Cross-validation Error Research Articles

A Flexible Theory for Catalysis: Learning Alkaline Oxygen Reduction on Complex Solid Solutions within the Ag-Pd-Pt-Ru Composition Space.

Compositionally complex materials such as high-entropy alloys and oxides have the potential to be efficient platforms for catalyst discovery because of the vast chemical space spanned by these novel materials. Identifying the composition of the most active catalyst materials, however, requires unraveling the descriptor-activity relationship, as experimentally screening the multitude of possible element ratios quickly becomes a daunting task. In this work, we show that inferred adsorption energy distributions of *OH and *O on complex solid solution surfaces within the space spanned by the system Ag-Pd-Pt-Ru are coupled to the experimentally observed electrocatalytic performance for the oxygen reduction reaction. In total, the catalytic activity of 1582 alloy compositions is predicted with a cross-validated mean absolute error of 0.042 mA/cm2 by applying a theory-derived model with only two adjustable parameters. Trends in the discrepancies between predicted electrochemical performance values of the model and the measured values on thin film surfaces subsequently provide insight into the alloys' surface compositions during reaction conditions. Bridging this gap between computationally modeled and experimentally observed catalytic activities, not only reveals insight into the underlying theory of catalysis but also takes a step closer to realizing exploration and exploitation of high-entropy materials.

Performance of a Handheld MicroNIR Instrument for Determining Protein Levels in Sorghum Grain Samples.

Near infrared (NIR) spectroscopy is widely used for evaluating quality traits of cereal grains. For evaluating protein content of intact sorghum grains, parallel NIR calibrations were developed using an established benchtop instrumentation (Perten DA-7250) as a baseline to test the efficacy of an adaptive handheld instrument (VIAVI MicroNIR OnSite-W). Spectra were collected from 59 grain samples using both instruments at the same time. Cross-validated calibration models were validated with 33 test samples. The selected calibration model for DA-7250 with a coefficient of determination (R2) = 0.98 and a root mean square error of cross validation (RMSECV) = 0.41% predicted the protein content of a test set with R2 = 0.94, root mean square error of prediction (RMSEP) = 0.52% with a ratio of performance to deviation (RPD) of 4.13. The selected model for the MicroNIR with R2 = 0.95 and RMSECV = 0.62% predicted the protein content of the test set with R2 = 0.87, RMSEP = 0.76% with an RPD of 2.74. In comparison, the performance of the DA-7250 was better than the MicroNIR, however, the performance of the MicroNIR was also acceptable for screening intact sorghum grain protein levels. Therefore, the MicroNIR instrument may be used as a potential tool for screening sorghum samples where benchtop instruments are not appropriate such as for screening samples in the field or as a less expensive option compared with benchtop instruments.

Towards the automation of NIR spectroscopy to assess vineyard water status spatial–temporal variability from a ground moving vehicle

Irrigation has a strong impact in terms of yield regulation and grape and wine quality, so the implementation of precision watering systems would facilitate the decision-making process about the water use efficiency and the irrigation scheduling in viticulture. The objectives of this work were two-fold. On one hand, to compare and assess grapevine water status using two different spectral devices assembled in a mobile platform and to evaluate their capability to map the spatial variability of the plant water status in two commercial vineyards from July to early October in season 2021, and secondly to develop an algorithm capable of automate the spectral acquisition process using one of the two spectral sensors previously tested. Contemporarily to the spectral measurements collected from the ground vehicle at solar noon, stem water potential (Ψs) was used as the reference method to evaluate the grapevine water status. Calibration and prediction models for grapevine water status assessment were performed using the Partial least squares (PLS) regression and the Variable Importance in the Projection (VIP) method. The best regression models returned a determination coefficient for cross validation (R2cv) and external validation (R2p) of 0.70 and 0.75 respectively, and the standard error of cross validation (RMSECV) values were lower than 0.105 MPa and 0.128 MPa for Tempranillo and Graciano varieties using a more expensive and heavier near-infrared (NIR) spectrometer (spectral range 1200–2100 nm). Remarkable models were also built with the miniaturized, low-cost spectral sensor (operating between 900–1860 nm) ranging from 0.69 to 0.71 for R2cv, around 0.74 in both varieties for R2p and the RMSECV values were below 0.157 MPa, while the RMSEP values did not exceed 0.151 MPa in both commercial vineyards. This work also includes the development of a software which automates data acquisition and allows faster (up to 40% of time saving in the field) and more efficient deployment of the developed algorithm. The encouraging results presented in this work demonstrate the great potential of this methodology to assess the water status of the vineyard and estimate its spatial variability in different commercial vineyards, providing useful information for better irrigation scheduling.

