Root Mean Square Error Of Cross-validation Research Articles

Temperature Correction of Spectra to Improve Solute Concentration Monitoring by In Situ Ultraviolet and Mid-Infrared Spectrometries toward Isothermal Local Model Performance.

Changes in temperature can significantly affect spectroscopic-based methods for in situ monitoring of processes. As varying temperature is inherent to many processes, associated temperature effects on spectra are unavoidable, which can hinder solute concentration determination. Ultraviolet (UV) and mid-infrared (IR) data were acquired for l-ascorbic acid (LAA) in MeCN/H2O (80:20 w/w) at different concentrations and temperatures. For both techniques, global partial least squares (PLS) models for prediction of LAA concentration constructed without preprocessing of the spectra required a high number of latent variables to account for the effects of temperature on the spectra (root mean square error of cross validation (RMSECV) of 0.18 and 0.16 g/100 g solvent, for UV and IR datasets, respectively). The PLS models constructed on the first derivative spectra required fewer latent variables, yielding variable results in accuracy (RMSECV of 0.23 and 0.06 g/100 g solvent, respectively). Corresponding isothermal local models constructed indicated improved model performance that required fewer latent variables in the absence of temperature effects (RMSECV of 0.01 and 0.04 g/100 g solvent, respectively). Temperature correction of the spectral data via loading space standardization (LSS) enabled the construction of global models using the same number of latent variables as the corresponding local model, which exhibited comparable model performance (RMSECV of 0.06 and 0.04 g/100 g solvent, respectively). The additional chemometric effort required for LSS is justified if prediction of solute concentration is required for in situ monitoring and control of cooling crystallization with an accuracy and precision approaching that attainable using an isothermal local model. However, the model performance with minimal preprocessing may be sufficient, for example, in the early phase development of a cooling crystallization process, where high accuracy is not always required. UV and IR spectrometries were used to determine solubility diagrams for LAA in MeCN/H2O (80:20 w/w), which were found to be accurate compared to those obtained using the traditional techniques of transmittance and gravimetric measurement. For both UV and IR spectrometries, solubility values obtained from models with LSS temperature correction were in better agreement with those determined gravimetrically. In this first example of the application of LSS to UV spectra, significant improvement in the predicted solute concentration is achieved with the additional chemometric effort. There is no extra experimental burden associated with the use of LSS if a structured approach is employed to acquire calibration data that account for both temperature and concentration.

Prediction of chicken meatball quality through NIR spectroscopy and multivariate analysis

Near Infrared (NIR) Spectroscopy leads a great opportunity to replace the expensive and time-consuming chemical conventional analysis for determination of the quality of meat products. This study was conducted aiming to evaluate the feasibility of NIRS and to establish a rapid assessment method to easily predict the quality of chicken meatball. Samples of meatball (n=123) were collected from Golden Harvest Company of Bangladesh. After collecting sample, spectra were obtained prior to analysis and a total of 369 NIRs were collected and stored in computer by DLP NIR scan Nano Software. To generate reference data 123 meatball samples were analyzed for proximate components, instrumental color CIE L*, a*, b*, and pH of meatball. After that a partial least square regression model for calibration and cross validation were developed for data analysis using The Unscrambler X software. Accuracies of the calibration models were evaluated using the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), coefficient of calibration (R²C) and coefficient of cross validation (R2 CV). Calibration equations were developed from reference data using partial least squares regressions. The standard deviation is 2.41, 0.14, 2.1, 0.41, 1.31, 0.31, 1.26, 0.38, and 0.38 for L*, a*, b*, pH, DM, moisture, CP, EE and ash respectively which indicates that all values are adequate for analytical purposes. Predictive ability of the models was assessed by coefficient of determination of cross-validation (R2 CV) and root mean square error of cross-validation. Predictions were good (R2 CV=0.84) for lightness (L*), (R2 CV=0.72) for redness (a*), (R2 CV=0.77) for yellowness (b*), (R2 CV=0.78) for pH, (R2 CV=0.73) for CP, (R2 CV=0.83) for EE (R2 CV=0.72) for moisture, (R2 CV=0.72) for DM and (R2 CV=0.74) for ash. From the results, it can be concluded that NIRS can be used for the rapid assessment of physico-chemical traits of chicken meatball.

