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African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density ( R 2 = 0.79, relative Root Mean Square Error, rRMSE = 1.9%) and tree cover ( R 2 = 0.78, rRMSE = 0.3%). EVI provided the best model for shrub density ( R 2 = 0.82) and shrub cover ( R 2 = 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees ( R 2 = 0.76), shrubs ( R 2 = 0.83), and grass ( R 2 = 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees’ and shrubs’ variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems. [ABSTRACT FROM AUTHOR]

Core-shell nanocarriers are increasingly being adapted in cosmetic and dermatological fields, aiming to provide an increased penetration of the active pharmaceutical or cosmetic ingredients (API and ACI) through the skin. In the final form, the nanocarriers (NC) are usually prepared in hydrogels, conferring desired viscous properties for topical application. Combined with the high chemical complexity of the encapsulating system itself, involving numerous ingredients to form a stable core and quantifying the NC and/or the encapsulated active without labor-intensive and destructive methods remains challenging. In this respect, the specific molecular fingerprint obtained from vibrational spectroscopy analysis could unambiguously overcome current obstacles in the development of fast and cost-effective quality control tools for NC-based products. The present study demonstrates the feasibility to deliver accurate quantification of the concentrations of curcumin (ACI)-loaded alginate nanocarriers in hydrogel matrices, coupling partial least square regression (PLSR) to infrared (IR) absorption and Raman spectroscopic analyses. With respective root mean square errors of 0.1469 ± 0.0175% w/ w and 0.4462 ± 0.0631% w/ w, both approaches offer acceptable precision. Further investigation of the PLSR results allowed to highlight the different selectivity of each approach, indicating only IR analysis delivers direct monitoring of the NC through the quantification of the Labrafac®, the main NC ingredient. Raman analyses are rather dominated by the contribution of the ACI which opens numerous perspectives to quantify the active molecules without interferences from the complex core-shell encapsulating systems thus positioning the technique as a powerful analytical tool for industrial screening of cosmetic and pharmaceutical products. [ABSTRACT FROM AUTHOR]

Fourier transform infrared with attenuated total reflectance (FTIR-ATR) and Raman spectroscopy combined with partial least square regression (PLSR) were applied for the prediction of phenolic compounds and antioxidant activity in honey. Standards of catechin, syringic, vanillic, and chlorogenic acids were used for the identification and quantification of the individual phenolic compounds in six honey varieties using HPLC–DAD. Total antioxidant activity (TAC) and ferrous chelating capacity were measured spectrophotometrically. For the establishment of PLSR model, Raman spectra with Savitzky-Golay smoothing in wavenumber region 1500–400 cm −1 was used while for FTIR–ATR the wavenumber regions of 1800–700 and 3000–2800 cm −1 with multiplicative scattering correction (MSC) and Savitzky–Golay smoothing were used. The determination coefficients (R 2 ) were ranged from 0.9272 to 0.9992 for Raman while from 0.9461 to 0.9988 for FTIT-ART. The FTIR–ATR and Raman demonstrated to be simple, rapid and nondestructive methods to quantify phenolic compounds and antioxidant activities in honey. [ABSTRACT FROM AUTHOR]

Hyperspectral reflectance provides an alternative method to soil’s physical and chemical analysis in laboratory for the estimation of soil properties in large range. In order to achieve rapid measurement of the soil heavy metal content in well-facilitied capital farmland construction areas, 154 soil samples at 0-30 cm depth were collected as research objects, which were from well-facilitied capital farmland construction areas in Xinzheng City, Henan Province. The raw hyperspectral reflectance of soil samples was measured by the standard procedure with a spectrometer of ASD Field Spec3 equipped with a high intensity contact probe under the laboratory conditions. Meanwhile, the contents of Cr, Cd, Zn, Cu, and Pb in these soil samples were analyzed. The 116 samples were used for building hyperspectral estimation models and the other 38 samples were used for model validation. In the next, the raw spectral reflectance of 400-2400 nm after multiplicative scatter correction and Savitzky-Golay was transformed to 2 spectral indices, i.e. first order differential reflectance(FDR) and second order differential reflectance(SDR). The correlation coefficient between the 3 kinds of spectral indices and Cr, Cd, Zn, Cu, Pb content was analyzed by Pearson correlation analysis. Then, the correlation coefficients (P<0.01) of the 3 spectral indices were got in significant test, which could be used to extract significant bands. At last, we used partial least squares regression (PLSR) method to build quantitative inversion models of soil heavy metal content based on significant bands for this study area, respectively. The prediction accuracies of these models were assessed by comparing determination coefficients ( R2 ), root mean squared error (RMSE) and relative percent deviation (RPD) between the prediction and validation values. Based on these, the optimal models were selected. The spatial distribution map of Cr, Cd, Zn, Cu and Pb content was made by geographical interpolation. The results showed that, conducting the first order differential reflectance and second order differential reflectance transformation on raw soil spectral data, could highlight the hidden spectral reflectivity characteristics effectively. Among all of the 3 spectral indices based on PLSR model, the model of second order differential reflectance about Cr could obtain more robust prediction accuracies, its values of R2 was 0.88，its values of RPD was 1.68; the model of the raw spectral reflectance (R) of 400-2 400 nm after multiplicative scatter correction（MSC）and Savitzky-Golay（SG）about Cd、 Zn and Cu could obtain more robust prediction accuracies, their values of R2 were 0.70, 0.88 and 0.99, their values of RPD were 1.50, 2.05 and 3.36 respectively; Pb could obtain more robust prediction accuracies, their values of R2 was 0.93，its values of RPD was 3.16. The optimum model of soil heavy metal was used to interpolate the soil heavy metal content; the content of Zn was in accordance with the standard of soil environmental quality, and the contents of Cr, Cd, Cu and Pb met the soil environmental quality standard Ⅱ, but the contents in some well-facilitied capital farmland construction areas were more than the soil background value. This study provides a reference for the real-time monitoring of soil basic information in well-facilitied capital farmland construction areas by hyperspectral inversion model. [ABSTRACT FROM AUTHOR]