Near Infrared Spectroscopy for Prediction of Yeast and Mould Counts in Black Soldier Fly Larvae, Feed and Frass: A Proof of Concept.

The use of black soldier fly larvae (BSFL) grown on different organic waste streams as a source of feed ingredient is becoming very popular in several regions across the globe. However, information about the easy-to-use methods to monitor the safety of BSFL is a major step limiting the commercialization of this source of protein. This study investigated the ability of near infrared (NIR) spectroscopy combined with chemometrics to predict yeast and mould counts (YMC) in the feed, larvae, and the residual frass. Partial least squares (PLS) regression was employed to predict the YMC in the feed, frass, and BSFL samples analyzed using NIR spectroscopy. The coefficient of determination in cross validation (R2CV) and the standard error in cross validation (SECV) obtained for the prediction of YMC for feed were (R2cv: 0.98 and SECV: 0.20), frass (R2cv: 0.81 and SECV: 0.90), larvae (R2cv: 0.91 and SECV: 0.27), and the combined set (R2cv: 0.74 and SECV: 0.82). However, the standard error of prediction (SEP) was considered moderate (range from 0.45 to 1.03). This study suggested that NIR spectroscopy could be utilized in commercial BSFL production facilities to monitor YMC in the feed and assist in the selection of suitable processing methods and control systems for either feed or larvae quality control.

An algorithm for enhancement of audio content classification

Presently, fast proliferation of information enforces novel challenges on content management. Further, computerized audio classification along-with content description is considered as valuable method to manage audio contents. In general, classification involves two steps. First, is the processing of accessible data in economical ways to deliver explanatory features. Second is how accurate features of undetermined tests is evaluated to choose classifier. In this paper, k-neighbor algorithm with machine learning is proposed for feature extraction as well as content classification/description. This algorithm enhances Quality of Service parameters of classifiers. Here, development of training as well as testing data set is developed to increase the classifier accuracy. A test engine set-up bed using simulation tool MATLAB is designed to estimate the implementation performance of the algorithm. A range of features are studied to evaluate effectiveness in terms of accuracy, zero crossing rate (ZCR) and spectral roll frequency. From the experimentation results, it is observed that the proposed algorithm can achieve accuracy of 95.8% for 2 sec window length as compare with k-neighbor algorithm. A total enhancement of 11% is achieved with cross validation error of 29.6. A superior assortment of training fabric to extract few additional useful features can enhance accuracy further.

An algorithm for enhancement of audio content classification

Presently, fast proliferation of information enforces novel challenges on content management. Further, computerized audio classification along-with content description is considered as valuable method to manage audio contents. In general, classification involves two steps. First, is the processing of accessible data in economical ways to deliver explanatory features. Second is how accurate features of undetermined tests is evaluated to choose classifier. In this paper, k-neighbor algorithm with machine learning is proposed for feature extraction as well as content classification/description. This algorithm enhances Quality of Service parameters of classifiers. Here, development of training as well as testing data set is developed to increase the classifier accuracy. A test engine set-up bed using simulation tool MATLAB is designed to estimate the implementation performance of the algorithm. A range of features are studied to evaluate effectiveness in terms of accuracy, zero crossing rate (ZCR) and spectral roll frequency. From the experimentation results, it is observed that the proposed algorithm can achieve accuracy of 95.8% for 2 sec window length as compare with k-neighbor algorithm. A total enhancement of 11% is achieved with cross validation error of 29.6. A superior assortment of training fabric to extract few additional useful features can enhance accuracy further.

