Use of low cost near-infrared spectroscopy, to predict pasting properties of high quality cassava flour.

Determination of pasting properties of high quality cassava flour using rapid visco analyzer is expensive and time consuming. The use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits. However, model development and validation are necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740-1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated. Based on calibration statistics (R2, RMSE and MAE), we found that model calibrations using glmnet provided the best model for breakdown viscosity, peak viscosity and pasting temperature. The glmnet model using the first derivative, peak viscosity had calibration and validation accuracy of R2 = 0.56 and R2 = 0.51 respectively while breakdown had calibration and validation accuracy of R2 = 0.66 and R2 = 0.66 respectively. We also found out that stacking of pre-treatments with Moving Average, Savitzky Golay, First Derivative, Second derivative and Standard Normal variate using glmnet model resulted in calibration and validation accuracy of R2 = 0.65 and R2 = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy for screening purposes. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties.

Feasibility of low-cost particulate matter sensors for long-term environmental monitoring: Field evaluation and calibration.

Low-cost sensor networks offer the potential to reduce monitoring costs while providing high-resolution spatiotemporal data on pollutant levels. However, these sensors come with limitations, and many aspects of their field performance remain underexplored. During October to December 2023, this study deployed two identical low-cost sensor systems near an urban standard monitoring station to record PM2.5 and PM10 concentrations, along with environmental temperature and humidity. Our evaluation of the monitoring performance of these sensors revealed a broad data distribution with a systematic overestimation; this overestimation was more pronounced in PM10 readings. The sensors showed good consistency (R2 > 0.9, NRMSE<5 %), and normalization residuals were tracked to assess stability, which, despite occasional environmental influences, remained generally stable. A lateral comparison of four calibration models (MLR, SVR, RF, XGBoost) demonstrated superior performance of RF and XGBoost over others, particularly with RF showing enhanced effectiveness on the test set. SHAP analysis identified sensor readings as the most critical variable, underscoring their pivotal role in predictive modeling. Relative humidity consistently proved more significant than dew point and temperature, with higher RH levels typically having a positive impact on model outputs. The study indicates that, with appropriate calibration, sensors can supplement the sparse networks of regulatory-grade instruments, enabling dense neighborhood-scale monitoring and a better understanding of temporal air quality trends.

A Design Method for an SVM-Based Humidity Sensor for Grain Storage.

One of the crucial factors in grain storage is appropriate moisture content, which plays a significant role in reducing storage losses and ensuring quality. However, currently available humidity sensors on the market fail to meet the demands of modern large-scale grain storage in China in terms of price, size, and ease of implementation. Therefore, this study aims to develop an economical, efficient, and easily deployable grain humidity sensor suitable for large-scale grain storage environments. Simultaneously, it constructs humidity calibration models applicable to three major grain crops: millet, rice, and wheat. Starting with the probe structure, this study analyzes the ideal probe structure for grain humidity sensors. Experimental validations are conducted using millet, rice, and wheat as experimental subjects to verify the accuracy of the sensor and humidity calibration models. The experimental results indicate that the optimal length of the probe under ideal conditions is 0.67 m. Humidity calibration models for millet, rice, and wheat are constructed using SVM models, with all three models achieving a correlation coefficient R2 greater than 0.9. The measured data and model-calculated data show a linear relationship, closely approximating y = x, with R2 values of all three fitted models above 0.9. In conclusion, this study provides reliable sensor technological support for humidity monitoring in large-scale grain storage and processing, with extensive applications in grain storage and grain safety management.

Quantitative determination of dopamine in the presence of interfering substances supported by machine learning tools.

In the field of neuroscience as well as in the clinical setting, the neurotransmitter dopamine (DA) is an analyte which is important for research as well as medical purposes. There are plenty of methods available to measure dopamine quantitatively, with voltammetric ones such as differential pulse voltammetry (DPV) being among the most convenient and simple ones. However, dopamine often occurs, either naturally or because of the requirements of involved enzymatic systems, alongside substances that can influence the signal it produces upon electrochemical conversion. An example for such substances is the magnesium ion, which itself is not electrochemically active in the potential range needed for DA oxidation, but influences the dopamine signal. We have characterized the properties of DPV signals subject to the interaction between DA and Mg2+ and show that, although these properties are changing in a nonlinear fashion when both concentrations are varying, relatively simple linear mathematical models can be used to determine dopamine concentrations quantitatively in the presence of magnesium ions. The focus of this study is thus, the mathematical treatment of experimental data in order to overcome an analytical problem and not the investigation of the chemical background of DA-Mg2+ interaction.

