infrared ◽

Nir Spectroscopy ◽

Chemical Compounds ◽

Spectroscopic Techniques ◽

High Throughput Analysis ◽

Laboratory Methods

Compared to traditional laboratory methods, spectroscopic techniques (e.g., near infrared, hyperspectral imaging) provide analysts with an innovative and improved understanding of complex issues by determining several chemical compounds and metabolites at once, allowing for the collection of the sample “fingerprint”. These techniques have the potential to deliver high-throughput options for the analysis of the chemical composition of grapes in the laboratory, the vineyard and before or during harvest, to provide better insights of the chemistry, nutrition and physiology of grapes. Faster computers, the development of software and portable easy to use spectrophotometers and data analytical methods allow for the development of innovative applications of these techniques for the analyses of grape composition.

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

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033517726724 ◽

2017 ◽

Vol 25 (5) ◽

pp. 330-337 ◽

Cited By ~ 5

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.

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Using NIR and ATR-FTIR spectroscopy to rapidly detect compression wood inPinus radiata

Canadian Journal of Forest Research ◽

10.1139/cjfr-2013-0329 ◽

2014 ◽

Vol 44 (7) ◽

pp. 820-830 ◽

Cited By ~ 14

Author(s):

J. Paul McLean ◽

Guangwu Jin ◽

Maree Brennan ◽

Michél K. Nieuwoudt ◽

Philip J. Harris

Keyword(s):

Chemical Composition ◽

Compression Wood ◽

Near Infrared ◽

Water Saturation ◽

Nir Spectroscopy ◽

Analytical Techniques ◽

Gradual Change ◽

Least Squares Regression ◽

Dimensional Changes ◽

Discriminatory Analysis

Compression wood has undesirable properties for structural timber and for paper production. Traditional methods of detecting it are often time consuming and subjective. This study aimed to rapidly and impartially detect compression wood through the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and diffuse reflectance near infrared (NIR) spectroscopy. Compression wood and non-compression wood samples were obtained from young Pinus radiata D. Don trees grown in New Zealand. Longitudinal dimensional changes were measured during drying or water saturation of the samples; lignin and galactose contents were determined using conventional analytical techniques. Chemical composition was here a more reliable discriminator between wood types than longitudinal dimensional changes. It was shown that partial least-squares regression (PLS-R) or discriminatory analysis (PLS-DA) could be used to build models on the training samples that could discriminate between wood types of the independently grown validation samples. Ultimately, both types of spectroscopies could be used to discriminate between compression wood and non-compression wood either through prediction or discriminatory analysis with equal success. Investigation into spectral differences between wood types, including sequential mixtures of wood types, showed that for the mid-IR region absorbance at a well-resolved lignin band could be used to discriminate compression wood from non-compression wood. For NIR, a similar investigation showed that absorbance values at four separate wavenumbers or the 6000–5600 cm−1region of the first derivative spectra were required for that discrimination. It is proposed that if there is a gradual change in the chemical composition of compression wood with its severity, then IR spectroscopy could feasibly be used to rapidly determine compression wood severity.

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Review: NIR Spectroscopy as a Suitable Tool for the Investigation of the Horticultural Field

Agronomy ◽

10.3390/agronomy9090503 ◽

2019 ◽

Vol 9 (9) ◽

pp. 503 ◽

Cited By ~ 7

Author(s):

Cattaneo ◽

Stellari

Keyword(s):

Chemical Composition ◽

Process Control ◽

Water Absorption ◽

Near Infrared ◽

Raw Materials ◽

Nir Spectroscopy ◽

Innovative Approach ◽

Different Types ◽

Key Issues ◽

Horticultural Products

The last 10 years of knowledge on near infrared (NIR) applications in the horticultural field are summarized. NIR spectroscopy is considered one of the most suitable technologies of investigation worldwide used as a nondestructive approach to monitoring raw materials and products in several fields. There are different types of approaches that can be employed for the study of key issues for horticultural products. In this paper, an update of the information collected from the main specific International Journals and Symposia was reported. Many papers showed the use of NIR spectroscopy in the horticultural field, and the literature data were grouped per year, per product, and per application, such as studies of direct (chemical composition) and indirect (physical and sensorial) properties (P), process control (PC), and authenticity and classification studies (AC). A mention was made of a recent innovative approach that considers the contribution of water absorption in the study of biological systems.