In a quest to use Raman spectroscopy as an optical diagnostic tool, we recorded Raman spectra of 32 dengue virus (DENV)-infected and 28 healthy sera samples in the near-infrared spectral range (540 to 1700 cm−1) using laser at 785 nm as the excitation source. We observed clear differences in the Raman spectra of DENV-infected sera as compared with those of healthy individuals. Here, as a result of our study, we report 12 unique Raman bands associated with DENV-infected sera that are not reported earlier. After applying analysis of variance and t-test ( p < 0.05) on these 12 bands, six Raman bands at 630 ( N-acetylglucosamine), 883 (in-plane bending (ring) of deoxyribose), 1218 (amide III- β conformation from C6H5-C stretching vibrations of tryptophan and phenylalanine), 1273 (amide-III), 1623 (tryptophan) and 1672 cm−1 (ceramide) were found only in the DENV-infected sera. The remaining six Raman bands at 716 (lipids), 780 (Uracil-based ring breathing mode), 828 (ring breathing tyrosine), 840 ( α-anomers), 1101 (ν(C-N) of lipids and DNA) and 1150 cm−1(glycogen/carotenoids) were only found in healthy sera. Two types of classification models, principal component analysis and linear discriminant analysis, were employed to develop principal component analysis-linear discriminant analysis model that has provided diagnostic accuracy 96.50%, sensitivity 93.44%, and specificity 100%. This indicates that these 12 Raman bands have the potential to be used as biomarkers for optical diagnosis of DENV infection. This study provides a new insight for future research in the field of optical diagnosis using Raman spectroscopy. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

BACKGROUND Ochratoxin A ( OTA) contamination is highly prevalent in a variety of agricultural products including the commercially important coffee bean. As such, rapid and accurate detection methods are considered necessary for the identification of OTA in green coffee beans. The goal of this research was to apply Fourier transform near infrared spectroscopy to detect and classify OTA contamination in green coffee beans in both a quantitative and qualitative manner. RESULTS PLSR models were generated using pretreated spectroscopic data to predict the OTA concentration. The best model displayed a correlation coefficient ( r) of 0.814, a standard error of prediction ( SEP and bias of 1.965 µg kg−1 and 0.358 µg kg−1, respectively. Additionally, a PLS-DA model was also generated, displaying a classification accuracy of 96.83% for a non- OTA contaminated model and 80.95% for an OTA contaminated model, with an overall classification accuracy of 88.89%. CONCLUSION The results demonstrate that the developed model could be used for detecting OTA contamination in green coffee beans in either a quantitative or qualitative manner. © 2016 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Notoginseng is a classical traditional Chinese medical herb, which is of high economic and medical value. Notoginseng powder (NP) could be easily adulterated with Sophora flavescens powder (SFP) or corn flour (CF), because of their similar tastes and appearances and much lower cost for these adulterants. The objective of this study is to quantify the NP content in adulterated NP by using a rapid and non-destructive visible and near infrared (Vis-NIR) spectroscopy method. Three wavelength ranges of visible spectra, short-wave near infrared spectra (SNIR) and long-wave near infrared spectra (LNIR) were separately used to establish the model based on two calibration methods of partial least square regression (PLSR) and least-squares support vector machines (LS-SVM), respectively. Competitive adaptive reweighted sampling (CARS) was conducted to identify the most important wavelengths/variables that had the greatest influence on the adulterant quantification throughout the whole wavelength range. The CARS-PLSR models based on LNIR were determined as the best models for the quantification of NP adulterated with SFP, CF, and their mixtures, in which the rP values were 0.940, 0.939, and 0.867 for the three models respectively. The research demonstrated the potential of the Vis-NIR spectroscopy technique for the rapid and non-destructive quantification of NP containing adulterants.

The effects of increasing ambient ozone (O3) concentrations on food security has become a major concern as the demand for agricultural productivity is projected to increase significantly over the next several decades. In this contribution, the responses of common soybean genotypes (AK-HARROW, PI88788, DWIGHT, PANA, and WILLIAMS82) to ambient O3 are characterized using hyperspectral data and foliar biophysical, mineral nutrient concentrations and soybean yield. Specifically, leaf reflectance spectra measured at different growth stages and canopy layers were used to examine the spectral indices that were most strongly correlated with leaf physiological status. The effects of elevated O3 on six important nutrients (K, Ca, Mg, Fe, Mn and Cu) were evaluated by analyzing the variations in nutrient concentrations at two critical growth stages with increasing ambient O3 concentration using Partial Least Square Regression (PLSR). Lastly, the identified best spectral indices and the robust nutrient prediction models were extrapolated to the entire growth period to explore their ability to track the effects of ambient O3 concentrations on soybean physiology and nutrient uptake. The results showed that fluorescence yield (ΔF/Fm’) and photochemical quenching (qP) appear to be good indicators of soybean physiological responses to O3 stress that are echoed by the harvest index (HI). Newly identified normalized difference spectral index (NDSI) [R416, R2371] always had the highest correlation (R2 > 0.6) with ΔF/Fm’, qP and electron transport rate (ETR, μmol m−2 s−1) compared to the published indices. Additionally, there were significant and broad spectral regions in visible and near infrared region that were well-correlated with ΔF/Fm’ and selected NDSIs that were applicable to satellite observations. The results of nutrient modeling using PLSR explained 54–87% of the variance in nutrient concentrations, and the predicted mineral nutrient accumulation throughout the growing season reflected the responses of ozone tolerant and sensitive genotypes well. NDSI [R416, R2371] demonstrated great potential in regard to its sensitivity in tracking plant physiological responses to changing ambient O3 concentrations. The outcome of this research has potential implications for development of space-based observation of large-scale crop responses to O3 damage, as well as for biotechnological breeding efforts to improve ozone tolerance under future climate scenarios.