Development of novel calibration model(s) to predict whole-body density in professional football players.

INTRODUCTION: Questions continue to be raised about the validity that is in existence to estimate Db, in professional male footballer players. METHODS: Phase 1: n = 28 anthropometric variables were used on n = 206 footballers, using regression analyses to determine standard error of estimate and R2. A cut-off correlation coefficient set at r = 0.950 and 90% R2. Phase 2: all variables (z-scores, x- = 0.0, SD = ±1.0) to help reduce heteroscedasticity, ß, r, t, significance of t and P-values were calculated. Phase 3: a forced stepwise-backwards regression analysis approach with nine predictors which met the acceptance criteria (r = 0.950, R2 = 90% and ß weights) was used to develop a 'best fit' and a 'practical' calibration model. Phase 4: cross-validation of the two newly developed calibration method using LoA. RESULTS: The 'best fit' model SEM (0.115 g ml-1), the highest R2 (6.6%) (P ≤ 0.005), whereas the 'practical' calibration model SEM (0.115 g ml-1), R2 (4.7%) (P ≤ 0.005) with r values = 0.271 and 0.596 and R2 (%) coefficients = 0.3526 for the 'best fit' and 'practical' calibration models, respectively (P = 0.01). CONCLUSIONS: The two calibration models supported an ecologically and statistically valid contribution and can provide sound judgements about professional footballers' body composition.

The Use of a Closed Feed Frame for the Development of Near-Infrared Spectroscopic Calibration Model to Determine Drug Concentration.

PURPOSE: This study evaluates the use of the closed feed frame as a material sparing approach to develop near-infrared (NIR) spectroscopic calibration models for monitoring blend uniformity. The effect of shear induced by recirculation on NIR spectra was also studied. METHODS: Calibration models were developed using NIR spectra obtained in the closed feed frame for two cases. For case 2, blends that flowed through the open feed frame were predicted with the model. The shear effect of the feed frame on the blends was assessed through the characterization of powder properties before and after recirculation. RESULTS: The physical characterization of the blends confirmed that the powder properties were not altered after recirculation within the closed feed frame. Both calibration models provided highly accurate predictions of the test sets with low bias (0.03% w/w and -0.06% w/w) and relative standard error of prediction (1.9% and 3.7%), respectively. The predictive performance of the calibration models was not affected by the shear effect. CONCLUSION: Recirculation within the closed feed frame did not change the physical properties of the blends studied. The prediction of blends flowing through the open feed frame was possible with a calibration model developed in the closed feed frame. The closed feed frame could reduce the materials needed to develop calibration models by more than 90%.

PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy.

Near-infrared spectroscopic (NIR) technique was used, for the first time, to predict volatile phenols content, namely guaiacol, 4-methyl-guaiacol, eugenol, syringol, 4-methyl-syringol and 4-allyl-syringol, of aged wine spirits (AWS). This study aimed to develop calibration models for the volatile phenol's quantification in AWS, by NIR, faster and without sample preparation. Partial least square regression (PLS-R) models were developed with NIR spectra in the near-IR region (12,500-4000 cm-1) and those obtained from GC-FID quantification after liquid-liquid extraction. In the PLS-R developed method, cross-validation with 50% of the samples along a validation test set with 50% of the remaining samples. The final calibration was performed with 100% of the data. PLS-R models with a good accuracy were obtained for guaiacol (r2 = 96.34; RPD = 5.23), 4-methyl-guaiacol (r2 = 96.1; RPD = 5.07), eugenol (r2 = 96.06; RPD = 5.04), syringol (r2 = 97.32; RPD = 6.11), 4-methyl-syringol (r2 = 95.79; RPD = 4.88) and 4-allyl-syringol (r2 = 95.97; RPD = 4.98). These results reveal that NIR is a valuable technique for the quality control of wine spirits and to predict the volatile phenols content, which contributes to the sensory quality of the spirit beverages.

Occupant-based energy upgrades selection for Canadian residential buildings based on field energy data and calibrated simulations.

Occupant behavior in residential buildings has a direct impact on the effectiveness of energy-saving measures. In order to realize a buildings' carbon mitigation targets, the impact of individual occupancy profiles needs to be integrated with building simulation models. This paper introduces a decision support framework as a potential solution to make energy performance upgrade choices based on different occupancy profiles. This framework has been demonstrated through a case study of two single-family detached homes in Canada, which were highly instrumented with sensors for monitoring energy input and output. The case studies represented two common occupancy profiles-(1) a family of four (consisting of 2 working adults and 2 teenagers); and (2) a retired couple. Firstly, calibrated energy models were developed by using one-year energy use data collected through an intrusive load monitoring technique. Secondly, energy upgrade combinations were considered for each profile and tested for additional investment, payback period and greenhouse gas (GHG) emissions. Lastly, the most suitable combination of energy upgrade for each profile was ranked using a multi-criteria decision-making method (e.g., TOPSIS). Results indicated that the retired couple used more energy than the family of four and required energy upgrades with usually higher payback periods to achieve the same level of GHG emission reduction. The results of this research are timely for energy policymaking and developing best management practices, which need to be implemented along with the deployment of more stringent building standards and codes.

