NIR Models Research Articles

Assessment of ATR-NIR and ATR-MIR spectroscopy as an analytical tool for the quantification of the total polyphenols in Dendrobium huoshanense.

In this study, the potentiality of applying attenuated total reflectance near-infrared (ATR-NIR) and attenuated total reflectance mid-infrared (ATR-MIR) techniques combined with a partial least squares (PLS) regression technology to quantify the total polyphenols (TPs) in Dendrobium huoshanense (DHS) was investigated and compared. The real TP contents in the DHS samples were analysed using methods of reference. The capability of the two IR spectroscopic techniques to quantify the TPs in DHS was assessed by the root-mean-square error of calibration (RMSEC) and determination coefficients (R2 ). The results showed that both NIR and MIR might be used as a fast and simple tool to replace traditional chemical assays for the determination of the TP contents in DHS, and the best NIR model showed slightly better prediction performance [root-mean-square error of prediction (RMSEP): 0.307, R2 : 0.9122, ratio performance deviation (RPD): 4.43] than the best MIR model (RMSEP: 0.440, R2 : 0.9069, RPD: 3.09). Results from this study indicated that both the NIR and MIR models could be used to quantify the TP in DHS, and ATR-NIR appeared to be the more predominant and more robust technique for the quantification of the TP in DHS.

Immediate measurement of fuel characteristics of bio-char using NIRS

Low temperature pyrolysis is effective to improve the fuel quality of wheat straw and corn stover. To determine the pyrolysis condition and fully utilizing the solid product (bio-char), the fuel characteristics of the bio-char should be measured. It costs a lot of time and money to finish all the measurement, which really retards the development of the biomass industry. This study is supposed to immediately predict the proximate analysis parameters, ultimate analysis parameters and heat value of sixty-five bio-char using NIRS. The results show the fuel characteristics of the bio-char vary in a very big range. The NIRS models can predict the VM, FC, ash, HHV, oxygen, carbon, hydrogen contents of the bio-char accurately. The Rc and Rcv2 value are all bigger than 0.92 and the RPD are bigger than 3.59. The NIRS models can also tell the high or low content of moisture and nitrogen. Only for the sulphur, more work need to do to improve the accuracy of the NIRS models. Predicting the fuel characteristic of the bio-char only by scanning the NIR spectrum, which costs only 10 seconds, will really be helpful for the biomass industry.

Evaluation of PSC on Container Ships Under Improved NIR Ship Targeting Model

Based on Improved New Inspection Regime (I-NIR) ship targeting model of Tokyo MoU, the impact of Port State Control (PSC) on container ships was assessed. Ship targeting is the first step in PSC. The proper method to target ship can help to find sub-standard ships and improve inspection efficiency. Through analyzing the data in recent 6 years, comparing the PSC overview of container ships both under old as well as new NIR models, and taking advantages of case study to evaluate the impact on ship risk level, the results show that I-NIR has a great impact on the inspection frequency and risk level of container ships in the Asia-Pacific region. Relevant shipping companies should actively adjust the strategy to prepare for PSC with the new characteristics under I-NIR.

Simultaneous detection of quality and safety in spinach plants using a new generation of NIRS sensors

Near infrared (NIR) spectrophotometers require study of the spectral acquisition process, so that they can be used for quality and safety assessment of horticultural products. The aim of this work was to optimize the use of two NIR spectrophotometers for analysing spinach plants in situ and online: a manual, portable instrument based on Linear Variable Filter (LVF) technology (MicroNIR™ 1700), suitable for analysis in the field, and during harvest and storage; and a Fourier Transform (FT)-NIR instrument (Matrix-F) suitable for the online analysis in the sorting lines. 195 spinach plants were used to predict the quality (texture, dry matter and soluble solid contents) and safety (nitrate content) parameters. Using the MicroNIR™ 1700 to take 6 spectra per spinach leaf resulted in NIRS models of predictive capacity which enable to screen spinach plants in situ and decide on their industrial destination according to their nitrate content. For the Matrix-F instrument, a single spectrum taken online for the intact product (either moving or not) on the conveyor belt was sufficient to establish product quality and safety during industrial processing. The results also showed that the use of both instruments could form a complementary strategy for global monitoring, allowing spinach plants to be analysed throughout the food supply chain.

