Diffuse Reflectance Mid-infrared Fourier Transform Spectroscopy Research Articles

Plant footprint decreases the functional diversity of molecules in topsoil organic matter after millions of years of ecosystem development

AbstractAimTheory suggests that the diversity of molecules in soil organic matter (SOM functional diversity) provides key insights on multiple ecosystem services. We aimed to investigate how and why SOM functional diversity and composition change as topsoils develop, and its implications for key soil functions (e.g., from nutrient pool to water regulation).LocationWe reported data on 16 soil chronosequences globally distributed in nine countries from six continents.Time Period2016–2017.Major Taxa StudiedSoil microbes (bacteria and fungi) and vascular plants.MethodsSOM functional diversity and composition without mineral interference were measured using diffuse reflectance mid‐infrared Fourier transform spectroscopy (DRIFT). We aimed to characterize the main environmental factors related to SOM functional diversity and composition. Also, we calculated the links among SOM functional diversity and key soil functions.ResultsWe found that SOM functional diversity declines after millions of years of soil formation (pedogenesis). We further showed that increases in plant cover and productivity led to a higher ratio of reduced (e.g., alkanes) over oxidized carbon forms (i.e., C: O‐functional groups ratio), which was positively correlated to SOM functional diversity as soils age. Our findings indicated that the plant footprint (i.e., the accumulation of plant‐derived material promoting the C: O‐functional group ratio) would explain the reduction of SOM functional diversity as ecosystems develop. Moreover, the dissimilarity in SOM composition consistently increased with soil age, with the soil development stage emerging as the main predictor of SOM dissimilarity across contrasting biomes.Main ConclusionsOur global survey contextualized the natural history of SOM functional diversity and composition during long‐term soil development. Together, we showed how plant footprint drives the losses of SOM functional diversity with increasing age, which might provide a novel mechanism to explain typically reported losses in ecosystem functions during ecosystem retrogression.

Rapid discrimination of adulteration in Radix Astragali combining diffuse reflectance mid-infrared Fourier transform spectroscopy with chemometrics

Fraud in the global food and related products supply chain is becoming increasingly common due to the huge profits associated with this type of criminal activity and yet strategies to detect fraudulent adulteration are still far from robust. Herbal medicines such as Radix Astragali suffer adulteration by the addition of less expensive materials with the objective to increase yield and consequently the profit margin. In this paper, diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) was used to detect the presence of Jin Quegen in Radix Astragali. 900 fake samples of Radix Astragali produced by 6 different regions were constructed at the levels of 2%, 5%, 10%, 30% and 50% (w/w). DRIFTS data were analyzed using unsupervised classification method such as principal component analysis (PCA), and supervised classification method such as linear discrimination analysis (LDA), K-nearest neighbor (K-NN), linear discrimination analysis combining K-nearest neighbor (LDA-KNN) and partial least squares discriminant analysis (PLS-DA). The results of PCA showed that it was feasible to detect the adulteration of Radix Astragali by the combination of drift technique and chemometrics. PLS-DA obtained the best classification results in all four supervised methods with mean-centralization as the data preprocessing method, the prediction accuracy of PLS-DA model for the six groups of sample ranged from 95.00% to 98.33%. At the same time, LDA-KNN also achieved good classification results, and its correct prediction rate were also between 86.67% and 100.0%. The prediction results confirmed that the combination of DRIFTS technology and chemometrics can distinguish the amount of adulteration present in Radix Astragali. Additionally, the innovative strategy designed can be used to test the fraud of various forms of herbal medicine in other products.

