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1
Bray, J. G. P., R. Viscarra Rossel, and A. B. McBratney. "Diagnostic screening of urban soil contaminants using diffuse reflectance spectroscopy." Soil Research 47, no. 4 (2009): 433. http://dx.doi.org/10.1071/sr08068.
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There is increasing demand for cheap and rapid screening tests for soil contaminants in environmental consultancies. Diffuse reflectance spectroscopy (DRS) in the visible-near infrared (vis-NIR) and mid infrared (MIR) has the potential to meet this demand. The aims of this paper were to develop diagnostic screening tests for heavy metals and polycyclic aromatic hydrocarbons (PAH) in soil using vis-NIR and MIR DRS. Cadmium, copper, lead, and zinc were analysed, as were total PAH and benzo[a]pyrene. An ordinal logistic regression technique was used for the screening and predictions of either contaminated or uncontaminated soil at different thresholds. We calculated the rates of false positive and false negative predictions and derived Receiver Operating Characteristic curves to explore how the choice of a threshold affects their proportion. Zinc and copper had the best prediction accuracies of the heavy metals, with 89% and 85%, respectively. Cadmium and lead had the lowest prediction accuracies, with 68% and 67%, respectively. PAH predictions averaged 78.9%. With an average prediction accuracy of 79.9%, MIR analysis was only slightly more accurate than vis-NIR analysis, which had an average prediction accuracy of 77.5%. However, vis-NIR may be used in situ, thereby reducing cost and time of analysis and providing diagnosis in ‘real-time’. DRS in the vis-NIR can substantially decrease both the time and cost associated with screening for soil contaminants.
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VALDES, E. V., L. G. YOUNG, I. McMILLAN, and J. E. WINCH. "ANALYSIS OF HAY, HAYLAGE AND CORN SILAGE SAMPLES BY NEAR INFRARED REFLECTANCE SPECTROSCOPY." Canadian Journal of Animal Science 65, no. 3 (1985): 753–60. http://dx.doi.org/10.4141/cjas85-088.
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Separate calibrations for hay, haylage and corn silage were developed to predict chemical composition by near infrared reflectance spectroscopy (NIR). A scanning type of NIR instrument was used to select the best set of wavelengths (λ) while a filter type was used to evaluate the calibrations. Reflectance (R) was recorded as log (1/R). Bias (nonrandom error) was corrected for each set of samples before the NIR analysis. Percent crude protein (CP), acid detergent fiber (ADF), calcium (Ca) and phosphorus (P) were studied in the hay samples. In addition, potassium (K) and magnesium (Mg) were included for the haylage and corn silage samples. Six hundred samples, including calibration (C) and prediction sets (PRE1 and PRE2) were used. PRE1 samples came from the same population as the C samples, whereas PRE2 samples were obtained in a different year. Accuracy of the predictions was assessed by the coefficients of determination (r2), standard error of the estimate (SEE), and coefficients of variation (CV). Crude protein was the parameter best predicted by NIR with r2, SEE and CV ranging from 0.72 to 0.96, 0.43 to 1.17 and 5.6 to 10.4, respectively. The highest SEE for crude protein were associated with the PRE2 samples for haylage and hay samples (1.09 and 1.17), respectively. NIR predictions of ADF had r2, SEE and CV values ranging from 0.21 to 0.92, 1.44 to 2.53 and 5.3% to 7.9%, respectively. Corn silage had the lowest SEE for ADF in both C and PRE1 sets. Predicting mineral contents by NIR gave high CV (10.5%–34.5%) and low r2 values (0.02–0.75). Calcium predictions had the highest variability, and P and Mg predictions the lowest.These results indicate that CP was successfully predicted by NIR. The higher SEE values for ADF may have been due to variation in the wet chemistry values of some samples. Minerals were not adequately predicted by NIR as assessed by r2, SEE and CV values. Key words: Near infrared reflectance spectroscopy, forage, chemical analysis
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Parent, Elizabeth Jeanne, Serge-Étienne Parent, and Léon Etienne Parent. "Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling." PLOS ONE 16, no. 7 (2021): e0233242. http://dx.doi.org/10.1371/journal.pone.0233242.
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Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transformation to avoid numerical biases. Machine learning can relate numerous independent variables that may impact on NIR spectra to assess particle-size distribution. Our objective was to reach high IRS prediction accuracy across a large range of PSD methods and soil properties. A total of 1298 soil samples from eastern Canada were IR-scanned. Spectra were processed by Stochastic Gradient Boosting (SGB) to predict sand, silt, clay and carbon. Slope and intercept of the log-log relationships between settling time and suspension density function (SDF) (R2 = 0.84–0.92) performed similarly to NIR spectra using either ilr-transformed (R2 = 0.81–0.93) or raw percentages (R2 = 0.76–0.94). Settling times of 0.67-min and 2-h were the most accurate for NIR predictions (R2 = 0.49–0.79). The NIR prediction of sand sieving method (R2 = 0.66) was more accurate than sedimentation method(R2 = 0.53). The NIR 2X gain was less accurate (R2 = 0.69–0.92) than 4X (R2 = 0.87–0.95). The MIR (R2 = 0.45–0.80) performed better than NIR (R2 = 0.40–0.71) spectra. Adding soil carbon, reconstituted bulk density, pH, red-green-blue color, oxalate and Mehlich3 extracts returned R2 value of 0.86–0.91 for texture prediction. In addition to slope and intercept of the SDF, 4X gain, method and pre-treatment classes, soil carbon and color appeared to be promising features for routine SGB-processed NIR particle-size analysis. Machine learning methods support cost-effective soil texture NIR analysis.
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Pozza, L. E., T. F. A. Bishop, U. Stockmann, and G. F. Birch. "Integration of vis-NIR and pXRF spectroscopy for rapid measurement of soil lead concentrations." Soil Research 58, no. 3 (2020): 247. http://dx.doi.org/10.1071/sr19174.
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Heavy metals accumulate in soil over time and, with changing land use, humans may be exposed to elevated contaminant concentrations. It is therefore important to delineate contaminated sites in the most efficient and accurate manner. Sensors, such as portable X-ray fluorescence (pXRF) and visible near-infrared (vis-NIR) spectroscopy predict metal concentrations more rapidly and in a less hazardous manner compared to traditional laboratory analytical methods. The current study explored the potential for integrating vis-NIR and pXRF outputs to improve lead predictions in fine- (<62.5 µm) and whole-fraction (<2 mm) soil samples. A multi-stage approach was taken to compare different data treatments and combination methods for the prediction of whole-fraction lead content. Data treatment included principal component analysis, and combination methods included concatenation of pXRF and vis-NIR spectra before modelling, and Granger–Ramanathan model averaging of pXRF and vis-NIR model outputs. The most accurate predictions of whole-fraction lead were obtained by Granger–Ramanathan model averaging of vis-NIR Cubist predictions and Compton-normalised pXRF output: Lin’s Concordance Correlation Coefficient (LCCC) = 0.95, root mean square error (RMSE) = 86.4 mg kg–1, Bias < 0.001 mg kg–1 and ratio of performance to inter-quartile range (RPIQ) = 0.37. The most suitable modelling method was then used to predict fine-fraction lead, which provided a similarly accurate model fit (LCCC = 0.94, RMSE = 84.2 mg kg–1, Bias < 0.001 mg kg–1 and RPIQ = 0.34), indicating the potential to reduce the number of samples required for fine-fraction processing. In addition, the quality of the prediction interval estimates was examined – an important aspect in modelling which is underutilised in current literature related to soil spectroscopy.
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Yang, Meihua, Songchao Chen, Xi Guo, Zhou Shi, and Xiaomin Zhao. "Exploring the Potential of vis-NIR Spectroscopy as a Covariate in Soil Organic Matter Mapping." Remote Sensing 15, no. 6 (2023): 1617. http://dx.doi.org/10.3390/rs15061617.
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Robust soil organic matter (SOM) mapping is required by farms, but their generation requires a large number of samples to be chemically analyzed, which is cost prohibitive. Recently, research has shown that visible and near-infrared (vis-NIR) reflectance spectroscopy is a fast and accurate technique for estimating SOM in a cost-effective manner. However, few studies have focused on using vis-NIR spectroscopy as a covariate to improve the accuracy of spatial modeling. In this study, our objective was to compare the mapping accuracy from a spatial model using kriging methods with and without the covariate of vis-NIR spectroscopy. We split the 261 samples into a calibration set (104) for building the spectral predictive model, a test set for generating the vis-NIR augmented set from the prediction of the fitted spectral predictive model (131), and a validation set (26) for evaluating map accuracy. We used two datasets (235 samples) for Kriging: a laboratory-based dataset (Ld, observations from calibration and test datasets) and a laboratory-based dataset with vis-NIR augmented predictions (Au.p, observations from calibration and predictions from test dataset), a laboratory-based dataset with vis-NIR spectra as the covariance (Ld.co) and augmented dataset with predictions using vis-NIR with vis-NIR spectra for the covariance (Au.p.co). The first one to seven accumulated principal components of vis-NIR spectra were used as the covariates when we used the measurement of Ld.co and Au.p.co. The map accuracy was evaluated by the validation set for the four datasets using Kriging. The results indicated that adding vis-NIR spectra as covariates had great potential in improving the map accuracy using kriging, and much higher accuracies were observed for Ld.p.co (RMSE of 5.51 g kg−1) and Au.p.co (RMSE of 5.66 g kg−1) than without using vis-NIR spectra as covariates for Ld (RMSE of 7.12 g kg−1) and Au.p (RMSE of 7.69 g kg−1). With a similar model performance to Ld.p.co, Au.p.co can reduce the cost of laboratory analysis for 60% of soil samples, demonstrating its advantage in cost-efficiency for spatial modeling of soil information. Therefore, we conclude that vis-NIR spectra can be used as a cost-effective technique to obtain augmented data to improve fine-resolution spatial mapping of soil information.