Non-destructive measurement of soluble solids content of three melon cultivars using portable visible/near infrared spectroscopy

In this study, a non-destructive method using visible/near infrared (Vis/NIR) spectroscopy was investigated to predict the soluble solids content (SSC) of intact melons (Cucumis melo L.) cv. ‘Manao’, ‘Jinhongbao’, ‘Xizhoumi’. A set of 360 samples (120 melons of each cultivar) was used to develop the calibration model, and two location (stylar-end and equatorial locations) models were investigated independently. The samples' spectra were obtained by a portable Vis/NIR photo-diode array spectrometer operated in reflectance mode. Multiplicative scatter correction (MSC), first derivative and Savizky-Golay (SG) smoothing in turn were applied to the obtained spectra. The region from 750 to 950 nm was selected to develop NIR models combined with the partial least squares (PLS) regression method. The results indicated that the stylar-end of the intact melon was the proper location to evaluate the SSC in the intact melon due to its suitable and exclusive physiological structure. A competitive adaptive reweighted sampling (CARS) algorithm was used to select effective wavelengths. Results showed that the CARS algorithm had great potential for simplifying the variables. Furthermore, another 195 samples were used for external prediction to evaluate the CARS-PLS model's accuracy and stability, which resulted in a high determination coefficient (R2p = 0.83) and a low root mean square error (RMSEP = 0.73 oBrix).

Optimal management of oil content variability in olive mill batches by NIR spectroscopy

Total oil content (OC) is one of the main parameters used to characterize the whole of olives entering a commercial mill, quantified by the total fresh weight of the lot and the oil concentration (%) assessed in a representative sample on olive paste, by means of chemical extraction. Nuclear magnetic resonance (NMR) and NIR spectroscopy are alternative methods even at individual olives. This work evaluates several strategies to calibrate precise NIR models for the estimation of the total OC. To this end, 278 olives were analysed covering whole season variability in terms of olive fresh-weight and the corresponding OC by chemical extraction in 31 batches. The average spectra from hyperspectral NIR images (1003–2208 nm) were computed for each fruit and the actual OC (g) of those olives determined by NMR (0.09 to 1.29 g with a precision of 0.017 g). According to the results, current batch based assessment of the OC (Soxhlet, %) in mills only reproduces 44% of the underlying heterogeneity, despite being the factory standard. The incorporation of individual NIR spectra (278) to the 31 Soxhlet values of the batches allows a 67% explanation of the OC (%) of olives. When estimating OC (g) gathering individual fresh weight and the estimation of oil concentration in olives, a standard error of prediction of 0.061 g is reached (r2 = 0.93), a precision value that approaches the potential limit according to the NMR reference (0.017 g).

Real-time process quality control of ramulus cinnamomi by critical quality attribute using microscale thermophoresis and on-line NIR.

Real-time process quality control of ramulus cinnamomi (cassia twig) is still a challenge in pharmaceutical industry. Rapid critical quality attribute (CQA) determination of ramulus cinnamomi is essential for quality control. Microscale thermophoresis (MST) was used to investigate the CQA of ramulus cinnamomi by the interaction with biomacromolecule. There was a good affinity between cinnamaldehyde and human serum albumin (HSA) with Ka as 2.1722×103mol/L. It was an excellent combination of similarity to ibuprofen with same binding force as discovered as hydrogen bond and van der Waals force. Furthermore, regarding cinnamaldehyde as CQA, on-line near-infrared was used to monitor pilot extraction process of ramulus cinnamomi combined with high performance liquid chromatography (HPLC). Quantitative model was established with Rpre2 as 0.9798 and RMSECV as 0.0993, suggesting the NIR model was so robust and accurate for pilot process quality control. This method provided a perfect guideline for rapid CQA determination and real-time process quality control of Chinese materia medica (CMM) based on a vital CQA.

