Search

Phenolic compounds constitute an essential part of the plant's secondary metabolites and play a crucial role in ecosystem functioning, including nutrient cycling and plant defence against biotic and abiotic stressors. Quantifying the phenolic compounds across global biomes is important for monitoring the biological diversity and ecosystem processes. However, our understanding of foliar phenolic compounds remains limited, particularly regarding how they vary among temperate tree species and whether their variation and absorption features can be assessed using spectroscopy at the leaf level. In this study, we examined the relationships between the spectral properties of fresh leaves from temperate tree species and two ecologically important phenolic compounds (i.e., total phenol and tannin). We sampled the leaves of four dominant tree species (i.e., English oak, European beech, Norway spruce, and Scots pine) across two European temperate forest sites. Continuum removal was applied to the leaf spectra to enhance the assessment of the subtle absorption features that correlate with the phenolic content. Total phenol and tannin concentrations were estimated by comparing the performance of two empirical methods, namely partial least squares regression (PLSR) and Gaussian processes regression (GPR). Our results showed a large range of variation in total phenol and tannin between temperate tree species (p < 0.05). Spectral analysis revealed persistent and distinct phenolic absorption features near 1666 nm in the spectra of English oak, Norway spruce and European beech, whereas Scots pine exhibited a weaker absorption feature near 1653 nm. Regression results showed that both PLSR and GPR accurately estimated total phenol and tannin across temperate tree species, with informative bands for predicting these two traits well-corresponded between the two models utilised. Our results also suggested that total phenol was overall more accurately predicted than tannin regardless of employed methods. The most accurate estimations were achieved using PLSR with the continuum-removed SWIR spectra (total phenol: R 2=0.79, NRMSE=9.95%; tannin: R 2=0.59, NRMSE=14.53%). Testing the models established for individual species or forest types revealed variability in their prediction performances, with these specific models demonstrating lower accuracy (R 2=0.47–0.69 and 0.34–0.54 for total phenol and tannin, respectively) compared to the cross-species model. Our study extends the understanding of absorption features of phenolic compounds in common temperate tree species and demonstrates the potential for a generalised spectroscopy model to predict foliar phenolic compounds across temperate forests. These findings provide a foundation for mapping and monitoring phenolic compounds in temperate forests at the canopy level using airborne and spaceborne imaging spectroscopy. [ABSTRACT FROM AUTHOR]

Abstract Ammopiptanthus mongolicus , the only drought-resistant, leguminous, evergreen shrub in the desert region of China, is endangered due to climate change and its growth stages urgently need to be non-destructively detected. Although many spectral indexes have been proposed for characterizing vegetation, the relationships are often inconsistent, making it challenging to characterize the status of vegetation across all growth stages. This study investigated the Spectral Features of the endangered desert plant A. mongolicus at different growth stages, and extracted the identified Spectral Features for the establishment of detection and discrimination models using Partial Least Square Regression (PLSR) and Fisher Linear Discriminate Analysis (FLDA), respectively. The results showed spectral reflectance of A. mongolicus differed across different growth stages and it generally increased with the degree of senescence. Poor performance was found in the single factor model, with RMSE ranging from 20.34 to 27.39 or Overall Accuracy of 60% in the validation datasets. The multivariate PLSR model, based on Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), Physiological Reflectance Index (PhRI) and Plant Senescence Reflectance Index (PSRI), turned out to be accurate in detecting the growth stages, with R 2 of 0.89 and RMSE of 12.46, and the performance of the multivariate FLDA model based on 14 Spectral Features was acceptable, with an Overall Accuracy of 89% in the validation datasets. This research provides useful insights for timely and non-destructively discriminating different growth stages by using multivariate PLSR and FLDA analysis. Graphical abstract Unlabelled Image Highlights • A. mongolicus is the only drought-resisting leguminous evergreen shrub in arid China. • It is the first reported field study on in-situ monitoring of the endangered plant. • Hyperspectral measurement was conducted over A. mongolicus at different growth stage. • PLSR and FLDA are applicable to timely and non-destructively detect plant status. • The results have potential implications on vegetation and desert management. [ABSTRACT FROM AUTHOR]