Understanding the temporal patterns of leaf traits is critical in determining the seasonality and magnitude of terrestrial carbon, water, and energy fluxes. However, we lack robust and efficient ways to monitor the temporal dynamics of leaf traits. Here we assessed the potential of leaf spectroscopy to predict and monitor leaf traits across their entire life cycle at different forest sites and light environments (sunlit vs. shaded) using a weekly sampled dataset across the entire growing season at two temperate deciduous forests. The dataset includes field measured leaf-level directional-hemispherical reflectance/transmittance together with seven important leaf traits [total chlorophyll (chlorophyll a and b ), carotenoids, mass-based nitrogen concentration (N mass ), mass-based carbon concentration (C mass ), and leaf mass per area (LMA)]. All leaf traits varied significantly throughout the growing season, and displayed trait-specific temporal patterns. We used a Partial Least Square Regression (PLSR) modeling approach to estimate leaf traits from spectra, and found that PLSR was able to capture the variability across time, sites, and light environments of all leaf traits investigated (R 2 = 0.6–0.8 for temporal variability; R 2 = 0.3–0.7 for cross-site variability; R 2 = 0.4–0.8 for variability from light environments). We also tested alternative field sampling designs and found that for most leaf traits, biweekly leaf sampling throughout the growing season enabled accurate characterization of the seasonal patterns. Compared with the estimation of foliar pigments, the performance of N mass , C mass and LMA PLSR models improved more significantly with sampling frequency. Our results demonstrate that leaf spectra-trait relationships vary with time, and thus tracking the seasonality of leaf traits requires statistical models calibrated with data sampled throughout the growing season. Our results have broad implications for future research that use vegetation spectra to infer leaf traits at different growing stages. [ABSTRACT FROM AUTHOR]

This study optimizes the application of portable Near Infrared-Acousto Optically Tunable Filter (NIR) device to meet the increasing demand for cost-effective, non-invasive and easy-to-use methods for measuring physical and chemical properties during olive fruit development. Fruits from different phenotypically cultivars were sampled for firmness, total and specific phenols detection by HPLC, total anthocyanins, chlorophyll and carotenoids detection by spectrophotometry. On the same fruits, a portable NIR device in diffuse reflectance mode was employed for spectral detections. Predictive models for firmness, chlorophyll, anthocyanins, carotenoids and rutin were developed by Partial Least Square analysis. Oleuropein, verbascoside, 3,4-DHPEA-EDA, and total phenols were used to develop a validation model. Internal cross-validation was applied for calibration and predictive models. The standard errors for calibration, cross-validation, prediction, and RPD ratios (SD/SECV) were calculated as references for the model effectiveness. The determination of the optimal harvesting time facilitates the production of high quality extra virgin olive oil and table olives. [ABSTRACT FROM AUTHOR]

There has been increasing interest from both academics and practitioners in sustainable consumption and production (SCP) behaviour. The literature has mainly focused on antecedents and indicators for SCP behaviour, but scholars are yet to develop frameworks that provide insights into SCP behaviour. To address this gap, this paper develops a theoretical model grounded in institutional and agency theories that explicates the role of top management beliefs and participation when dealing with institutional pressures that impact upon SCP behaviour by facilitating information sharing and reducing behavioural uncertainty. Based on a sample of 167 responses from a survey with Indian organizations, we test the model using partial least squared regression-based structural equation modelling (PLSR SEM). Our results indicate the role of top management commitment as a mediator between institutional pressures and SCP behaviour and the role of beliefs as shaping the commitment of top managers towards strengthening SCP behaviour. We further suggest that participation plays a significant role in quality information sharing, which is important in reducing behavioural uncertainty among stakeholders. Finally we outline our research limitations and further research directions. [ABSTRACT FROM AUTHOR]

Periodic and regular assessment of land degradation in arid regions of the world is essential for implementing suitable corrective measures in time. Assessment of soil properties based on soil sampling from hot arid tracts followed by laboratory analysis is a formidable task. Reflectance spectroscopy appears to be an emerging technology for the assessment of soils in extreme environment. In this study, soil spectral library of 138 soil samples from hot arid western Rajasthan have been created in visible, near-infrared and short wave infrared (350-2500 nm) region of the electromagnetic spectrum along with the measurements of basic soil properties. Further, spectral reflectance-based algorithms have been developed for rapid assessment of soil resources of arid regions. Results showed that sand and clay content may be satisfactorily estimated from linear models involving principal components (PCs) or derived band reflectance as the input variables ( R = 0.41-0.43). Organic carbon (OC) content of soil was also found satisfactorily correlated with spectral data ( R = 0.27). Derived band reflectance corresponding to Operational Land Imager bands of Landsat-8 has been found best to predict soil properties. Soil OC content has been found to be best estimated by derived spectral band data corresponding to spectral bands of IRS-P6 satellite. Partial least square regression-based models were found even better than the PCs-based and band reflectance-based multiple regression models for estimating soil properties. Thus, the present study indicates that soil spectral reflectance data captured by remote sensing satellites may have a great potential for rapid assessment of soil resources in arid regions. [ABSTRACT FROM AUTHOR]

Conventional methods of soil nitrogen extraction are time consuming, expensive and tedious. Remote sensing and Geographical Information System technologies can be used for the rapid and efficient prediction of the presence of soil nitrogen. However, studies are limited by and large to fields of larger and homogeneous units. This research concentrates on the prediction of topsoil nitrogen from harvested, scattered and small-sized agricultural fields of India using hyperspectral data. Spaceborne hyperspectral Hyperion data are used for the prediction of the presence of nitrogen. Multivariate partial least square regression method was used to predict the presence of nitrogen from reflectance. Reflectance data were pretreated using moving average and Savitzky–Golay filters which resulted in moderate prediction ofR20.65 and 0.63 for calibration and validation, respectively. It can be inferred that Hyperion data can be effectively used for the prediction of the presence of soil nitrogen with a moderate level of accuracy even in case of scattered fields and fields of sizes approximately equal to the spatial resolution of the satellite. [ABSTRACT FROM PUBLISHER]