NIR model transfer of alkali-soluble polysaccharides in Poria cocos with piecewise direct standardization

Objective To investigate the feasibility for establishing near-infrared spectra model transfer with traditional Chinese medicine. Method Poria cocos, a kind of fungi (called “ Fuling” in Chinese), was used as an investigative object. NIR quantification model transfer of alkali-soluble polysaccharides in Poria cocos was studied. Spectra were obtained from Thermo Antaris II (master device) and Buchi NIRFlex N-500 (slave device), respectively. Procedures and factors that may have effects on the performance of spectral standardization have been investigated. Spline interpolation was applied to align spectra from two different instruments, and piecewise direct standardization was employed to standardize spectra from slave instrument. Full cross-validation was used to evaluate the result of model transfer. Results Spectral pre-processing including multiplicative signal correction and wavelength selection played an important role in operating the model transfer. Spectra within the region of 9000–6500 cm−1 provided relatively good transfer result. In addition, increasing window size had positive effect on improving the spectral transfer. In this study, a window size of 13 provided the best spectra standardization result. Test validation with five samples produced the best full cross-validation result with the values of correlation coefficient of calibration ([Formula: see text]), correlation coefficient of prediction ([Formula: see text]) and root mean square error of prediction as 0.86, 0.73 and 2.3, respectively. Conclusion In this study, many factors influenced the quality of the transferred slave spectra. And it is hard to provide a pretty good quantitative model transfer result about alkali-soluble polysaccharide in Poria cocos. The following factors have great effects on the quality of model transfer: (i) multiplicative signal correction which is necessary for model building; (ii) increasing the window size which has a positive effect on slave spectra standardization; (iii) a proper wavelength region benefits for model building; (iv) a proper scale of standardization group including proper samples is necessary.

Vibrational spectroscopy as a green technology for predicting nutraceutical properties and antiradical potential of early-to-late apricot genotypes

Vibrational spectroscopy is considered a green alternative to the traditional analytical methods for quantitative determination of antioxidants in agricultural products. In this study, the potential of NIR and MIR spectroscopy for the prediction of antioxidant compounds in seven early-to-late apricot genotypes from different locations was reported. NIR and MIR calibration models were developed using Partial Least Squares Regression (PLS) with cross-validation, and the performance of final models was evaluated using Rp2, root mean square error of prediction (RMSEP) and relative predicted determinant (RPD). Five independent PLS models were developed, using both NIR and MIR spectra from 300 samples per year and their corresponding total phenols (TPs), total flavans (FLs), total carotenoids (TCs), antiradical capacity (AC) reference values, yielding coefficient on average always > 0.95. Very good models were obtained for TPs (Rp2 = 0.99, RMSEP = 0.73 g kg−1 GAE, RPD = 12.0; Rp2 = 0.99, RMSEP = 0.95 g kg−1 GAE, RPD = 9.9 for MIR and NIR models, respectively), and FLs (Rp2 = 0.99, RMSEP = 0.99 g kg−1 CAT, RPD = 8.5; Rp2 = 0.98, RMSEP = 1.10 g kg−1 CAT, RPD = 6.4, for MIR and NIR models, respectively). Besides, both MIR and NIR models for total carotenoids (TCs) quantification showed a satisfactorily predictive capability (Rp2 = 0.98, RMSEP = 0.01 g kg-1 β-carotene, RPD = 6.5; Rp2 = 0.95, RMSEP = 0.01 g kg-1 β-carotene, RPD = 4.5, for MIR and NIR models, respectively). Good model performances were also obtained for the prediction of antiradical potential of samples, developed herein for the first time on freeze-dried apricot samples. In general, ATR-MIR demonstrated advantages over NIR in predicting all nutraceutical parameters, whereas similar model performances were obtained for the antiradical potential tested versus ABTS+• using MIR and NIR spectral regions. Results reported in the present study suggest that vibrational spectroscopy, both in MIR and NIR range, is adequate to predict the nutraceutical properties of apricots for screening purposes quickly and sustainably.

