Soil water repellency (WR) is a widespread phenomenon caused by aggregated organic matter (OM) and layers of hydrophobic organic substances coating the surface of soil particles. These substances have a very low surface free energy, reducing a soil's water attraction. There is focus on WR due to its effects on germination, root growth, liquid–vapour dynamics, surface erosion and leaching of chemicals through fingered flow paths. However, common techniques for measuring WR are time-consuming and expensive. Meanwhile, it is well established that visible near infrared (vis-NIR) spectroscopy is a reliable method for determining soil OM. Potentially it could therefore provide fast measurements of WR through autocorrelation with OM. The aim of this study was to test the feasibility of vis-NIR spectroscopy for estimating the WR of soils with a small gradient in soil organic carbon (SOC) and texture, and to evaluate the effect of soil pretreatment on the predictive ability of WR models. A total of 87 soil samples from an agricultural coarse sandy field in Denmark were analysed for SOC, particle size fractions, water content and WR. Soil samples were scanned with a vis-NIR sensor (350–2500 nm) after air- and oven-drying at 60°C and 105°C. WR, expressed as liquid surface tension (mN m−1), was determined using the molarity of ethanol droplet test. Partial least squares regression models of SOC, texture and water content showed no predictive ability ( r2 values between 0.10 and 0.51). However, successful models ( r2 = 0.85) were generated for WR. The majority of bands important in the vis-NIR region of WR models were related to different components of OM indicating that, across the investigated field, WR was related to specific hydrophobic components of soil OM rather than to the total amount of carbon. A lower prediction error of the WR model for soils dried at 105°C (1.93 mN m−1) than at 60°C (2.52 mN m−1) can be explained by a lower range of WR values for the soils dried at 105°C. Moreover, a higher temperature reduced the number of absorption bands related to OM, indicating a degradation of hydrocarbon groups and a more hydrophobic character of the soil.

Lignin composition [syringyl/guaiacyl (S/G) ratio] and cellulose content in wood have an important bearing on pulp yield. This paper deals with the development of a calibration model for S/G ratio using five Eucalyptus species from different sites by diffuse reflectance near infrared spectroscopy. The model was constructed with 120 samples covering an S/G ratio range from 1.8 to 3.6, determined by pyrolysis-gas chromatography-mass spectrometry. The calibration plot has an R2 value of 0.825 which was validated in E. camaldulensis, E. urophylla and E. pellita. Variation in S/G ratio was studied in more than 3000 E. camaldulensis trees across three diverse sites in southern India. The S/G ratio was lower in a low rainfall site (Mahabubnagar) compared to a higher rainfall site (Ongole). A positive correlation ( R2 = 0.72) was observed between S/G ratio and Kraft pulp yield in E. camaldulensis. Alkali consumption in Kraft pulping experiments was inversely proportional to the S/G ratio ( R2 = 0.914).

Models used to evaluate leaching of contaminants to groundwater are very sensitive to sorption coefficients ( Kd). These models need reliable Kd data at the field scale, but the number of samples required makes the classic batch sorption experiments inappropriate for this purpose. Since visible–near infrared (vis–NIR) spectroscopy is an inexpensive and fast method, it has been used for predicting soil properties related to soil sorption capacity. In this study, we aimed to predict the spatial variation of Kd from vis–NIR spectra for two contaminants: phenanthrene (sorbed on organic fractions) and glyphosate (sorbed on mineral fractions). Forty-five bulk soil samples were collected from an agricultural field in Estrup, Denmark, in a 15 m × 15 m grid. Samples were air-dried, sieved to 2 mm and analysed for selected soil properties. Sorption coefficients were obtained from a batch equilibration experiment. Soil samples were measured with a bench-top spectrometer covering the vis–NIR range between 400 nm and 2500 nm. Partial least squares regression with full cross-validation was used to correlate the soil spectra with Kd values and soil properties. The sorption coefficients ranged from 345 L kg−1 to 886 L kg−1 and from 162 L kg−1 to 536 L kg−1 for phenanthrene and glyphosate, respectively. The regression coefficients showed that phenanthrene sorption was correlated with total organic carbon, aluminium oxides and cation exchange capacity, and glyphosate sorption with clay minerals and iron oxides. By means of the vis–NIR spectra we were able to predict phenanthrene ( R2 = 0.95, RMSECV = 31 L kg−1) and glyphosate ( R2 = 0.79, RMSECV = 45 L kg−1) sorption capacities. A model using vis–NIR spectra plus pH values improved the prediction of glyphosate sorption capacity ( R2 = 0.88, RMSECV = 34 L kg−1). The models obtained from vis–NIR spectra successfully predicted Kd within the investigated field, indicating the potential of vis–NIR spectroscopy as a fast method for determining Kd for input to leaching risk assessment models. However, further studies of different soil types and geographical scales are needed to confirm our findings.