Near-infrared spectroscopy as a process analytical technology tool for monitoring performance of membrane filtration in a whey protein fractionation process.

A successful transition from a laboratory proof-of-principle to a functioning industrial Process Analytical Technology (PAT) application of spectroscopy and chemometrics is still an active area of research and development. A comprehensive understanding on how the design and implementation of the optical instrumentation affect the data quality, and how this will affect the performance of the prediction models is vital for the successful implementation of in-line monitoring. In a previous study, we have demonstrated that near-infrared spectroscopy (NIRS), combined with chemometric techniques, can be used to quantify α-lactalbumin and β-lactoglobulin in aqueous whey solutions. This work demonstrates the potential, both at-line and in-line, of monitoring a protein fractionation process in a full-scale production. An in-line near infrared spectrometer (NIRS) was used to monitor the individual protein concentrations in a protein fractionation process over 20 days. In addition, samples were extracted from the process and analysed with at-line NIRS and an in-house HPLC reference method. The developed models could predict β-lactoglobulin and α-lactalbumin concentrations with satisfactory precision and accuracy, yielding a root mean square error of cross-validation of 0.08 and 0.18 w/w% proteins, respectively. For the first time, the whey proteins concentrations were measured continuously in a production facility to demonstrate the potential of NIRS for at-line and in-line rapid monitoring of protein composition in industrial whey streams. A PAT tool was developed from NIRS data where Partial Least Squares regression modelling and an Exponentially Weighted Moving Average filtering were used to extract and visualize valuable information on the process dynamics and performance. The study demonstrates that continuous monitoring by in-line NIRS allowed elucidation of some process behaviours not readily detectable with the current sampling frequency and that it offers novel process optimisation opportunities by providing increased process understanding and decision support information concerning preventive maintenance and real-time process validation.

Feature selection for a continental-scale geospatial model of environmental sound levels.

Modeling environmental sound levels over continental scales is difficult due to the variety of geospatial environments. Moreover, current continental-scale models depend upon machine learning and therefore face additional challenges due to limited acoustic training data. In previous work, an ensemble of machine learning models was used to predict environmental sound levels in the contiguous United States using a training set composed of 51 geospatial layers (downselected from 120) and acoustic data from 496 geographic sites from Pedersen, Transtrum, Gee, Lympany, James, and Salton [JASA Express Lett. 1(12), 122401 (2021)]. In this paper, the downselection process, which is based on factors such as data quality and inter-feature correlations, is described in further detail. To investigate additional dimensionality reduction, four different feature selection methods are applied to the 51 layers. Leave-one-out median absolute deviation cross-validation errors suggest that the number of geospatial features can be reduced to 15 without significant degradation of the model's predictive error. However, ensemble predictions demonstrate that feature selection results are sensitive to variations in details of the problem formulation and, therefore, should elicit some skepticism. These results suggest that more sophisticated dimensionality reduction techniques are necessary for problems with limited training data and different training and testing distributions.