In-line near infrared spectroscopy for the prediction of moisture content in the tapioca starch drying process

Moisture content is an important parameter measured in tapioca starch production as this parameter has been shown to correlate strongly with the quality of the finished product. However, there is currently no in-line sensor which can be used to directly measure the moisture content of the product in real time.The objective of the present work was to study the use of an in-line measurement which can be introduced at the end of the drying process for tapioca starch moisture content evaluation. Either in-line NIR data or at-line NIR data was used to develop the necessary calibration models for evaluating the moisture content. Furthermore, calibration models were also developed by pooling the in-line and at-line data. Its performance was then verified using additional in-line data. The NIR model developed using 100% of the at-line data and 50% of the in-line data was validated using the unused 50% of the inline data. This model was shown to provide better performance in moisture content prediction with an SEP of 0.61% and a bias of 0.001%. In addition, the results showed that the at-line spectrum can also be used for the calibration model development to predict the moisture content of the samples scanned by an in-line spectrometer. However, the in-line spectrometer installation on a pneumatic conveying circular tube where tapioca starch and air mixed was found to be complicated due to significant vibration. This caused additional variation in the data with time. Therefore, it is concluded that the most suitable place for installing a spectrometer would be at a position involving a low pressure, or where the stream flow of a product is steadier in order to avoid the dynamic mixing of the product within the drying tube affecting the uncertainty of NIR scattering during the measurement.

Data fusion strategy in quantitative analysis of spectroscopy relevant to olive oil adulteration

Olive oil adulteration with various less expensive edible oils represents a great danger for consumers. Spectrometry has been used to detect olive oil adulteration with other oil, but we need more robust and accurate model. Therefore, this work investigated the combination of infrared (NIR) and mid infrared (MIR) spectroscopy for the quantification of rapeseed oil in olive oil blends. Furthermore, a partial least squares (PLS) model was established to predict the concentration of the adulterant. Models constructed using baseline correction by combination of standard normal variate (SNV), SG smoothing and vector normalization pretreatments, respectively. Three data fusion strategies (low, mid and high-level) have been applied to take advantage of the synergistic effect of the information obtained from NIR and MIR. We chose algorithm (SPA) to extract spectral features for mid-level data fusion. Binary linear regression used in high-level data fusion. We selected the best pretreatment for final evaluation according to the evaluation parameters (R2 of calibration and validation, RMSECV and RMSEP). NIR, MIR and data fusion models were evaluated by comparing the R2 of validation and RMSEP (root mean square error of prediction). The RMSEP of low-level (3.44), high-level (2.86) data fusion were better than NIR(7.09), MIR(4.04), mid-level(6.09)and the validation coefficient of determination R2 of low-level data fusion (0.975) and high-level data fusion (0.988) are better than the NIR (0.896) and MIR (0.966). Results showed that：(1) NIR and MIR are fast and non-destructive testing tools to detect the extra-virgin olive oil adulteration with rapeseed oil. (2) Low-level data fusion can effectively improve model prediction accuracy. (3) SPA reduced the number of variables, but it did not improved the results. (4) High-level data fusion strategy can be used as a reliable tool for quantitative analysis.

