Root Mean Square Error Of Cross-validation Research Articles

Rapid prediction method of ZIF-8 immobilized Candida rugosa lipase activity by near-infrared spectroscopy

Candida rugosa lipase (CRL, EC3.1.1.3) is one of the main enzymes synthesizing esters, and ZIF-8 was chosen as an immobilization carrier for lipase. Enzyme activity testing often requires expensive reagents as substrates, and the experiment processes are time-consuming and inconvenient. As a result, a novel approach based on near-infrared spectroscopy (NIRs) was developed for predicting CRL/ZIF-8 enzyme activity. The absorbance of the immobilized enzyme catalytic system was evaluated using UV–Vis spectroscopy to investigate the amount of CRL/ZIF-8 enzyme activity. The powdered samples' near-infrared spectra were obtained. The sample’s enzyme activity data were linked with each sample’s original NIR spectra to establish the NIR model. A partial least squares (PLS) model of immobilized enzyme activity was developed by coupling spectral preprocessing with a variable screening technique. The experiments were completed within 48 h to eliminate inaccuracies between the reduction in enzyme activity with increasing laying-aside time throughout the test and the NIRs modeling. The root-mean-square error of cross-validation (RMSECV), the correlation coefficient of validation set (R) value, and the ratio of prediction to deviation (RPD) value were employed as assessment model indicators. The near-infrared spectrum model was developed by merging the best 2nd derivative spectral preprocessing with the Competitive Adaptive Reweighted Sampling (CARS) variable screening method. This model's root-mean-square error of cross-validation (RMSECV) was 0.368 U/g, the correlation coefficient of calibration set (R_cv) value was 0.943, the root-mean-square error of prediction (RMSEP) set was 0.414 U/g, the correlation coefficient of validation set (R) value was 0.952, and the ratio of prediction to deviation (RPD) was 3.0. The model demonstrates that the fitting relationship between the predicted and the reference enzyme activity value of the NIRs is satisfactory. The findings revealed a strong relationship between NIRs and CRL/ZIF-8 enzyme activity. As a result, the established model could be implemented to quantify the enzyme activity of CRL/ZIF-8 quickly by including more variations of natural samples. The prediction method is simple, rapid, and adaptable to be the theoretical and practical basis for further studying other interdisciplinary research work in enzymology and spectroscopy.

Determination of the glass transition temperature of an epoxy prepreg by Near Infrared Spectroscopy

An aged epoxy prepreg's glass transition temperature (Tg) is determined using Near Infrared Spectroscopy (NIRS). A miniaturized Near Infrared (NIR) spectrometer and a full-scaled FTNIR spectrometer are used for Tg determination and different measuring setups are tested for the miniaturized spectrometer. Partial least square (PLS) regression is used to develop calibration models by utilizing different spectral pretreatments. It is shown that with the miniaturized spectrometer and a suitable measurement setup, PLS model qualities similar to those of the FTNIR spectrometer can be achieved in cross-validation (CV), with a determination coefficient of CV (R2CV) of about 0.97 and a root mean square error of cross-validation (RMSECV) of approx. 0.7 °C in the measuring range between 25 °C and 42 °C. Cost-effective, miniaturized NIR spectrometers are thus a suitable technology for the quality assurance of prepregs.

Enrichment of barley grain in some biologically active molecules by microbial fermentation and their characterisation by infrared spectroscopy

