The use of near infrared spectroscopy to predict foliar nutrient levels of hydroponically grown teak seedlings

2021 ◽  
pp. 096703352110256
Author(s):  
William Andrew Whittier ◽  
Gary R Hodge ◽  
Juan Lopez ◽  
Carole Saravitz ◽  
Juan Jose Acosta
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Natural Populations ◽  
Nir Spectroscopy ◽  
Tectona Grandis ◽  
Nutrient Status ◽  
Timber Species ◽  
Destructive Sampling ◽  
Irreversible Damage ◽  
Foliar Nutrient

Due to a combination of durability, strength, and aesthetically pleasing color, teak ( Tectona grandis L.f.) is globally regarded as a premier timber species. High value, in combination with comprehensive harvesting restrictions from natural populations, has resulted in extensive teak plantation establishment throughout the tropics and subtropics. Plantations directly depend on the production of healthy seedlings. In order to assist growers in efficiently diagnosing teak seedling nutrient issues, a hydroponic nutrient study was conducted at North Carolina State University. The ability to accurately diagnose nutrient disorders prior to the onset of visual symptoms through the use of near infrared (NIR) technology will allow growers to potentially remedy seedling issues before irreversible damage is done. This research utilized two different near infrared (NIR) spectrometers to develop predictive foliar nutrient models for 13 nutrients and then compared the accuracy of the models between the devices. Destructive leaf sampling and laboratory grade NIR spectroscopy scanning was compared to nondestructive sampling coupled with a handheld NIR device used in a greenhouse. Using traditional wet lab foliar analysis results for calibration, nutrient prediction models for nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), sulfur (S), copper (Cu), molybdenum (Mo), magnesium (Mg), boron (B), calcium (Ca), manganese (Mn), iron (Fe), sodium (Na), and zinc (Z) were developed using both NIR devices. Models developed using both techniques were good for N, P, and K (R2 > 0.80), while the B model was adequate only with the destructive sampling procedure. Models for the remaining nutrients were not suitable. Although destructive sampling and desktop scanning procedure generally produced models with higher correlations they required work and time for sample preparation that might reduce the value of this NIR approach. The results suggest that both destructive and nondestructive sampling NIR calibrations can be useful to monitor macro nutrient status of teak plants grown in a nursery environment.

Near infrared spectroscopy of Eucalyptus pellita for foliar nutrients and the potential for real-time monitoring of trees in fertiliser trial plots

2021 ◽  
pp. 096703352110079
Author(s):  
Agustan Alwi ◽  
Roger Meder ◽  
Yani Japarudin ◽  
Hazandy A Hamid ◽  
Ruzana Sanusi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Acacia Mangium ◽  
Nir Spectroscopy ◽  
Nutrient Status ◽  
Significant Loss ◽  
Eucalyptus Pellita ◽  
Operational Decision Making ◽  
Fertiliser Application ◽  
Planting Distance

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.

Non-destructive estimation of fibre morphological parameters and chemical constituents of Tectona grandis L.f. wood by near infrared spectroscopy

2021 ◽  
pp. 096703352199911
Author(s):  
SR Shukla ◽  
S Shashikala ◽  
M Sujatha
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Chemical Constituents ◽  
Fibre Diameter ◽  
Tectona Grandis ◽  
Cost Effective ◽  
Wood Products ◽  
Timber Species ◽  
Morphological Parameters ◽  
Mean Square Errors

Near infrared (NIR) spectroscopy is developing as an advanced and non-invasive tool in the wood, wood products and forestry sectors. It may be applied as a rapid and cost effective technique for assessment of different wood quality parameters of timber species. In the present study, NIR spectra of heartwood samples of Tectona grandis (teak) were collected before measuring fibre morphological parameters (fibre length, fibre diameter and fibre lumen diameter)and main chemical constituents (cellulose, hemicellulose, lignin and extractives) using maceration and wet chemistry methods respectively. Multivariate partial least squares (PLS) regression was applied to develop the calibration models between measured values of wood parameters and NIR spectral data. Pre-processing of NIR spectra demonstrated better predictions based on higher values of correlation coefficient for estimation (R2), validation (Rcv 2 ), ratio of performance to deviation (RPD), and lower values of root mean square errors of estimation (RMSEE), cross-validation (RMSECV) and number of latent variable (rank). Internal cross-validation was used to find the optimum rank. Robust calibrations models with high R2 (>0.87), low errors and high RPD values (> 2.93) were observed from PLS analysis for fibre morphological parameters and main chemical constituents of teak. These linear models may be applied for rapid and cost effective estimation of different fibre parameters and chemical constituents in routine testing and evaluation procedures for teak.

scholarly journals Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Sylvio Barbon ◽  
Ana Paula Ayub da Costa Barbon ◽  
Rafael Gomes Mantovani ◽  
Douglas Fernandes Barbin
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Chicken Meat ◽  
Poultry Meat ◽  
Spectral Information ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Meat Samples

Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIEL∗a∗b∗, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with 77.2% precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.