Spectroscopy approach to methanol detection in waste fat methyl esters.

Second-generation biodiesel manufactured from waste cooking oils (WCO) and inedible animal fats (AF) are one of the alternatives to the first generation (1G) vegetable oil-based biodiesel. In this study, a quality control method is proposed to evaluate methanol content in waste fat methyl esters and is based on near infrared spectroscopy (NIR) combined with multivariate analysis. More specifically, calibration models are constructed using partial least squares regression (PLS) for the prediction of methanol content in rapeseed oil methyl ester (ROME), waste cooking oil methyl ester (WCOME), chicken fat methyl ester (CFME) and pork fat methyl ester (PFME) by Vis-NIR spectrometer. The calibration models are based on the absorbance spectra and computed data from five wavelength regions of 400-2170 nm, 780-2170 nm, 1400-2170 nm, 1400-1600 nm and 1970-2170 nm. For the cases with the highest prediction ability obtained in this study, the coefficient of determination of the model's goodness-of-fit for methanol concentrations range 0-5% (v/v) was R2 > 0.990, and for concentrations 0-1% (v/v) was R2 > 0.994, indicating the spectroscopic approach effectiveness in methanol content detection relevant to the biofuel quality assessment. A pseudo-univariate limits of detection (LODpu) and quantification (LOQpu) as well as ratio of performance to deviation (RPD) were used to confirm the validity and to evaluate the practical applicability of developed models. In addition, the obtained results indicate the possibility of developing a transmission sensor for online monitoring of the production process and the quality of biofuel.

Direct determination of Ca, K, Mg, Na, P, S, Fe and Zn in bivalve mollusks by wavelength dispersive X-ray fluorescence (WDXRF) and laser-induced breakdown spectroscopy (LIBS).

This study aims to develop methods for determination of Ca, K, Mg and Na by laser-induced breakdown spectroscopy (LIBS) and Ca, K, Mg, Na, P, S, Fe and Zn by wavelength dispersive X-ray fluorescence (WDXRF) in pressed pellets bivalve mollusks. LIBS and WDXRF calibration models were built with references values determined by inductively coupled plasma optical emission spectrometry (ICP OES) after acid digestion. The calibration models for LIBS and WDXRF were obtained from 28 samples (14 for calibration and 14 for validation). It was possible to implement a validation between LIBS and WDXRF methods for elements Ca, K, Mg and Na. The proposed calibration model obtained using LIBS and WDXRF data presented a good correlation with reference values obtained by ICP OES.

Raman spectroscopy for wine analyses: A comparison with near and mid infrared spectroscopy.

Routine wine analysis are commonly employed to ensure the quality and safety standards, and to meet consumers' demands and legal requirements. In the last decades, efforts have been done in order to replace the traditional analytical techniques by vibrational spectroscopic techniques such as near infrared (NIR) and mid infrared (MIR) spectroscopy. The potential of these techniques has already been proved by several studies that revealed their ability for the determination of several wine parameters with high levels of precision and accuracy. Raman spectroscopy, (which is also a vibrational technique), was much less explored in the wine industry. In this work, the ability of Raman spectroscopy for routine wine analysis was evaluated and compared to NIR and MIR spectroscopy. Several calibration models were developed aiming the quantitative assessment of alcoholic strength, density, total acidity, volatile acidity, total sugars and pH in white wines. For this purpose, partial least squares (PLS) regression was employed, enabling the correlation between reference results and spectral information obtained by NIR, MIR and Raman spectroscopy. Results revealed the better performance of MIR spectroscopy for the measurement of alcoholic strength (R2P = 0.99, RMSEP=1.77%, and RER=56.41), and total acidity (R2P = 0.98, RMSEP=2.02%, and RER=49.46). Raman spectroscopy was pointed out as the most suitable for the determination of total sugars (R2P = 0.97, RMSEP=5.12%, RER=19.52), and pH (R2P = 0.90, RMSEP=4.92%, RER=20.34). The three techniques presented similar results in what referred the assessment of density (R2P = 0.96, 0.98, and 0.97, RMSEP=4.72%, 3.90%, and 3.80%, for Raman, MIR, and NIR respectively). None of the three techniques seemed to be suitable for the accurate determination of volatile acidity (R2P <0.78, RMSEP>14.32%, and RER<6.98).