The chemical and mineralogical analyses of art have important cultural, scholarly and historic applications. Investigations can be performed to learn about the history of a particular object, or to investigate techniques for conservation and restoration purposes. In this study, a suite of historic artists' pigments, synthetic samples and end members were analysed to detect specific characteristics that can be applied to their identification and differentiation from other pigments. We combined and compared reflectance spectroscopy (RS; 350–2500 nm) with X-ray diffractometry (XRD) for this purpose. We focused on pigments rich in iron oxides and oxy-hydroxides, specifically ochres, siennas, umbers and “red oxides”. It was found that these two techniques are often complementary, and have different strengths and weaknesses. XRD was found to be able to detect a wider range of accessory minerals than RS, and its strength lies in discrimination on the basis of mineral structure. It is less sensitive than RS for detection of poorly crystalline/amorphous phases and fine-grained components. RS is very sensitive to detection and discrimination of different Fe-oxy-hydroxides in the wavelength region below ∼1200 nm. At longer wavelengths (>1200 nm), reflectance spectra can detect the presence of accessory minerals that possess strong absorption features. The analytical strength of RS lies in discrimination on the basis of composition, which is usually linked to specific crystallographic structures. RS and XRD data acquired for powdered mineral pigments can be successfully integrated for the identification of sub-groups within the iron oxy-hydroxide-rich group of pigments, but require further investigation for individual sample discrimination. RS and XRD are also able to verify, disprove or refine identification of the phase(s) that make up these pigments. A major practical advantage of RS over XRD is that RS is a non-destructive, non-contact technique, whereas XRD normally requires removal of a sample from a cultural artefact. One of the main findings is that the reflectance spectra of the pigments present in the mixtures retained their diagnostic absorption features even when mixed with linseed oil. Our results show that RS can be used to discriminate various Fe oxy-hydroxide-rich pigments as well as confirm the organic nature of binders; this has important implications for restoration and conservation of cultural artefacts.

High-throughput and non-destructive methods for quantifying the content of the stilbene compounds of Scots pine ( Pinus sylvestris L.) heartwood are needed in the breeding for decay resistance of heartwood timber. In this study, near infrared (NIR) spectroscopy calibrations were developed for a large collection of solid heartwood increment core samples in order to predict the amount of the stilbene pinosylvin (PS), its monomethyl ether (PSM) and their sum (STB). The resulting models presented quite accurate predictions in an independent validation set with R2V values ranging between 0.79 and 0.91. The accuracy of the models strongly depended on the chemical being calibrated, with the lowest accuracy for PS, intermediate accuracy for PSM and highest accuracy for STB. The effect of collecting one, two or more (up to five) spectra per sample on the calibration models was studied and it was found that averaging multiple spectra yielded better accuracy as it may account for the heterogeneity of wood along the increment core within and between rings. Several statistical pretreatments of the spectra were tested and an automatic selection of wavenumbers prior to calibration. Without the automatic selection of wavenumbers, a first derivative of normalised spectra yielded the best accuracies, whereas after the automatic selection of wavenumbers, no particular statistical pretreatment appeared to yield better results than any other. Finally, the automatic selection of wavenumbers slightly improved the accuracy of the models for all traits. These results demonstrate the potential of NIR spectroscopy as a high-throughput and non-destructive phenotyping technique in tree breeding for the improvement of decay resistance in heartwood timber.

Near infrared (NIR) diffuse reflectance spectroscopy was investigated to simultaneously determine the particle size (physical attribute) and active ingredient lobetyolin (chemical attribute) of Codonopsis radix. Laser diffraction and high-performance liquid chromatography were used as reference methods to determine particle size and lobetyolin content, respectively. Several spectral pretreatment methods were compared, with first derivative combined with nine-point Savitzky–Golay smoothing filter as the best method for establishing the partial least-squares models of particle size and lobetyolin. Then, synergy interval partial least squares (SiPLS) and backward interval partial least squares (BiPLS) were compared. The results showed that BiPLS was the appropriate method for establishing the particle size model; the root mean square error of calibration ( RMSEC), root mean square error of cross-validation ( RMSECV) and root mean square error of prediction ( RMSEP) values were 34.3 μm, 36.1 μm and 29.2 μm, respectively, and the values of Rcal2, Rcv2 and rpre2 were 0.92, 0.91 and 0.94, respectively. The ratio of performance to deviation ( RPD) was 4.1. Meanwhile, SiPLS was the optimal method for establishing the lobetyolin model; the RMSEC, RMSECV, RMSEP values were 0.052 mg g−1, 0.059 mg g−1 and 0.054 mg g−1, respectively, and the values of Rcal2, Rcv2 and rpre2 were 0.87, 0.84 and 0.83, respectively. The RPD was 2.5. According to the variable importance in projection (VIP) scores and the variable selection method of SiPLS, 1210–1296 nm was the second overtone of C–H; 2070–2156 nm and 2242–2328 nm were the combination of C–O, O–H and C–H. Therefore, the results showed that NIR could be used to determine physical and chemical properties simultaneously.