Application of Deep-Learning Algorithm Driven Intelligent Raman Spectroscopy Methodology to Quality Control in the Manufacturing Process of Guanxinning Tablets.

Coupled with the convolutional neural network (CNN), an intelligent Raman spectroscopy methodology for rapid quantitative analysis of four pharmacodynamic substances and soluble solid in the manufacture process of Guanxinning tablets was established. Raman spectra of 330 real samples were collected by a portable Raman spectrometer. The contents of danshensu, ferulic acid, rosmarinic acid, and salvianolic acid B were determined with high-performance liquid chromatography-diode array detection (HPLC-DAD), while the content of soluble solid was determined by using an oven-drying method. In the establishing of the CNN calibration model, the spectral characteristic bands were screened out by a competitive adaptive reweighted sampling (CARS) algorithm. The performance of the CNN model is evaluated by root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), coefficient of determination of calibration (), coefficient of determination of cross-validation (), and coefficient of determination of validation (). The values for soluble solid, salvianolic acid B, danshensu, ferulic acid, and rosmarinic acid are 0.9415, 0.9246, 0.8458, 0.8667, and 0.8491, respectively. The established model was used for the analysis of three batches of unknown samples from the manufacturing process of Guanxinning tablets. As the results show, Raman spectroscopy is faster and more convenient than that of conventional methods, which is helpful for the implementation of process analysis technology (PAT) in the manufacturing process of Guanxinning tablets.

Estimation of Aboveground Biomass of Potatoes Based on Characteristic Variables Extracted from UAV Hyperspectral Imagery

Aboveground biomass (AGB) is an important indicator for crop-growth monitoring and yield prediction, and accurate monitoring of AGB is beneficial to agricultural fertilization management and optimization of planting patterns. Imaging spectrometer sensors mounted on unmanned aerial vehicle (UAV) remote-sensing platforms have become an important technical method for monitoring AGB because the method is convenient, rapidly collects data and provides image data with high spatial and spectral resolution. To confirm the feasibility of UAV hyperspectral remote-sensing technology to estimate AGB, this study acquired hyperspectral images and measured AGB data over the potato bud, tuber formation, tuber growth, and starch-storage periods. The canopy spectrum obtained in each growth period was smoothed by using the Savitzky–Golay filtering method, and the spectral-reflection feature parameters, spectral-location feature parameters, and vegetation indexes were extracted. First, a Pearson correlation analysis was performed between the three types of characteristic spectral parameters and AGB, and the spectral parameters that reached a significant level of 0.01 in each growth period were selected. Next, the spectral parameters reaching a significance of 0.01 were optimized and screened by moving window partial least squares (MWPLS), Monte Carlo uninformative variable elimination (MC-UVE), and random frog (RF) methods, and the final model parameters were determined according to the thresholds of the root mean square error of cross-validation (RMSEcv), the reliability index, and the selected probability. Finally, the three optimal characteristic spectral parameters and their combinations were used to estimate the potato AGB in each growth period by combining the partial least squares regression (PLSR) and Gaussian process regression (GPR) methods. The results show that, (i) ranked from high to low, vegetation indexes, spectral-location feature parameters, and spectral-reflection feature parameters in each growth period are correlated with the AGB, and these correlations all first improve and then degrade in going from the budding period to the starch-storage period. (ii) The AGB estimation model based on the characteristic variables screened by the three methods in each growth period is most accurate with RF, less so with MC-UVE, and least accurate with MWPLS. (iii) Estimating the AGB with the same variables combined with the PLSR method in each growth period is more accurate than the corresponding GPR method, but the estimations produced by the two methods both show a trend of first improving and then worsening from the budding period to the starch-accumulation period. The accuracy of the estimation models constructed by PLSR and GPR from high to low is based on comprehensive variables, vegetation indexes, spectral-location feature parameters and spectral-reflection feature parameters. (iv) When combined with the RF-PLSR method to estimate AGB in each growth period, the best R2 values are 0.65, 0.68, 0.72, and 0.67, the corresponding RMSE values are 167.76, 162.98, 160.77, and 169.24 kg/hm2, and the corresponding NRMSE values are 19.76%, 16.01%, 15.04%, and 16.84%. The results of this study show that a variety of characteristic spectral parameters may be extracted from UAV hyperspectral images, that the RF method may be used for optimizing and screening, and that PLSR regression provides accurate estimates of the potato AGB. The proposed approach thus provides a rapid, accurate, and nondestructive way to monitor the growth status of potatoes.

