Quantitative Structure–Retention Relationship Analysis of Polycyclic Aromatic Compounds in Ultra-High Performance Chromatography

Carbohydrate levels are important regulators of the growth and yield of tree crops. Current methods for measuring foliar carbohydrate concentrations are time consuming and laborious, but rapid imaging technologies have emerged with the potential to improve the effectiveness of tree nutrient management. Carbohydrate concentrations were predicted using hyperspectral imaging (400–1000 nm) of leaves of the evergreen tree crops, avocado, and macadamia. Models were developed using partial least squares regression (PLSR) and artificial neural network (ANN) algorithms to predict carbohydrate concentrations. PLSR models had R2 values of 0.51, 0.82, 0.86, and 0.85, and ANN models had R2 values of 0.83, 0.83, 0.78, and 0.86, in predicting starch, sucrose, glucose, and fructose concentrations, respectively, in avocado leaves. PLSR models had R2 values of 0.60, 0.64, 0.91, and 0.95, and ANN models had R2 values of 0.67, 0.82, 0.98, and 0.98, in predicting the same concentrations, respectively, in macadamia leaves. ANN only outperformed PLSR when predicting starch concentrations in avocado leaves and sucrose concentrations in macadamia leaves. Performance differences were possibly associated with nonlinear relationships between carbohydrate concentrations and reflectance values. This study demonstrates that PLSR and ANN models perform well in predicting carbohydrate concentrations in evergreen tree-crop leaves.

Quantitative structure-retention relationship of selected imidazoline derivatives on α1-acid glycoprotein column

Motivated by recent research on two-phase flow void fraction measurement, this paper presents a study on void fraction measurements of a lubricant air-oil two-phase flow using a near infrared (NIR) optical-fiber spectrometer associated with partial least squares (PLS) regression models. To overcome the measurement inaccuracy due to interface scattering, this study used PLS regression models to analyse the spectrum of air-oil two-phase flow. First, an NIR optical-fiber spectrometer experimental system was developed. Second, a flow regime analysis was conducted for the bubble flow transition inside the experimental system. Finally, the PLS regression model validations, the void fraction measurement accuracies, and the uncertainties of the experimental system were evaluated and discussed, and the different PLS regression models were compared in detail. The results indicate that the NIR optical-fiber spectrometer combined with the PLS regression model can achieve void fraction measurements of lubricants in air-oil two-phase flow, with a maximum squared correlation coefficient (R 2) of 0.981, a minimum Type A uncertainty of 0.039% and a minimum expanded uncertainty of 0.077%.

The objective of this study was to compare the predictions of the protein contents and hardness values by partial least squares regression (PLSR) and principal components regression (PCR) models for bulk samples of Canadian wheat, which were obtained from different locations and crop years. Wheat samples of Canada Western Red Spring (CWRS), Canada Western Hard White Spring (CWHWS), Canada Western Soft White Spring (CWSWS), and Canada Prairie Spring Red (CPSR) classes were obtained from nearby agricultural farms in the main wheat growing locations in the Provinces of Alberta, Saskatchewan, and Manitoba from 2007, 2008, and 2009 crop years. Wheat samples were conditioned to moisture levels of 13, 16, and 19 % (wet basis) and pooled together for developing the regression models. A database of the near-infrared (NIR) hyperspectral image cubes of bulk samples of wheat classes was created in the wavelength region of 960–1,700 nm with 10 nm intervals. Reference protein contents and hardness values were determined using the Dumatherm method and single kernel characterization system (SKCS), respectively. A tenfold cross-validation was used for the ten-factor partial least squares regression (PLSR) and principal components regression (PCR) models for prediction purposes. Prediction performances of regression models were assessed by calculating the estimated mean square errors of prediction (MSEP), standard error of cross-validation (SECV), and correlation coefficient (r). Using the full data set in the protein prediction study, the ten-component PLSR model gave 1.76, 1.33, and 0.68 for the estimated MSEP, SECV, and r, respectively, which were better than the results for the ten-component PCR model (2.02, 1.42, and 0.62, respectively). For the hardness prediction, the estimated MSEP, SECV, and r values were 147.7, 12.15, and 0.82, respectively, for the ten-component PLSR model using the full data set. The PLSR models prediction performances outperformed the PCR models for predicting protein contents and hardness of wheat.