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Xu, Qinghua, Menghua Qin, Yonghao Ni, Maurice Defo, Barbara Dalpke, and Gail Sherson. "Predictions of wood density and module of elasticity of balsam fir (Abies balsamea) and black spruce (Picea mariana) from near infrared spectral analyses." Canadian Journal of Forest Research 41, no. 2 (2011): 352–58. http://dx.doi.org/10.1139/x10-215.
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The predictions of properties for wood disc average are seldom reported, and they are important for sorting out logs based on their quality. The minimum near infrared (NIR) spectra required to predict wood disc average properties would also be of critical importance. In this study, calibration and prediction models for wood disc average properties were developed using NIR spectral data for balsam fir (Abies balsamea (L.) Mill.) and black spruce (Picea mariana (Mill.) B.S.P.) samples collected from 14 different sites across Newfoundland, Canada. The calibration was done against area-weighted average wood properties determined by SilviScan. NIR spectra were collected in 18 mm increments from the radial–longitudinal face of green and oven-dried samples. Results showed that using NIR spectra from three spots per wood strip was sufficient for the modeling and prediction for density and module of elasticity (MOE). The coefficients of determination ranged from 0.76 (MOE of green wood samples) to 0.88 (density of oven-dried wood samples). However, the microfibril angle (MFA) cannot be well predicted from either green wood or oven-dried wood NIR spectra. Our results further showed that the NIR spectra collected from oven-dried wood samples gave better calibration and prediction than those collected from green wood samples.
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Maudoux, Marc, Shou He Yan, and Sonia Collin. "Quantitative Analysis of Alcohol, Real Extract, Original Gravity, Nitrogen and Polyphenols in Beers Using NIR Spectroscopy." Journal of Near Infrared Spectroscopy 6, A (1998): A363—A366. http://dx.doi.org/10.1255/jnirs.225.
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This study was to develop a rapid and accurate NIR analysis method for determinations of alcohol, real extract, original gravity, total nitrogen and total polyphenols. Commercial European beers (110 samples) were used to create calibration models between EBC (European Brewing Committee) and NIR spectral data. The optimal correlation coefficients ( r) were 0.94 to 0.98 and the corresponding CV% (coefficients of validation variation) were 4.29, 6.53, 4.50, 6.06 and 4.74 for NIR predictions of alcohol, real extract, original gravity, nitrogen and polyphenols, respectively. The stepwise MLR calibration proved to be a good choice for measurements of alcohol and original gravity, while PLS regression models seem to be better for the predictions of the real extract, nitrogen and polyphenols. Comparisons of results from MLR and PLS, demonstrate that MLR methods (log 1/ R) are better than those of PLS (log 1/ R) in calibration and prediction sets. The reflection mode is better than those of transmission in all above cases.
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Feng, Xiaoyu, Rebecca A. Larson, and Matthew F. Digman. "Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure Nutrients." Remote Sensing 14, no. 4 (2022): 963. http://dx.doi.org/10.3390/rs14040963.
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Livestock manure is widely applied onto agriculture soil to fertilize crops and increase soil fertility. However, it is difficult to provide real-time manure nutrient data based on traditional lab analyses during application. Manure sensing using near-infrared (NIR) spectroscopy is an innovative, rapid, and cost-effective technique for inline analysis of animal manure. This study investigated a NIR sensing system with reflectance and transflectance modes to predict N speciation in dairy cow manure using a spiking method. In this study, 20 dairy cow manure samples were collected and spiked to achieve four levels of ammoniacal nitrogen (NH4-N) and organic nitrogen (Org-N) concentrations that resulted in 100 samples in each spiking group. All samples were scanned and analyzed using a NIR system with reflectance and transflectance sensor configurations. NIR calibration models were developed using partial least square regression analysis for NH4-N, Org-N, total solid (TS), ash, and particle size (PS). Coefficient of determination (R2) and root mean square error (RMSE) were selected to evaluate the models. A transflectance probe with a 1 mm path length had the best performance for analyzing manure constituents among three path lengths. Reflectance mode improved the calibration accuracy for NH4-N and Org-N, whereas transflectance mode improved the model predictability for TS, ash, and PS. Reflectance provided good prediction for NH4-N (R2 = 0.83; RMSE = 0.65 mg mL−1) and approximate predictions for Org-N (R2 = 0.66; RMSE = 1.18 mg mL−1). Transflectance was excellent for TS predictions (R2 = 0.97), and provided good quantitative predictions for ash and approximate predictions for PS. The correlations between the accuracy of NH4-N and Org-N calibration models and other manure parameters were not observed indicating the predictions of N contents were not affected by TS, ash, and PS.
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Mammadov, Elton, Michael Denk, Amrakh I. Mamedov, and Cornelia Glaesser. "Predicting Soil Properties for Agricultural Land in the Caucasus Mountains Using Mid-Infrared Spectroscopy." Land 13, no. 2 (2024): 154. http://dx.doi.org/10.3390/land13020154.
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Visible-near infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy are increasingly being used for the fast determination of soil properties. The aim of this study was (i) to test the use of MIR spectra (Agilent 4300 FTIR Handheld spectrometer) for the prediction of soil properties and (ii) to compare the prediction performances of MIR spectra and Vis-NIR (ASD FieldSpecPro) spectra; the Vis-NIR data were adopted from a previous study. Both the MIR and Vis-NIR spectra were coupled with partial least squares regression, different pre-processing techniques, and the same 114 soil samples, collected from the agricultural land located between boreal forests and semi-arid steppe belts (Kastanozems). The prediction accuracy (R2 = 0.70–0.99) of both techniques was similar for most of the soil properties assessed. However, (i) the MIR spectra were superior for estimating CaCO3, pH, SOC, sand, Ca, Mg, Cd, Fe, Mn, and Pb. (ii) The Vis-NIR spectra provided better results for silt, clay, and K, and (iii) the hygroscopic water content, Cu, P, and Zn were poorly predicted by both methods. The importance of the applied pre-processing techniques was evident, and among others, the first derivative spectra produced more reliable predictions for 11 of the 17 soil properties analyzed. The spectrally active CaCO3 had a dominant contribution in the MIR predictions of spectrally inactive soil properties, followed by SOC and Fe, whereas particle sizes and hygroscopic water content appeared as confounding factors. The estimation of spectrally inactive soil properties was carried out by considering their secondary correlation with carbonates, clay minerals, and organic matter. The soil information covered by the MIR spectra was more meaningful than that covered by the Vis-NIR spectra, while both displayed similar capturing mechanisms. Both the MIR and Vis-NIR spectra seized the same soil information, which may appear as a limiting factor for combining both spectral ranges. The interpretation of MIR spectra allowed us to differentiate non-carbonated and carbonated samples corresponding to carbonate leaching and accumulation zones associated with topography and land use. The prediction capability of the MIR spectra and the content of nutrient elements was highly related to soil-forming factors in the study area, which highlights the importance of local (site-specific) prediction models.
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Aaljoki, Kari. "Automated Quality Assurance of Online NIR Analysers." Journal of Automated Methods and Management in Chemistry 2005, no. 2 (2005): 44–49. http://dx.doi.org/10.1155/jammc.2005.44.
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Modern NIR analysers produce valuable data for closed-loop process control and optimisation practically in real time. Thus it is highly important to keep them in the best possible shape. Quality assurance (QA) of NIR analysers is an interesting and complex issue because it is not only the instrument and sample handling that has to be monitored. At the same time, validity of prediction models has to be assured. A system for fully automated QA of NIR analysers is described. The system takes care of collecting and organising spectra from various instruments, relevant laboratory, and process management system (PMS) data. Validation of spectra is based on simple diagnostics values derived from the spectra. Predictions are validated against laboratory (LIMS) or other online analyser results (collected from PMS). The system features automated alarming, reporting, trending, and charting functions for major key variables for easy visual inspection. Various textual and graphical reports are sent to maintenance people through email. The software was written with Borland Delphi 7 Enterprise. Oracle and PMS ODBC interfaces were used for accessing LIMS and PMS data using appropriate SQL queries. It will be shown that it is possible to take actions even before the quality of predictions is seriously affected, thus maximising the overall uptime of the instrument.
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Guo, Cheng, Jin Zhang, Wensheng Cai, and Xueguang Shao. "Enhancing Transferability of Near-Infrared Spectral Models for Soluble Solids Content Prediction across Different Fruits." Applied Sciences 13, no. 9 (2023): 5417. http://dx.doi.org/10.3390/app13095417.