Nondestructive evaluation of SW-NIRS and NIR-HSI for predicting the maturity index of intact pineapples

Determination of optimum maturity and ripeness of fruit is essential in the production of processed fruit, including pineapples, but this is difficult to achieve consistently by visual grading in commercial factories. Therefore, this study tested two nondestructive techniques for predicting the maturity index of intact pineapple. These were transmittance short wavelength near infrared spectroscopy (SW-NIRS) in the wavelength range of 665–955 nm and reflectance near infrared hyperspectral imaging (NIR-HSI) in the wavelength range of 935–1720 nm. The number of samples used for calibration was 120 for both SW-NIRS and NIR-HSI. The maturity index and spectral information of individual pineapple fruit were acquired from both techniques and analysed using the same procedure. Then, partial least squares regression (PLSR) was used to establish the models for predicting the maturity index of each intact fruit. The leave-one-out cross validation was used for evaluating the performance of the models. The results showed that both techniques gave reliable performance in predicting the maturity index of individual fruit, with a coefficient of determination considering cross validation (Rcv2) for the prediction of the maturity index of 0.70 and a root mean square error in cross validation (RMSECV) of 2.16 when using SW-NIRS and Rcv2 of 0.72 and RMSECV of 1.68 when using NIR-HSI. It was therefore concluded that both SW-NIRS and NIR-HSI had the potential for use in nondestructive analysis of the maturity of intact pineapple fruit in fruit processing factories.

Establishment of a NIR-based methodology for tracking the blend homogeneity of HTPB propellant slurry in the mixing process

The hydroxyl-terminated polybutadiene (HTPB) propellants with high level of solid loadings from 80 wt% to 90 wt% consist of aluminum (Al) powder, ammonium perchlorate (AP) and HTPB. The Al/AP/HTPB adhesive system full of solid grains appears high viscosity against flow. Therefore, the mixing is a crucial procedure in the production as it directly affects the structural integrity of the finished product. This work focused on the feasibility of tracking the blend homogeneity of Al/AP/HTPB adhesive system in the mixing process through using the near-infrared (NIR) spectroscopy and orthogonal partial least squares discrimination analysis (OPLS-DA). The OPLS-DA classification models were created by variable selection, spectral pretreatment and latent variables (LVs) optimization. It had been demonstrated that the developed models presented an excellent predictability with the root mean square error of cross-validation (RMSECV) for slurries in Ⅰ, Ⅱ groups of 0.1261 and 0.0789, respectively. Meanwhile, the well-fitted models for slurries in Ⅰ, Ⅱ groups with the squared correlation coefficient (R2) of 0.806 and 0.980, exhibited separately an acceptable predictive capability with the predictive squared correlation coefficient (Q2) > 0.5. Furthermore, Euclidean distance and move block standard deviation (MBSD) as reference methods were used to validate the predictive performance of the developed models with respect to the blend homogeneity of HTPB propellant slurry. The experimental results showed that the terminal time for each batch of slurry reaching to ideal uniformity predicted by Euclidean distance/MBSD and OPLS-DA were both at 26–30 min. Therefore, it had been proved that the method we proposed was a potential tool to monitor the variation of the uniform state of HTPB propellant slurry in the mixing process.

Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework.