This study aimed to compare stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and support vector machine regression (SVMR) for estimating soil total nitrogen (TN) contents with laboratory visible/near-infrared reflectance (Vis/NIR) of selected coarse and heterogeneous soils. Moreover, the effects of the first (1st) vs. second (2nd) derivative of spectral reflectance and the importance wavelengths were explored. The TN contents and the Vis/NIR were measured in the laboratory. Several methods were employed for Vis/NIR data pre-processing. The SMLR, PLSR and SVMR models were calibrated and validated using independent datasets. Results showed that the SVMR and the PLSR models had similar performances, and better performances than the SMLR. The spectral bands near 1450, 1850, 2250, 2330 and 2430 nm in the PLSR model were important wavelengths. In addition, the 1st derivative was more appropriate than the 2nd derivative for spectral data pre-processing. PLSR was the most suitable method for estimating TN contents in this study. SVMR may be a promising technique, and its potential needs to be further explored. Moreover, the future studies using outdoor and airborne/satellite hyperspectral data for estimating TN content are necessary for testing the findings.

Monitoring of crops during their growth period is essential to increase the profitability of production and reduce costs. Unmanned aerial vehicles (UAVs) have been introduced as an appropriate tool in precision farming, due to their affordability and high-spatial resolution data. The potential of plant height and vegetation indices derived from UAV-based RGB imagery at 12-leaf growth stage (V12) is investigated for prediction of corn forage yield. Using the structure from motion techniques, the crop height model (CHM) was created by subtracting crop surface model from digital elevation model. Next, by evaluating the CHM using ground measurements of plant height, it was shown that UAV imagery provides high capability in modeling the plant height ( R 2 = 0.85). Pearson’s correlation coefficient ( R ) between variables showed that the plant height had the highest correlation with forage yield ( R = 0.84). Furthermore, among the vegetation indices, visible atmospherically resistant index with R = 0.77, normalized difference index with R = 0.75, and excess red with R = 0.74 were better than other indices. Finally, the partial least squares regression (PLSR) model was constructed using plant height and vegetation indices to predict forage yield. The beta regression coefficients of the PLSR model were used to identify the most relevant predictors, and the final PLSR model was rebuilt with relevant predictors. The PLSR analysis confirmed that the plant height was the most important predictor, although integrating the vegetation indices in the PLSR model has slightly improved the prediction model ( R 2 = 0.74, RMSE = 0.316 kg / m2, and relative RMSE = 12.39 % ), compared to the model that used only plant height ( R 2 = 0.702, RMSE = 0.339 kg / m2, and relative RMSE = 13.29 % ). Overall, these results indicate that the UAV-based images are efficient and cost-effective data to predict corn forage yield as well as plant height modeling.

We describe the development of a novel method for real-time in situ characterization of polycyclic aromatic hydrocarbons (PAHs) in submerged freshwater sediments. Laser-induced fluorescence (LIF) spectroscopy, a mature technique for PAH characterization in terrestrial sediments, was adapted for shipboard use. A cone penetrometer-type apparatus was designed for probe penetration at a constant rate (1 cm/s) to a depth of 3 m. A field-portable LIF system was used for in situ measurements in which the output of a pulsed excimer laser was transmitted by optical fiber to a sapphire window (6.4-mm o.d.) in the probe wall; fluorescent emission was collected by a separate optical fiber for transmission to the spectrometer on deck. Four wavelengths (340, 390, 440, 490 nm) were selected via optical delay lines, and multiple-wavelength waveforms were created. These multiple-wavelength waveforms contain information on the fluorescence frequency, intensity, and emission decay rate. Field testing was conducted at 10 sites in Milwaukee Harbor (total PAH concentrations ranged from approximately 10 to 650 microg/g); conventional sediment core samples were collected concurrently. The core samples were analyzed by EPA methods 3545 (pressurized fluid extraction, PFE) and 8270C (gas chromatography-mass spectrometry, GC-MS) for PAHs. A partial least-squares regression (PLSR) model wasthen created based on laboratory LIF measurements and PFE-GC-MS of the core samples. The PLSR model was applied to the in situ field test data, and 13 of the 16 EPA-regulated PAHs were quantified with a relative error of <30% overall (the remaining three PAHs were found at levels insufficient to quantify). We additionally describe preliminary source apportionment relationships that were revealed by the PLSR model for the in situ LIF measurements.