A FTIR-ATR coupled to chemometry method was first-ever tested in monitoring the effects of a range of bacterial, yeast and fungal fermentations on nutrient and chemical composition of barley. Chemically analysed contents of samples taken at 24 h intervals were predicted by a partial least square regression (PLSR) model. Moreover, secondary protein components (SPC) of barley protein were measured by a Gaussian curve fitting of amid I spectral region. The results showed that crude ash content of barley increased significantly in Aspergillus oryzae+Bacillus subtilis fermentation and crude protein content in Lactobacillus salivarius fermentation. Organic acid content (especially lactic acid) increased in all fermentations of barley with bacteria, fungal and yeast, while crude fiber and anti-nutritional contents such as phytic acid and tannin decreased. On the other hand, PLSR model based on second derivate spectral data has highly acceptable performance characteristics in predicting protein, ash, fibre, lipid and starch: low RMSEV (root mean squared error of calibration) of 0.0003–1.24 with r2 = 0.999 and RMSECV (root mean squared error of cross validation) of 0.004–1.88 with R2 = 0.988, high recovery rate (closeness to analysed value) of almost 100.0, high precision VCp% (variance coefficient of prediction) of less than 5.0% and RPD (relative performance determinant) values of over 2.0. Barley protein was seen to be subjected to a useful hydrolysis, degradation and aggregation of microbial fermentation, as measured in SPC percentages. The decreased percentages of β-sheet, α-helix components and random coil associated with increased percentage of β-turn component is more likely an indication of improved protein quality. We recommended the model employed herein is robust and can replace the analytical methods of wet chemistry in feed analysis in nutritional studies and in routine analysis by the industry to monitor in line fermentation processes.

Label-Free Quantification of Nanoencapsulated Piperonyl Esters in Cosmetic Hydrogels Using Raman Spectroscopy.

Raman spectroscopy is a well-established technique for the molecular characterisation of samples and does not require extensive pre-analytical processing for complex cosmetic products. As an illustration of its potential, this study investigates the quantitative performance of Raman spectroscopy coupled with partial least squares regression (PLSR) for the analysis of Alginate nanoencapsulated Piperonyl Esters (ANC-PE) incorporated into a hydrogel. A total of 96 ANC-PE samples covering a 0.4% w/w-8.3% w/w PE concentration range have been prepared and analysed. Despite the complex formulation of the sample, the spectral features of the PE can be detected and used to quantify the concentrations. Using a leave-K-out cross-validation approach, samples were divided into a training set (n = 64) and a test set, samples that were previously unknown to the PLSR model (n = 32). The root mean square error of cross-validation (RMSECV) and prediction (RMSEP) was evaluated to be 0.142% (w/w PE) and 0.148% (w/w PE), respectively. The accuracy of the prediction model was further evaluated by the percent relative error calculated from the predicted concentration compared to the true value, yielding values of 3.58% for the training set and 3.67% for the test set. The outcome of the analysis demonstrated the analytical power of Raman to obtain label-free, non-destructive quantification of the active cosmetic ingredient, presently PE, in complex formulations, holding promise for future analytical quality control (AQC) applications in the cosmetics industry with rapid and consumable-free analysis.

A simple but effective evaluation criterion for parameters optimization of EPO and its application to moisture insensitive prediction of soil organic matter

Field measurement using portable NIR spectrometer is becoming a popular way to provide in situ, rapid estimation of soil organic matter content. However, in situ NIR spectra is altered by external environmental factors, such as soil moisture. The external parameter orthogonalization (EPO) has been recognized as one of the most effective method to minimize effect of soil moisture on spectra. In general, the root mean squared error of cross validation (RMSECV) was mainly used as an evaluation criterion to optimize the parameters involved in EPO algorithm, which ensures that the wet soil spectra corrected by EPO could have a good quantitative performance, but ignores the spectral similarity between the dry soil spectra and the wet soil spectra corrected by EPO. In this study, we present a simple but effective evaluation criterion for optimization of EPO, which we refer to as the mean of spectral correlation coefficient (MSCC). Compared with the commonly used RMSECV criterion, the spectra corrected by EPO (MSCC) are more similar to the corresponding dry soil spectra, and MSCC is more robust to different spectral pretreatment methods. In addition, the quantitative results of MSCC are equivalent to those of RMSECV, and sometimes slightly better than those of RMSECV.