Highlights: [•] Hyperspectral imaging was used to determine drip loss and pH in salmon fillets. [•] The predictive abilities of PLSR models with two spectral ranges were compared. [•] Important wavelengths were selected to reduce redundancy of hyperspectral images. [•] Multivariate models were built for predicting drip loss and pH with good accuracy. [•] We developed image processing algorithm to obtain drip loss and pH distribution maps. [ABSTRACT FROM AUTHOR]

Maillard reaction products (MRPs) were prepared from sunflower peptide and D-xylose with or without L-cysteine (PXC or PX) by heating over a range of temperatures (80–140°C) for 2.0h and a pH of 7.4 and subsequently the products were sensory evaluated. Partial least square regression (PLSR) was performed to analyze the correlation among data of quantitative sensory descriptive analysis, peptides, GC–MS and free amino acid (FAA) data of PXCs and PXs. Results revealed that MRPs formed at 120°C with cysteine addition (PXC-120) had greater meat-like flavor, mouthfulness and continuity taste compared to other MRPs. Molecular weight distribution showed that the presence of cysteine inhibited the low molecular weight (LMW) peptide cross-linking but accelerated the high molecular weight (HMW) peptide degradation with increasing temperature. Furthermore, results showed that the peptide above 5kDa has a significant negative contribution to sensory attributes of PXCs, while the peptide between 1 and 5kDa showed no significant but positive influence on PX sensory attributes. Sulfur containing compounds showed a significant and positive correlation to sensory attributes of PXCs while nitrogen containing compounds and furan were significantly but negatively correlated to sensory attributes of PXCs. [ABSTRACT FROM AUTHOR]

The quantitative analysis of surface-enhanced Raman spectra using scattering nanoparticles has shown the potential and promising applications in in vivo molecular imaging. The diverse approaches have been used for quantitative analysis of Raman spectra information, which can be categorized as direct classical least squares models, full spectrum multivariate calibration models, selected multivariate calibration models, and latent variable regression (LVR) models. However, the working principle of these methods in the Raman spectra application remains poorly understood and a clear picture of the overall performance of each model is missing. Based on the characteristics of the Raman spectra, in this paper, we first provide the theoretical foundation of the aforementioned commonly used models and show why the LVR models are more suitable for quantitative analysis of the Raman spectra. Then, we demonstrate the fundamental connections and differences between different LVR methods, such as principal component regression, reduced-rank regression, partial least square regression (PLSR), canonical correlation regression, and robust canonical analysis, by comparing their objective functions and constraints. We further prove that PLSR is literally a blend of multivariate calibration and feature extraction model that relates concentrations of nanotags to spectrum intensity. These features (a.k.a. latent variables) satisfy two purposes: the best representation of the predictor matrix and correlation with the response matrix. These illustrations give a new understanding of the traditional PLSR and explain why PLSR exceeds other methods in quantitative analysis of the Raman spectra problem. In the end, all the methods are tested on the Raman spectra datasets with different evaluation criteria to evaluate their performance. [ABSTRACT FROM PUBLISHER]

Highlights: [•] The reflectance of yellow rust, powdery mildew and aphid were measured and compared at leaf level. [•] Most efficient bands and spectral features were identified for discriminating stressors. [•] The overall accuracy of 0.75 was achieved for stressors discrimination by FLDA. [•] The damage intensity of diseases/insects was accurately estimated by a PLSR model. [ABSTRACT FROM AUTHOR]

Highlights: [•] Near infrared (NIR), attenuated total reflection (ATR), imaging/mapping approaches. [•] Recent technological and methodological advances. [•] Multivariate analytical approaches. [•] Selected examples in the fields of phytomics and bioanalysis. [•] Discussion of future trends. [ABSTRACT FROM AUTHOR]

Following the recent success in quantitative analysis of essential fatty acid compositions in a commercial microencapsulated fish oil (μEFO) supplement, we extended the application of portable attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopic technique and partial least square regression (PLSR) analysis for rapid determination of total protein contents-the other major component in most commercial μEFO powders. In contrast to the traditional chromatographic methodology used in a routine amino acid analysis (AAA), the ATR-FTIR spectra of the μEFO powder can be acquired directly from its original powder form with no requirement of any sample preparation, making the technique exceptionally fast, noninvasive, and environmentally friendly as well as being cost effective and hence eminently suitable for routine use by industry. By optimizing the spectral region of interest and number of latent factors through the developed PLSR strategy, a good linear calibration model was produced as indicated by an excellent value of coefficient of determination R = 0.9975, using standard μEFO powders with total protein contents in the range of 140-450 mg/g. The prediction of the protein contents acquired from an independent validation set through the optimized PLSR model was highly accurate as evidenced through (1) a good linear fitting ( R = 0.9759) in the plot of predicted versus reference values, which were obtained from a standard AAA method, (2) lowest root mean square error of prediction (11.64 mg/g), and (3) high residual predictive deviation (6.83) ranked in very good level of predictive quality indicating high robustness and good predictive performance of the achieved PLSR calibration model. The study therefore demonstrated the potential application of the portable ATR-FTIR technique when used together with PLSR analysis for rapid online monitoring of the two major components (i.e., oil and protein contents) in finished μEFO powders in the actual manufacturing setting. [ABSTRACT FROM AUTHOR]