High-throughput assessment of soil carbonate minerals in urban environments

As urbanization expands, interest in understanding soil properties and functions in urban environments has also increased. Urban soils play an important role in the global carbon (C) cycle. Due to natural and human influences on various forms of C, quantifying soil inorganic carbon (SIC) becomes critical in urban areas. In this study, the suitability of diffuse reflectance mid-infrared Fourier-transform spectroscopy (mid-DRIFTS) to rapidly estimate calcite and dolomite concentrations was tested. Also, the effectiveness of HCl acid pre-treatment to remove carbonates to enable proper estimation of soil organic carbon (SOC) in urban soils was evaluated. Specific mid-infrared frequencies known to be associated with carbonates, i.e. dolomite and calcite, were identified, and specific peak areas were measured and calibrated with results obtained with the volumetric calcimeter method. Multiple linear regression was used to estimate total carbonates with peaks areas at 2930 cm−1, 2515 cm−1 and 730 cm−1 resulting in a 3.72% root mean squared error of an independent validation set (RMSEV). Greater prediction accuracy of calcite (875 cm−1) and dolomite (875 cm−1 and 730 cm−1) were achieved using single peaks in bivariate relationships resulting in RMSEV of 1.27% and 4.22%, respectively. Soil samples were treated with 1 M HCl for 24 h to remove carbonates, and total carbonate, dolomite, and calcite contents were estimated again using both volumetric calcimeter and mid-DRIFTS. The reduction in carbonate concentrations post-acid treatment was considerable (97% for dolomite, and 96% for calcite and total carbonates), but was not complete. Thus, this could compromise quantification of other soil carbon fractions such as total SOC. Using mid-DRIFTS peak areas of CO3−2 functional groups represents a robust alternative for rapid, fast, and low-cost laboratory measurements of soil carbonates, and this high-throughput approach may be used in assays aiming to minimize carbonate interference in SOC estimations.

DRIFTS band areas as measured pool size proxy to reduce parameter uncertainty in soil organic matter models

Abstract. Soil organic matter (SOM) turnover models predict changes in SOM due to management and environmental factors. Their initialization remains challenging as partitioning of SOM into different hypothetical pools is intrinsically linked to model assumptions. Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) provides information on SOM quality and could yield a measurable pool-partitioning proxy for SOM. This study tested DRIFTS-derived SOM pool partitioning using the Daisy model. The DRIFTS stability index (DSI) of bulk soil samples was defined as the ratio of the area below the aliphatic absorption band (2930 cm−1) to the area below the aromatic–carboxylate absorption band (1620 cm−1). For pool partitioning, the DSI (2930 cm−1 ∕ 1620 cm−1) was set equal to the ratio of fast-cycling ∕ slow-cycling SOM. Performance was tested by simulating long-term bare fallow plots from the Bad Lauchstädt extreme farmyard manure experiment in Germany (Chernozem, 25 years), the Ultuna continuous soil organic matter field experiment in Sweden (Cambisol, 50 years), and 7 year duration bare fallow plots from the Kraichgau and Swabian Jura regions in southwest Germany (Luvisols). All experiments were at sites that were agricultural fields for centuries before fallow establishment, so classical theory would suggest that a steady state can be assumed for initializing SOM pools. Hence, steady-state and DSI initializations were compared, using two published parameter sets that differed in turnover rates and humification efficiency. Initialization using the DSI significantly reduced Daisy model error for total soil organic carbon and microbial carbon in cases where assuming a steady state had poor model performance. This was irrespective of the parameter set, but faster turnover performed better for all sites except for Bad Lauchstädt. These results suggest that soils, although under long-term agricultural use, were not necessarily at a steady state. In a next step, Bayesian-calibration-inferred best-fitting turnover rates for Daisy using the DSI were evaluated for each individual site or for all sites combined. Two approaches significantly reduced parameter uncertainty and equifinality in Bayesian calibrations: (1) adding physicochemical meaning with the DSI (for humification efficiency and slow SOM turnover) and (2) combining all sites (for all parameters). Individual-site-derived turnover rates were strongly site specific. The Bayesian calibration combining all sites suggested a potential for rapid SOM loss with 95 % credibility intervals for the slow SOM pools' half-life being 278 to 1095 years (highest probability density at 426 years). The credibility intervals of this study were consistent with several recently published Bayesian calibrations of similar two-pool SOM models, i.e., with turnover rates being faster than earlier model calibrations suggested; hence they likely underestimated potential SOM losses.

Soil sample drying temperature affects specific organic mid-DRIFTS peaks and quality indices

Peak area (PA) integration of diffuse reflectance mid infrared Fourier transform spectroscopy (DRIFTS) spectra is useful to study soil organic matter (SOM) quality and as a potential modelling proxy. Residual water of soil samples after drying affects DRIFTS spectra, mainly in the region >2500 cm−1. Therefore, SOM quality related PA at different wavenumbers should be influenced to varying degrees. We studied how absolute, normalized and relative PA related to SOM functional groups (at 2930, 1620, 1530 and 1159 cm−1), as well as their ratios were influenced by oven drying of soil samples at increasing temperatures (from 32 °C to 105 °C). All organic and even mineral associated PA significantly increased with drying temperature. The PA of aliphatic CH stretching (2930 cm−1), located on the shoulder of the broad OH stretching PA, was influenced most strongly. Our results indicate that using 105 °C as drying temperature and storing samples in a desiccator is the best way to minimize water interference. These findings apply to relative PA and ratios of organic compounds, while no effect of drying temperatures on PA correlations to C or N were found.