An advanced variable selection method based on information gain and Fisher criterion reselection iteration for multivariate calibration

The difficulty of analyzing high-dimensional data makes dimensionality reduction essential, and variable selection is widely used as a dimensionality reduction method to improve the interpretability of models. A variable selection method based on information gain and Fisher's criterion reselection iteration (IFRI) is proposed in this paper. It selects variables that have strong associations with the property of interest by two classical feature selection functions to improve interpretability of the selected variables. At first, information gain was employed to pre-select “global” feature variables with strong ability to distinguish between different classes, and Fisher's criterion was applied to re-select key variables further from the selected “global” variables that enable larger inter-class variance and smaller intra-class variance for the samples. Pearson correlation coefficients were then applied to eliminate variables with strong linear correlation and a series of subsets were obtained iteratively. At the end, cross-validation (CV) was used to calculate the obtained subset, and the one with the lowest root mean square error of cross-validation (RMSECV) was considered optimal. Combining with partial least squares (PLS), it was applied to the analysis of nicotine and total sugar content in tobacco samples. By comparing and validating with two representative variable selection algorithms, CARS and MC-UVE, IFRI has been demonstrated to be an effective and applicable method to establish high-performance models by selecting a few key variables. Differing from most methods, IFRI does not need to depend on the PLS model parameters in the process of variable selection, which makes it more interpretable. Moreover, the selected variables by IFRI are reproducible for the same dataset. Apparently, the principle of the method makes it not limited to spectral data and can be applied to the variable selection of different types of high-dimensional data by combining it with other modeling methods, for example it can be combined with qualitative discriminant models to classify tobacco or used to select critical chemical components affecting the tobacco quality.

Rapid detection of pork gelatin in ice cream samples by using non-destructive FT-NIR spectroscopy and Partial least squares-discriminant analysis

This study aimed at developing a fast and low-cost detection method to discriminate between ice cream samples containing pork or non-pork gelatin by using Fourier Transform Near Infrared (FT-NIR) spectroscopy and Partial Least Squares Discriminant Analysis (PLS-DA). Forty two samples of ice cream were used, among which twenty three samples were adulterated with different levels i.e. 1%, 5%, 10%, and 20 % of pork gelatin (Non-Halal). Whereas, the remaining nineteen samples containing only cow gelatin (Halal) were used as a control. All the ice cream samples were measured with the FT-NIR spectrophotometer in the reflection mode. Spectra were collected in the wavenumber range from 10000 to 4000 cm−1 (1000 to 2500 nm). The results show that the PLS-DA model with Unit Vector Normalization (UVN) spectral transformations for 1% pork gelatin adulteration is the optimal one which was based on a compromise between the lowest value of root mean square error of cross validation (RMSECV) for the calibration set. The lowest value of root mean square error of prediction (RMSEP) for the test set, the least number of factors and the percentage of correctly classified samples, the Halal and Non-Halal, for both calibration and test sets. This newly developed method is fast, involves simple sample preparation and is low cost.

Development of a quantitative method to evaluate the printability parameters of water-based ink using visible and near infrared spectroscopy

Water-based inks are widely used in green packaging and printing. The printability parameters of water-based inks, such as viscosity (alcohol concentration (AC)) and color (toning additive concentration (toning yellow concentration/toning red concentration, TYC/TRC)), can only be controlled manually in many printing companies. The printability parameters of water-based inks with different additives were analyzed using spectral preprocessing, variable selection, and model-building methods with visible and near infrared (vis-NIR) spectral data (380∼980 nm). Model performance was compared using the root mean square error of cross-validation (RMSEC) and the coefficient of determination (R2). The results of the experiment indicate that the viscosity of the water-based inks can be quantitatively predicted using the principal component analysis and back propagation neural network model (PCA-BPNN) combined with Savitzky-Golay (SG) smoothing in the spectral subrange, which is superior to the PLS regression model. The R2c and r2p of the PCA-BPNN model were up to 0.998 and 0.993, and the RMSEC and RMSEP values obtained were 0.21 and 0.34. Similarly, the concentration of toning yellow and toning red can be quantitively predicted using the PCA-BPNN model combined with SG smoothing in the 617∼726 nm spectral range, which is better than iPLS regression model. These results indicate that the use of vis-NIR spectroscopy and chemometrics is a promising strategy, reliable for predicting the printability parameters of water-based inks, and provides the technical basis for subsequent implementation of online inspection.