Robust soil mapping at the farm scale with vis–NIR spectroscopy

SummarySustainable agriculture practices are often hampered by the prohibitive costs associated with the generation of fine‐resolution soil maps. Recently, several papers have been published highlighting how visible and near infrared (vis–NIR) reflectance spectroscopy may offer an alternative to address this problem by increasing the density of soil sampling and by reducing the number of conventional laboratory analyses needed. However, for farm‐scale soil mapping, previous studies rarely focused on sample optimization for the calibration of vis–NIR models or on robust modelling of the spatial variation of soil properties predicted by vis–NIR spectroscopy. In the present study, we used soil vis–NIR spectroscopy models optimized in terms of both number of calibration samples and accuracy for high‐resolution robust farm‐scale soil mapping and addressed some of the most common pitfalls identified in previous research. We collected 910 samples from 458 locations at two depths (A, 0–0.20 m; B, 0.80–1.0 m) in the state of São Paulo, Brazil. All soil samples were analysed by conventional methods and scanned in the vis–NIR spectral range. With the vis–NIR spectra only, we inferred statistically the optimal set size and the best samples with which to calibrate vis–NIR models. The calibrated vis–NIR models were validated and used to predict soil properties for the rest of the samples. The prediction error of the spectroscopic model was propagated through the spatial analysis, in which robust block kriging was used to predict particle‐size fractions and exchangeable calcium content for each depth. The results indicated that statistical selection of the calibration samples based on vis–NIR spectra considerably decreased the need for conventional chemical analysis for a given level of mapping accuracy. The methods tested in this research were developed and implemented using open‐source software. All codes and data are provided for reproducible research purposes.HighlightsVis–NIR spectroscopy enables an increase in sampling density with little additional cost.Guided selection of vis–NIR calibration samples reduced the need for conventional soil analysis.Error of spectroscopic model prediction was propagated by spatial analysis.Maps from the vis–NIR augmented dataset were almost as accurate as those from conventional soil analysis.

Log‐ratio transformation is the key to determining soil organic carbon fractions with near‐infrared spectroscopy

SummaryInformation about soil organic carbon fractions is important in understanding the vulnerability of soil carbon to climate change and land management. Soil organic carbon can be divided into fractions that are labile and others that are more stable. All existing methods to fractionate soil organic carbon are time consuming and complex. Near‐infrared reflectance spectroscopy (NIRS) is a rapid analytical technique. In this study we evaluated and optimized the use of NIRS to predict soil organic carbon fractions with the constraint that the carbon fractions (labile and stabilized) should add up to 100% (total organic carbon content). We used samples from two datasets from agricultural and forest sites in Germany (dataset A) and Europe (dataset B). Samples were fractionated by two different methods (density and physical‐chemical fractionation) as reference methods. Soil samples were scanned in the NIR range and calibration models were developed using partial least squares regression. The key to improving model performance was the log‐ratio transformation proposed by Aitchison for compositional data that enabled us to model the fractions as dependent variables. Traditional methods for the optimization of NIRS models, such as selection of wavelength range and pretreatment of spectra, showed no effective reduction in error. With the constraint that both fractions add to 100% (log‐ratio transformation), the ratio of performance to deviation (RPD) increased from 1.6 to 2.8 for the labile C fraction and from 1.5 to 3.2 for the stabilized C fraction. Root mean square error of cross‐validation of the labile fraction was 4.3 g C kg−1for dataset A and 2.7 g C kg−1for dataset B, which corresponds to anR2of 0.88–0.80 and RPD of 2.9–2.2, respectively. The models performed equally well for different soil textures and land‐use types. With log‐ratio transformation, more precise calibrations of NIRS for C fractions could be obtained.HighlightsNear‐infrared spectroscopy can predict soil carbon fractions at different spatial extentsLog‐ratio transformation satisfies the constraint that fractions should add up to 100%Log‐ratio transformation is the key to improving calibrations for soil C fractions.The NIR models were tested with two fractionation schemes comprising different land uses and soil texture

Comparison of Cation Exchange Capacity Estimated from Vis–NIR Spectral Reflectance Data and a Pedotransfer Function

Core Ideas Conventional methods for measuring CEC are time consuming and costly. A Vis–NIRS model and pedotransfer function (PTF) were developed for CEC determination. The Vis–NIRS model estimated CEC accurately for different soil types. The Vis–NIRS CEC model performed better than the PTF based on clay and organic C contents. Knowledge of the cation exchange capacity (CEC) for soils or other porous media is very important for civil engineering and agricultural applications. However, the standard laboratory methods to measure CEC are costly and laborious. The aim of this research was to develop a visible–near‐infrared spectroscopy (Vis–NIRS, 400–2500 nm) calibration model to predict CEC based on multivariate analysis and to compare the predictive ability of Vis–NIRS with that of a pedotransfer function (PTF). For this purpose, reference CEC was measured by the ammonium acetate method for 235 soil samples, collected from 21 countries. Diffuse spectral reflectance data were also collected by using a NIRSTM DS2500 spectrometer. The model was constructed on a calibration subset (80%) and evaluated with a validation subset (20%) using partial least squares regression. The Vis–NIRS calibration model was sufficiently robust based on the cross‐validation results [R2 = 0.79, RMSE of cross‐validation values of 7.9 cmolc kg−1 and bias = −0.14]. The independent validation of the Vis–NIRS model showed good prediction accuracy, regardless of sample origin (RMSE of prediction value of 5.0 cmolc kg−1 and ratio of performance to interquartile distance value of 4.5). Moreover, the Vis–NIRS prediction performance was superior to that of the PTF, which was influenced by the sample origin (RMSE values of 11.5 cmolc kg−1). The better prediction of CEC by the Vis–NIRS calibration model suggests that it is due to the co‐variation of CEC with clay (type and content) and organic C content.