Estimating economic benefit of sugar beet based on three-dimensional computer vision: a case study in Inner Mongolia, China

Fractional-order Savitzky–Golay filter for pre-treatment of on-line vis–NIR spectra to predict phosphorus in soil

Assessment of the human albumin in acid precipitation process using NIRS and multi-variable selection methods combined with SPA

Quantification and classification of vegetable oils in extra virgin olive oil samples using a portable near-infrared spectrometer associated with chemometrics

&amp;lt;p&amp;gt;Fire is an important element of the ecosystems, nevertheless, high severity fires can have negative impacts on the ecosystems as consequence of the high temperatures reaches. High temperatures normally have detrimental impacts on soil properties. The objective of this work was to determine the relationship of spectral reflectance and soil pH, electrical conductivity (EC), carbonates (CaCO&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;) and total carbon (TC) content after a medium to high severity wildfire occurred in Croatia using linear and nonlinear calibration models.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Soils were sampled 2 days after a medium to high severity wildfire in Zadar County, Croatia. A total of 120 soil samples (0-5 cm) were collected from three different treatments (n= 40 per treatment): control (C), mean severity (MS), high severity (HS). Soil pH, EC, CaCO&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; and TC content were determined using standard laboratory methods. &amp;amp;#160;Soil spectral measurements were carried out using a portable spectroradiometer (20 per treatment, 60 in total). Linear statistical model - partial least squares regression (PLSR) and non-linear - artificial neural network (ANN) were generated to estimate changes in soil pH, EC, CaCO&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; and TC content based on the original spectral reflectance and its first derivative in form of principal components (PC). One-way ANOVA revealed pH values were significantly different in all three treatments. EC, CaCO&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; and TC were significantly higher in HS plots compared with the other treatments.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Different wildfire severity indicated very collinear soil spectral response, but with certain variations of reflectance intensity. Control samples showed a higher reflectance than MS and HS samples. This is attributed to the low pH and TC content. Low reflectance of MS and HS samples could be explained by their increased pH and TC values. Soil pH was the only parameter that showed a high R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; and low root mean squared error (RMSE) after Savitzky Golay smoothing and the first derivation. In PLSR model, strong to very strong correlation and low RMSE were obtained. ANN model also showed a high R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; and lower RMSE for all properties except pH. Both models showed satisfactory results for prediction of the studied soil properties. ANN model predicted EC, CaCO&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;, and TC better, while PLSR proved to be a better model for pH prediction.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Key words:&amp;lt;/strong&amp;gt; soil reflectance, fire severity, principal components, partial least squares regression, neural networks&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Acknowledgements&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This work was supported by Croatian Science Foundation through the project &amp;quot;Soil erosion and degradation in Croatia&amp;quot; (UIP-2017-05-7834) (SEDCRO).&amp;lt;/p&amp;gt;