Notoginseng is a classical traditional Chinese medical herb, which is of high economic and medical value. Notoginseng powder (NP) could be easily adulterated with Sophora flavescens powder (SFP) or corn flour (CF), because of their similar tastes and appearances and much lower cost for these adulterants. The objective of this study is to quantify the NP content in adulterated NP by using a rapid and non-destructive visible and near infrared (Vis-NIR) spectroscopy method. Three wavelength ranges of visible spectra, short-wave near infrared spectra (SNIR) and long-wave near infrared spectra (LNIR) were separately used to establish the model based on two calibration methods of partial least square regression (PLSR) and least-squares support vector machines (LS-SVM), respectively. Competitive adaptive reweighted sampling (CARS) was conducted to identify the most important wavelengths/variables that had the greatest influence on the adulterant quantification throughout the whole wavelength range. The CARS-PLSR models based on LNIR were determined as the best models for the quantification of NP adulterated with SFP, CF, and their mixtures, in which the rp values were 0.940, 0.939, and 0.867 for the three models respectively. The research demonstrated the potential of the Vis-NIR spectroscopy technique for the rapid and non-destructive quantification of NP containing adulterants. [ABSTRACT FROM AUTHOR]

Abstract: Sewage sludge application to agricultural soils is a common practice in several countries in the European Union. Nevertheless, the application dose constitutes an essential aspect that must be taken into account in order to minimize environmental impacts. In this study, near infrared reflectance spectroscopy (NIRS) was used to estimate in sewage sludge samples several parameters related to agronomic and environmental issues, such as the contents in organic matter, nitrogen and other nutrients, metals and carbon fractions, among others. In our study (using 380 biosolid samples), two regression models were fitted: the common partial least square regression (PLSR) and the penalized signal regression (PSR). Using PLSR, NIRS became a feasible tool to estimate several parameters with good goodness of fit, such as total organic matter, total organic carbon, total nitrogen, water-soluble carbon, extractable organic carbon, fulvic acid-like carbon, electrical conductivity, Mg, Fe and Cr, among other parameters, in sewage sludge samples. For parameters such as C/N ratio, humic acid-like carbon, humification index, the percentage of humic acid-like carbon, the polymerization ratio, P, K, Cu, Pb, Zn, Ni and Hg, the performance of NIRS calibrations developed with PLSR was not sufficiently good. Nevertheless, the use of PSR provided successful calibrations for all parameters. [Copyright &y& Elsevier]

Wheat aphid, Sitobion avenae F. is one of the most destructive pests that emerge in northwest China almost every year, impacting on the production of winter wheat. Hyperspectral remote sensing has been demonstrated to be superior to a traditional method in detecting diseases and pests. In this study, spectral features (SFs) were examined by four methods to detect aphid density of wheat leaf and model was established to estimate aphid density using partial least square regression (PLSR). A total of 60 wheat leaves with different aphid densities were selected. Aphid density of the leaves was first visually estimated, and then the reflectance of leaves was measured in the spectral range of 350–2500 nm using a spectroradiometer coupling with a leaf clip. A total of 48 spectral features were obtained and examined via correlation analysis, independent t-test by spectral derivative method, continuous removal method, continuous wavelet analysis (CWA) and commonly used vegetation indices for stress detection. Based on variable importance in projection (VIP), five spectral features (VIP\geq 1) were selected from 17 spectral features due to their strong correlation with aphid density (R^2\geq 0.5) to establish the model for estimating aphid density by PLSR. The result showed that the model had a great potential in detecting aphid density with a relative root mean square error (RMSE) of 15 and a coefficient of determination (R^2) of 0.77. [ABSTRACT FROM PUBLISHER]

Abstract: In this study we developed a technique to early and rapidly estimate seed yield using hyperspectral images of oilseed rape leaves in the visible and near infrared (VIS–NIR) region (380–1030nm). Hyperspectral images of leaves were acquired four times from field trials in China between seedling until pods stage. Seed yield data on individual oilseed rape plants were collected during the local harvest season in 2011. Partial least square regression (PLSR) was applied to relate the average spectral data to the corresponding actual yield. We compared four PLSR models from four growing stages. The best fit model with the highest coefficients of determination () of 0.71 and the lowest root mean square errors (RMSEP) of 23.96 was obtained based on the hyperspectral images from the flowering stage (on March 25, 2011). The loading weights of this resulting PLSR model were used to identify the most important wavelengths and to reduce the high dimensionality of the hyperspectral data. The new PLSR model using the most relevant wavelengths (543, 686, 718, 741, 824 and 994nm) performed well (, RMSEP=23.72) for predicting seed weights of individual plants. These results demonstrated that hyperspectral imaging system is promising to predict the seed yield in oilseed rape based on its leaves in early growing stage. [Copyright &y& Elsevier]

Abstract: Powdery mildew (Blumeria graminis) is one of the most destructive diseases, which has a significant impact on the production of winter wheat. Detecting powdery mildew via spectral measurement and analysis is a possible alternative to traditional methods in obtaining the spatial distribution information of the disease. In this study, hyperspectral reflectances of normal and powdery mildew infected leaves were measured with a spectroradiometer in a laboratory. A total of 32 spectral features (SFs) were extracted from the lab spectra and examined through a correlation analysis and an independent t-test associated with the disease severity. Two regression models: multivariate linear regression (MLR) and partial least square regression (PLSR) were developed for estimating the disease severity of powdery mildew. In addition, the fisher linear discriminant analysis (FLDA) was also adopted for discriminating the three healthy levels (normal, slightly-damaged and heavily-damaged) of powdery mildew with the extracted SFs. The experimental results indicated that (1) most SFs showed a clear response to powdery mildew; (2) for estimating the disease severity with SFs, the PLSR model outperformed the MLR model, with a relative root mean square error (RMSE) of 0.23 and a coefficient of determination (R 2) of 0.80 when using seven components; (3) for discrimination analysis, a higher accuracy was produced for the heavily-damaged leaves by FLDA with both producer’s and user’s accuracies over 90%; (4) the selected broad-band SFs revealed a great potential in estimating the disease severity and discriminating severity levels. The results imply that multispectral remote sensing is a cost effective method in the detection and mapping of powdery mildew. [Copyright &y& Elsevier]

Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression (PLSR) models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM+ data. The established models were optimized according to the variable importance for projection (VIP) criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test ( p<0.05). The simplified models without insignificant variables (VIP <1) performed as well as the full model but with less computation time. The relative root mean square error (RMSE%) was 29% for canopy closure density, and 58% for above-ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above-ground biomass. [ABSTRACT FROM AUTHOR]

This study was conducted to understand the factors that affect the acceptance of various perilla porridges and compare the sensory characteristics that drive consumer acceptance of perilla porridges across Korean and Chinese cultures. A descriptive analysis was performed on 8 samples, which were made with 2 types of perilla oils, 3 types of perilla powders, 1 type of roasted perilla seeds, and 2 types of ground perilla porridges, by 10 Korean and 9 Chinese panelists. The Korean panelists generated more descriptors than the Chinese panel, and the Korean panelists used descriptors that were related to their individual experiences or cultural backgrounds. According to the results of consumer test, attributes such as glossy surface, salty flavor, and savory flavor determined positive preferences in the Korean panel, and attributes such as glossy surface, sesame oil odor, sesame oil flavor, porridge with milk, whiteness, light flavor, salty odor, and mouthfeel of porridge determined preference in the Chinese panel. [ABSTRACT FROM AUTHOR]

Abstract: Airborne hyperspectral remote sensing was adapted to establish a general-purpose model for quantifying nitrogen content of rice plants at the heading stage using three years of data. There was a difference in dry mass and nitrogen concentration due to the difference in the accumulated daily radiation (ADR) and effective cumulative temperature (ECT). Because of these environmental differences, there was also a significant difference in nitrogen content among the three years. In the multiple linear regression (MLR) analysis, the accuracy (coefficient of determination: R 2, root mean square of error: RMSE and relative error: RE) of two-year models was better than that of single-year models as shown by R 2 ≥0.693, RMSE≤1.405gm−2 and RE≤9.136%. The accuracy of the three-year model was R 2 =0.893, RMSE=1.092gm−2 and RE=8.550% with eight variables. When each model was verified using the other data, the range of RE for two-year models was similar or increased compared with that for single-year models. In the partial least square regression (PLSR) model for the validation, the accuracy of two-year models was also better than that of single-year models as R 2 ≥0.699, RMSE≤1.611gm−2 and RE≤13.36%. The accuracy of the three-year model was R 2 =0.837, RMSE=1.401gm−2 and RE=11.23% with four latent variables. When each model was verified, the range of RE for two-year models was similar or decreased compared with that for single-year models. The similarities and differences of loading weights for each latent variable depending on hyperspectral reflectance might have affected the regression coefficients and the accuracy of each prediction model. The accuracy of the single-year MLR models was better than that of the single-year PLSR models. However, accuracy of the multi-year PLSR models was better than that of the multi-year MLR models. Therefore, PLSR model might be more suitable than MLR model to predict the nitrogen contents at the heading stage using the hyperspectral reflectance because PLSR models have more sensitive than MLR models for the inhomogeneous results. Although there were differences in the environmental variables (ADR and ECT), it is possible to establish a general-purpose prediction model for nitrogen content at the heading stage using airborne hyperspectral remote sensing. [Copyright &y& Elsevier]

Hyperspectral visible near infrared reflectance spectroscopy (VNIRRS) and geostatistical methods are considered for precision soil mapping. This study evaluated whether VNIR or geostatistics, or their combined use, could provide efficient approaches for assessing the soil spatially and associated reductions in sample size using soil samples from a 32 ha area (800 × 400 m) in northern Turkey. Soil variables considered were CaCO, organic matter, clay, sand and silt contents, pH, electrical conductivity, cation exchange capacity (CEC) and exchangeable cations (Ca, Mg, Na and K). Cross-validation was used to compare the two approaches using all grid data ( n = 512), systematic selections of 13, 25 and 50% of the data and random selections of 13 and 25% for calibration; the remaining data were used for validation. Partial least squares regression (PLSR) analysis was used for calibrating soil properties from first derivative VNIR reflectance spectra (VNIRRS), whereas ordinary-, co- and regression-kriging were used for spatial prediction. The VNIRRS-PLSR method provided better prediction results than ordinary kriging for soil organic matter, clay and sand contents, ( R values of 0.56-0.73, 0.79-0.85, 0.65-0.79, respectively) and smaller root mean squared errors of prediction (values of 2.7-4.1, 37.4-43, 46.9-61, respectively). The EC, pH, Na, K and silt content were predicted poorly by both approaches because either the variables showed little variation or the data were not spatially correlated. Overall, the prediction accuracy of VNIRRS-PLSR was not affected by sample size as much as it was for ordinary kriging. Cokriging (COK) and regression kriging (RK) were applied to a combination of values predicted by VNIR reflectance spectroscopy and measured in the laboratory to improve the accuracy of prediction of the soil properties. The results showed that both COK and RK with VNIRRS estimates improved the predictions of soil variables compared to VNIRRS and OK. The combined use of VNIRRS and multivariate geostatistics results in better spatial prediction of soil properties and enables a reduction in sampling and laboratory analyses. [ABSTRACT FROM AUTHOR]

This paper analyzes the drop transfer process in gas metal arc welding in short-circuit transfer mode (GMAW-S) in order to develop an optimized spatter rate model that can be used on line. According to thermodynamic characters and practical behavior, a complete arcing process is divided into three sub-processes: arc re-ignition, energy output and shorting preparation. Shorting process is then divided as drop spread, bridge sustention and bridge destabilization. Nine process variables and their distribution are analyzed based on welding experiments with high-speed photos and synchronous current and voltage signals. Method of variation coefficient is used to reflect process consistency and to design characteristic parameters. Partial least square regression (PLSR) is utilized to set up spatter rate model because of severe correlativity among the above characteristic parameters. PLSR is a new multivariate statistical analysis method, in which regression modeling, data simplification and relativity analysis are included in a single algorithm. Experiment results show that the regression equation based on PLSR is effective for on-line predicting spatter rate of its corresponding welding condition. [ABSTRACT FROM AUTHOR]