Comparing Visible–Near‐Infrared Spectroscopy and a Pedotransfer Function for Predicting the Dry Region of the Soil‐Water Retention Curve

Core Ideas The Campbell–Shiozawa (CS) model was fitted to dry region data. The CS model was anchored to soil water content at −106 cm H2O matric potential. The model's parameters were well predicted from vis‐NIR spectra or a PTF. Both the vis‐NIRS and PTF models gave good predictions of dry region water retention. The soil‐water retention curve (SWRC) at the dry end, also known as soil water vapor sorption isotherms, is essential for the modeling of water vapor transport, microbial activity, and biological processes such as plant water uptake in the vadose zone. Measurement of detailed soil water vapor sorption isotherms (WSIs) can be time consuming. Therefore, we propose rapid, inexpensive methodologies (visible–near‐infrared spectroscopy [vis–NIRS] and a pedotransfer function [PTF]) to predict the Campbell–Shiozawa (CS) model parameters to obtain the WSIs. Water vapor sorption isotherms were measured on 144 soil samples with a vapor sorption analyzer. The CS semi‐logarithmic‐linear function anchored at a soil‐water matric potential of −106 cm H2O (log|−106| = pF 6) was fitted to the measured data because it accurately characterizes the WSIs. Thereafter, a vis–NIRS calibration model and a PTF, based on clay and organic C contents, were developed and used to predict the two reference CS model parameters (α and W6). Both parameters were predicted with a reasonable degree of accuracy using vis–NIRS and the PTF (for α, RMSE values of 0.0041 and 0.0025, and for W6, RMSE values of 0.0042 and 0.0034 for vis–NIRS and the PTF, respectively). Based on the predicted α and W6 values, the predicted WSIs compared closely with the measured isotherms for individual soil samples from each field. At the field scale, the vis–NIRS model performed marginally better than the PTF. Thus, it is evident that the use of vis–NIRS or PTFs provides a relatively inexpensive approach to predicting soil water sorption isotherms.

Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy

Visible and near-infrared (Vis–NIR) spectroscopy is used to estimate soil organic matter (SOM). Spectral preprocessing techniques and multivariate modeling methods play important roles in the quantitative analysis of SOM. First and second derivatives (i.e., the conventional integer order derivatives) are commonly used spectral derivatives, which, however, may ignore some detailed spectral information regarding SOM. Here, we presented a fractional order derivative (FOD) method to preprocess the reflectance spectra. Robust modeling methods are still required for accurate estimation of SOM. Local modeling technique (memory-based learning, MBL) was introduced to compare with two global modeling approaches, namely, partial least square (PLS) and random forest (RF). A total of 535 topsoil samples were gathered from Hubei Province, Central China, with their reflectance spectra and SOM contents measured in the laboratory. FOD was allowed to vary from 0 to 2 with an increment of 0.25 at each step. Coefficient of determination (R2) and ratio of the performance to deviation (RPD) were employed as performance statistics during validation. Results showed that with the increase of derivative order, the baseline drifts and overlapping peaks were gradually removed but the spectral strength decreased concurrently. Higher derivative order reflectance (i.e., 1.5-order, 1.75-order, and 2-order reflectance) were more susceptible to spectral noise interferences. The correlation coefficient of SOM with FOD processed spectra at some specific wavelengths was larger than that with the original reflectance. MBL performed better than PLS and RF, regardless of FOD transformation. Calibration with 0.25-order reflectance and MBL provided the most accurate estimation of SOM, with an RPD of 2.23. Our results confirm the effectiveness of FOD and local modeling (MBL) in the development of Vis–NIR models for SOM estimation.

