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For the first time, a prototype HS-GC-MS-IMS dual-detection system is presented for the analysis of volatile organic compounds (VOCs) in fields of quality control of brewing hop. With a soft ionization and drift time-based ion separation in IMS and a hard ionization and m/z-based separation in MS, substance identification in the case of co-elution was improved, substantially. Machine learning tools were used for a non-targeted screening of the complex VOC profiles of 65 different hop samples for similarity search by principal component analysis (PCA) followed by hierarchical cluster analysis (HCA). Partial least square regression (PLSR) was applied to investigate the observed correlation between the volatile profile and the α-acid content of hops and resulted in a standard error of prediction of only 1.04% α-acid. This promising volatilomic approach shows clearly the potential of HS-GC-MS-IMS in combination with machine learning for the enhancement of future quality assurance of hops. [ABSTRACT FROM AUTHOR]

Desi ghee, obtained by buffalo and cow milk, is highly expensive because it contains valuable vitamins and conjugated linoleic acid (CLA). Its high demand and cost result in to its adulteration with inferior banaspati ghee. In this study, Fluorescence spectroscopy along with multivariate analysis has been utilised for the detection and quantification of adulteration. Spectroscopic analysis showed that buffalo ghee contains more vitamins and CLA than cow, whereas cow ghee is enriched with beta-carotene. For multivariate analysis, principle component analysis (PCA) and partial least square regression (PLSR) have been applied on the spectral data for the determination of adulteration. PLSR model was authenticated by predicting 23 unknown samples including 3 commercial brands of desi ghee. The root mean square error in prediction (RMSEP) of unknown samples was found to be 1.7 which is a reasonable value for quantitative prediction. Due to non-destructive and requiring no sample pre-treatment, this method can effectively be employed as on line characterization tool for the food safety assurance. [ABSTRACT FROM AUTHOR]

The level of groundwater can also be used in monitoring desertification and land degradation. In this study, three models, namely: partial least square regression, artificial neural networks (ANN), and adaptive neuro-fuzzy inference system, were used to monitor and predict the level of groundwater and the land degradation index via the Iranian Model of Desertification Potential Assessment method. The groundwater data of 24 Piezometric wells from 2002 to 2016 were also collated to predict the groundwater level. In all models, 70% of the data were applied for training, while 30% of data were employed for testing and validation. Monthly rainfall, topographic wetness index, distance of the river (m), latitude and longitude of Piezometers in the Universal Transverse Mercator coordinate system were the inputs, and the level of groundwater was the output of each method. The prediction performance of both training and testing sets is evaluated by R2 and MSE. Looking at statistical inferences, we found that ANN has the highest efficiency (R2 = 0.96, MSE = 0.71 m) which agree with other findings. We combined the results of ANN with ordinary kriging (OK) and produced a groundwater condition map. According to the potential desertification map and groundwater level index, the potential of desertification had become severe since 2002 and was at a rate of 60% of land area, which, due to incorrect land management in 2016, increased to almost 98% of the land surface in the study area. Again between 2002 and 2016, the land area with low degradation risk decreased from 38,030 ha (39% of the study area) to zero ha in 2016. In 2016, there was no moderate land degradation risk. Using ANN, we predicted that around 99% of the area (95,206 ha) was severely degraded in 2017 and according to groundwater level index, the land degradation increased by 100%. This implies that the area deserves urgent care and reclamation. We also used latitude and longitude of Piezometers as input variables which improved the model. In addition to the target variable, latitude and longitude play important roles in OK and decreased the total error of two combined models. [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]

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]

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]

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]

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]