Machine learning models for fast and isothermal hydrothermal liquefaction of biomass: Comprehensive experiment and prediction of various product fraction yields

Hydrothermal liquefaction (HTL) shows promise in biocrude productions from wet biomass. In this study, phycocyanin conversion was performed under an extensive range of conditions (set-point temperatures of 300–500 °C and holding times of 20–1800 s), covering both isothermal and fast HTL processes. Based on 226 sets of product fraction yields obtained from this study and literature, machine learning models, such as multiple linear regression, decision regression tree, random forest, gradient boosting regression, and support vector regression were developed. The model with the highest predictive accuracy for biocrude yield was further used to perform both single and multi-task predictions of other product yields. The results show that fast HTL at 500 °C and 40 s produced the highest biocrude yield (35.0 wt%) and energy recovery (55.2%), which were 11.9 wt% and 15.9% higher than those obtained by conventional isothermal HTL at 300 °C and 1800 s. Biocrude predominantly consisted of nitrogenous heterocyclic compounds, leading to its high nitrogen content (∼10 wt%). In addition, water quality analysis shows that the chemical oxygen demand, ammonia and total phosphorus contents of the aqueous phase by-products were much higher than the discharge standards and required further treatments. Cross-validation and testing error analysis show that the random forest model exhibited the highest predictive accuracy for the biocrude yield, with a testing R2 of 0.93. Additionally, the random forest model could predict other product fraction yields with desirable accuracies. According to the feature importance analysis, the most important factor for biocrude production from fast HTL was the residence time, followed by the reaction temperature.

Environmentally sustainable DRS-FTIR probe assisted by chemometric tools for quality control analysis of cinnarizine and piracetam having diverged concentration ranges: Validation, greenness, and whiteness studies

A novel diffuse reflectance fourier transform infrared spectroscopic method accompanied by chemometrics was optimized to fulfill the white analytical chemistry and green analytical chemistry principles for the quantification of cinnarizine and piracetam for the first time without any prior separation in their challenging pharmaceutical preparation, which has a pretty substantial difference in the concentration of cinnarizine/piracetam (1:16). Furthermore, the suggested method was used for cinnarizine/piracetam dissolution testing as an effective alternative to traditional methods. For the cinnarizine/piracetam dissolution tests, we used a dissolution vessel with 900 mL of phosphate buffer pH 2.5 at 37 °C ± 0.5 °C, then the sampling was carried out by frequent withdrawal of 20 µl samples from the dissolution vessel at a one-minute interval, over one hour, then representative fourier transform infrared spectra were recorded. To create a partial-least-squares regression model, a fractional factorial design with 5 different levels and 2 factors was used. This led to the creation of 25 mixtures, 15 as a calibration set and 10 as a validation set, with varying concentration ranges: 1–75 and 16–1000 μg/mL for cinnarizine/piracetam, respectively. Upon optimization of the partial-least-squares regression model, in terms of latent variables and spectral region, root mean square error of cross-validation of 0.477 and 0.270, for cinnarizine/piracetam respectively, were obtained. The optimized partial-least-squares regression model was further validated, providing good results in terms of recovery% (around 98 to 102 %), root mean square error of prediction (0.436 and 3.329), relative root mean square error of prediction (1.210 and 1.245), bias-corrected mean square error of prediction (0.059 and 0.081), and limit of detection (0.125 and 2.786) for cinnarizine/piracetam respectively. Ultimately, the developed method was assessed for whiteness, greenness, and sustainability using five assessment tools. the developed method achieved a greener national environmental method index and complementary green analytical procedure index quadrants with higher eco-scale assessment scores (91), analytical greenness metric scores (0.87), and red–greenblue 12 algorithm scores (89.7) than the reported methods, showing high practical and environmental acceptance for quality control of cinnarizine/piracetam.

Development of NIRS calibration curves for sugars in baked sweetpotato.