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Near-infrared (NIR) spectroscopy is widely used for non-destructive detection of fruit quality, but the transferability of NIR models between different fruits is still a challenge. This study investigates the transferability of NIR models from strawberry to grape and apple using two case studies. A total of 94 strawberry, 80 grape, and 125 apple samples were measured for their soluble solids content (SSC) and NIR spectra. Partial least squares (PLS) regression was used to establish a model for predicting strawberry SSC, with an acceptable root mean square error of prediction (RMSEP) and correlation coefficient (R) of 0.53 °Brix and 0.91, respectively. Directly applying the strawberry model to grape and apple spectra significantly degrades the performance, increasing the RMSEP up to 3.47 and 16.40, respectively. Spectral preprocessing can improve the predictions for all three fruits, but the bias cannot be eliminated. Global modeling produces a generalized model, but the prediction for strawberry degrades. Calibration transfer with SS-PFCE and PLS correction, which are calibration methods without standard samples, was found to be an effective way to improve the prediction of grape and apple spectra using the strawberry model. Therefore, calibration transfer may be a feasible way for improving the transferability of NIR models for multiple fruits.
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Schimleck, Laurence R., and Robert Evans. "ESTIMATION OF WOOD STIFFNESS OF INCREMENT CORES BY NEAR INFRARED SPECTROSCOPY: THE DEVELOPMENT AND APPLICATION OF CALIBRATIONS BASED ON SELECTED CORES." IAWA Journal 23, no. 3 (2002): 217–24. http://dx.doi.org/10.1163/22941932-90000299.
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Eight Pinus radiata D. Don (Radiata pine) increment core samples representative of a total of thirty-two increment cores were selected for the development of an EL(SS) (longitudinal modulus of elasticity calculated from SilviScan-2 data) calibration based on NIR spectra obtained from the radial–longitudinal face of each sample in 10-mm increments. The primary aim of the work was to investigate if an EL(SS) calibration developed using a subsample of cores representative of a larger set provided better predictions of EL(SS) than those reported in Schimleck et al. (2002a). The EL(SS) calibration was developed using eight factors giving an excellent relationship between SilviScan-2 determined EL(SS) and NIR fitted EL(SS) (coefficient of determination (R2) = 0.97) and a low standard error of calibration (SEC) (0.91 GPa).To test the EL(SS) calibration, NIR spectra were obtained in 10-mm sections from the radial–longitudinal face of two intact P. radiata increment cores and EL(SS) of each section predicted. NIR estimates of EL(SS) were in excellent agreement with EL(SS) determined using SilviScan-2 data, with R2 of 0.99 (core A) and 0.98 (core B). Standard error of predictions (SEP) of 1.6 GPa (core A) and 1.2 GPa (core B) were obtained. Both sets of predictions closely followed the patterns of EL(SS) radial variation determined experimentally. EL(SS) calibration based on NIR spectra obtained from a set of representative cores can provide excellent predictions of EL(SS). The predictions were superior to those reported in Schimleck et al. (2002a).
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Schimleck, Laurence, Joseph Dahlen, Seung-Chul Yoon, Kurt Lawrence, and Paul Jones. "Prediction of Douglas-Fir Lumber Properties: Comparison between a Benchtop Near-Infrared Spectrometer and Hyperspectral Imaging System." Applied Sciences 8, no. 12 (2018): 2602. http://dx.doi.org/10.3390/app8122602.
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Near-infrared (NIR) spectroscopy and NIR hyperspectral imaging (NIR-HSI) were compared for the rapid estimation of physical and mechanical properties of No. 2 visual grade 2 × 4 (38.1 mm by 88.9 mm) Douglas-fir structural lumber. In total, 390 lumber samples were acquired from four mills in North America and destructively tested through bending. From each piece of lumber, a 25-mm length block was cut to collect diffuse reflectance NIR spectra and hyperspectral images. Calibrations for the specific gravity (SG) of both the lumber (SGlumber) and 25-mm block (SGblock) and the lumber modulus of elasticity (MOE) and modulus of rupture (MOR) were created using partial least squares (PLS) regression and their performance checked with a prediction set. The strongest calibrations were based on NIR spectra; however, the NIR-HSI data provided stronger predictions for all properties. In terms of fit statistics, SGblock gave the best results, followed by SGlumber, MOE, and MOR. The NIR-HSI SGlumber, MOE, and MOR calibrations were used to predict these properties for each pixel across the transverse surface of the scanned samples, allowing SG, MOE, and MOR variation within and among rings to be observed.
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Hallett, R. A., J. W. Hornbeck, and M. E. Martin. "Predicting Elements in White Pine and Red Oak Foliage with Visible-Near Infrared Reflectance Spectroscopy." Journal of Near Infrared Spectroscopy 5, no. 2 (1997): 77–82. http://dx.doi.org/10.1255/jnirs.101.
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Near infrared (NIR) reflectance spectroscopy was evaluated for its effectiveness at predicting Al, Ca, Fe, K, Mg and Mn concentrations in white pine ( Pinus strobus L.) and red oak ( Quercus rubra L.) foliage. A NIR spectrophotometer was used to scan 470 dried, ground foliage samples. These samples were used to develop calibration equations using a modified partial least squares (MPLS) regression technique. For the calibration equations, concentrations of Al, Ca, Fe, K, Mg and Mn as determined by acid digestion and laboratory analysis were regressed against second-difference absorbance values measured from 400 to 2498 nm. The regression models developed by NIR reflectance spectroscopy were unable to predict Fe. Predictions were satisfactory for Al, Ca, K, Mn and Mg. It still is uncertain which mineral/organic associations are being detected by NIR reflectance spectroscopy. Future applications may include prediction of element concentrations in the forest canopy via remote sensing.
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Welle, Roland, Katrin Zähle, Carlos Hildebrand, Konrad Kräling, and Willi Greten. "Application of near Infrared Spectroscopy on-Combine for Canola Breeding." Journal of Near Infrared Spectroscopy 15, no. 5 (2007): 317–25. http://dx.doi.org/10.1255/jnirs.736.
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Oil yield and agronomic properties are the most important targets for canola breeders ( Brassica napus L.) in northern Europe. In order to enhance Pioneer's canola oil yield breeding efforts, oil content was measured with near infrared (NIR) spectroscopy directly on a harvester (NIR spectroscopy on-combine) and, in addition, moisture, protein and glucosinolate content were determined. NIR spectroscopy, coupled with rapid harvesting, can significantly improve the quality and speed of oil yield determinations so they occur within a very narrow harvest time window. Moisture, oil, protein and glucosinolate calibrations were developed with 449 samples from the 2004 to 2005 harvests, comprising spectra from four diode array NIR spectrometers mounted on-combine. Applying these calibrations to an independent dataset from the 2006 harvest resulted in standard errors of prediction ( SEP) and coefficients of determination ( r2) of 0.41% and 0.93 for moisture, 0.7% and 0.84 for oil, 0.61% and 0.81 for protein, 4.0% and 0.22 for glucosinolates, respectively. Combining calibrations generated from the four instruments gave optimal predictions. Omitting data from any instrument decreased accuracy and precision, although dropping each instrument had a different effect on the measured values of the constituents. Each instrument produced very similar moisture and constituent predictions with a common sample set as indicated by high r2 and, thus, very similar ranking properties. Analysis of variance with the on-combine determinations led to lower residual variance for oil and similar variance for protein compared to those obtained with classical methods of sampling and laboratory NIR analyses. In summary, the results demonstrate that NIR spectroscopy on-combine is very promising to enhance breeding canola for higher oil yield.
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Tran, Jonathan, Simone Vassiliadis, Aaron C. Elkins, Noel O. I. Cogan, and Simone J. Rochfort. "Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in Cannabis Sativa." Sensors 23, no. 5 (2023): 2607. http://dx.doi.org/10.3390/s23052607.
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Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography–mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II–Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4–100% accuracy prediction), the handheld device also performed well (83.1–100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.
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Jones, P. D., L. R. Schimleck, G. F. Peter, R. F. Daniels, and A. Clark III. "Nondestructive estimation of Pinus taeda L. wood properties for samples from a wide range of sites in Georgia." Canadian Journal of Forest Research 35, no. 1 (2005): 85–92. http://dx.doi.org/10.1139/x04-160.
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Preliminary studies based on small sample sets show that near infrared (NIR) spectroscopy has the potential for rapidly estimating many important wood properties. However, if NIR is to be used operationally, then calibrations using several hundred samples from a wide variety of growing conditions need to be developed and their performance tested on samples from new populations. In this study, 120 Pinus taeda L. (loblolly pine) radial strips (cut from increment cores) representing 15 different sites from three physiographic regions in Georgia (USA) were characterized in terms of air-dry density, microfibril angle (MFA), and stiffness. NIR spectra were collected in 10-mm increments from the radial longitudinal surface of each strip and split into calibration (nine sites, 729 spectra) and prediction sets (six sites, 225 spectra). Calibrations were developed using untreated and mathematically treated (first and second derivative and multiplicative scatter correction) spectra. Strong correlations were obtained for all properties, the strongest R2 values being 0.83 (density), 0.90 (MFA), and 0.93 (stiffness). When applied to the test set, good relationships were obtained (Rp2 ranged from 0.80 to 0.90), but the accuracy of predictions varied depending on math treatment. The addition of a small number of cores from the prediction set (one core per new site) to the calibration set improved the accuracy of predictions and importantly minimized the differences obtained with the various math treatments. These results suggest that density, MFA, and stiffness can be estimated by NIR with sufficient accuracy to be used in operational settings.