Spatially Offset Raman Spectroscopic (SORS) Analysis of Wine Alcoholic Fermentation: A Preliminary Study

Spatially offset Raman spectroscopy (SORS) is a non-invasive analytical technique that allows the analysis of samples through a container. This makes it an effective tool for studying food and beverage products, as it can measure the sample without being affected by the packaging or the container. In this study, a portable SORS equipment was used for the first time to analyse the alcoholic fermentation process of white wine. Different sample measurement arrangements were tested in order to determine the most effective method for monitoring the fermentation process and predicting key oenological parameters. The best results were obtained when the sample was directly measured through the glass container in which the fermentation was occurring. This allowed for the accurate monitoring of the process and the prediction of density and pH with a root mean square error of cross-validation (RMSECV) of 0.0029 g·L−1 and 0.04, respectively, and R2 values of 0.993 and 0.961 for density and pH, respectively. Additionally, the sources of variability depending on the measurement arrangements were studied using ANOVA-Simultaneous Component Analysis (ASCA).

Achieving clinically optimal balance between accuracy and simplicity of a formula for manual use: Development of a simple formula for estimating liver graft weight with donor anthropometrics.

In developing a formula for manual use in clinical settings, simplicity is as important as accuracy. Whole-liver (WL) mass is often estimated using demographic and anthropometric information to calculate the standard liver volume or recommended graft volume in liver transplantation. Multiple formulas for estimating WL mass have been reported, including those with multiple independent variables. However, it is unknown whether multivariable models lead to clinically meaningful improvements in accuracy over univariable models. Our goal was to quantitatively define clinically meaningful improvements in accuracy, which justifies an additional independent variable, and to identify an estimation formula for WL graft weight that best balances accuracy and simplicity given the criterion. From the Japanese Liver Transplantation Society registry, which contains data on all liver transplant cases in Japan, 129 WL donor-graft pairs were extracted. Among the candidate models, those with the smallest cross-validation (CV) root-mean-square error (RMSE) were selected, penalizing model complexity by requiring more complex models to yield a ≥5% decrease in CV RMSE. The winning model by voting with random subsets was fitted to the entire dataset to obtain the final formula. External validity was assessed using CV. A simple univariable linear regression formula using body weight (BW) was obtained as follows: WL graft weight [g] = 14.8 × BW [kg] + 439.2. The CV RMSE (g) and coefficient of determination (R2) were 195.2 and 0.548, respectively. In summary, in the development of a simple formula for manually estimating WL weight using demographic and anthropometric variables, a clinically acceptable trade-off between accuracy and simplicity was quantitatively defined, and the best model was selected using this criterion. A univariable linear model using BW achieved a clinically optimal balance between simplicity and accuracy, while one using body surface area performed similarly.

Rapid determination of lead (Pb) in the soil-plant system by laser-induced breakdown spectroscopy (LIBS): case study of Pb-pollution from perovskite solar cells.

With the widespread usage of lead (Pb)-containing perovskite solar cells (PSCs), it is critical to monitor Pb pollution from PSCs in the environment. Among different analytical techniques, laser-induced breakdown spectroscopy (LIBS) has demonstrated good performance in the fast quantification of many elements in solid samples, without using toxic and expensive chemical reagents. Therefore, LIBS offers significant potential for detecting and quantifying Pb in the environment. In this study, a Pb migration model in the PSCs-soil-Houttuynia plants system was assessed based on the LIBS data. The Pb transfer rates and the Pb uptake coefficients were calculated to evaluate Pb migration from PSCs to plants. The results showed that the R2 of quantitative results were all greater than 0.98, with the root-mean-square error of cross-validation (RMSECV) being less than 1.53 wt.%. Furthermore, above 49% Pb from PSCs was swiftly diffused into soil under watering conditions, while Houttuynia plants absorbed over 10% Pb from polluted soil. This study revealed that Pb leakage from PSCs should not be underestimated and that LIBS is a viable and fast analytical method for monitoring Pb in the environment.