Estimation of gelatin content in historical papers using NIR spectroscopy

2011 ◽  
Author(s):  
M Ormsby ◽  
T Barrett ◽  
J. B. Lang ◽  
J. Mazurek ◽  
M. Schilling
Keyword(s):  
Gas Chromatography ◽  
Amino Acid ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Destructive Sampling ◽  
Percentage Points ◽  
Gelatin Concentration ◽  
The Difference ◽  
Gas Chromatography Mass Spectroscopy ◽  
Non Destructive

<p>Gelatin sizing was a key ingredient during the handpapermaking era. The gelatin concentration in historical papers has never been well documented, however, because measuring the gelatin content required destructive sampling. In this project we developed a non-destructive method using near infrared (NIR) spectroscopy. Gelatin concentrations of 40 historical papers from the 15<sup>th</sup>-18<sup>th</sup> centuries were determined from amino acid (AA) concentrations by using gas chromatography/mass spectroscopy. These values were combined with NIR spectra from the same papers to generate a model for predicting concentrations of unknowns. If a NIR measurement predicted a gelatin concentration in the range 0-6 percent then there is a 95% probability that the difference between the NIR model value and a destructive AA measurement would be between -1.6 and +1.3 percentage points. For 6-8 percent there is a 95% probability the difference would be between -2.0 and +1.5 percentage points, and for 8-12 percent the difference is between -3.0 and +2.0 percentage points. In a study of 159 specimens from books, loose leaves, and artworks printed from 1460-1791, the means for all papers were quite high in the 15<sup>th</sup> century and dropped an average of 20% every 50 years. Possible explanations for the decline are offered.</p>

Classification of Automobile Lubricant by Near-Infrared Spectroscopy Combined with Machine Classification

2011 ◽  
Vol 460-461 ◽  
pp. 667-672
Author(s):  
Yun Zhao ◽  
Xing Xu ◽  
Yong He
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Svm Model

The main objective of this paper is to classify four kinds of automobile lubricant by near-infrared (NIR) spectral technology and to observe whether NIR spectroscopy could be used for predicting water content. Principle component analysis (PCA) was applied to reduce the information from the spectral data and first two PCs were used to cluster the samples. Partial least square (PLS), least square support vector machine (LS-SVM), and Gaussian processes classification (GPC) were employed to develop prediction models. There were 120 samples for training set and test set. Two LS-SVM models with first five PCs and first six PCs were built, respectively, and accuracy of the model with five PCs is adequate with less calculation. The results from the experiment indicate that the LS-SVM model outperforms the PLS model and GPC model outperforms the LS-SVM model.

Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef

2021 ◽  
pp. 096703352110495
Author(s):  
Cassius EO Coombs ◽  
Robert R Liddle ◽  
Luciano A González
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Consumer Confidence ◽  
Spectroscopic Methods ◽  
Marbling Score ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Beef Steak

The present study analysed the ability for portable near infrared reflectance (NIR) and Raman spectroscopy sensors to differentiate between grass-fed and grain-fed beef. Scans were made on lean and fat surfaces of 108 beef steak samples labelled as grass-fed ( n = 54) and grain-fed ( n = 54), with partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) used to develop discrimination models which were tested on independent datasets. Furthermore, PLS-DA was used to predict visual marbling score and days on feed (DOF). The NIR spectra accurately discriminated between grass- and grain-fed beef on both fat (91.7%, n = 92) and lean (88.5%, n = 96), as did Raman (fat 95.2%, n = 82; lean 69.6%, n = 68). Fat scanning using NIR spectroscopy moderately predicted DOF (r2val = 0.53), though Raman and NIR spectroscopy lean prediction models for DOF and marbling were less precise (r2val < 0.50). It can be concluded that portable NIR and Raman spectrometers can be used successfully to differentiate grass-fed from grain-fed beef and therefore aid retail and consumer confidence.

scholarly journals Rapid Quality Control of Woodchip Parameters Using a Hand-Held Near Infrared Spectrophotometer

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1413
Author(s):  
Elena Leoni ◽  
Manuela Mancini ◽  
Daniele Duca ◽  
Giuseppe Toscano
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Calorific Value ◽  
Industrial Applications ◽  
Suitable Alternative ◽  
Real Time Analysis ◽  
Solid Biofuels ◽  
Solid Biofuel ◽  
Infrared Spectrophotometer

Near infrared spectroscopy is a non-invasive and rapid technique to support the analysis of solid biofuels such as woodchip, which is considered as a suitable alternative for energy production, according to European goals for fossil fuel reduction. Chemical and physical properties of the woodchip influence combustion performance, so the most discriminant parameters such as moisture and ash content and gross calorific value were constantly monitored. The aim of this study was the development of prediction models for these three parameters with the use of a hand-held NIR spectrometer. Laboratory analyses were carried out to evaluate the quality of several Italian samples from a power plant, and PLS regression models were developed to test prediction accuracy. Moreover, the most relevant wavelengths were investigated to discriminate chemical compounds influence. Prediction models demonstrated the capacity of handheld MicroNIR instrument to be considered a practical tool for solid biofuel quality assessment. As a consequence, NIR spectroscopy improved real-time analysis and made it suitable for practical and industrial applications, as supported by the recent Italian standard UNI/TS 11765.