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Quantitative reflectance spectroscopy as an alternative to traditional wet lab analysis of foliar chemistry: near-infrared and mid-infrared calibrations compared

Canadian Journal of Forest Research ◽

10.1139/x05-037 ◽

2005 ◽

Vol 35 (5) ◽

pp. 1122-1130 ◽

Cited By ~ 28

Author(s):

Andrew D Richardson ◽

James B Reeves III

Keyword(s):

Chemical Composition ◽

Near Infrared ◽

Nir Spectroscopy ◽

Predictive Ability ◽

Reflectance Spectroscopy ◽

Mid Infrared ◽

Mir Spectroscopy ◽

Wet Lab ◽

The Difference ◽

Primitive State

Quantitative reflectance spectroscopy offers an alternative to traditional analytical methods for the determination of the chemical composition of a sample. The objective of this study was to develop a set of spectroscopic calibrations to determine the chemical composition (nutrients, carbon, and fiber constituents, determined using standard wet lab methods) of dried conifer foliage samples (N = 72), and to compare the predictive ability of calibrations based on three different spectral regions: visible and shortwave near infrared (VISsNIR, 400- to 1100-nm wavelengths), near infrared (NIR, 1100- to 2500-nm wavelengths), and mid infrared (MIR, 2500- to 25 000-nm wavelengths). To date, most quantitative reflectance spectroscopy has been based on the VISsNIRNIR, and the ability of MIR calibrations to predict the composition of tree foliage has not been tested. VISsNIR calibrations were clearly inferior to those based on longer wavelengths. For 8 of 11 analytes, the MIR calibrations had the lowest standard error of cross-validation, but in most cases the difference in accuracy between NIR and MIR calibrations was small, and against an independent validation set, there was no clear evidence that either spectral region was superior. Although quantitative MIR spectroscopy is at a more primitive state of development than NIR spectroscopy, these results demonstrate that the mid infrared has considerable promise for quantitative analytical work.

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A fully convolutional neural network-based regression approach for effective chemical composition analysis using near-infrared spectroscopy in cloud

10.37965/jait.2020.0037 ◽

2021 ◽

Author(s):

Daiyu Jiang ◽

Gang Hu ◽

Guanqiu Qi ◽

Neal Mazur

Keyword(s):

Quantitative Analysis ◽

Chemical Composition ◽

Near Infrared ◽

Tobacco Industry ◽

Nir Spectroscopy ◽

Composition Analysis ◽

Convolutional Network ◽

Tobacco Leaves ◽

Chemical Composition Analysis ◽

Complex Features

As one chemical composition, nicotine content has an important influence on the quality of tobacco leaves. Rapid and non-destructive quantitative analysis of nicotine is an important task in the tobacco industry. Near-infrared (NIR) spectroscopy as an effective chemical-composition analysis technique has been widely used. In this paper, we propose a one-dimensional Fully Convolutional Network (1D-FCN) model to quantitatively analyze the nicotine composition of tobacco leaves using NIRspectroscopy data in a cloud environment. This 1D-FCN model uses one-dimension convolution layers to directly extract the complex features from sequential spectroscopy data. It consists of five convolutional layers and two full connection layers with the max-pooling layer replaced by a convolutional layer to avoid information loss.Cloud computing techniques are used to solve the increasing requests of large-size data analysis and implement data sharing and accessing.Experimental results show that the proposed 1D-FCN model can effectively extract the complex characteristics inside the spectrum and more accurately predict the nicotine volumes in tobacco leaves than other approaches. This research provides a deep learning foundation for quantitative analysis of NIR spectra data in the tobacco industry.

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Near Infrared (NIR) Spectroscopy to Predict Physical Properties of Acacia mangium at Three Different Age Classes

Wood Research Journal ◽

10.51850/wrj.2013.4.1.7-12 ◽

2017 ◽

Vol 4 (1) ◽

pp. 7-12

Author(s):

Lina Karlinasari ◽

Merry Sabed

Keyword(s):

Moisture Content ◽

Physical Properties ◽

Wood Density ◽

Near Infrared ◽

Acacia Mangium ◽

Nir Spectroscopy ◽

Solid Wood ◽

Partial Least Square ◽

Least Square ◽

Calibration Model

Near Infrared (NIR) spectroscopy has been used to predict several properties of wood. This is one of the nondestructive testing (NDT) methods providing fast and reliable wood characterization analysis which can be applied in various manufacture industry, included forest sector, in control and process monitoring task. Moisture content and wood density are important properties related to strength properties. The aim of this study was to evaluate NIR technique in obtaining calibration models for determining moisture content and wood density of Acacia mangium in the age of 5, 6, 7 years-old. Spectra were measured in both solid and ground wood samples. Laboratory testing of physical properties were determined by volumetric and gravimetric methods. The laboratory values were correlated with the NIR spectra using multivariate analysis statistic of Partial Least Square (PLS). The calibration-validation model of this relationship was evaluated by using the coefficient of determination (R2), root means square error of calibration (RMSEC) and cross-validation (RMSECV) values. Generally, a better accuracy was obtained by using calibration model of ground wood compared to that of solid wood samples. At age of 7 years-old, the R2 allowed the use of NIR spectra of solid samples to develop calibration and validation model, especially for wood density. Based on ratio of performance to deviation (RPD) and RMSE, ground samples demonstrated a higher value of RPD, RMSEC, and RMSECV compared to solid wood for all properties.

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