Highlights • A comparison of WorldView-2 and Sentinel-2 to map Andean wetlands was performed. • A one-class classifier and PLSR were used to map and estimate soil moisture. • The sensors performed equally well for mapping wetlands. • Sentinel-2 outperforms WorldView-2 in the prediction of soil moisture. • The approach could be used to enhance the Chilean wetland inventory. Abstract In the Maipo watershed, situated in central Chile, mining activities are impacting high altitude Andean wetlands through the consumption and exploitation of water and land. As wetlands are vulnerable and particularly susceptible to changes of water supply, alterations and modifications in the hydrological regime have direct effects on their ecophysiological condition and vegetation cover. The aim of this study was to evaluate the potential of Worldview-2 and Sentinel-2 sensors to identify and map Andean wetlands through the use of the one-class classifier Bias support vector machines (BSVM), and then to estimate soil moisture content of the identified wetlands during snow-free summer using partial least square regression. The results obtained in this research showed that the combination of remote sensing data and a small sample of ground reference measurements enables to map Andean high altitude wetlands with high accuracies. BSVM was capable to classify the meadow areas with an overall accuracy of over ∼78% for both sensors. Our results also indicate that it is feasible to map surface soil moisture with optical remote sensing data and simple regression approaches in the examined environment. Surface soil moisture estimates reached r2 values of up to 0.58, and normalized mean square errors of 19% using Sentinel-2 data, while Worldview-2 estimates resulted in non-satisfying results. The presented approach is particularly valuable for monitoring high-mountain wetland areas with limited accessibility such as in the Andes. [ABSTRACT FROM AUTHOR]

Chemical absorption using amines is a suitable method to separate CO 2 from CO 2 rich natural gas stream. An instantaneous monitoring of CO 2 concentration in amine solvent is essential for an efficient chemical absorption process. A spectroscopic technique such as Raman spectroscopy along with multivariate modeling is considered as a robust and fast analytical method. It has been applied to monitor CO 2 concentration in a chemical absorption process. However, these studies are limited to low CO 2 loadings (<0.5 mol CO2 /mol amine ) and cannot be extrapolated to high CO 2 loading conditions. The evaluation of Raman method at high CO 2 loading is essential for the application at high pressure gas streams. In the present study, Raman spectroscopy is non-invasively applied to monitor CO 2 concentration in aqueous amines (DEA, MDEA, and their blends) over a wide range of CO 2 loadings (0.04–1.3 mol CO2 /mol amine ). The partial least square regression (PLSR) calibration models are developed and validated accordingly. The prediction accuracy is reported using determination coefficient (R 2 ) and root mean square error (RMSE). The average validation R 2 V and RMSE V for all the studied systems are calculated as 0.94 and 0.064 mol CO2 /mol amine respectively. These values show that Raman spectroscopy with PLSR is a promising technique to monitor CO 2 concentration for a wide range of CO 2 loading. The improvement in CO 2 monitoring is expected to enhance the process efficiency of natural gas processing plants. [ABSTRACT FROM AUTHOR]

The aim of this research was to correlate the chemical and physical characteristics of bitumen samples with the Fourier Transform infrared (FTIR) spectroscopy. Based on the FTIR analyses of 32 bitumen samples of different refineries, viscosity and ageing states using the attenuated total reflection (ATR) technology with multiple reflections, the gained spectra were evaluated with the chemometrical approach including multivariate analysis methods. With the Linear Discriminant Analysis (LDA), the bitumen samples could be distinguished according to the refinery even if the grades and the ageing states of the samples varied. In addition, the Partial Least Square Regression (PLSR) enables the determination of linear combinations for the description of different chemical, conventional and rheological parameters including the asphaltene content c asp , the softening point T R & B , the needle penetration PEN , the complex shear modulus | G ∗ | and the phase angle δ . Thereby, the separated consideration of the refineries allows the prediction of the parameters within the permitted limits according to the respective standards. Only for the softening point, the deviation of the calculated values exceeds the permitted deviations. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

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]

• 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]

• 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]