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Schimleck, Laurence R., Peter D. Kube, Carolyn A. Raymond, Anthony J. Michell, and Jim French. "Estimation of whole-tree kraft pulp yield of Eucalyptus nitens using near-infrared spectra collected from increment cores." Canadian Journal of Forest Research 35, no. 12 (2005): 2797–805. http://dx.doi.org/10.1139/x05-193.
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Eucalyptus nitens (Deane and Maiden) Maiden (shining gum) is widely grown for kraft pulp production. Improving the kraft pulp yield of E. nitens increases plantation profitability but traditional assessment is slow and expensive, which hinders improvement. Near-infrared (NIR) spectroscopy provides a rapid and inexpensive method for estimating pulp yield, but studies have been limited to estimating whole-tree pulp yield using whole-tree composite samples obtained destructively. For whole-tree pulp-yield calibrations to be used non-destructively they must be applied to increment cores. In this study we used a Tasmanian E. nitens whole-tree pulp yield calibration to estimate the whole-tree pulp yields of trees from a site not included in the calibration. This was done using NIR spectra from increment cores and whole-tree composite chips. Predictions of whole-tree pulp yield based on increment cores were better than those obtained using whole-tree composite chips. The accuracy of pulp-yield predictions was greatly improved by adding a small number of prediction-set samples to the calibration sets. Calibrations for estimating whole-tree pulp yield were also obtained using NIR spectra from milled cores and whole-tree composite chips. The calibrations had similar statistics, indicating that it is possible to obtain calibrations for estimating whole-tree pulp yield based on increment-core NIR spectra.
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Shimbori, Chisato, and Yohei Kurata. "Nondestructive measurement of water content in hardwood leaves using near-infrared spectroscopy." BioResources 12, no. 4 (2017): 9244–52. http://dx.doi.org/10.15376/biores.12.4.9244-9252.
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Near infrared (NIR) spectroscopy was applied to conduct nondestructive measurements of water content in hardwood leaves. The authors developed a prediction method using a partial least squares regression (PLSR) analysis of NIR spectra data of six hardwood species. The pretreated spectra were compared by the full spectral range (1200 nm to 2500 nm) and short spectral ranges (1300 nm to 1600 nm [short range 1 (S1)] and 1800 nm to 2100 nm [S2]). Good prediction results were obtained for the full spectral range with six species. The correlation coefficient for prediction of each of the species ranged from 0.94 to 0.97, and the root mean standard error of prediction ranged from 1.59 to 7.72. Compared with the full spectral analysis, predictions based on S1 and S2 were less accurate. However, leaf water content could be predicted based on measurements in the S1 and S2 ranges. It was worth comparing the wavelengths in a preliminary experiment. In this research, NIR spectroscopy was a powerful nondestructive technique for determining the moisture content of tree leaves.
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Gelinski, Estela Kamile, Fabiane Hamerski, Marcos Lúcio Corazza, and Alexandre Ferreira Santos. "Biodiesel Synthesis Monitoring using Near Infrared Spectroscopy." Open Chemical Engineering Journal 12, no. 1 (2018): 95–110. http://dx.doi.org/10.2174/1874123101812010095.
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Objective: Biodiesel is a renewable fuel considered as the main substitute for fossil fuels. Its industrial production is mainly made by the transesterification reaction. In most processes, information on the production of biodiesel is essentially done by off-line measurements. Methods: However, for the purpose of control, where online monitoring of biodiesel conversion is required, this is not a satisfactory approach. An alternative technique to the online quantification of conversion is the near infrared (NIR) spectroscopy, which is fast and accurate. In this work, models for biodiesel reactions monitoring using NIR spectroscopy were developed based on the ester content during alkali-catalyzed transesterification reaction between soybean oil and ethanol. Gas chromatography with flame ionization detection was employed as the reference method for quantification. FT-NIR spectra were acquired with a transflectance probe. The models were developed using Partial Least Squares (PLS) regression with synthetic samples at room temperature simulating reaction composition for different ethanol to oil molar ratios and conversions. Model predictions were then validated online for reactions performed with ethanol to oil molar ratios of 6 and 9 at 55ºC. Standard errors of prediction of external data were equal to 3.12%, hence close to the experimental error of the reference technique (2.78%), showing that even without using data from a monitored reaction to perform calibration, proper on-line predictions were provided during transesterification runs. Results: Additionally, it is shown that PLS models and NIR spectra of few samples can be combined to accurately predict the glycerol contents of the medium, making the NIR spectroscopy a powerful tool for biodiesel production monitoring.
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Aykas, Didem Peren, Christopher Ball, Ahmed Menevseoglu, and Luis E. Rodriguez-Saona. "In Situ Monitoring of Sugar Content in Breakfast Cereals Using a Novel FT-NIR Spectrometer." Applied Sciences 10, no. 24 (2020): 8774. http://dx.doi.org/10.3390/app10248774.
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This research demonstrates simultaneous predictions of individual and total sugars in breakfast cereals using a novel, handheld near-infrared (NIR) spectroscopic sensor. This miniaturized, battery-operated unit based on Fourier Transform (FT)-NIR was used to collect spectra from both ground and intact breakfast cereal samples, followed by real-time wireless data transfer to a commercial tablet for chemometric processing. A total of 164 breakfast cereal samples (60 store-bought and 104 provided by a snack food company) were tested. Reference analysis for the individual (sucrose, glucose, and fructose) and total sugar contents used high-performance liquid chromatography (HPLC). Chemometric prediction models were generated using partial least square regression (PLSR) by combining the HPLC reference analysis data and FT-NIR spectra, and associated calibration models were externally validated through an independent data set. These multivariate models showed excellent correlation (Rpre ≥ 0.93) and low standard error of prediction (SEP ≤ 2.4 g/100 g) between the predicted and the measured sugar values. Analysis results from the FT-NIR data, confirmed by the reference techniques, showed that eight store-bought cereal samples out of 60 (13%) were not compliant with the total sugar content declaration. The results suggest that the FT-NIR prototype can provide reliable analysis for the snack food manufacturers for on-site analysis.
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Gholizadeh, Asa, João A. Coblinski, Mohammadmehdi Saberioon, et al. "vis–NIR and XRF Data Fusion and Feature Selection to Estimate Potentially Toxic Elements in Soil." Sensors 21, no. 7 (2021): 2386. http://dx.doi.org/10.3390/s21072386.
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Soil contamination by potentially toxic elements (PTEs) is intensifying under increasing industrialization. Thus, the ability to efficiently delineate contaminated sites is crucial. Visible–near infrared (vis–NIR: 350–2500 nm) and X-ray fluorescence (XRF: 0.02–41.08 keV) spectroscopic techniques have attracted tremendous attention for the assessment of PTEs. Recently, the application of fused vis–NIR and XRF spectroscopy, which is based on the complementary effect of data fusion, is also increasing. Moreover, different data manipulation methods, including feature selection approaches, affect the prediction performance. This study investigated the feasibility of using single and fused vis–NIR and XRF spectra while exploring feature selection algorithms for the assessment of key soil PTEs. The soil samples were collected from one of the most heavily polluted areas of the Czech Republic and scanned using laboratory vis–NIR and XRF spectrometers. Univariate filter (UF) and genetic algorithm (GA) were used to select the bands of greater importance for the PTE prediction. Support vector machine (SVM) was then used to train the models using the full-range and feature-selected spectra of single sensors and their fusion. It was found that XRF spectra alone (primarily GA-selected) performed better than single vis–NIR and fused spectral data for predictions of PTEs. Moreover, the prediction models that were derived from the fused data set (particularly the GA-selected) enhanced the models’ accuracies as compared with the single vis–NIR spectra. In general, the results suggest that the GA-selected spectra obtained from the single XRF spectrometer (for As and Pb) and from the fusion of vis–NIR and XRF (for Pb) are promising for accurate quantitative estimation detection of the mentioned PTEs.
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P. Rueda, Marta, Ana Domínguez-Vidal, Víctor Aranda, and María José Ayora-Cañada. "Monitoring the Composting Process of Olive Oil Industry Waste: Benchtop FT-NIR vs. Miniaturized NIR Spectrometer." Agronomy 14, no. 12 (2024): 3061. https://doi.org/10.3390/agronomy14123061.
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Miniaturized near-infrared (NIR) spectrometers are revolutionizing the agri-food industry thanks to their compact size and ultra-fast analysis capabilities. This work compares the analytical performance of a handheld NIR spectrometer and a benchtop FT-NIR for the determination of several parameters, namely, pH, electrical conductivity (EC25), C/N ratio, and organic matter as LOI (loss-on-ignition) in compost. Samples were collected at different stages of maturity from a full-scale facility that processes olive mill semi-solid residue together with olive tree pruning residue and animal manure. Using an FT-NIR spectrometer, satisfactory predictions (RPD > 2.0) were obtained with both partial least squares (PLS) and support vector machine (SVM) regression, SVM clearly being superior in the case of pH (RMSEP = 0.26; RPD = 3.8). The superior performance of the FT-NIR spectrometer in comparison with the handheld spectrometer was essentially due to the extended spectral range, especially for pH. In general, when analyzing intact samples with the miniaturized spectrometer, sample rotation decreased RMSEP values (~20%). Nevertheless, a fast and simple assessment of compost quality with reasonable prediction performance can also be achieved on intact samples by averaging static measurements acquired at different sample positions.