scholarly journals The overall precision test for near infrared scanning and reference method for the determination of soluble solids content and pH of mango for processing factory

2020 ◽  
Vol 187 ◽  
pp. 04003
Author(s):  
Nphatsanan Saksangium ◽  
Panmanas Sirisomboon
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Reference Test ◽  
Infrared Scanning ◽  
Solids Content ◽  
Precision Test

Near infrared (NIR) spectroscopy is a rapid technique for nondestructive testing. Mango is popular fruit in Thailand. Therefore, The main aim of this paper is to report an overall precision of the NIR spectroscopy instruments and reference methods for determination at the beginning of the experiment for prediction models development to be in the mango applied processing factory.. Results showed that the repeatability of FT-NIR spectrometer and UV-VIS-NIR spectrometer were 0.00191 and 0.00529, respectively. The reproducibility of FT-NIR spectrometer and UV-VIS-NIR spectrometer were 0.00323 and 0.03561, respectively. Repeatability of reference test of TSS and pH were 0.1657 and 0.0827. Therefore, the R2max of TSS and pH were 0.9825 and 0.9504 which indicates that it is possible to develop NIR model for prediction of total soluble solids and pH.

Improvement of Prediction Models for Nondestructive Detection of TVB-N Using Dual-Band Vis/NIR Spectroscopic Technique

2017 ◽  
Vol 60 (4) ◽  
pp. 1075-1082 ◽  
Author(s):  
Wenxiu Wang ◽  
Yankun Peng
Keyword(s):  
Light Source ◽  
Spectral Region ◽  
Near Infrared ◽  
Prediction Models ◽  
Great Influence ◽  
Nir Spectroscopy ◽  
Dual Band ◽  
Coefficient Of Determination ◽  
Band Selection ◽  
Nondestructive Detection

Abstract. This article discusses the influence of light source and band selection on prediction model performance. Two spectra acquisition systems for visible (Vis) and near-infrared (NIR) spectroscopy with a ring light source and a point light source were set up and compared based on the coefficient of variation (CV), signal-to-noise ratio (SNR), spectrum area change rate (ACR), and model results. Reflectance spectra of 61 pork samples were collected, and anomalous samples were eliminated by Monte Carlo method based on model cluster analysis. Partial least squares (PLS) models for total volatile basic nitrogen (TVB-N) based on a single spectral region (350-1100 nm or 1000-2500 nm) and a dual spectral region (350-2500 nm) were built to compare the influence of band choice. Based on the optimal chosen band, characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS), and a new PLS model was established. The results showed that spectra acquired with the ring light source had better stability and achieved optimal prediction models. The dual spectral region, which contained more comprehensive information on TVB-N, yielded better results than any single spectral region. Based on the dual-band spectra, a simplified PLS model using feature variables achieved a coefficient of determination in the prediction set (Rp2) of 0.8767 and standard error of prediction (SEP) of 2.8354 mg per 100 g. The results demonstrated that the choice of light source and modeling band had great influence on prediction results, and improvement of models would promote the application of Vis/NIR spectroscopy in on-line or portable detection. Keywords: Band selection, Light source, Nondestructive detection, Pork, TVB-N, Vis/NIR spectroscopy.

scholarly journals A high-throughput quantification of resin and rubber contents in Parthenium argentatum using near-infrared (NIR) spectroscopy

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Zinan Luo ◽  
Kelly R. Thorp ◽  
Hussein Abdel-Haleem
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Full Range ◽  
Germplasm Collection ◽  
Nir Spectroscopy ◽  
The United States ◽  
Screening Tools ◽  
Rapid Screening ◽  
Parthenium Argentatum ◽  
Better Than

Abstract Background Guayule (Parthenium argentatum A. Gray), a plant native to semi-arid regions of northern Mexico and southern Texas in the United States, is an alternative source for natural rubber (NR). Rapid screening tools are needed to replace the current labor-intensive and cost-inefficient method for quantifying rubber and resin contents. Near-infrared (NIR) spectroscopy is a promising technique that simplifies and speeds up the quantification procedure without losing precision. In this study, two spectral instruments were used to rapidly quantify resin and rubber contents in 315 ground samples harvested from a guayule germplasm collection grown under different irrigation conditions at Maricopa, AZ. The effects of eight different pretreatment approaches on improving prediction models using partial least squares regression (PLSR) were investigated and compared. Important characteristic wavelengths that contribute to prominent absorbance peaks were identified. Results Using two different NIR devices, ASD FieldSpec®3 performed better than Polychromix Phazir™ in improving R2 and residual predicative deviation (RPD) values of PLSR models. Compared to the models based on full-range spectra (750–2500 nm), using a subset of wavelengths (1100–2400 nm) with high sensitivity to guayule rubber and resin contents could lead to better prediction accuracy. The prediction power of the models for quantifying resin content was better than rubber content. Conclusions In summary, the calibrated PLSR models for resin and rubber contents were successfully developed for a diverse guayule germplasm collection and were applied to roughly screen samples in a low-cost and efficient way. This improved efficiency could enable breeders to rapidly screen large guayule populations to identify cultivars that are high in rubber and resin contents.

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