Predicting partitioning of radiolabelled 14C-PFOA in a range of soils using diffuse reflectance infrared spectroscopy

The aim of this study was to establish partitioning coefficients (Kd) of perfluorooctanoic acid (PFOA) in a wide range of soils and determine if those values can be predicted from soil properties using multiple linear regression (MLR) and from infrared spectra of soils using partial least squares regression (PLSR). For 100 different soils, the Kd values of spiked radiolabelled 14C-PFOA ranged from 0.6 to 14.8 L/kg and significantly decreased with soil depth (p < 0.05) due to soil properties that change with depth. The MLR modelling revealed that PFOA sorption was significantly (p < 0.05) influenced, in decreasing order, by organic carbon (OC) content, silt-plus-clay content and soil pH. Soils were partitioned into all soils and surface soils alone. The MLR models using OC, silt-plus-clay content and pH together explained most of the variation in sorption in all soils as well as surface soils alone (0-15 cm). However, correlations between soil properties and Kd values in some soils could not be explained by the MLR model. Modelling of Kd prediction in soils with PLSR and diffuse reflectance (mid) infrared Fourier transform spectroscopy (DRIFT) showed comparable success in explaining the predictions of Kd values, including some of the outliers identified in the MLR model. The PLSR loading weights suggested that quartz, and possibly pyrophyllite minerals, were inversely correlated with the Kd values. Given that MLR requires a-priori characterisation of a range of soil properties and PLSR-DRIFT is a method based on the direct relationship between spectra and soil components, mid-infrared spectroscopy may be a more economical and rapid technique to predict the solid-liquid partitioning of PFOA in soils.

Assessing the authenticity of black pepper using diffuse reflectance mid-infrared Fourier transform spectroscopy coupled with chemometrics

Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) coupled with chemometrics was used to examine the possibility of verifying the origin and authenticity of black pepper in this study. DRIFTS were obtained for 150 pure black pepper samples of different geographical origin (Kerala, HaiNan, DongNai, Sarawak, Fujian) and 1200 fake black pepper (adulterated with sorghum or Sichuan pepper, doping levels from 5% to 50%, respectively) by recording within a spectroscopic range of 4000–400 cm−1. Collected data were treated using principal component analysis (PCA), genetic algorithm optimized support vector machine (GA-SVM) and partial least squares discriminant analysis (PLS-DA). PCA model of the DRIFTS revealed the clustering trend of the pure and fake black pepper samples. Accurate and reliable classification results were obtained by DRIFTS combined GA-SVM and PLS-DA model. For the pure black pepper sample, the training set and prediction set of GA-SVM and PLS-DA models all achieved 100% correct recognition rate. For the fake black pepper sample, the total recognition rate of GA-SVM and PLS-DA model were greater than 98% for the training sets and more than 96% for the predicted sets, respectively. The results of this study demonstrated that DRIFTS method combined with chemometric tools can be successfully applied to detect mislabeling and adulteration of black pepper.

Comparison and application of fluorescence EEMs and DRIFTS combined with chemometrics for tracing the geographical origin of Radix Astragali

Selection of the appropriate method for traceability may be of great interest for the characterization of food authenticity and to reveal falsifications. The possibility of tracing the geographical origins of Radix Astragali based on diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) technique and fluorescence fingerprints (EEMs) technique was investigated in this work. DRIFTS technique combined with PCA and PLS-DA and EEMs technique combined with M-PCA and N-PLS-DA were used to determine the geographical origin of Radix Astragali samples, respectively. DRIFTS-PLS-DA provided total recognition rates of 98.4% for all Radix Astragali samples in the training sets and 94.6% in the predicted sets. Compared with the DRIFTS, EEMs combined with chemometrics obtained more accurate recognition results. The total recognition rates (RRs) of the training sets and prediction sets obtained with EEMs-N-PLS-DA were all 100%. The good classification results of fluorescence fingerprints technique should be attributed mainly to two reasons. One reason is that three-dimensional fluorescence spectrum can provide more information than two-dimensional DRIFTS, and the other reason is that fluorescence spectrum has higher sensitivity and selectivity than the DRIFTS. Therefore, fluorescence fingerprint (EEMs) technique combined with chemometrics results more adequate for tracing the food geographical origin. It should be noted that the more the analysis target contains fluorescent substances, the more accurate results are obtained by using the fluorescent fingerprint method. Conversely, if the classification object contains very few fluorescent substances, the classification result may not be as good as the DRIFTS method. Furthermore, due to relatively cumbersome operation of fluorescence method, EEMs fluorescence method is unsuitable for rapid analysis as compared to infrared method.