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Xiaobo, Zou, Li Yanxiao, and Zhao Jiewen. "Using Genetic Algorithm Interval Partial Least Squares Selection of the Optimal near Infrared Wavelength Regions for Determination of the Soluble Solids Content of “Fuji” Apple." Journal of Near Infrared Spectroscopy 15, no. 3 (2007): 153–59. http://dx.doi.org/10.1255/jnirs.732.
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A near infrared (NIR) spectroscopy acquisition device was developed in this study using an apple as the test sample. With this device, the apple was rolled while collecting the NIR spectra. The feasibility of using efficient selection of wavelength regions in Fourier transform NIR for a rapid and conclusive determination of the inner qualities of fruit such as soluble solids content (SSC) of apples was investigated. Graphically-oriented local multivariate calibration modelling procedures called genetic algorithm interval partial least-squares (GA-iPLS) were applied to select efficient spectral regions that provide the lowest prediction error, in comparison to the full-spectrum model. The optimal SSC predictions were obtained from a seven-factor model using five intervals among 40 intervals selected by GA-iPLS. In the determination, a root mean square error of prediction of 0.42 °Brix for SSC of apples was obtained. The result demonstrated that the new method is a very useful and effective method for developing high precision PLS models based on optimal wavelength regions.
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Rungchang, Saowaluk, Sila Kittiwachana, Sujitra Funsueb, et al. "Nondestructive Determination of Tocopherol and Tocotrienol in Vitamin E Powder Using Near- and Mid-Infrared Spectroscopy." Foods 13, no. 24 (2024): 4079. https://doi.org/10.3390/foods13244079.
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Vitamin E is an essential nutrient, but its poor water solubility limits food and pharmaceutical applications. The usability of vitamin E can be enhanced via modification methods such as encapsulation, which transforms the physical state of vitamin E from a liquid to a powder. This study examined the efficacy of near-infrared (NIR) and mid-infrared (MIR) spectroscopy in identifying and predicting various vitamin E derivatives in vitamin E-encapsulated powder (VEP). An MIR analysis revealed the fundamental C–H vibrations of vitamin E in the range of 2700–3250 cm−1, whereas an NIR analysis provided information about the corresponding combination, first, and second overtones in the range of 4000–9000 cm−1. The MIR and NIR data were analyzed using a principal component analysis to characterize the VEP. Partial least squares (PLS) regression was applied to predict the content of individual vitamin E derivatives. PLS cross-validation revealed that NIR analysis provides more reliable predictive accuracy and precision for the contents of vitamin E derivatives, achieving a higher coefficient of determination for prediction (Q2) (0.92–0.99) than MIR analysis (0.20–0.85). For test set validation, the NIR predictions exhibited a significant level of accuracy, as indicated by a high ratio of prediction to deviation (RPD) and Q2. Furthermore, the PLS models developed using the NIR data had statistically significant predictive performance, with a high RPD (1.54–3.92) and Q2 (0.66–0.94). Thus, NIR spectroscopy is a valuable nondestructive technique for analyzing vitamin E samples, while MIR spectroscopy serves as a useful method for confirming its presence.
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Chadalavada, Keerthi, Krithika Anbazhagan, Adama Ndour, et al. "NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals." Sensors 22, no. 10 (2022): 3710. http://dx.doi.org/10.3390/s22103710.
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Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R2 ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.
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Cecchinato, Alessio, Sara Pegolo, and Giovanni Bittante. "379 ASAS-EAAP Talk: Precision Phenotyping using Infrared Spectroscopy to Improve the Quality of Animal Products." Journal of Animal Science 98, Supplement_4 (2020): 140. http://dx.doi.org/10.1093/jas/skaa278.258.
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Abstract There is an ever-growing interest in research oriented towards the improvement of quality of animal products. In this context, one major operational bottleneck is the possibility to collect quality indicators over the meat and dairy chains and for selective breeding purposes. The use of near-infrared (NIR) and the Fourier-transformed infrared (FTIR) spectroscopy techniques have been proven to be powerful precision phenotyping tools for high-throughput meat and milk quality assessment. Such technologies allow scoring large number of animals and/or derived-products for novel (predicted) phenotypes and indicator traits to set-up potential new payment systems and boost the genetic improvement. One important step in the use of NIR and FTIR tools is the definition of the “gold standard” as the infrared-based predictions could act only as indicators traits. Indeed, the definition of a robust calibration set, the assessment of repeatability and reproducibility of the reference (i.e., gold standard) as well as the detection of random and systematic errors are crucial steps. Once the reference phenotype has been defined, different statistical methodologies could be applied to infrared spectra data. For instance, the partial least squares regression (PLS) is a multivariate regression method commonly used to build up prediction models using NIR and FTIR spectra data. However, the implementation of advanced statistical approaches, such as Bayesian approaches and machine learning methods, might allow us to achieve more robust and accurate predictions. In this talk, we will describe and discuss some of the challenges and potentials of NIR and FTIR tools for large-scale precision phenotyping. Some examples include the use of NIR and Visible-NIR (Vis-NIR) for assessing meat quality parameters (also using portable instruments able to collect spectra directly from the muscle surface at the slaughterhouse) and the use of FTIR for predicting several traits related to fine milk composition and technological traits in dairy cattle.
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Schimleck, Laurence R., and Robert Evans. "ESTIMATION OF MICROFIBRIL ANGLE OF INCREMENT CORES BY NEAR INFRARED SPECTROSCOPY." IAWA Journal 23, no. 3 (2002): 225–34. http://dx.doi.org/10.1163/22941932-90000300.
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Eight Pinus radiata D. Don (Radiata pine) increment core samples representative of a total of thirty-two increment cores were selected. NIR spectra were obtained from the radial–longitudinal face of each core in 10-mm increments and used to develop a microfibril angle (MFA) calibration. The MFA calibration was developed using seven factors giving an excellent relationship between SilviScan-2 determined MFA and NIR fitted MFA (coefficient of determination (R2) = 0.95) and a standard error of calibration (SEC) of 1.8 degrees.The MFA calibration was used to predict the MFA of NIR spectra obtained in 10-mm sections from the radial–longitudinal face of two intact P. radiata increment cores. NIR predicted MFA was found to be in excellent agreement with MFA determined by SilviScan-2, with R2 of 0.98 (core A) and 0.96 (core B). The standard error of prediction (SEP) for core A (1.0 degree) was much lower than for core B (2.5 degrees). Both sets of predictions closely followed the patterns of MFA radial variation determined by SilviScan-2. NIR spectroscopy provides a rapid method for determining MFA variation in increment cores and is suitable for the routine analysis of large numbers of samples.
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Amondi, Mercy, Jared Ombiro, Zephania Birech, and Duke Oeba. "Predicting Hass Avocado Maturity with NIR Spectroscopy for Non-invasive Dry Matter Estimation in Hass Avocados." Asian Journal of Research and Reviews in Physics 8, no. 4 (2024): 53–65. http://dx.doi.org/10.9734/ajr2p/2024/v8i4175.
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Aims: To develop a rapid, non-invasive method for predicting Hass avocado maturity using near- infrared diffuse reflectance spectroscopy (NIR-DRS) combined with machine learning algorithms, and to identify the optimal NIR wavelength range for accurate dry matter content prediction. Study Design: An experimental design involving spectral data collection from Hass avocados and the development of machine learning models for dry matter prediction. Methodology: Spectral data from 200 Hass avocados were collected using near-infrared diffuse reflectance spectroscopy (900-2500 nm). To improve the quality of the spectral data and reduce noise, standard normal variate was used to correct for scattering and remove unwanted variability in the spectral data. PCA was then performed to reduce the dimension of the spectral data while retaining the most significant variance. Following preprocessing, machine learning models, including Convolutional Neural Networks (CNN), were trained to predict dry matter content, and the optimal wavelength range was determined for accurate prediction. Results: The CNN model demonstrated superior performance for dry matter prediction with R² of 0.91 in the testing set. The wavelength range of 1000-1500 nm was identified as optimal, offering accurate predictions while reducing computational complexity. This range shows potential for developing cost-effective NIR devices for real-time maturity assessment. Conclusion: NIR spectroscopy combined with machine learning offers a non-invasive, accurate method for predicting avocado dry matter, with potential applications for quality control in the avocado industry. The findings demonstrate that focusing on specific wavelength ranges can lead to more affordable and efficient NIR solutions.
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Huang, Yuanping, Xiaoxi Sun, Keke Liao, Lujia Han, and Zengling Yang. "Real-time and field monitoring of the key parameters in industrial trough composting process using a handheld near infrared spectrometer." Journal of Near Infrared Spectroscopy 28, no. 5-6 (2020): 334–43. http://dx.doi.org/10.1177/0967033520939323.