Developing Near‐ and Mid‐Infrared Spectroscopy Analysis Methods for Rapid Assessment of Soil Quality in Illinois

Core Ideas Lithology‐based libraries may support reusable NIR models to predict soil quality. Monte Carlo feature selection and spectral refinement of NIR‐ and MIR‐based models. Use of randomly drawn validation datasets and overestimation of RPD. Estimation of plant growth via NDVI and relation to NIR‐ and MIR‐based models. Rapid, affordable assessment of soil quality (SQ) is needed to maintain soil health. We sought to build reusable near‐ (NIR) and mid‐infrared‐ (MIR) based models using a regional library of accepted SQ indicators and Normalized Difference Vegetation Index (NDVI). This described soil health in 228 topsoil samples from 57 grain fields in Illinois paired to compare the influence of conventional, organic or conservation‐tillage practices. Predictive models for all SQ indicators were developed with Partial Least Squares Regression (PLSR) using: (i) whole NIR or MIR spectra, (ii) refined spectra that included features associated with organic functional groups, or (iii) refined spectra that were identified as important using Monte Carlo feature selection (MCFS). Comparison of model building methods using randomly or systematically divided calibration and validation sets suggests that the random method overestimated model Residual Prediction Deviation (RPD). Use of systematically selected validation sets produced acceptable (RPDs ≥ 1.4) models for soil organic C (SOC), total N (TN), and exchangeable Ca and Mg using whole NIR spectra. Use of spectral refinement techniques improved MIR‐based models for SOC, TN, and exchangeable Ca to RPDs ≥ 1.4. Poor performance of models predicting other SQ indicators, including particulate organic matter (POM), potentially mineralizable N (PMN), and fluorescein diacetate hydrolysis (FDA), is attributed to the influence of mineralogy associated with different types of loess. Use of lithology‐based libraries may support reusable and affordable NIR‐based models to predict SOC, TN, Ca and Mg. The addition of weather or moisture co‐variates may be needed to build models reliant on NDVI.

On-line measurement of activation energy of ground bamboo using near infrared spectroscopy

Abstract On-line measurement of activation energy (Ea) is very important in supporting the thermal conversion process. The main objective of this study was to evaluate the Ea of ground bamboo using near infrared spectroscopy in real time. 80 bamboo samples with different diameters were selected using random sampling. Ea was determined using the Coats-Redfern method, and Ea of reaction order (n) at n = 1 and n≠1 was investigated. The performance of on-line measurement predicted by PLS modelling for Ea at n = 1 and Ea at n≠1 showed coefficients of determination of 0.781 and 0.714, respectively; standard error of prediction of 5.249 and 6.858 kJ/mol, respectively; and bias values of −1.0628 and −1.871 kJ/mol, respectively. Both PLS models were found to be fair and could be applied toward screening. The results showed that the vibration bands of lignocellulosic components (CH2, hemicellulose, cellulose, and lignin) highly influenced model development. Moreover, internal relationships were identified among Ea, the pre-exponential factor (A), and n, such as A (1/min) = 63251 × e0.2200×Ea (at n = 1), A (1/min) = 33719 × e0.2267×Ea (at n≠1), and n = 0.008 × Ea+0.254. These relationships can be used to evaluate A and n if Ea is known. In the case of this study, Ea was forecasted using an NIR model.