The impacts of four potential bioenergy crops on soil carbon dynamics as shown by biomarker analyses and DRIFT spectroscopy

AbstractPerennial bioenergy crops accumulate carbon (C) in soils through minimally disturbing management practices and large root inputs, but the mechanisms of microbial control over C dynamics under bioenergy crops have not been clarified. Root‐derived C inputs affect both soil microbial contribution to and degradation of soil organic matter resulting in differing soil organic carbon (SOC) concentrations, storage, and stabilities under different vegetation regimes. Here, we measured biomarker amino sugars and neutral sugars and used diffuse reflectance mid‐infrared Fourier transform spectroscopy (DRIFTS) to explore microbial C contributions, degradation ability, and SOC stability, respectively, under four potential bioenergy crops, M.×giganteus (Miscanthus × giganteus), switchgrass (Panicum virgatum L.), a mixed prairie, and a maize (Zea mays L.)–maize–soybean (Glycine max(L.) Merr.) (MMS) rotation over six growing seasons. Our results showed that SOC concentration (g/kg) increased by 10.6% in mixed prairie over the duration of this experiment and SOC storage (Mg/ha) increased by 17.0% and 15.6% in switchgrass and mixed prairie, respectively. Conversion of row crops to perennial grasses maintained SOC stability and increased bacterial residue contribution to SOC in M.×giganteus and switchgrass by 20.0% and 15.0%, respectively, after 6 years. Degradation of microbe‐derived labile SOC was increased in M.×giganteus, and degradation of both labile and stable SOC increased in MMS rotation. These results demonstrate that microbial communities under perennial grasses maintained SOC quality, while SOC quantity increased under switchgrass and mixed prairie. Annual MMS rotation displayed decreases in aspects of SOC quality without changes in SOC quantity. These findings have implications for understanding microbial control over soil C quantity and quality under land‐use shift from annual to perennial bioenergy cropping systems.

Ecological risk assessment on heavy metals in soils: Use of soil diffuse reflectance mid-infrared Fourier-transform spectroscopy

The bioavailability of heavy metals in soil is controlled by their concentrations and soil properties. Diffuse reflectance mid-infrared Fourier-transform spectroscopy (DRIFTS) is capable of detecting specific organic and inorganic bonds in metal complexes and minerals and therefore, has been employed to predict soil composition and heavy metal contents. The present study explored the potential of DRIFTS for estimating soil heavy metal bioavailability. Soil and corresponding wheat grain samples from the Yangtze River Delta region were analyzed by DRIFTS and chemical methods. Statistical regression analyses were conducted to correlate the soil spectral information to the concentrations of Cd, Cr, Cu, Zn, Pb, Ni, Hg and Fe in wheat grains. The principal components in the spectra influencing soil heavy metal bioavailability were identified and used in prediction model construction. The established soil DRIFTS-based prediction models were applied to estimate the heavy metal concentrations in wheat grains in the mid-Yangtze River Delta area. The predicted heavy metal concentrations of wheat grain were highly consistent with the measured levels by chemical analysis, showing a significant correlation (r2 > 0.72) with acceptable root mean square error RMSE. In conclusion, DRIFTS is a promising technique for assessing the bioavailability of soil heavy metals and related ecological risk.

Effects of soil composition and preparation on the prediction of particle size distribution using mid-infrared spectroscopy and partial least-squares regression

Soil composition and preparation can affect prediction accuracy using diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS). In the present study, we evaluated the effect of soil composition, preparation and carbonate content on the accuracy of particle size distribution (PSD) predictions in four contrasting sets of soils, including calcareous soils, using partial least-squares regression (PLSR). The soils were scanned as &lt;2- and &lt;0.1-mm fine-ground samples. Regression calibrations were derived for individual soil sets, as well as a composite of the four sets. Predictions for clay and sand for the &lt;2-mm composite calibration resulted in good accuracy (coefficient of determination R2=0.78; ratio of the standard deviation of reference values to the prediction error (RPD)=2.2), but were less accurate for clay in the calcareous soils (R2=0.70–0.78; RPD=1.8–1.1) and similarly accurate for sand (R2=0.68–0.80; RPD=1.7–2.2). Predictions for silt were poor. Accuracies improved by fine grinding (R2=0.88, RPD=2.9 for clay; R2=0.84, RPD=2.9 for sand). It was concluded that single, large and highly variable sets rather than site-specific calibrations could be used for the PSD predictions of specific soil sets. Changes in the PLSR loading weights, resulting from grinding, could be linked to an improved access of the infrared beam to the soil matrix by removal or dilution of surface coatings, resulting in a reduction of inter- and intraparticulate heterogeneity.

Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) for rapid identification of dried sea cucumber products from different geographical areas

In this study, diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS), principal components analysis (PCA) and soft independent modeling of class analogy (SIMCA) were performed on the classification of dried sea cucumber samples of the same species from various geographical areas using fingerprint region (1700–600 cm −1). A set of 96 individual dried sea cucumbers ( Apostichopus japonicus Selenka) grown up in four different geographical regions (Rongcheng, 16 individuals; Weihai, 20 individuals; Yantai, 40 individuals; Dalian, 20 individuals) in East China Sea were analyzed and successfully differentiated into four classes according to the distribution of geographical areas in spite of small differences in IR fingerprints. The result shows that the environmental influencing factor is the key factor contributing to the classification of sea cucumbers. And, this study also demonstrates that DRIFTS is a convenient tool for fast differentiation of seafoods from various geographical areas, such as dried sea cucumber products.

Classification of Italian honeys by mid-infrared diffuse reflectance spectroscopy (DRIFTS)

Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) and multivariate statistical analysis methods were used for the identification and classification of honey from different floral sources. The 82 honey samples (robinia, chestnut, citrus, polyfloral) were scanned by DRIFTS in the region 4000–600 cm −1 and also transformed in 1st and 2nd derivatives. Spectral data were analyzed by principal component analysis, general discriminant analysis and classification tree analysis. Classification accuracy near 100% was obtained by discriminant and classification tree analyses. Classification models were successfully validated with one-third leave out method and a classification of about 100% were achieved.

Quantified NOx adsorption on Pt/K/gamma-Al2O3 and the effects of CO2 and H2O

A multi-component NOx-trap catalyst consisting of Pt and K supported on γ-Al2O3 was studied at 250°C to determine the roles of the individual catalyst components, to identify the adsorbing species during the lean capture cycle, and to assess the effects of H2O and CO2 on NOx storage. The Al2O3 support was shown to have NOx trapping capability with and without Pt present (at 250°C Pt/Al2O3 adsorbs 2.3μmols NOx/m2). NOx is primarily trapped on Al2O3 in the form of nitrates with monodentate, chelating and bridged forms apparent in Diffuse Reflectance mid-Infrared Fourier Transform Spectroscopy (DRIFTS) analysis. The addition of K to the catalyst increases the adsorption capacity to 6.2μmols NOx/m2, and the primary storage form on K is a free nitrate ion. Quantitative DRIFTS analysis shows that 12% of the nitrates on a Pt/K/Al2O3 catalyst are coordinated on the Al2O3 support at saturation.When 5% CO2 was included in a feed stream with 300ppm NO and 12% O2, the amount of K-based nitrate storage decreased by 45% after 1h on stream due to the competition of adsorbed free nitrates with carboxylates for adsorption sites. When 5% H2O was included in a feed stream with 300ppm NO and 12% O2, the amount of K-based nitrate storage decreased by only 16% after 1h, but the Al2O3-based nitrates decreased by 92%. Interestingly, with both 5% CO2 and 5% H2O in the feed, the total storage only decreased by 11%, as the hydroxyl groups generated on Al2O3 destabilized the K–CO2 bond; specifically, H2O mitigates the NOx storage capacity losses associated with carboxylate competition.

Determining cement composition by Fourier transform infrared spectroscopy

A diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) method is described for obtaining high quality Fourier transform infrared (FTIR) spectra of cements. DRIFT spectra of synthetic C 3 S, C 2 S, C 3 A, and C 4 AF and of pure gypsum, bassanite, anhydrite, syngenite, and calcite are shown. Typical spectra of American Petroleum Institute class G and class A cements display characteristic features which can be related qualitatively to variations in the constituent minerals. For quantitative analysis, the FTIR spectra of 156 cements of varied origin and known elemental composition have been used to construct multivariate calibration models. These relate the spectrum to composition (expressed in terms of nine mineral components and five minor oxides) and allow the composition of unknown cements to be determined rapidly from the FTIR spectrum alone. Error estimates are given.