Predicting proximate composition of mutton by fourier transform-near infrared spectroscopy

A study was conducted to predict the proximate composition viz., moisture, crude protein, crude fat and total ash contents of mutton by Fourier transform-near infrared (FT-NIR) spectroscopy.Atotal of 200 longissimus dorsi muscle samples of mutton were collected from the retail meat outlets in and around Chennai in of September, 2020. The correlation coefficient ((R2)between true (conventional) and prediction (FT-NIR) values for moisture, crude protein, crude fat and total ash in mutton were 0.98, 0.98, 0.89, and 0.84, respectively. The root mean square errors of cross-validation (RMSECV) were 0.106, 0.095, 0.188 and 0.034, respectively. Ratios of prediction to deviation (RPD) were 7.80, 8.08, 3.15 and 2.53, respectively. Nonsignificant (P>0.05) difference was observed between the true and predicted values for all the proximate composition. It was concluded that FT-NIR spectroscopy canbeusedas a promisingtoolforassessing theproximate composition of mutton.

Raman spectroscopy for the quantitative analysis of Lornoxicam in solid dosage forms

AbstractIn this study, Raman spectroscopy is used to characterize different formulations of active pharmaceutical ingredient (API) of Lornoxicam, a nonsteroidal anti‐inflammatory drug for qualitative and quantitative analyses. The Raman spectral characteristic peaks associated with API and excipients are identified, which increase or decrease as their concentrations change in different formulations. The spectral response was analyzed qualitatively and quantitatively by using chemometrics tools such as principal component analysis (PCA) model and partial least squares regression (PLSR) analysis model, respectively. The characteristic Raman spectral features associated with the different formulations of API of Lornoxicam were used to differentiate different groups of Raman spectral data using PCA. By using PLSR model, root mean square error of cross validation (RMSECV) was found to be 0.76 mg during the development of prediction model and according to the (R2) value, the goodness of the model was found to be 0.99. Furthermore, the concentration of API in the blind sample was measured. In comparison to the 15/35 mg (w/w), this concentration was projected to be 14.13/35 mg (w/w). Raman spectroscopy along with PCA and PLSR analyses is a useful tool for determining the concentration of Lornoxicam in pharmaceutical samples, both qualitatively and quantitatively.

Identification of Browning in Human Adipocytes by Partial Least Squares Regression (PLSR), Infrared Spectral Biomarkers, and Partial Least Squares Discriminant Analysis (PLS-DA) Using FTIR Spectroscopy

We aimed to identify the browning of white adipocytes using partial least squares regression (PLSR), infrared spectral biomarkers, and partial least squares discriminant analysis (PLS-DA) with FTIR spectroscopy instead of molecular biology. PLSR helps distinguish human beige adipocytes treated with norepinephrine and rosiglitazone. When PLSR was based on the selected regions of 3997–3656 and 1618–938 cm−1, PLSR achieved an R2 of cross-validation of 88.95, a root mean square error of cross validation (RMSECV) of 2.13, and a ratio performance deviation (RPD) of 3.01. Infrared spectral biomarkers [1635 cm−1 (β-sheet amide I), 879–882, 860–3 cm−1 (A-form helix), and 629–38 cm−1 (OH out-of-plane bending)] were identified in human beige adipocytes based on spectral differences between human beige adipocytes and human white adipocytes, principal component analysis-linear discriminant analysis (PCA-LDA) cluster vector, U-test, and Fisher’s score per wavenumber. PLS-DA yielded a useful classification of adipocytes and expression distribution of adipogenesis genes in adipocytes. PLSR, infrared spectral biomarkers, and PLS-DA using FTIR spectroscopy are proposed as effective tools for identifying specific biological activities in a limited environment through features that do not require labeling and are relatively inexpensive in terms of time and labor.

Network pharmacology and fingerprint for the integrated analysis of mechanism, identification and prediction in Panax notoginseng.