The use of near infrared (NIR) spectroscopy was proved to be a useful tool for quality analysis of fruits. A bifurcated fiber type NIR spectrometer, with a detection range of 800∼2500 nm by InGaAs detector, was used to evaluate the firmness of peaches. Anisotropy of NIR spectra and firmness of peaches in relation to detecting positions of different parts (including three latitudes and three longitudes) were investigated. Both spectra absorbency and firmness of peach were influenced by longitudes (i, ii, iii) and latitudes (A, B, C). For modeling, two thirds of the samples were used as the calibration set and the remaining one third were used as the validation or prediction set. Partial least square regression (PLSR) models for different longitude and latitude spectra and for the whole fruit show that collecting several NIR spectra from different longitudes and latitudes of a fruit for NIR calibration modeling can improve the modeling performance. In addition, proper spectra pretreatments like scattering correction or derivative also can enhance the modeling performance. The best results obtained in this study were from the holistic model with multiplicative scattering correction (MSC) pretreatment, with correlation coefficient of cross-validation r cv=0.864, root mean square error of cross-validation RMSECV=6.71 N, correlation coefficient of calibration r=0.948, root mean square error of calibration RMSEC=4.21 N and root mean square error of prediction RMSEP=5.42 N. The results of this study are useful for further research and application that when applying NIR spectroscopy for objectives with anisotropic differences, spectra and quality indices are necessarily measured from several parts of each object to improve the modeling performance. [ABSTRACT FROM AUTHOR]

The correct recognition of sweet orange (Citrus sinensis L. Osbeck) variety accessions at the nursery stage of growth is a challenge for the productive sector as they do not show any difference in phenotype traits. Furthermore, there is no DNA marker able to distinguish orange accessions within a variety due to their narrow genetic trace. As different combinations of canopy and rootstock affect the uptake of elements from soil, each accession features a typical elemental concentration in the leaves. Thus, the main aim of this work was to analyze two sets of ten different accessions of very close genetic characters of three varieties of fresh citrus leaves at the nursery stage of growth by measuring the differences in elemental concentration by laser-induced breakdown spectroscopy (LIBS). The accessions were discriminated by both principal component analysis (PCA) and a classifier based on the combination of classification via regression (CVR) and partial least square regression (PLSR) models, which used the elemental concentrations measured by LIBS as input data. A correct classification of 95.1% and 80.96% was achieved, respectively, for set 1 and set 2. These results showed that LIBS is a valuable technique to discriminate among citrus accessions, which can be applied in the productive sector as an excellent cost–benefit tool in citrus breeding programs. [ABSTRACT FROM AUTHOR]

Xylella fastidiosa is a bacterial pathogen affecting many plant species worldwide. Recently, the subspecies pauca (Xfp) has been reported as the causal agent of a devastating disease on olive trees in the Salento area (Apulia region, southeastern Italy), where centenarian and millenarian plants constitute a great agronomic, economic, and landscape trait, as well as an important cultural heritage. It is, therefore, important to develop diagnostic tools able to detect the disease early, even when infected plants are still asymptomatic, to reduce the infection risk for the surrounding plants. The reference analysis is the quantitative real time-Polymerase-Chain-Reaction (qPCR) of the bacterial DNA. The aim of this work was to assess whether the analysis of hyperspectral data, using different statistical methods, was able to select with sufficient accuracy, which plants to analyze with PCR, to save time and economic resources. The study area was selected in the Municipality of Oria (Brindisi). Partial Least Square Regression (PLSR) and Canonical Discriminant Analysis (CDA) indicated that the most important bands were those related to the chlorophyll function, water, lignin content, as can also be seen from the wilting symptoms in Xfp-infected plants. The confusion matrix of CDA showed an overall accuracy of 0.67, but with a better capability to discriminate the infected plants. Finally, an unsupervised classification, using only spectral data, was able to discriminate the infected plants at a very early stage of infection. Then, in phase of testing qPCR should be performed only on the plants predicted as infected from hyperspectral data, thus, saving time and financial resources. [ABSTRACT FROM AUTHOR]

• We constructed harmonisation functions for soil extractable P in arable lands. • Separate functions are provided for acidic and calcareous soils. • Models are constructed based on easily available soil properties. • Additional soil properties improve performance of harmonization functions in calcareous soils. Phosphorus (P) is a nutrient essential to living organisms and ecosystems. Accurate information regarding extractable soil P is necessary for agricultural management and environmental quality. Direct measurements of extractable soil P at large scales are usually impeded by considerable time, labour, and economic resources required for implementation. To meet agronomic and environmental monitoring needs, multiple extraction methods have been developed worldwide to estimate the different components of soil P. In France, three extraction methods are used, namely the Dyer method for acidic soils, Joret-Hébert for calcareous soils, and Olsen for all soils. Therefore, it is difficult to compare data obtained nationwide for monitoring purposes. Consequently, it is of significant importance to develop pedotransfer functions (PTFs) to harmonise extractable soil P data obtained from different extraction methods with the assistance of other easily available predictors from soil information systems. In this study, we used an extensive dataset from the French soil-monitoring programme for the calibration and evaluation of PTFs. We implemented the partial least squares regression to relate extractable P measured by the Dyer or Joret-Hébert method to extractable P determined by the Olsen method considering 14 soil properties (total P 2 O 5 , pH, cation exchange capacity (CEC), CaCO 3 , soil texture (clay, silt and sand contents), total organic carbon, and exchangeable Fe, Al, CaO, Mn, MgO, and K 2 O). We constructed patrimonial models by selecting the most important predictors. According to the results of 10 iterations cross-validation, the average R2, root mean-square error (RMSE), and mean error (ME) of the PTF of calcareous soils were 0.66, 25.81, and −0.11 mg kg−1, whereas those of acidic soils were 0.70, 24.02, and −0.87 mg kg1, respectively. The Joret-Hébert P 2 O 5 , silt, pH, total P 2 O 5 , CEC, and K were the most important predictors for estimating Olsen P 2 O 5 in calcareous soils, whereas Dyer P 2 O 5 , exchangeable Al, K, and pH were the most important predictors for estimating Olsen P 2 O 5 in acidic soils. We observed that the explanatory power of the soil properties was more important in calcareous than in acidic soils. As expected, the proxies of Olsen P 2 O 5 , namely, Dyer P 2 O 5 and Joret-Hébert P 2 O 5 , were the most important variables in modelling Olsen P 2 O 5 variations. In addition, the relationship between Olsen P 2 O 5 and Dyer P 2 O 5 was much stronger than that between Olsen P 2 O 5 and Joret-Hébert P 2 O 5. The results confirmed the feasibility of estimating extractable P in soil by PTFs that were constructed using statistical methods, such as partial least squares regression. The addition of more predictors that are related to agricultural practices and topography attributes may improve the prediction accuracy. [ABSTRACT FROM AUTHOR]