Assessment of simpler calibration models in the development and validation of a fast postmortem multi-analyte LC-QTOF quantitation method in whole blood with simultaneous screening capabilities using SWATH acquisition.

In postmortem toxicology, fast methods can provide a triage to avoid unnecessary autopsies. Usually, this requires multiple qualitative and quantitative analytical methods. The aim of the present study was to develop a postmortem LC-QTOF method for simultaneous screening and quantitation using easy sample preparation and reduced alternative calibration models. Hence, a method for 24 highly relevant substances in forensic toxicology was fully validated using the following calibration models: one-point external, one-point internal via corresponding deuterated standards, multi-point external daily calibration, and multi-point external weekly calibration. Two hundred microliters of postmortem blood were spiked with internal deuterated standard mixture and extracted by acetonitrile protein precipitation. Analysis was performed on a Sciex 6600 QTOF instrument with ESI+ mode using data-independent acquisition (DIA) namely sequential window acquisition of all theoretical mass spectra (SWATH). Validation of the different calibration models included selectivity, autosampler stability, recovery, matrix effects, accuracy, and precision for 24 substances. In addition, corresponding deuterated analogs of 52 substances were included to the internal standard mix for semi-quantitative concentration assessment. The simple protein precipitation provided recoveries higher than 55 and 75% for all analytes at low and high concentrations, respectively. Accuracy and precision criteria (bias and imprecision ± 15 and ± 20% near the limit of quantitation) were fulfilled by the different calibration models for most analytes. The validated method was successfully applied to more than 100 authentic postmortem samples and 3 proficiency tests. Furthermore, the one-point internal calibration via corresponding deuterated standard proved to be a considerably time saving technique for 76 analytes. Graphical abstract One-point and multi-point calibration and the resulting beta-tolerance intervals from method validation.

Standardization of milk infrared spectra for the retroactive application of calibration models.

The objective of this study was to standardize the infrared spectra obtained over time and across 2 milk laboratories of Canada to create a uniform historical database and allow (1) the retroactive application of calibration models for prediction of fine milk composition; and (2) the direct use of spectral information for the development of indicators of animal health and efficiency. Spectral variation across laboratories and over time was inspected by principal components analysis (PCA). Shifts in the PCA scores were detected over time, leading to the definition of different subsets of spectra having homogeneous infrared signal. To evaluate the possibility of using common equations on spectra collected by the 2 instruments and over time, we developed a standardization (STD) method. For each subset of data having homogeneous infrared signal, a total of 99 spectra corresponding to the percentiles of the distribution of the absorbance at each wavenumber were created and used to build the STD matrices. Equations predicting contents of saturated fatty acids, short-chain fatty acids, and C18:0 were created and applied on different subsets of spectra, before and after STD. After STD, bias and root mean squared error of prediction decreased by 66% and 32%, respectively. When calibration equations were applied to the historical nonstandardized database of spectra, shifts in the predictions could be observed over time for all investigated traits. Shifts in the distribution of the predictions over time corresponded to the shifts identified by the inspection of the PCA scores. After STD, shifts in the predicted fatty acid contents were greatly reduced. Standardization reduced spectral variability between instruments and over time, allowing the merging of milk spectra data from different instruments into a common database, the retroactive use of calibrations equations, or the direct use of the spectral data without restrictions.

Azithromycin assay in drug formulations: Validation of a HPTLC method with a quadratic polynomial calibration model using the accuracy profile approach.

Many different assaying high performance thin layer chromatography (HPTLC) methods have been developed and validated in order to be used in routine analysis in different analytical fields. Validation often starts by the evaluation of the linearity of the calibration curve. Frequently, if the correlation coefficient is close to one, the linear calibration curve model is considered to be proper to predict the unknown concentration in the sample. But is this simple model effective to assess the behavior of the response of an HPTLC method as a function of concentration. To answer this question, a method for the determination of azithromycin by HPTLC has been developed and validated following both the classical approach and that based on the accuracy profile. Silica gel plates with fluorescence indicator F254 and chloroform - ethanol - 25% ammonia 6:14:0.2 (v/v/v) as mobile phase were used. Analysis was carried out in reflectance mode at 483nm. The RF of azithromycin was 0.53. The validation based on the classical approach, shows that the behavior is not linear, even though r2=0.999 because the lack of fit test is significant (P<0.05). Validation based on the accuracy profile approach considering both the straight line and the quadratic regression model, show that the former results is a ß-expectation tolerance interval outside the acceptance limits, while with the latter, this interval is within the limits of ±5% acceptability for a range which extends from 0.2 to 1.0µg/zone. With the quadratic model, the method showed to be precise and accurate.

