Root Mean Square Error Of Cross-validation Research Articles

Determination of oil quality during crispy pork rind frying: Near infrared spectra and color values as predictors

This study provides a comprehensive analysis of oil degradation during the frying of various semi-finished pork rinds, including high-fat (HF), non-fat (NF), streaky (SK), and thin (TN) types. By quantitatively assessing parameters such as peroxide, acid, anisidine, and Totox values, we sought to elucidate the complex interactions and effects on oil quality during the frying process. The oil samples underwent evaluation utilizing a near-infrared (NIR) spectrometer and the color variations in the frying oil during the process. The resulting data highlighted significant correlations between color parameters and oil quality. The NIR models exhibited an impressive and robust predictive proficiency with high correlation coefficients values (R > 0.9 for all measured parameters) and low root mean square errors of cross-validation (RMSECVs). They promise rapid, non-destructive, and accurate evaluation of oil quality during frying processes. Additionally, the study identified oil color as a viable, low-cost alternative for predicting oil quality parameters where sophisticated spectroscopic equipment is unavailable.

Estimating S-wave amplitude for earthquake early warning in New Zealand: Leveraging the first 3 seconds of P-Wave

This study addresses the critical question of predicting the amplitude of S-waves during earthquakes in Aotearoa New Zealand (NZ), a highly earthquake-prone region, for implementing an Earthquake Early Warning System (EEWS). This research uses ground motion parameters from a comprehensive dataset comprising historical earthquakes in the Canterbury region of NZ. It explores the potential to estimate the damaging S-wave amplitude before it arrives, primarily focusing on the initial P-wave signals. The study establishes nine linear regression relationships between P-wave and S-wave amplitudes, employing three parameters: peak ground acceleration, peak ground velocity, and peak ground displacement. Each relationship’s performance is evaluated through correlation coefficient (R), coefficient of determination (R²), root mean square error (RMSE), and 5-fold Cross-validation RMSE, aiming to identify the most predictive empirical model for the Canterbury context. Results using a weighted scoring approach indicate that the relationship involving P-wave Peak Ground Velocity (Pv) within a 3-second window strongly correlates with S-wave Peak Ground Acceleration (PGA), highlighting its potential for EEWS. The selected empirical relationship is subsequently applied to establish a P-wave amplitude (Pv) threshold for the Canterbury region as a case study from which an EEWS could benefit. The study also suggests future research exploring complex machine learning models for predicting S-wave amplitude and expanding the analysis with more datasets from different regions of NZ.

A comparative study of acoustic and ultrasonic nondestructive testing for evaluating melon quality

Abstract Melon (Cucumis melo L.) is a high-value agricultural commodity known worldwide for its sweet taste and crisp flesh texture, which are important factors for quality and consumer acceptance. Unfortunately, quality testing and determining the optimal harvest time for achieving desired melon characteristics are traditionally performed through destructive methods. The aim of this study was to explore the potential of acoustic and ultrasonic tests for predicting the physicochemical properties of Honey Globe melons (Cucumis melo L. var. inodorus). A total of 100 melon samples were used in this study. For the nondestructive ultrasonic testing, attenuation values served as its variable, whereas in acoustic testing, variables included frequency, magnitude, short-term energy, and zero-moment. Fruit’s flesh firmness and total soluble solids (TSS) as physicochemical quality properties were determined using destructive tests. The calibration phase involved 80 melon samples, employing a K-Fold Cross Validation approach with ten folds, done on Partial Least Square Regression (PLS) modeling. Another 20 melon samples were used for blind testing. Reliability evaluation was done on key metrics, consisting of R2 values, RMSEC (Root Mean Square Error of Calibration), RMSECV (Root Mean Square Error of Cross-Validation), RMSEP (Root Mean Square Error of Prediction), and RPD (Ratio of Performance to Deviation). Analysis results on these metrics collectively support the conclusion that both ultrasonic and acoustic methods exhibit their potential to predict the firmness properties of melon fruits. The best evaluation result that has been conducted for the ultrasonic test uses attenuation, age, and density as predictors to predict fruit firmness, with R2 = 0.763 and RPD = 2.945, while the acoustic test achieved the best result with magnitude used as a predictor to predict fruit firmness with R2 = 0.718 and RPD = 2.230. However, evaluation metrics on the prediction of total soluble solids for both nondestructive tests were still not good enough for application with low R2 and RPD value.

Detection of adulteration of cattle and buffalo meat through NIRS and chemometric analysis

The objective of this study was to evaluate the efficacy of near-infrared (NIR) reflectance spectroscopy in detecting adulteration in cattle and buffalo meat. A total of 16 samples were tested, 2 of which were pure and 14 were adulterated. The beef samples were adulterated by mixing buffalo meat in the range of 0-28% (w/w) at approximately 2% increments according to weight. To detect adulteration, DLP® NIRscan™ Nano Software was used to gather spectra. The Unscrambler X program was used to develop calibration and validation models utilizing principal component regression and partial least squares. Root mean square error of calibration (RMSEc), root mean square error of cross-validation (RMSEcv), coefficient of calibration (R2 c ), and coefficient of cross-validation (R2 cv) were used to assess the accuracy of the calibration models. The R2 value of 0.90 or above indicates that the regression model is excellent. For the PCR model, the predicted R2 cv value was 0.73 and for the PLSR model, the predicted R2 cv value was 0.98 through leverage correction. In cross-validation, the R2 cv value was 0.65 for both the PCR and PLSR models. According to the findings, it is suggested that NIR spectroscopy is a reasonably efficient method for detecting adulteration in cattle meat with buffalo meat.