Variability in sugar content between raw and cooked sweetpotato storage roots impact nutritional and dietary importance with implications for consumer preference. High-throughput phenotyping is required to breed varieties that satisfy consumer preferences. Near-infrared reflectance spectroscopy (NIRS) calibration curves were developed for analysing sugars in baked storage roots using 147 genotypes from a population segregating for sugar content and other traits. The NIRS prediction curves had high coefficients of determination in calibration (R2 c) of 0.96 (glucose), 0.93 (fructose), 0.96 (sucrose), and 0.96 (maltose). The corresponding coefficients of determination for cross-validation (R2 cv) were 0.92 (glucose), 0.89 (fructose), 0.96 (sucrose) and 0.93 (maltose) and were similar to the R2 c for all sugars measured. The ratios of the standard deviation of the reference set to the standard error of cross-validation were greater than three for all sugars. These results confirm the applicability of the NIRS curves in efficiently determining sugar content in baked sweetpotato storage roots. External validation was performed on an additional 70 genotypes. Coefficients of determination (r2) were 0.88 (glucose), 0.88 (fructose), 0.86 (sucrose) and 0.49 (maltose). The results were comparable to those found for the calibration and cross-validation in fructose, glucose, and sucrose, but were moderate for maltose due to the low variability of maltose content in the population. NIRS can be used for screening sugar content in baked sweetpotato storage roots in breeding programs and can be used to assist with the development of improved sweetpotato varieties that better meet consumer preferences. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Application of peripheral neural information to model and control agonist/antagonistic muscular coordination dynamics using the Poincaré approach

Recovering motor activation signals from peripheral nerve fascicles is the most acknowledged strategy for the control of prostheses driven by electroneurogram. Yet, restoring signals capturing correct muscular co-activation dynamics is still a challenge. This study aims to identify, through a novel hybrid paradigm, a reference model for muscular co-activation dynamics, whose parameters could be controlled by electroneurogram. A dataset related to an animal study was exploited. Muscular co-activation was modelled in the phase space using the Poincaré plot. The information of a selected electroneurogram channel about gait events was extracted and employed for the first time to design the Poincaré section. Utilising the intersection points, proposed iterated maps, X- and Y-maps, were trained to develop a reference model. The cross-validated prediction error of the model was 2.06 × 10-4 ± 0.0011 and 7.17 × 10-6 ± 4.59 × 10-5 for the X- and Y-maps, respectively. Analogous large- and small-scale patterns of the recurrence plots corresponding to sample model-generated and true data verified their similar dynamical regimes. Low variability of the model parameters, intra- and inter-trially, corroborated generalisability of the model. The viability of exploiting electroneurogram for modelling and predicting muscular co-activation was established. Leveraging reference model not only simplifies, but also opens up new possibilities for near-natural control of prostheses.

Using Internet of Things (IoT), Near-Infrared Spectroscopy (NIRS), and Hyperspectral Imaging (HSI) to Enhance Monitoring and Detection of Grain Pests in Storage and Handling Operators

To reduce the use of insecticides, silo operators are reconsidering their practices and implementing integrated pest management (IPM) to manage insect infestations. IPM requires the early detection of insects to react before infestation spread or to isolate infested lots. Depending on their position in the storage and handling chain, operators will favor monitoring or rapid detection tools. To simplify monitoring in storage, an internet-connected trap has been designed. It includes a camera located above a tank that allows for the captured insects to be counted. A total of 89 traps were installed in elevators for a proof-of-concept phase. Compared to sample monitoring, the traps detected an average of three additional insect species in an infested batch. To improve the detection of insects in wheat, methods for detecting and quantifying live adult insects (Sitophilus oryzae, Rhyzoperta dominica, and Tribolium confusum) using NIRS and HSI have been developed. The used instruments, a near-infrared spectrometer and a hyperspectral camera, allow for an in-flow analysis, which reduces sampling errors. The cross-validation errors of the NIRS models ranged from 2.44 insects/kg to 2.56 insects/kg, and the prediction error of the HSI ones ranged from 0.70 insect/kg to 2.07 insect/kg, depending on the insect species.