Panax notoginseng (Burkill) F. H. Chen ex C. H. Chow, is a well-known herb with multitudinous efficacy. In this study, a series of overall analyses on the action mechanism, component content, origin identification, and content prediction of P. notoginseng are conducted. The purpose was to analyse the mechanism of pharmacological efficacy, differences between contents and groups of P. notoginseng from different origins, and to identify the origin and predict the content. The P. notoginseng samples from four different origins were used for analysis by the database, network pharmacology (Q-marker) and fingerprint analysis [high-performance liquid chromatography (HPLC), attenuated total reflectance Fourier-transform infrared (ATR-FTIR) and near-infrared (NIR)] combined with data fusion strategy (low- and feature-level). Four saponins were identified as Q-markers, and exerted pharmacological effects on signalling pathways through 24 core targets. The qualitative and quantitative analysis of HPLC showed that there were differences among groups and different origins. Therefore, considering the need to treat diseases, combined with network database and network pharmacology, the suitable producing areas were determined through the mechanism of action and the required saponin content. The low-level data fusion successfully identified the origin and predicted the content of P. notoginseng from different origins. The accuracy rate of each evaluation index of the partial least squares discriminant analysis (PLS-DA) model was 1, and the t-SNE (t-distributed stochastic neighbor embedding) visualisation results were good. The coefficient of determination (R2 ) of the partial least squares regression (PLSR) model ranged from 0.9235-0.9996, and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)range is 0.301-1.519. This study was designed to provide a sufficient theoretical basis for the quality control of P. notoginseng.

Component Prediction of Antai Pills Based on One-Dimensional Convolutional Neural Network and Near-Infrared Spectroscopy

Convolutional neural networks (CNNs) are widely used for image recognition and text analysis and have been suggested for application on one-dimensional data as a way to reduce the need for preprocessing steps. In this study, the performance of one-dimensional convolutional neural network (1DCNN) machine learning algorithm was investigated for regression analysis of Antai pills spectral data. This algorithm was compared with other chemometric methods, including support vector machine regression (SVR) and partial least-square regression (PLSR) methods. The results showed that the 1DCNN model outperformed the PLSR and SVR models with similar data preprocessing for the three analytes (wogonoside, scutellarin, and ferulic acid) in Antai pills. Taking wogonoside as an example, the indices such as the correction coefficient of determination ( R v 2 ), the root mean-squared error of cross validation (RMSECV) for calibration set, the prediction coefficient of determination ( R p 2 ), and the root mean-squared error of prediction (RMSEP) obtained by PLSR modeling were 0.9340, 0.5568, 0.9491, and 0.5088; the indices obtained by SVR modeling were 0.9520, 0.4816, 0.9667, and 0.4117; and the indices obtained by 1DCNN modeling were 0.9683, 0.3397, 0.9845, and 0.2807, respectively. The evaluation metrics of 1DCNN are better than those of PLSR and SVR, and the prediction effect is the best, proving that 1DCNN has a good generalization ability. Especially with outliers of spectra, PLSR’s R p 2 decreased by 0.0181, SVR’s R v 2 decreased by 0.01, and 1DCNN’s R v 2 increased by 0.0009 and R p 2 decreased by 0.0057. The evaluation indices of 1DCNN have no significant change in comparison with no outliers and can still show good performance, which reflects the inclusiveness of the 1DCNN model for outliers. Simultaneously, the feasibility and robustness of the 1DCNN model in the application of near-infrared spectroscopy was verified, which has a certain application value.

Non-Destructive Quality Evaluation of Tropical Fruit (Mango and Mangosteen) Purée Using Near-Infrared Spectroscopy Combined with Partial Least Squares Regression

Mango and mangosteen are commercially important tropical fruits with a short shelf life. Fruit processing is one of the alternatives to extend the shelf life of these fruits. Purée is one of the processed products of fresh fruit. In this research, the quality of mango and mangosteen purée was analyzed. Titratable acidity (TA) and total soluble solids (TSS) were predicted using non-destructive near-infrared (NIR) spectroscopy. A partial least squares regression (PLSR) model was developed based on the NIR spectra with a wavelength ranging from 800 to 2500 nm. The PLSR model returned a coefficient of determination (r2) and a ratio of prediction to deviation (RPD) of 0.955 and 4.7 for TSS, and 0.784 and 2.2 for TA, in the mango purée. Similarly, the best model was selected for the TSS prediction in the mangosteen purée through PLSR, with an r2, a root mean square error of cross-validation (RMSECV), and RPD of 0.799, 0.3% malic acid, and 2.2, respectively. The results show the possible application of NIR spectroscopy in the product processing line, although a larger number of samples with wide variation in future studies are needed as an input to update the model, in order to obtain a more robust model.