Background:Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations.Objectives:To develop a daily, high-resolution ambient CO exposure prediction model at the city scale.Methods:We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (, , and ) concentrations.Results:The CO model had spatial cross-validation (CV) and root-mean-square error (RMSE) of (ppm), respectively; the model had temporal CV and RMSE of 0.61 and , respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads.Discussion:The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889

Study on hyperspectral monitoring model of soil total nitrogen content based on fractional-order derivative

Realizing the real-time nondestructive monitoring of soil total nitrogen (STN) content is an important task to promote precision agriculture development. In this study, a water regulation experiment was performed. Winter wheat and summer soybean soils were taken as the research objects. The effects of fractional-order derivatives (FOD) on soil spectral reflectance were studied, and the hyperspectral monitoring models of STN content were constructed based on the full spectrum band and the uninformative variable elimination (UVE) -extracted band. The main results were as follows: The FOD showed a gradual change in the integer-order derivative (IOD). When calculating the FOD, the more fitting points there were, the smaller the FOD spectrum, and the higher the correlation with the STN content. In the range of orders 0–1, the correlation increased with increasing order. In the range of orders 1–2, the correlation first decreased and then increased. When constructing the model, stepwise multiple linear regression (SMLR) was used, but the accuracies of the SMLR models were low. Afterward, the bands were extracted by UVE, and the models were constructed by partial least squares regression (PLSR). The number of bands extracted by UVE ranged from 11 to 534, mainly in the range of 1000–2450 nm. Under the same conditions, the accuracy of the PLSR model constructed with the UVE-extracted bands was higher than that of the model constructed with the full band spectrum. Among all PLSR models, when the number of fitting points, the order, and the number of bands were 40, 0.67, and 272, respectively, the model accuracy was the highest. The coefficient of determination of cross-validation (R2cv), root mean square error of cross-validation (RMSEcv), coefficient of determination of validation (R2v), root mean square error of validation (RMSEv), and relative analysis error of validation (RPDv) of the model were 0.7317, 0.2244 g·kg−1, 0.7937, 0.1976 g·kg−1, and 2.1904, respectively. This model could realize the hyperspectral monitoring of STN content in wheat and soybean fields. In this study, hyperspectral spectroscopy was used to monitor the STN content, which provided a theoretical basis and technical support for the observations.

Rapid and low-cost liquid biopsy with ATR-FTIR spectroscopy to discriminate the molecular subtypes of breast cancer

Breast cancer (BC) is the most prevalent cancer worldwide. The prognosis and survival of these patients are directly related to the diagnostic stage. Even so, the gold standard screening method (mammography) has a long waiting period, high rates of false positives, anxiety for patients, and consequently delays the diagnosis by core needle biopsy (invasive method). Alternatively, the Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy is a noninvasive, low-cost, rapid, and reagent-free technique that generates the spectral metabolomic profile of biomolecules. This makes it possible to assess systemic repercussions, such as the BC carcinogenesis process. Blood plasma samples (n = 56 BC and n = 18 controls) were analyzed in the spectrophotometer in the ATR-FTIR mode. For the exploratory analysis of the data, interval Principal Component Analysis (iPCA) was used, and for predictive chemometric modeling, the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) algorithm with validation by leave-one-out cross-validation. iPCA in the region of 1118–1052 cm−1 (predominantly DNA/RNA bands) showed significant clustering of molecular subtypes and control. The OPLS-DA model achieved 100% accuracy with only 1 latent variable and Root Mean Square Error of Cross-Validation (RMSECV) < 0.005 for all molecular subtypes and control. The wavenumbers (cm−1) with the highest iPCA peaks (loadings: 1117, 1089, 1081, 1075, 1057, and 1052) were used as input to MANOVA (Wilks' Lambda, p < 0.001 between molecular subtypes and control). The rapid and low-cost detection of BC molecular subtypes by ATR-FTIR spectroscopy would plausibly allow initial screening and clinical management, improving prognosis, reducing mortality and costs for the health system.