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Producing organic fertilizer by aerobic composting is an effective way to solve the livestock manure pollution problem and to achieve economic utilization of the valuable resource. To control the composting process effectively and ensure the quality of such organic fertilizers, it is necessary to quantify the key parameters and provide timely feedback of their changes during the composting process. In the industrial field, the traditional laboratory analysis is being transferred into process analysis. This study explored the application of real-time and field monitoring of the key parameters in the industrial trough composting process using handheld near infrared (NIR) spectroscopy and evaluated its ability to accurately predict these changes. The results showed that the handheld NIR could accurately detect moisture content (MC), total nitrogen (TN), total carbon (TC), the carbon/nitrogen (C/N) ratio, organic matter (OM) and electrical conductivity (EC) during the trough composting process, with excellent predictions for MC, good predictions for TN and OM, approximate predictions for TC, C/N ratio and EC. Changes in NIR-predicted values and measured values were consistent as the composting process progressed. The handheld NIR sensor shows good potential for real-time and field monitoring of the composting process and organic fertilizer quality assurance.
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Sitorus, Agustami, and Ravipat Lapcharoensuk. "Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy." Sensors 24, no. 7 (2024): 2362. http://dx.doi.org/10.3390/s24072362.
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Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R2 of 0.886–0.999, RMSE of 0.370–6.108%, and Bias of −0.176–1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future.
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Riley, Mark R., and Loreto C. Cánaves. "FT-NIR Spectroscopic Analysis of Nitrogen in Cotton Leaves." Applied Spectroscopy 56, no. 11 (2002): 1484–89. http://dx.doi.org/10.1366/00037020260377805.
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Near-infrared spectroscopy was evaluated as a means to quantify the nitrogen content in fresh cotton leaves ( Gossypium hirsutum L. var. Delta Pine 90) subjected to a factorial design experiment of varying nitrogen and water applications. Absorbance spectra were collected in the 10 000–4000 cm−1 (1000–2500 nm) region from fresh cotton leaves over a two month portion of the growing season. Total nitrogen content was quantified by a wet chemistry Kjeldahl method for validation purposes. Partial least-squares regression analysis, using an automated grid search method, selected the spectral region 6041 to 5651 cm−1 (1650–1770 nm) for analysis based on having the lowest standard error of prediction of total nitrogen content. This region includes protein spectral features. Nitrogen predictions resulted in a correlation coefficient of 0.83, and a standard error of prediction of 0.29% for nitrogen levels ranging from 3.1 to 5.2% total nitrogen. This approach has promise for providing rapid plant chemical analyses for cotton crop fertilization management purposes.
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Gavan, Alexandru, Liora Colobatiu, Andrei Mocan, Anca Toiu, and Ioan Tomuta. "Development of a NIR Method for the In-Line Quantification of the Total Polyphenolic Content: A Study Applied on Ajuga genevensis L. Dry Extract Obtained in a Fluid Bed Process." Molecules 23, no. 9 (2018): 2152. http://dx.doi.org/10.3390/molecules23092152.
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This study describes an innovative in-line near-infrared (NIR) process monitoring method for the quantification of the total polyphenolic content (TPC) of Ajuga genevensis dry extracts. The dry extract was obtained in a fluidized bed processor, by spraying and adsorbing a liquid extract onto an inert powder support. NIR spectra were recorded continuously during the extract’s spraying process. For the calibration of the in-line TPC quantification method, samples were collected during the entire process. The TPC of each sample was assessed spectroscopically, by applying a UV-Vis reference method. The obtained values were further used in order to develop a quality OPLS prediction model by correlating them with the corresponding NIR spectra. The final dry extract registered good flowability and compressibility properties, a concentration in active principles three times higher than the one of the liquid extract and an overall process yield of 85%. The average TPC’s recovery of the NIR in-line prediction method, compared with the reference UV-Vis one, was 98.7%, indicating a reliable monitoring method which provided accurate predictions of the TPC during the process, permitting a good process overview and enabling us to establish the process’s end point at the exact moment when the product reaches the desired TPC concentration.
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Korda, David, and Tomáš Kohout. "Silicate Mineralogy from Vis–NIR Reflectance Spectra." Planetary Science Journal 5, no. 4 (2024): 85. http://dx.doi.org/10.3847/psj/ad2685.
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Abstract The asteroid composition is the key to understanding the origin and evolution of the solar system. The composition is imprinted at specific wavelengths of the asteroid reflectance spectra. We wish to find the optimal wavelength range and step of reflectance spectra that contain sufficient information about S-complex asteroids while keeping the data volume as low as possible. We especially aim for the ASPECT instrument on board the Milani/Hera CubeSat that will observe the S-complex binary asteroid (65803) Didymos–Dimorphos. We use labeled reflectance spectra of the most common silicate found in meteorites, namely olivine, orthopyroxene, clinopyroxene, and their mixtures. The spectra are interpolated to various wavelength grids. We use convolutional neural networks and train them with the labeled interpolated reflectance spectra. The reliability of the network outputs is evaluated using standard regression metrics. We do not find any significant dependence between the error of the model predictions and normalization position, fineness of coverage within the 1 μm band, and wavelength step up to 50 nm. High-precision predictions of the olivine and orthopyroxene modal abundances are obtained using spectra that cover wavelengths from 750 to 1050 nm and from 750 to 1250 nm, respectively. For high-precision predictions of the olivine chemical composition, the spectra should cover wavelengths from 750 to 1550 nm. The orthopyroxene chemical composition can be estimated from spectra that cover wavelengths from 750 to 1350 nm. We design a simple web interface through which everybody can use the pretrained models.
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Yang, Hai Qing, Bo Yan Kuang, and Abdul M. Mouazen. "Prediction of Soil TN and TC at a Farm-Scale Using VIS-NIR Spectroscopy." Advanced Materials Research 225-226 (April 2011): 1258–61. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.1258.
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Building cost-effective models is of academic and practical value for fast measurement of soil properties, especially at a farm-scale. The aim of this study is to build quantitative models for soil total nitrogen (TN) and total carbon (TC) using visible and near infrared (VIS-NIR) spectroscopy. Dried samples (n=122) collected from an experimental farm, at Silsoe, Bedfordshire, United Kingdom, were scanned from 350 to 2500 nm at 1-nm intervals. Samples were divided into a calibration set (75%) and an independent validation set (25%). A partial least squares regression (PLSR) with leave-one-out cross validation was carried out based on different spectral ranges. Result shows that the best predictions (R2>0.90 and RPD>3.3) are achieved for TN using the VIS range (400-700nm) and for TC using the VIS-NIR range (400-2500nm). It is concluded that VIS-NIR spectroscopy coupled with PLSR can be adopted for the prediction of soil TN and TC at a farm-scale.
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McVey, Claire, Una Gordon, Simon A. Haughey, and Christopher T. Elliott. "Assessment of the Analytical Performance of Three Near-Infrared Spectroscopy Instruments (Benchtop, Handheld and Portable) through the Investigation of Coriander Seed Authenticity." Foods 10, no. 5 (2021): 956. http://dx.doi.org/10.3390/foods10050956.
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The performance of three near-infrared spectroscopy (NIRS) instruments was compared through the investigation of coriander seed authenticity. The Thermo Fisher iS50 NIRS benchtop instrument, the portable Ocean Insights Flame-NIR and the Consumer Physics handheld SCiO device were assessed in conjunction with chemometric modelling in order to determine their predictive capabilities and use as quantitative tools through regression analysis. Two hundred authentic coriander seed samples and ninety adulterated samples were analysed on each device. Prediction models were developed and validated using SIMCA 15 chemometric software. All instruments correctly predicted 100% of the adulterated samples. The best models resulted in correct predictions of 100%, 98.5% and 95.6% for authentic coriander samples using spectra from the iS50, Flame-NIR and SCiO, respectively. The development of regression models highlighted the limitations of the Flame-NIR and SCiO for quantitative analysis, compared to the iS50. However, the results indicate their use as screening tools for on-site analysis of food, at various stages of the food supply chain.
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Schimleck, L. R., and Y. Yazaki. "Analysis of Black Wattle (Acacia mearnsii De Wild) Bark by Near Infrared Spectroscopy." Holzforschung 57, no. 5 (2003): 527–32. http://dx.doi.org/10.1515/hf.2003.078.
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Summary The analysis of two sets of Acacia mearnsii De Wild (Black Wattle) samples by near infrared (NIR) spectroscopy is reported. Set 1 samples were characterised in terms of hot water extractives, Stiasny value and polyflavanoid content. Set 2 samples were characterised by nine different parameters, including tannin content. NIR spectra were obtained from the milled bark of all samples and calibrations developed for each parameter. Calibrations developed for hot water extractives and polyflavanoid content (set 1) gave very good coefficients of determination (R2) and performed well in prediction. Set 2 calibrations were generally good with total and soluble solids, tannin content, Stiasny value-2 and UV-2, all having R2 greater than 0.8. Owing to the small number of set 2 samples, no predictions were made using the calibrations. The strong relationships obtained for many parameters in this study indicates that NIR spectroscopy has considerable potential for the rapid assessment of the quality of extractives in A. mearnsii bark.
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Wang, Zheng, Jianli Ding, and Zipeng Zhang. "Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features." Sensors 22, no. 3 (2022): 1194. http://dx.doi.org/10.3390/s22031194.