Calibrations developed using near infrared (NIR) spectroscopy to determine the quality of fruits or vegetables are usually applicable to a single species. The ability to determine the quality of several species using a common calibration would have advantages in some situations. A method to develop a common NIR calibration model that could be applied to many fruit species was examined. NIR spectra of apples, pears and persimmons were measured in the short-wavelength region using an interactance method. Partial least-squares (PLS) regressions based on second-derivative spectra were performed for Brix-value determination using calibration samples comprising each fruit species independently and the three species combined. Each single species calibration model predicted the Brix value in validation samples of the same species with a low standard error of performance ( SEP) (0.34–0.40°Brix (°Bx) and with low bias (0.01–0.08 °Bx) but with much higher SEP and bias errors when used to predict the Brix value in other species. The common calibration model developed from the combined sample set predicted Brix values in the apples, pears and persimmons with an SEP = 0.43 °Bx and a bias of −0.03 °Bx.

Feasibility of rapid estimation of haematocrit (Ht) in cattle was studied by near infrared (NIR) spectroscopy in the short wavelength (SW, 700–1050 nm) region. The blood samples, held in standard vacuum blood collection tubes, were measured by a custom-designed, portable NIR spectrometer equipped with a Li-ion battery. Sufficient evidence for estimation of Ht was presented by searching the informative wavelength regions and comparing the root mean square errors of prediction ( RMSEP) with moving window partial least square regression (MW-PLSR). The result of the MW-PLSR indicated the importance of the water absorption band with a maximum at 970 nm and the absorption band due to deoxy-haemoglobin with a maximum at 760 nm in estimating Ht. Due to light scattering from blood cells, the optical density of the blood does not have a linear relationship to Ht, especially in the low Ht range. In order to improve the prediction accuracy of Ht, an appropriate transformation was proposed for Ht. We confirmed the usefulness of the SW-NIR in estimating Ht, and hope that this technique will replace the time-consuming conventional centrifuge method.

In this investigation, Fourier transform-near infrared (FT-NIR) spectroscopy was used to determine the curcumin content in commercial turmeric herbal medicines. Two different sample presentations originating from a turmeric capsule and powder were evaluated and compared using an FT-NIR spectrometer equipped with a 30-capsule sample plate in the wavenumber region of 11,500–6000 cm−1 (transmittance mode) and a 6-vial sample plate in the wavenumber region of 10,000–4000cm−1 (reflectance mode), respectively. One hundred and forty samples of turmeric herbal medicines were divided into two sets: a training set with 110 samples and a prediction set with 30 samples. High-quality FT-NIR spectra for the turmeric powder containing the informative regions of curcumin (6850, 6000 and 4600 cm−1) were obtained while collecting FT-NIR data on the powder samples in the glass vials. A successful partial least-squares calibration model for curcumin content in commercial turmeric herbal medicine powders was then obtained by utilising the entire spectral region (10,000–4000 cm−1) as well as a second derivative spectral pretreatment containing seven principal components. The best model for predicting curcumin in 30 test samples returned a coefficient of determination ( r2) of 0.98, a root mean squares error of prediction ( RMSEP) of 0.13% that was only 1.35 times the standard error of laboratory and a ratio of RMSEP to the standard deviation of curcumin content in the prediction set ( RPD) of 7.76. These statistical values confirmed the high predictive performance of this model. All these results confirm that the use of turmeric herbal medicine powder and a wide informative wavenumber range of 10,000–4000 cm−1 can increase the efficacy of FT-NIR spectroscopy for the rapid and non-destructive analysis of curcumin content in commercial turmeric herbal medicines. By contrast, models developed for determining curcumin in turmeric contained in capsules were not suitable for analytical purposes.