Combination of multispectral imagery, environmental data and thermography for on-the-go monitoring of the grapevine water status in commercial vineyards

Efficient irrigation in viticulture requires objective and representative monitoring of the vineyard water status variability. In this work the combination of multispectral, environmental and thermal data (using an infrared radiometer) acquired simultaneously on-the-go (at midday), from a ground moving vehicle (moving at 3 km/h) was tested to assess the vineyard stem water potential (Ψs) and its spatial variability (three different irrigation treatments were imposed) over two seasons in north Spain. Partial least squares (PLS) cross-validation regression models involving the canopy temperature (Tc), environmental and spectral variables yielded determination coefficients (R2cv) of ~ 0.63 and root mean square error of cross-validation (RMSECV) between 0.124 MPa and 0.206 MPa in the two seasons. Linear discriminant analysis (LDA) involving only the variables used to build the regression models was run to distinguish among low, medium and high water stressed vines, yielding an average percentage of correct classification samples of 74%. The satisfactory performance of the multivariate models involving thermal, environmental and spectral data to either estimate or classify the plant water status within a vineyard supports the approach towards the combination of different data source to improve the capabilities of thermography itself. The inclusion of vegetative spectral indices in the regression and classification models of grapevine water status may provide real-time feedback on grapevine water use as influenced by actual vegetative growth, abiotic and/or biotic stress patterns. This combined approach can be seen as an advancement from existing solutions to assess plant water status variability given the simplicity and potential to automation of the thermal sensor employed and the integration of environmental and canopy vigour data into the model.

Near infrared spectroscopy for the quality control of rice bran

Objective : The objective of this study was to examine the accuracy of estimating the nutrient content in rice bran as a feed ingredient by using Near Infrared (NIR) technology compared to proximate analysis method. Methods : Rice bran was used as feed sample to compare the results of data analysis using these two methods. In this study the Fourier Transform Near-infrared used infrared with wavelength of 12500-4000 cm -1 . The OPUS 7.8 software was integrated with NIRS. The model was made by using Partial Least Square regression analysis to correlate the result of the spectrum and the result of proximate analysis method. Results: In this study, the nutrient content in rice bran available in markets, which commonly are Crude Protein (CP), ether extract (EE), and Crude Fiber (CF), are quite varied and wide in range, so that The curve prediction/true showed up wider and did not cover the entire surface of the line. In consequence two outliers were removed so that the accuracy value could be increased in measuring the quality of animal feed as indicated by the improvement of the coefficient of determination or R 2 , Root Mean Square Error for Cross Validation (RMSECV), and Standard Error (SE). Conclusions. NIR is a useful tool to estimate nutrient composition of rice bran available in the market especially for CP, EE, and CF by removing two outliers. The Prediction / True curve does not widen after removing two outliers, and can improve R 2 , RMSECV, and SE values

NIR Validation and Calibration of Van Soest cell wall constituents (ADF, NDF, and ADL) of Available Corn Silage in Bangladesh

This study was undertaken at the Feed Quality Control Laboratory, DLS, Savar, Dhaka, to calibrate and validate corn silage nutritional parameters- Van Soest cell wall constituents (ADF, NDF, and ADL) in the Near-infrared Spectrophotometer (Bruker-MPA, Germany) systems monochromator (700- 9500 nm) range was used for the rapid analysis of available corn silage. Almost 52 samples were analyzed at the QC lab wet chemistry laboratory to know the available nutrients. In the 2nd part of this study, developed local calibration equations in the NIRS using OPUS (Optical User Software) to relate the spectral data and corresponding wet chemistry values. A Quartz sample cup was used to hold the sample on the Infrared light scanner and used XPM was MPAII sphere macrosample_64_rotating_Res16-DLS.XPM. Fresh samples were dried and ground through 2mm screen for the analysis. The value for each component was placed into the calibration group for NIRS equation development. After calibration in NIR, the root means a square error of estimation (RMSEE) for the determination of ADF%, ADL%, and NDF%, were, 1.56, 0.47, and 1.15, with the correlation coefficient (r2) of 93.31, 95.43, and 97.86 respectively which are very close to the mean laboratory values. Whereas after cross-validation, RMSECV (Root Mean Square Error Cross-Validation) were 1.97, 0.598, and 1.55, along with the r2 values 87.02%, 90.23%, and 95.32%, respectively. The accuracy% of the predicted values in NIRS was between 98.94-100.16% which are very close to the mean laboratory values. It can be concluded that NIR could be a potential instrument to predict the nutritional quality of corn silage in Bangladesh.

Prediction of Soluble Solids and Lycopene Content of Processing Tomato Cultivars by Vis-NIR Spectroscopy.