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The soil organic matter (SOM) content is a key factor affecting the function and health of soil ecosystems. For measurements of land reclamation and soil fertility, SOM monitoring using visible and near-infrared spectroscopy (Vis-NIR) is one approach to quantifying soil quality, and Vis-NIR is important for monitoring the SOM content in a broad and nondestructive manner. To investigate the influence of environmental factors and Vis-NIR spectroscopy in estimating SOM, 249 soil samples were collected from the Werigan–Kuqa oasis in Xinjiang, China, and their spectral reflectance, SOM content and soil salinity were measured. To classify and improve the prediction accuracy, we also take into account the soil salinity content as a variable indicator. Relevant environmental variables were extracted using remote sensing datasets (land-use/land-cover (LULC), digital elevation model (DEM), World Reference Base for Soil Resources (WRB), and soil texture). On the basis of Savitzky–Golay (S-G) smoothing and first derivative (FD) preprocessing of the original spectrum, three clusters were obtained by K-means clustering through the use of Vis-NIR and used as spectral classification variables. Using Vis-NIR as Model 1, Vis-NIR combined with spectral classification as Model 2, environmental variables as Model 3, and the combination of all the above variables (Vis-NIR, spectral classification, environmental variables, and soil salinity) as Model 4, a SOM content estimation model was constructed using partial least squares regression (PLSR). Using the 249 soil samples, the modeling set contained 166 samples and the validation set contained 83 samples. The results showed that Model 2 (validation r2 = 0.78) was better than Model 1 (validation r2 = 0.76). The prediction accuracy for Model 4 (validation r2 = 0.85) was better than Model 2 (validation r2 = 0.78). Among these, Model 3 was the worst (validation r2 = 0.39). Therefore, the combination of environmental variables with Vis-NIR spectroscopy to estimate SOM content is an important method and has important implications for improving the accuracy of SOM predictions in arid regions.
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Sundaram, Jaya, Chari V. Kandala, Christopher L. Butts, Charles Y. Chen, and Victor Sobolev. "Nondestructive NIR Reflectance Spectroscopic Method for Rapid Fatty Acid Analysis of Peanut Seeds." Peanut Science 38, no. 2 (2011): 85–92. http://dx.doi.org/10.3146/ps10-3.1.
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ABSTRACT Near Infrared Reflectance Spectroscopy (NIRS) was used to rapidly and nondestructively analyze the fatty acid concentration present in peanut seeds samples. Absorbance spectra were collected in the wavelength range from 400 nm to 2500 nm using NIRS. The oleic, linoleic and palmitic fatty acids were converted to their corresponding methyl esters and their concentrations were measured using a gas chromatograph (GC). Partial least square (PLS) analysis was performed on a calibration set, and models were developed for prediction of fatty acid concentrations. The best model was selected based on the coefficient of determination (R2), Root Mean Square Error of Prediction, residual percent deviation (RPD) and correlation coefficient percentage between the gas chromatography measured values and the NIR predicted values. The NIR reflectance model developed yielded RPD values of three and above for prediction of the three fatty acids, indicating that this nondestructive method would be suitable for fatty acid predictions in peanut seeds.
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Wetherill, G. Z., I. Murray, and C. A. Glasbey. "Analysis of artificial mixtures of pure chemicals by near-infrared reflectance." Journal of Agricultural Science 114, no. 3 (1990): 253–57. http://dx.doi.org/10.1017/s0021859600072634.
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SUMMARYCompositional analysis of feeds and other materials by near-infrared reflectance (NIR) has been proposed as a cheap and rapid alternative to traditional wet chemical methods. A theoretical basis for NIR measurements is needed and may be obtained from the study of artificial mixtures of pure chemicals.Mixtures of lactose, casein and sodium oleate, in widely differing concentrations, were analysed by NIR. Principal component analysis was used to study the variations between spectra, and multiple linear regressions gave predictors of sample compositions from the spectra. Optical densities at most combinations of wavelengths gave good predictions of sample compositions because there was much less unexplained variation between NIR spectra than would occur between natural samples.
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Tavares, Marcos Silva, Carlos Augusto Alves Cardoso Silva, Jamile Raquel Regazzo, et al. "Performance of Machine Learning Models in Predicting Common Bean (Phaseolus vulgaris L.) Crop Nitrogen Using NIR Spectroscopy." Agronomy 14, no. 8 (2024): 1634. http://dx.doi.org/10.3390/agronomy14081634.
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Beans are the main direct source of protein consumed by humans in the world and their productivity is directly linked to nitrogen. The short crop cycle imposes the need for fast methodologies for N quantification. In this work, we evaluated the performance of four machine learning algorithms in nitrogen estimation using NIR spectroscopy, comparing predictions between complete spectral data and only intervals obtained with the variable importance in projection (VIP). Doses of 0, 50, 100, and 150 kg ha−1 of N were applied and leaf reflectance was collected. Weka software was used to test the algorithms. The selection of the most effective spectral zones was made with the variable importance in projection (VIP). The intervals of 700–740 nm and 983–995 nm were considered the most important for the study of nitrogen. More efficient predictions were verified for RF and KNN models (R2 = 0.89, RMSE = 2.23 g kg−1; and R2 = 0.80, RMSE = 2.89 g kg−1, respectively) when only the most important spectral regions were included. The efficiency of nitrogen prediction based on NIR reflectance combined with machine learning was verified, which can serve as an important tool in precision agriculture.
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Fu, Xianshu, Xiaoping Yu, Zihong Ye, and Haifeng Cui. "Analysis of Antioxidant Activity of Chinese Brown Rice by Fourier-Transformed Near Infrared Spectroscopy and Chemometrics." Journal of Chemistry 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/379327.
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This paper develops a rapid method using near infrared (NIR) spectroscopy for analyzing the antioxidant activity of brown rice as total phenol content (TPC) and radical scavenging activity by DPPH (2,2-diphenyl-2-picrylhydrazyl) expressed as gallic acid equivalent (GAE). Brown rice (n=121) collected from five producing areas was analyzed for TPC and DPPH by reference methods. The NIR reflectance spectra were measured with compact powders of samples and no treatment was used. Full-spectrum partial least squares (FS-PLS) and interval PLS (iPLS) were used as the regression methods to relate the antioxidant activity values to the NIR data. The spectral range of 4800–5600 cm−1plus 6000–6400 cm−1has the best correlation with TPC, while the range of 4400–5200 cm−1plus 6000–6400 cm−1is the most suitable for predicting DPPH. With standard normal variate (SNV) transformation and the selected wavelength ranges, the root mean squared error of prediction (RMSEP) is 0.062 mg GAE g−1for TPC and 0.141 mg GAE g−1for DPPH radical, respectively. The multiple correlation coefficients of predictions for TPC and DPPH are 0.962 and 0.974, respectively. The developed NIR method might have a potential application to quality control of brown rice in the domestic market.
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Hwang, Sung-Wook, Hyunwoo Chung, Taekyeong Lee, Hyo Won Kwak, In-Gyu Choi, and Hwanmyeong Yeo. "Investigation of NIR spectroscopy and electrical resistance-based approaches for moisture determination of logging residues and sweet sorghum." BioResources 18, no. 1 (2023): 2064–82. http://dx.doi.org/10.15376/biores.18.1.2064-2082.
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Techniques based on electrical resistance and near-infrared (NIR) spectroscopy were used to determine the moisture content (MC) of logging residues and sweet sorghum. The MC of biomass is a factor to be controlled that can affect the quality of final products. To accurately measure the moisture in fragmented materials, it is essential to increase the bulk density of the materials by compression. The low bulk density increased the error from the oven-drying MC and the variation between repeated measurements. The calculated correction factor made it possible to use a commercial wood moisture meter for biomass materials. Ordinary least squares regression models built with the electrical resistance data achieved coefficients of determination (R2) of 0.933 and 0.833 with root mean square errors (RMSE) of 0.505 and 0.891, respectively, for the MC predictions of logging residue and sweet sorghum. Partial least squares regression models combined with NIR spectroscopy achieved R2 of 0.942 and 0.958 with RMSE of 1.318 and 3.681 for logging residue and sweet sorghum, respectively. In contrast to the electrical resistance-based models, the NIR-based models could predict the MC regardless of the bulk density of the materials. Data transformation by the second derivative and removal of outliers contributed to the improvement of the prediction of the NIR-based models.
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Tahmoures, Mohammad, Afshin Honarbakhsh, Sayed Fakhreddin Afzali, Mehdi Nourzadeh Hadad, and Yaser Ostovari. "Quantifying salinity in calcareous soils through advanced spectroscopic models: A comparative study of random forests and regression techniques across diverse land use systems." PLOS ONE 19, no. 8 (2024): e0307853. http://dx.doi.org/10.1371/journal.pone.0307853.
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Precise prediction of soil salinity using visible, and near-infrared (vis-NIR) spectroscopy is crucial for ensuring food security and effective environmental management. This paper focuses on the precise prediction of soil salinity utilizing visible and near-infrared (vis-NIR) spectroscopy, a critical factor for food security and effective environmental management. The objective is to utilize vis-NIR spectra alongside a multiple regression model (MLR) and a random forest (RF) modeling approach to predict soil salinity across various land use types, such as farmlands, bare lands, and rangelands accurately. To this end, we selected 150 sampling points representatives of these diverse land uses. At each point, we collected soil samples to measure the soil salinity (ECe) and employed a portable spectrometer to capture the spectral reflectance across the full wavelength range of 400 to 2400 nm. The methodology involved using both individual spectral reflectance values and combinations of reflectance values from different wavelengths as input variables for developing the MLR and RF models. The results indicated that the RF model (RMSE = 4.85 dS m-1, R2 = 0.87, and RPD = 3.15), utilizing combined factors as input variables, outperformed others. Furthermore, our analysis across different land uses revealed that models incorporating combined input variables yielded significantly better results, particularly for farmlands and rangelands. This study underscores the potential of combining vis-NIR spectroscopy with advanced modeling techniques to enhance the accuracy of soil salinity predictions, thereby supporting more informed agricultural and environmental management decisions.