&amp;lt;p&amp;gt;Soil properties could be assessed with reflectance spectroscopy (soil spectroscopy, SS) in vis-NIR region (400-2500 nm) through absorption features found in soil spectra. A high spectral resolution (up to 1 nm) drives to high dimensional and multicollinear data. This issue is usually addressed prior to modelling with feature extraction methods such as Principal Component Analysis, or embedded methods such as Partial Least Squares Regression (PLSR). Feature Selection (FS) wrapper methods are promising dimensionality reduction approaches barely used in SS. The objective of this study was two-fold: i) evaluate the performance of FS wrapper methods built from Random Forest (RF) algorithm to predict soil organic matter (SOM), clay and carbonates using laboratory spectroscopy, ii) test the performance of FS methods for dimensionality reduction in SS. The reflectance of 100 soil samples from Sierra de las Nieves National Park (Spain), was measured under laboratory conditions using an ASD FieldSpec Pro JR. A spectral preprocessing method, Continumm Removal (CR), was applied to raw spectra. The RF wrapper considered two different feature searching approaches: Sequential Forward Selection (SFS) and Sequential Flotant Forward Selection (SFFS). The performance of RF with FS (RF-FS) was compared to that of Partial Least Squares Regression (PLSR) and RF (without FS). Models were evaluated with R-squared, root mean squared error (RMSE) and ratio of prediction to deviation (RPD).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;RF-FS models outperformed PLSR and RF models for the three SAP. RF-FS best models had a RPD of 2.19 for SOM, 1.64 for carbonates and 1.52 for clay, whereas PLSR models had RPD values of 1.59, 1.22 and 1.3, and RF 1.38, 1.23 and 1.23 for SOM, carbonates, and clay, respectively. Therefore, FS was useful in obtaining models with improved accuracy by reducing redundant features and avoiding multicollinearity (Hughes effect). The application of FS wrapper methods reduced the number of features in the RF-FS models to less than 1% of the starting features. Features were selected across all spectra from SOM and clay, and around 900, 1900 and 2350 nm for carbonates. This research, thus, shows an alternative to different feature extraction approaches for modelling soil properties based on FS methods and machine learning.&amp;lt;/p&amp;gt;

Previous studies demonstrated that quantitative structure retention relationships (QSRR) combined with the linear solvent strength (LSS) model allow for prediction of gradient reversed phase high performance liquid chromatography (HPLC) retention time for any analyte of a known molecular structure under defined HPLC conditions. The QSRR model derived at the selected gradient time was tested at the same gradient time. Presently, in the first step, experimental retention data for model sets of just 5 analytes were used to derive appropriate QSRR models at two gradient times. Additionally, a new molecular modeling approach based on a more accurate ab initio method was here proposed. Those QSRR models were used to further predict gradient retention times for sets of 16 test analytes belonging to polynuclear aromatic hydrocarbons (PAHs), at two selected gradient times. Then, applying linear solvent strength (LSS) theory, only predicted retention times for PAHs were used to find the optimal gradient conditions to separate them. Satisfactory predictions of gradient retention times for PAHs were obtained. Contrary to the previous achievements, the proposed QSRR provides the chance to predict retention of PAHs with the appropriate selectivity achieving the same sequence of analytes eluted in the experiment and during the simulation performed on the computer screen.

Developing green liquid chromatography (LC) methods is currently a cutting-edge field due to the recognized negative influence of toxic organic solvents on human health and nature. One of the available strategies for “greening” is the addition of cyclodextrin (CD) in the mobile phase. CDs are known for their ability to form inclusion complexes with various analytes, improving their solubility in the aqueous phase, thus reducing the retention times and, simultaneously, the organic solvent consumption. The presented concept could be extended to incorporate sustainability in the field of separation science, from the stage of method development to routine analysis, through the quantitative structure retention relationship (QSRR) modeling approach. This book chapter is aimed at presenting the possibilities of building and employing QSRR models in CD-modified high-performance liquid chromatography (HPLC), followed by investigating their potential utility in different areas of application. The complexity of CD-modified HPLC indicates the associated complexity of the QSRR modeling. This review provides an overview of CD structures and analytical techniques applied for characterization of the formed inclusion complexes. Special focus is placed on the practical and theoretical knowledge regarding the retention mechanisms and equilibria existing in CD-modified HPLC systems as an introduction to retention modeling. Molecular descriptors mostly applicable in modeling retention in HPLC, as well as complex association constants that can properly describe the formed inclusion complexes in CD-modified HPLC, are also presented. Further, machine learning techniques are discussed in terms of their utility and contribution in building good predictive QSRR models. Finally, the key experimental findings about the utility of the obtained QSRR models in retention prediction and evaluation of optimal conditions of the chromatographic method, and then thermodynamic parameters of complexation are presented, followed by theoretical discussion of possible benefits, drawbacks, and future perspectives of the introduced approach.