Tomato-based products are significant components of vegetable consumption. The processing tomato industry is unquestionably in need of a rapid definition method for measuring soluble solids content (SSC) and lycopene content. The objective was to find the best chemometric method for the estimation of SSC and lycopene content from visible and near-infrared (Vis-NIR) absorbance and reflectance data so that they could be determined without the use of chemicals in the process. A total of 326 Vis-NIR absorbance and reflectance spectra and reference measurements were available to calibrate and validate prediction models. The obtained spectra can be manipulated using different preprocessing methods and multivariate data analysis techniques to develop prediction models for these two main quality attributes of tomato fruits. Eight different method combinations were compared in homogenized and intact fruit samples. For SSC prediction, the results showed that the best root mean squared error of cross-validation (RMSECV) originated from raw absorbance (0.58) data and with multiplicative scatter correction (MSC) (0.59) of intact fruit in Vis-NIR, and first derivatives of reflectance (R2 = 0.41) for homogenate in the short-wave infrared (SWIR) region. The best predictive ability for lycopene content of homogenate in the SWIR range (R2 = 0.47; RMSECV = 17.95 mg kg–1) was slightly lower than that of Vis-NIR (R2 = 0.68; 15.07 mg kg–1). This study reports the suitability of two Vis-NIR spectrometers, absorbance/reflectance spectra, preprocessing methods, and partial least square (PLS) regression to predict SSC and lycopene content of intact tomato fruit and its homogenate.

At‐line monitoring of industrial corn seed classification quality by digital images analysis using smartphone

This study aimed to evaluate the feasibility of using the PhotoMetrix PRO® application to perform the selection of good quality corn seeds from defective ones, through digital images and the multivariate analysis of color elements. An optimization of the experimental conditions was carried out. Eighteen experiments were performed varying the size of the analysis recipient, the distance of the sample from the smartphone and the size of the region of interest (ROI). A calibration model was built using a partial least squares (PLS) regression model in the determined experimental conditions, and the prediction of external samples was then performed. The conditions that provided the smallest cross-validation error consisted of using the smaller chamber, the largest ROI, and the longest distance from the smartphone to the sample. The coefficient of determination obtained by the model was .976. In addition, the root-mean-square error of calibration (RMSEC) and the root-mean-square error of cross-validation (RMSECV) obtained were 1.2% and 4.8%, respectively. In the prediction, two outliers were observed, and the RMSEP dropped to 2.2% when they were removed. The proposed methodology shows great potential to contribute to the industrial process of corn seed production. It is fast, accurate, and low cost, allowing the analysis to be performed 15 times faster than the current process. Practical Applications Corn is one of the most important commodities in Brazil's economy. During the processing of corn seeds in the industry, those of good quality are selected among the others through parameters, such as color and density, for example. The gravity table and the Color Sorter machine are equipment used for this purpose. However, even if the machine's separation is effective, an important percentage of good seeds are wasted. To reduce the waste of good seeds remaining in the tailings of these machines, the operators carry out a manual inspection of tailings samples, determining the quantity of quality seeds present there for later adjustment of the machines. This visual and manual analysis becomes time-consuming, subjective, and imprecise. Thus, as an alternative, this study proposes the use of digital image and partial least square model built by a mobile app to determine the quantity of quality corn seeds in the machines' wastes.

Selection of the Effective Characteristic Spectra Based on the Chemical Structure and Its Application in Rapid Analysis of Ethanol Content in Gasoline.

Near-infrared (NIR) spectroscopy analysis is one of the most rapid detection methods for determining ethanol content in gasoline. Wavelength selection is a key step in the multivariate calibration analysis of NIR spectroscopy. To improve detection accuracy of ethanol content in gasoline and provide a simpler interpretation, we established NIR spectroscopy, a rapid analysis method based on the effective characteristic spectra. Five effective characteristic spectral bands were used according to the molecular structure of ethanol, followed by the development of four modeling schemes. The four modeling schemes spectra, NIR full spectra, and variable importance projection (VIP) spectra were used for modeling and analysis. The model was established based on the effective characteristic spectra without the interference spectra of aromatic hydrocarbons, achieving the best model performance. In addition, the model was further evaluated by internal cross-validation and external validation. The model’s evaluation parameters were as follows: the root mean square error of cross-validation (RMSECV) was 0.6193, the correlation coefficient of internal cross-validation (RCV2) was 0.9995, the root mean square error of prediction (RMSEP) was 0.5572, and the correlation coefficient of external prediction validation (RP2) was 0.9995. The effective characteristic spectra model had smaller RMSEP and RMSECV values, and larger RCV2 and RP2 values compared to the full spectra and VIP spectra models. In conclusion, the effective characteristic spectra model had the highest accuracy and could provide rapid analysis of the ethanol content in gasoline.