A new prediction model for total body water estimation by BIA in children with normal and excessive weight

Various methods, including bioelectrical impedance analysis (BIA), are used for total body water (TBW) estimation. The objective of our study by BIA was to develop a new predication model based on corrected TBW for normal adult BMI, a concept similar to the standardization of glomerular filtration rate by relating it to the average adult body surface area. We measured TBW by BIA in 335 children 3-21 years old with normal or excessive body weight. Based on our data, we derived a new prediction model for TBW (L) for females {[(72.784+0.4093×weight)∗Corrected TBW]/100} and males {[(57.944+0.6551×weight)∗Corrected TBW]/100}. For validation, we compared our prediction model with three other models on TBW by BIA and dilution methods. Our model's error size to predict TBW showed lower cross-validated root mean square error (CV-RMSE) as compared to three other models versus our dataset by BIA and two other datasets by dilution methods. Our model also showed a smaller error (2.059) in CV-RMSE as compared to other models by dilution methods (2.126, 2.873, and 4.384) for normal and excessive weight combined. This implies that our model is more robust when excessive weight individuals are included in the data.. Our prediction model for TBW estimation by BIA performs better as compared to some other models based on BIA and dilution method datasets. Furthermore, our prediction model is the only one that is devised to be applicable to children and young adults with both normal as well as excessive weight.

Estimating soil salinity using Gaofen-2 imagery: A novel application of combined spectral and textural features

Gaofen-2 (GF-2) imagery data has been playing an important role in environmental monitoring. However, the scarcity of spectral bands makes GF-2 difficult to use in soil salinity estimation. In this paper, we combined spectral and textual features for soil salinity estimation from GF-2 imagery. The spectral features comprised five classes of predictors: spectral value, vegetation index, salinity index, brightness index, and intensity index. Four gray-level co-occurrence matrix (GLCM) indices were used as the textural features. The least absolute shrinkage and selection operator (LASSO) was applied to select features. Four methods, namely, Random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and partial least squares regression (PLSR) were applied and compared. To this end, 211 soil samples were collected in the Yellow River Delta through field investigation. The results showed that GF-2 imagery could successfully estimate soil salinity by integrating spectral and texture features, and among the four methods, the RF had the highest accuracy with the determination coefficient for cross-validation (R2CV), a root mean square error for cross-validation (RMSECV), and the ratio of the standard deviation to the root mean square error of prediction (RPD) of 0.82, 2.36 g kg−1, and 2.28, respectively. Especially, the impact of different scale features on the soil salinity estimation accuracy was evaluated. The optimal window size for features was 9 × 9 pixels, and increasing or decreasing the window size will decrease the estimation accuracy. The study provides a novel application to soil salinity estimation from remote sensing imagery.

Detection of eucalyptus oil adulteration in Australian tea tree oil using UV–Vis and fluorescence spectroscopy

Pure Australian tea tree oil is a high-value commodity; however, cheaper tea tree oils are often adulterated with other substances such as eucalyptus oil. Detection of adulteration typically requires time-consuming analysis with expensive equipment. Consequently, rapid methods of screening for adulteration would be beneficial for the industry. This study examined whether the rapid, non-destructive and low-cost methods of ultraviolet-visible and fluorescence spectroscopy could be used to detect adulteration of tea tree oil with eucalyptus oil. Ultraviolet-visible spectroscopy showed moderate accuracy for predicting adulterant concentration (root mean square error of cross-validation (RMSECV) of 6.8% v/v). Moving window analysis was used to reduce the model wavelength range to just 55 nm, without a loss in predictive accuracy. Fluorescence spectroscopy also performed well, with an RMSECV of 2.9% v/v. Consequently, these techniques may be suitable for rapidly and cheaply screening tea tree oil samples for gross adulteration.