Three-point multivariate calibration models by correlation constrained MCR-ALS: A feasibility study for quantitative analysis of complex mixtures.

When it comes to address quantitative analysis in complex mixtures, Partial Least Squares (PLS) is often referred to as a standard first-order multivariate calibration method. The set of samples used to build the PLS regression model should ideally be large and representative to produce reliable predictions. In practice, however, the large number of calibration samples is not always affordable and the choice of these samples should be handled with care as it can significantly affect the accuracy of the predictive model. Correlation constrained multivariate curve resolution (CC-MCR) is an alternative regression method for first-order datasets where, unlike PLS, calibration and prediction stages are performed iteratively and optimized under constraints until the decomposition meets the convergence criterion. Both calibration and test samples are fitted into a unique bilinear model so that the number of calibration samples is no longer a critical issue. In this paper we demonstrate that under certain conditions CC-MCR models can provide for reasonable predictions in quantitative analysis of complex mixtures even when only three calibration samples are employed. The latter are defined as samples having the minimum, the maximum and the average concentration, providing for a simple and rapid strategy to build reliable calibration model. The feasibility of three-point multivariate calibration approach was assessed with several case studies featuring mixtures of different analytes in presence of interfering species. Satisfactory predictions with relative errors in the range 3-15% were achieved and good agreement with classical PLS models built from a larger set of calibration samples was observed.

Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for potassium and sodium intake.

We pooled data from 5 large validation studies (1999-2009) of dietary self-report instruments that used recovery biomarkers as referents, to assess food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). Here we report on total potassium and sodium intakes, their densities, and their ratio. Results were similar by sex but were heterogeneous across studies. For potassium, potassium density, sodium, sodium density, and sodium:potassium ratio, average correlation coefficients for the correlation of reported intake with true intake on the FFQs were 0.37, 0.47, 0.16, 0.32, and 0.49, respectively. For the same nutrients measured with a single 24HR, they were 0.47, 0.46, 0.32, 0.31, and 0.46, respectively, rising to 0.56, 0.53, 0.41, 0.38, and 0.60 for the average of three 24HRs. Average underreporting was 5%-6% with an FFQ and 0%-4% with a single 24HR for potassium but was 28%-39% and 4%-13%, respectively, for sodium. Higher body mass index was related to underreporting of sodium. Calibration equations for true intake that included personal characteristics provided improved prediction, except for sodium density. In summary, self-reports capture potassium intake quite well but sodium intake less well. Using densities improves the measurement of potassium and sodium on an FFQ. Sodium:potassium ratio is measured much better than sodium itself on both FFQs and 24HRs.

Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid, uric acid, dopamine and nitrite: application of non-bilinear voltammetric data for exploiting first-order advantage.

For the first time, several multivariate calibration (MVC) models including partial least squares-1 (PLS-1), continuum power regression (CPR), multiple linear regression-successive projections algorithm (MLR-SPA), robust continuum regression (RCR), partial robust M-regression (PRM), polynomial-PLS (PLY-PLS), spline-PLS (SPL-PLS), radial basis function-PLS (RBF-PLS), least squares-support vector machines (LS-SVM), wavelet transform-artificial neural network (WT-ANN), discrete wavelet transform-ANN (DWT-ANN), and back propagation-ANN (BP-ANN) have been constructed on the basis of non-bilinear first order square wave voltammetric (SWV) data for the simultaneous determination of ascorbic acid (AA), uric acid (UA), dopamine (DP) and nitrite (NT) at a glassy carbon electrode (GCE) to identify which technique offers the best predictions. The compositions of the calibration mixtures were selected according to a simplex lattice design (SLD) and validated with an external set of analytes' mixtures. An asymmetric least squares splines regression (AsLSSR) algorithm was applied for correcting the baselines. A correlation optimized warping (COW) algorithm was used to data alignment and lack of bilinearity was tackled by potential shift correction. The effects of several pre-processing techniques such as genetic algorithm (GA), orthogonal signal correction (OSC), mean centering (MC), robust median centering (RMC), wavelet denoising (WD), and Savitsky-Golay smoothing (SGS) on the predictive ability of the mentioned MVC models were examined. The best preprocessing technique was found for each model. According to the results obtained, the RBF-PLS was recommended to simultaneously assay the concentrations of AA, UA, DP and NT in human serum samples.