Application of energy dispersive X-ray fluorescence spectrometry and near-infrared reflectance spectroscopy combined with multivariate statistical analysis for discriminating the geographical origin of soybeans

Energy dispersive X-ray fluorescence spectrometry (ED-XRF) and near-infrared reflectance spectroscopy (NIRS), two non-destructive and rapid techniques, were explored to classify the geographical origin of soybeans. Multivariate statistical methods, including principal component analysis (PCA), discriminant analysis (DA), and modified partial least squares (MPLS), were applied as a chemometric tool to classify soybeans into two groups. A statistically significant difference was observed in the ED-XRF elemental determination of Al, P, S, Cl, Ca, Fe, Ni, Cu, Zn, Br, and Rb between domestic and imported soybeans. A total of 112 samples were almost perfectly classified according to their origin in the application of canonical DA with ED-XRF. NIRS based on DA application showed excellent classification results with 99.1% accuracy after optimizing spectral preprocessing. NIRS based on the MPLS model with leave-one-out cross-validation showed 100% prediction results using first or second derivative pretreatment of the raw spectrum with Salvitzy-Golay or Norris derivative smoothing techniques. The best calibration and validation statistics in the MPLS model for the root mean square error of calibration (RMSEC) and root mean square error of cross-validation (RMSECV) were found to be 0.132% (R2c = 0.9606) and 0.146% (R2cv = 0.9687), respectively. These results suggest that ED-XRF and NIRS combined with multivariate statistical analysis can be suitable technologies for the efficient determination of the geographical origin of soybeans.

Quantitative analysis of soil cadmium content based on the fusion of XRF and Vis-NIR data

Anthropogenic activities, such as sewage irrigation and the application of pesticides and fertilizers, are the main causes of Cadmium (Cd) pollution, which reduces soil quality and threatens the environment and human health. Although the traditional cadmium measurement methods are accurate, it involves complicated sample processing steps and complicated laboratory analysis, which is time-consuming and costly, and is often unfriendly to the environment. X-ray fluorescence (XRF) and visible near-infrared (vis–NIR) spectroscopy have been recognized as alternatives to measure soil heavy metal contamination in a cheap, fast, non-destructive, and environmentally conscious manner. In this study, 370 paddy soil samples from the Nanji area of Poyang Lake were taken as the research object, and the feasibility and effectiveness of XRF and vis-NIR spectroscopy for estimating soil chromium content were discussed, respectively, and the combination of the two was used to estimate soil Cd content. Combined with some spectral preprocessing methods, the quantitative analysis model of least squares support vector machine (LS-SVM) and leave-one-out cross validation with three different data fusions (Equal Weight Fusion, Coaddition Fusion and Outer Product Fusion) was established. The results showed the accuracy and stability of Equal Weight Fusion and Outer Product Fusion was better than the single spectrum quantitative analysis model. The model of Outer Product Fusion at LOOCV set had the best performance with the determination coefficient (R2) of 0.91, root mean square error of cross validation (RMSECV) of 0.12 ​mg/kg and the relative percent deviation (RPD) of 3.27, with R2 ​= ​0.90, RMSEP ​= ​0.13 ​mg/kg and RPD ​= ​3.11 in Prediction set, which can satisfy the detection requirements. This method was accurate, reliable, and can provide a reference for the research of soil heavy metal distribution investigation methods.

Estimating the Analytical Performance of Raman Spectroscopy for Quantification of Active Ingredients in Human Stratum Corneum.