Applying UV Hyperspectral Imaging for the Quantification of Honeydew Content on Raw Cotton via PCA and PLS-R Models

Cotton contamination by honeydew is considered one of the significant problems for quality in textiles as it causes stickiness during manufacturing. Therefore, millions of dollars in losses are attributed to honeydew contamination each year. This work presents the use of UV hyperspectral imaging (225–300 nm) to characterize honeydew contamination on raw cotton samples. As reference samples, cotton samples were soaked in solutions containing sugar and proteins at different concentrations to mimic honeydew. Multivariate techniques such as a principal component analysis (PCA) and partial least squares regression (PLS-R) were used to predict and classify the amount of honeydew at each pixel of a hyperspectral image of raw cotton samples. The results show that the PCA model was able to differentiate cotton samples based on their sugar concentrations. The first two principal components (PCs) explain nearly 91.0% of the total variance. A PLS-R model was built, showing a performance with a coefficient of determination for the validation (R2cv) = 0.91 and root mean square error of cross-validation (RMSECV) = 0.036 g. This PLS-R model was able to predict the honeydew content in grams on raw cotton samples for each pixel. In conclusion, UV hyperspectral imaging, in combination with multivariate data analysis, shows high potential for quality control in textiles.

Development of a new and rapid FTIR method using chemometric modeling techniques for the determination of lavandin adulteration in lavender essential oil

Lavender essential oil (LT) is subject to adulteration by malicious suppliers and is often sold mixed with the cheaper lavandin essential oil (LD). In this study, it is aimed to develop a new and rapid spectroscopic method by using chemometric modeling techniques for the qualitative and quantitative determination of LD adulteration in LT. For the discrimination of LD in LT, 36 principal component analysis (PCA) models were created and the model with the best statistical results was evaluated by the number of PCs in the explained variance and eigen value. Soft independent modeling by class analogy (SIMCA) was also employed to demonstrate the discrimination capabilities of the best PCA model. For the quantification of LD in LT, 36 partial least squares (PLS) regression models employing the spectral data were developed. To find out the best PLS model, the root mean square error of prediction (RMSEP), and root mean square error of cross-validation (RMSECV) were mainly used. It was found that the best models for discrimination and quantitative analysis were those using normal spectra in the region 2200–400 cm−1 (NDSR). The results obtained showed that LD adulteration in LT could be successfully detected over a wide concentration range, thanks to the combined use of FTIR spectroscopy and chemometry without using any toxic chemicals or pretreatments.

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.

Classification and quantification of sucrose from sugar beet and sugarcane using optical spectroscopy and chemometrics.

Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380nm. Spectral differences were observable in the ultraviolet-visible (UV-Vis) region from 200 to 600nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV-Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky-Golay were most acceptable with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV-Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries' agricultural policies.

Calibration for iron redox state and oxygen fugacity in silicate glasses from x-ray absorption spectroscopy

Prior work to quantify glass Fe-redox ratios in situ using x-ray absorption spectroscopy (XAS) has relied heavily on the pre-edge region. The pre-edge-based methods do not take advantage of other information in the spectra above the pre-edge, up to and past the main edge. This study updates the previous calibration (Dyar et al., 2016a) with additional experimental glasses spanning a wide range of geologically relevant compositions (SiO2 weight % = 38.5–78.0), equilibrated over a range of oxygen fugacity (fO2) values from reducing (IW-2) to oxidizing (air), for which Fe XAS analysis has been collected for the whole spectral region (pre-edge, main edge, EXAFS). The resulting dataset was used to evaluate multiple multivariate statistical models for their ability to accurately predict log fO2 or %Fe3+. Accuracies are estimated using cross-validated root mean square error, for which purpose unseen test data are held out from the regressions. Datasets covering small compositional ranges were tested and compared with datasets covering broad compositional ranges. These ‘stratified’ sets improved predictive accuracy in some cases; however, the extra complications associated with data stratification did not justify the small improvement in accuracy. Overall, a simple partial least squares (PLS) model based on all data collected over the entire XAS region was found to result in a universal calibration for determination of log fO2 and %Fe3+ in glasses.

The ricebean genome provides insight into Vigna genome evolution and facilitates genetic enhancement.

The ricebean genome provides insight into Vigna genome evolution and facilitates genetic enhancement.

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.