Raw pH fall-out as a sign of a mycorrhizal modifier of Sorghum sudanensis

The management of symbiotic Microbial Biota (MB) in the soil as agents that promote the yield and health of crops, is aimed at inducing modifications of the phenotype of plants, both over and under the ground. It is here shown, in Sorghumsudanensis plants, that: i) a simple response to MB inoculation is the result of the fall out of the raw pH; ii) the simple NIR scans of leaves can be considered to rapidly classify the outcomes; iii) the raw pH can be considered a key-variable of leaf modifications. An experiment was carried out on Sorghumsudanensis. The plants were seeded in pots and grown for 66 d, and then a control non-inoculated group (C) was compared with thirteen Arbuscular Mycorrhizae (AM) Glomus inoculated groups and with two commercial MB products. A total of 374 raw pH measurements conducted on the leaves showed that the 5.18 pH units in the C group were scaled by -1.9% (P<0.0336) in the MB group and by -3.4% in the AM group (P<0.0001), with a relevant diversity between groups. Direct discrimination of these three groups, by means of smart NIR-SCIO, showed a % reclassification of the C, MB and AM groups of 74%, 59% and 96% in the fresh leaves and of 65%, 51% and 94% in the dried ground leaves, respectively. The composition of the dried leaves, based on a set of 14 variables predicted via NIRS models, plus the total foliar dry weight and percentage, showed a typical increase in protein, ash and hemicellulose, and a typical decrease in the cellulose, dry matter, crude fiber and crop maturity index. These variables were related to the foliar pH, as a key-variable, by means of a PLS standard model (R2 0.81) in which a low pH steadily favored the dry mass weight and, to a lesser extent, the hemicellulose and the digestible NDF contents; on the other hand, a high pH increased the dry matter percentage and the cellulose content of the leaves. As expected, the leaves of the inoculated plants showed a more juvenile ontogenic status. The epigean botanical modifications can be considered harmonic expressions of a luxuriant symbiosis, as testified by the homologous NIR categorization. The outlook for a symbiotic agriculture, with mycorrhizal plants, should consider the raw pH as a multifaceted variable.

Spectral fusion by Outer Product Analysis (OPA) to improve predictions of soil organic C

Soil organic carbon (C) is an important indicator of agricultural and environmental quality. It improves soil fertility and helps to mitigate greenhouse gas emissions. Soil spectroscopy with either vis–NIR (350–2500 nm) or mid-IR (4000–400 cm−1) spectra have been used successfully to predict organic C concentrations in soil. However, research to improve predictions of soil organic C by simply combining vis–NIR and mid-IR spectra to model them together has been unsuccessful. Here we use the Outer Product Analysis (OPA) to fuse vis–NIR and mid-IR spectra by bringing them into a common spectral domain. Using the fused data, we derived models to predict soil organic C and compared its predictions to those derived with vis–NIR and mid-IR models separately. We analyzed 1259 tropical soil samples from surface and subsurface layers across agricultural areas in Central Brazil. Soil organic C contents were determined by a modified Walkley-Black method, and vis–NIR and mid-IR reflectance spectra were obtained with a FieldSpec Pro and a Nicolet 6700 Fourier Transformed Infrared (FT-IR), respectively. Reflectances were log-transformed into absorbances. The mean content of soil organic C was 9.14 g kg−1 (SD = 5.64 g kg−1). The OPA algorithm was used to emphasize co-evolutions of each spectral domain into the same one by multiplying the absorbances from both sets of spectra to produce a matrix with all possible products between them. Support Vector Machine with linear kernel function was used for the spectroscopic modeling. Predictions of soil organic C using vis–NIR, mid-IR, and fused spectra were statistically compared by the Tukey's test using the coefficient of determination (R2), root mean squared error (RMSE), and ratio of performance to interquartile distance (RPIQ). Absorbances in vis–NIR and mid-IR were emphasized in the common spectral domain presenting stronger correlations with soil organic C than individual ranges. Soil organic C predictions with the OPA fused spectra were significantly better (R2 = 0.81, RMSE = 2.42 g kg−1, and RPIQ = 2.87) than those with vis–NIR (R2 = 0.69, RMSE = 3.38 g kg−1, and RPIQ = 2.08) or mid-IR spectra (R2 = 0.77, RMSE = 2.90 g kg−1, and RPIQ = 2.43). Fusing vis–NIR and mid-IR spectra by OPA improves predictions of soil organic C.