Confocal Raman microscopy (CRM) has become a versatile technique that can be applied routinely to monitor skin penetration of active molecules. In the present study, CRM coupled to multivariate analysis (namely PLSR—partial least squares regression) is used for the quantitative measurement of an active ingredient (AI) applied to isolated (ex vivo) human stratum corneum (SC), using systematically varied doses of resorcinol, as model compound, and the performance is quantified according to key figures of merit defined by regulatory bodies (ICH, FDA, and EMA). A methodology is thus demonstrated to establish the limit of detection (LOD), precision, accuracy, sensitivity (SEN), and selectivity (SEL) of the technique, and the performance according to these key figures of merit is compared to that of similar established methodologies, based on studies available in literature. First, principal components analysis (PCA) was used to examine the variability within the spectral data set collected. Second, ratios calculated from the area under the curve (AUC) of characteristic resorcinol and proteins/lipids bands (1400–1500 cm−1) were used to perform linear regression analysis of the Raman spectra. Third, cross-validated PLSR analysis was applied to perform quantitative analysis in the fingerprint region. The AUC results show clearly that the intensities of Raman features in the spectra collected are linearly correlated to resorcinol concentrations in the SC (R2 = 0.999) despite a heterogeneity in the distribution of the active molecule in the samples. The Root Mean Square Error of Cross-Validation (RMSECV) (0.017 mg resorcinol/mg SC), The Root Mean Square of Prediction (RMSEP) (0.015 mg resorcinol/mg SC), and R2 (0.971) demonstrate the reliability of the linear regression constructed, enabling accurate quantification of resorcinol. Furthermore, the results have enabled the determination, for the first time, of numerical criteria to estimate analytical performances of CRM, including LOD, precision using bias corrected mean square error prediction (BCMSEP), sensitivity, and selectivity, for quantification of the performance of the analytical technique. This is one step further towards demonstrating that Raman spectroscopy complies with international guidelines and to establishing the technique as a reference and approved tool for permeation studies.

Rapid Quantitation of Coal Proximate Analysis by Using Laser-Induced Breakdown Spectroscopy

Proximate analysis of coal is of great significance to ensure the safe and economic operation of coal-fired and biomass-fired power generation units. Laser-induced breakdown spectroscopy (LIBS) assisted by chemometric methods could realize the prediction of coal proximate analysis rapidly, which makes up for the shortcomings of the traditional method. In this paper, three quantitative models were proposed to predict the proximate analysis of coal, including principal component regression (PCR), artificial neural networks (ANNs), and principal component analysis coupled with ANN (PCA-ANN). Three model evaluation indicators, such as the coefficient of determination (R2), root-mean-square error of cross-validation (RMSECV), and mean square error (MSE), were applied to measure the accuracy and stability of the models. The most accurate and stable prediction of coal proximate analysis was achieved by PCR, of which the average R2, RMSECV, and MSE values were 0.9944, 0.39%, and 0.21, respectively. Although the R2 values of ANN and PCA-ANN were greater than 0.9, the higher RMSECV and MSE values indicated that ANN and PCA-ANN were inferior to PCR. Compared with the other two models, PCR could not only achieve accurate prediction, but also shorten the modeling time.

Smartphone-based miniaturized, green and rapid methods for the colorimetric determination of sugar in soft drinks

Total sugar content of soft drinks was determined in a 3D-printed chamber equipped with an USB camera attached to the smartphone to take pictures directly from the vessels used for the reaction of monosaccharides with cupric ion from Benedict's reagent. The freely available PhotoMetrix UVC application was used to control the device and process images. Reaction time, total volume of solution and illumination were evaluated. Images were captured before and after centrifugation and the results were compared with official method (AOAC 923.09, Lane-Eynon titration). Partial least squares calibration was used reaching suitable values (R2 above 0.99; Root mean square error of calibration (RMSEC) and Root mean square error in cross-validation (RMSECV) values lower than 0.07 and 0.23 mg mL−1, respectively). The proposed methods presented agreements from 95.3 to 108.7% to the official one. Hundreds of samples can be analyzed in one hour and reductions in energy expenditure by 5.5 times and waste volume by 78 times were obtained in relation to the AOAC method.