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Szydlowski-Zanier, Nathalie, Marc Berger, François Wahl, and Denis Guillaume. "Performance of a near Infrared Spectrometer Equipped with an Autosampling Accessory." Journal of Near Infrared Spectroscopy 11, no. 2 (2003): 83–95. http://dx.doi.org/10.1255/jnirs.357.
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The performance of a Fourier-transform near infrared (FT-NIR) spectrometer equipped with an autosampling accessory (AutoSamplIR) using disposable vials with uncontrolled pathlength has been compared with the performance of an FT-NIR equipped with a manual vial sampling accessory using a reusable cell with calibrated pathlength. The effect of different parameters such as the signal-to-noise ratio, the stability of the purge and the linearity of absorbance have been evaluated by means of the NIR predictions of two physico-chemical properties of interest for gasoils, i.e the wt% of hydrogen and the cetane number (CN). It has been shown that an FT-NIR with the autosampling accessory can be used advantageously at low cost for quick and precise analyses of small amounts of sample.
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Rouxinol, Maria Inês, Maria Rosário Martins, Gabriela Carneiro Murta, João Mota Barroso, and Ana Elisa Rato. "Quality Assessment of Red Wine Grapes through NIR Spectroscopy." Agronomy 12, no. 3 (2022): 637. http://dx.doi.org/10.3390/agronomy12030637.
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Red wine grapes require a constant follow-up through analytical chemistry to assure the greatest wine quality. Wet chemical procedures are time-consuming and produce residues that are hard to eliminate. NIR (near infrared radiation) spectroscopy has been referred as an accurate, rapid, and cost-efficient technique to evaluate quality in many fruit species, both in field and in industry. The main objective of this study was to develop predictive models using NIR spectroscopy to quantify important quality attributes in wine grapes. Soluble solids content (SSC), titratable acidity (TA), total phenolic content, total flavonoids, total anthocyanins, and total tannins were quantified in four red wine grape varieties, ‘Aragonês’, ‘Trincadeira’, ‘Touriga Nacional’, and ‘Syrah’. Samples were collected during 2017 and 2018 along véraison. Prediction models were developed using a near-infrared portable device (Brimrose, Luminar 5030), and spectra were collected from entire grapes under near field conditions. Models were built using a partial least square regression (PLSR) algorithm and SSC, TA, total anthocyanins, and total tannins exhibited a determination coefficient of 0.89, 0.90, 0.87, and 0.88, respectively. The Residual Prediction Deviation (RPD) values of these models were higher than 2.3. The prediction models for SSC, TA, total anthocyanins, and total tannins have considerable potential to quantify these attributes in wine grapes. Total flavonoids and total phenolic content were predicted with a slightly lower capacity, with R2 = 0.72 and 0.71, respectively, and both with a RPD of 1.6, indicating a very low to borderline potential for quantitative predictions in flavonoids and phenols models.
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Sun, Jason, Rainer Künnemeyer, Andrew McGlone, and Philip Rowe. "Multispectral scattering imaging and NIR interactance for apple firmness predictions." Postharvest Biology and Technology 119 (September 2016): 58–68. http://dx.doi.org/10.1016/j.postharvbio.2016.04.019.
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Hrušková, M., M. Bednářová, and F. Novotný. "Wheat flour dough rheological characteristics predicted by NIRSystems 6500." Czech Journal of Food Sciences 19, No. 6 (2013): 213–18. http://dx.doi.org/10.17221/6610-cjfs.
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Rheological quality of wheat dough prepared from 114 wheat flour samples (wheat harvest 1998 and 1999) was assessed by help of farinograph and extensigraph. Spectra of all samples were measured on spectrograph NIRSystems 6500 NIR. Calibration equations with cross and independent validation for all rheological characteristics were computed by NIR Software ISI Present WINISI II using mPLS and PLS methods. The quality of prediction was evaluated by coefficients of correlation between measured and predicted values from cross and independent validation. A statistically significant dependence between predicted and measured values (with probability higher than 99%) was determined in all mentioned rheological characteristics in the case of cross validation. Only farinograph absorption, time of dough development and mixing tolerance were successfully predicted by independent validation. Predictions of extensigraph characteristics were not found out statistically significant probably due to a small number of tested samples.
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Rafiq, Hamza, Jens Hartung, Torsten Schober, et al. "Non-Destructive Near-Infrared Technology for Efficient Cannabinoid Analysis in Cannabis Inflorescences." Plants 13, no. 6 (2024): 833. http://dx.doi.org/10.3390/plants13060833.
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In the evolving field of cannabis research, scholars are exploring innovative methods to quantify cannabinoids rapidly and non-destructively. This study evaluates the effectiveness of a hand-held near-infrared (NIR) device for quantifying total cannabidiol (total CBD), total delta-9-tetrahydrocannabinol (total THC), and total cannabigerol (total CBG) in whole cannabis inflorescences. Employing pre-processing techniques, including standard normal variate (SNV) and Savitzky–Golay (SG) smoothing, we aim to optimize the portable NIR technology for rapid and non-destructive cannabinoid analysis. A partial least-squares regression (PLSR) model was utilized to predict cannabinoid concentration based on NIR spectra. The results indicated that SNV pre-processing exhibited superior performance in predicting total CBD concentration, yielding the lowest root mean square error of prediction (RMSEP) of 2.228 and the highest coefficient of determination for prediction (R2P) of 0.792. The ratio of performance to deviation (RPD) for total CBD was highest (2.195) with SNV. In contrast, raw data exhibited the least accurate predictions for total THC, with an R2P of 0.812, an RPD of 2.306, and an RMSEP of 1.651. Notably, total CBG prediction showed unique characteristics, with raw data yielding the highest R2P of 0.806. SNV pre-processing emerges as a robust method for precise total CBD quantification, offering valuable insights into the optimization of a hand-held NIR device for the rapid and non-destructive analysis of cannabinoid in whole inflorescence samples. These findings contribute to ongoing efforts in developing portable and efficient technologies for cannabinoid analysis, addressing the increasing demand for quick and accurate assessment methods in cannabis cultivation, pharmaceuticals, and regulatory compliance.
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Yang, Xueping, JH Cherney, MD Casler, and Paolo Berzaghi. "Forage calibration transfer from laboratory to portable near infrared spectrometers." Journal of Near Infrared Spectroscopy 31, no. 3 (2023): 126–40. http://dx.doi.org/10.1177/09670335231173136.
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Portable near infrared (NIR) spectrometers are now readily available on the market and with their smaller size, weight and cost have provided the opportunity to analyze forages both on farms and directly in the field. As new technologies and new portable NIR instruments become available on the market, calibrations for these instruments become a major constraint due to the costs and time necessary to collect reference data. This study evaluated techniques to transfer calibrations for alfalfa and grass forage samples that were developed for a scanning benchtop monochromator (FOSS 6500, 400–2498 nm, LAB) to a diode array instrument (AuroraNir, 950–1650 nm, DA), a digital light processing instrument (NIR-S-G1, 950–1650 nm, DLP) and a short wavelength instrument (SCiO, 740–1070 nm, SCIO). Alfalfa (N = 612) and grass (N = 516) samples from eight agronomic studies were analyzed by wet chemistry for crude protein, neutral detergent fiber (NDF), acid detergent fiber (ADF), in-vitro digestibility (IVTD) and NDF digestibility (NDFD) and divided into calibration, test-set, standardization and inoculation/prediction datasets. Different calibration transfer strategies were evaluated: Spectral Bias Correction (SBC), Shenk and Westerhaus algorithm (SW), Piecewise Direct Standardization (PDS), Dynamic Orthogonal Projection (DOP) or creating a new calibration using LAB predictions of the inoculation/prediction dataset as reference values. All computations for trimming, calibration, validation and standardization were developed using R. SBC with inoculation was an effective method to transfer calibrations for DA. Validation errors for DA transferred calibrations were about 15% lower than LAB for alfalfa data but 6% greater for grass data. For SCIO after DOP spectral adjustment, predicting errors were slightly greater than LAB for both data sets, while prediction errors with DLP were two to three times greater than LAB even after inoculation. PDS created spectral artifacts in the spectra of all three portables, which then resulted in large validation errors. Using LAB predictions as reference values was suitable only for DA, while DLP and DA had large prediction errors. This study showed that calibration sharing between a benchtop and portable instruments is challenging, but possible depending on the portable technologies and the transfer method. Spectral bias correction plus inoculation was the best method to transfer multivariate models for the forage components’ prediction from LAB to handhelds, particularly for DA. Application of DOP was beneficial for SCIO to successfully maintain performance of the original calibration, while for DLP the prediction models were not accurate. Additional studies are necessary to verify these transferring techniques can also be applied to fresh forages, allowing an easier and extended implementation of NIR analysis directly in fields.