The chemical composition of bee pollens differs greatly and depends primarily on the botanical origin of the product. Therefore, it is a crucially important task to discriminate pollens of different plant species. In our work, we aim to determine the applicability of microscopic pollen analysis, spectral colour measurement, sensory, NIR spectroscopy, e-nose and e-tongue methods for the classification of bee pollen of five different botanical origins. Chemometric methods (PCA, LDA) were used to classify bee pollen loads by analysing the statistical pattern of the samples and to determine the independent and combined effects of the above-mentioned methods. The results of the microscopic analysis identified 100% of sunflower, red clover, rapeseed and two polyfloral pollens mainly containing lakeshore bulrush and spiny plumeless thistle. The colour profiles of the samples were different for the five different samples. E-nose and NIR provided 100% classification accuracy, while e-tongue > 94% classification accuracy for the botanical origin identification using LDA. Partial least square regression (PLS) results built to regress on the sensory and spectral colour attributes using the fused data of NIR spectroscopy, e-nose and e-tongue showed higher than 0.8 R2 during the validation except for one attribute, which was much higher compared to the independent models built for instruments. [ABSTRACT FROM AUTHOR]

• Used chemical references to evaluate sensory profiles of 7 watermelon varieties. • Applied nose clips to investigate the impact of aroma on refreshing perception. • Refreshing was dominant and driven by wateriness, crispness, fresh, melon, and sweet. • Mealiness was a negative driver of refreshing. • Refreshing perception of watermelon increased with lower temperature. Watermelon (Citrullus lanatus) is known for its refreshing quality, though its sensory attributes have never been related to its perceived refreshment. Modified quantitative descriptive analysis by a trained panel was used to examine the sensory profile of seven watermelon varieties. Eleven attributes including perceived refreshing intensity were measured on a 0–10 line scale using chemical references. Watermelon samples were evaluated with and without nose clips to control orthonasal and retronasal aroma and temperature was included as a variable to observe their effects on perceived refreshment. The dominant watermelon attributes were wateriness, refreshing, crispness, sweet, mealiness, fresh, ripe, and melon. The varieties were best differentiated by refreshing (p < 0.001), crispness (p = 0.002), sweet (p < 0.001), mealiness (p = 0.016), green (p = 0.007), and sour perception (p < 0.001). Captivation and Excursion were the most refreshing varieties. Captivation, Excursion, and Seedless varieties were less refreshing when flavor perception was inhibited; ratings ranged from 6.8 to 7.2 without nose clips and 5.9–6.0 with nose clips (p = 0.002). Refreshing was most positively driven by wateriness, followed by crispness, fresh, melon, and sweet, and negatively driven by mealiness, as indicated by partial least square regression. Samples served cold were more refreshing (ratings of 7.1 without and 6.0 with nose clips) than those served at room temperature (ratings of 4.9 without and 3.5 with nose clips), p < 0.001. This study defined the sensory profile of seven watermelon varieties and showed that flavor, texture, and temperature were responsible for the refreshing perception of watermelon for the first time. [ABSTRACT FROM AUTHOR]

The content of chlorophyll, an important substance for photosynthesis in plants, is an important index used to characterize the photosynthetic rate and nutrient grade of plants. The real-time rapid acquisition of crop chlorophyll content is of great significance for guiding fine management and differentiated fertilization in the field. This study used the method of continuous wavelet transform (CWT) to process the collected visible and near-infrared spectra of a corn canopy. This task was conducted to extract the valuable information in the spectral data and improve the sensitivity of chlorophyll content assessment. First, a Savitzky–Golay filter and standard normal variable processing were applied to the spectral data to eliminate the influence of random noise and limit drift on spectral reflectance. Second, CWT was performed on the spectral reflection curve with 10 frequency scales to obtain the wavelet energy coefficient of the spectral data. The characteristic bands related to chlorophyll content in the spectral data and the wavelet energy coefficients were screened using the maximum correlation coefficient and the local correlation coefficient extrema, respectively. A partial least-square regression model was established. Results showed that the characteristic bands selected via local correlation coefficient extrema in a wavelet energy coefficient created a detection model with optimal accuracy. The determination coefficient (Rc2) of the calibration set was 0.7856, and the root-mean-square error (RMSE) of the calibration set (RMSEC) was 3.0408. The determination coefficient (Rv2) of the validation set is was 0.7364, and the RMSE of the validation set (RMSEV) was 3.3032. Continuous wavelet transform is a process of data dimension enhancement which can effectively extract the sensitive variables from spectral datasets and improve the detection accuracy of models. [ABSTRACT FROM AUTHOR]