Simple Method for Calcium Determination in Crude Oil Emulsion Using Digital Image Colorimetry

A portable device for digital image colorimetry (DIC) is reported for the determination of Ca in crude oil emulsions. ASTM D4807 was used for sample preparation for the isolation of saline species containing Ca. DIC was evaluated based on the complexation reactions with Ca using calcein, murexide, and calcon. Partial least squares (PLS) provided the best determination coefficients (higher than 0.990) for all complexing reagents. Significant interference of Mg was observed for the determination of Ca using calcein. PLS models with the lowest root mean square error of cross validation (RMSECV) values were obtained for murexide and calcon for a concentration of 0.250 mmol L−1 at pH of 13. The best conditions for camera focal length were 11 and 2 mm for murexide and calcon. For light intensity, the optimized conditions for murexide and calcon was 180 lux. Stabilities of color complexes over the time were 30 and 15 min for murexide and calcon, respectively. DIC/murexide provided the lowest root mean square error of prediction (RMSEP) and a linear response was observed from 0.5 to 10 mg L−1 Ca and the relative standard deviation (RSD) was lower than 10%. Significant differences were not observed between the results obtained by DIC/murexide and those obtained by microwave-assisted wet digestion (MAWD) with determination by inductively coupled plasma-optical emission spectrometry (ICP-OES). A limit of quantification of 0.50 mg kg−1 Ca were obtained using DIC/murexide. This approach is an alternative and useful method for the determination of Ca in crude oil samples, with advantages of miniaturization, low cost, and ease of operation.

Rapid Measurement of Antioxidant Properties of Dendrobium officinale Using Near-Infrared Spectroscopy and Chemometrics.

Dendrobium officinale (D. officinale), often used as a dual-use plant with herbal medicine and food applications, has attracted considerable attention for health-benefiting components and wide economic value. The antioxidant ability of D. officinale is of great significance to ensure its health care value and safeguard consumers' interests. However, the common analytical methods for evaluating the antioxidant ability of D. officinale are time-consuming, laborious, and costly. In this study, near-infrared (NIR) spectroscopy and chemometrics were employed to establish a rapid and accurate method for the determination of 2,2'-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) scavenging capacity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity, and ferric reducing antioxidant power (FRAP) in D. officinale. The quantitative models were developed based on the partial least squares (PLS) algorithm. Two wavelength selection methods, namely the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) method, were used for model optimization. The CARS-PLS models exhibited superior predictive performance compared to other PLS models. The root mean square errors of cross-validation (RMSECVs) for ABTS, FRAP, and DPPH were 0.44%, 2.64 μmol/L, and 2.06%, respectively. The results demonstrated the potential application of NIR spectroscopy combined with the CARS-PLS model for the rapid prediction of antioxidant activity in D. officinale. This method can serve as an alternative to conventional analytical methods for efficiently quantifying the antioxidant properties in D. officinale.

Quality control of naringenin-carbamazepine drug-drug cocrystal: Quantitative analytical method construction of ATR-FTIR and Raman combined with chemometrics

Naringenin, commonly found in citrus fruits, is one of the active pharmaceutical ingredient (API) in naringenin-carbamazepine drug-drug cocrystal (NR-CBZ). In the preparation of cocrystal, quantitative determination of NR-CBZ is essential for the quality control. In this paper, ATR-FTIR and Raman spectroscopies combined with partial least squares (PLS) and principal component regression (PCR) were used to quantify NR-CBZ in the mixtures. To improve the accuracy of the prediction models, median, denoising, multiple scattering correction (MSC), first and second order derivatives, Savitzky-Golay filter and SNV were used and their performance was evaluated using prediction errors in combination with different spectral ranges. Raman spectra, PLS combined with Savitzky-Golay filter over the entire spectral range were found to determine the best content prediction result for NR-CBZ. The root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and squared correlation coefficient (R2) of the model were 0.101, 0.132 and 0.870, respectively.

Estimation of Small-Stream Water Surface Elevation Using UAV Photogrammetry and Deep Learning

Unmanned aerial vehicle (UAV) photogrammetry allows the generation of orthophoto and digital surface model (DSM) rasters of terrain. However, DSMs of water bodies mapped using this technique often reveal distortions in the water surface, thereby impeding the accurate sampling of water surface elevation (WSE) from DSMs. This study investigates the capability of deep neural networks to accommodate the aforementioned perturbations and effectively estimate WSE from photogrammetric rasters. Convolutional neural networks (CNNs) were employed for this purpose. Two regression approaches utilizing CNNs were explored: direct regression employing an encoder and a solution based on prediction of the weight mask by an autoencoder architecture, subsequently used to sample values from the photogrammetric DSM. The dataset employed in this study comprises data collected from five case studies of small lowland streams in Poland and Denmark, consisting of 322 DSM and orthophoto raster samples. A grid search was employed to identify the optimal combination of encoder, mask generation architecture, and batch size among multiple candidates. Solutions were evaluated using two cross-validation methods: stratified k-fold cross-validation, where validation subsets maintained the same proportion of samples from all case studies, and leave-one-case-out cross-validation, where the validation dataset originates entirely from a single case study, and the training set consists of samples from other case studies. Depending on the case study and the level of validation strictness, the proposed solution achieved a root mean square error (RMSE) ranging between 2 cm and 16 cm. The proposed method outperforms methods based on the straightforward sampling of photogrammetric DSM, achieving, on average, an 84% lower RMSE for stratified cross-validation and a 62% lower RMSE for all-in-case-out cross-validation. By utilizing data from other research, the proposed solution was compared on the same case study with other UAV-based methods. For that benchmark case study, the proposed solution achieved an RMSE score of 5.9 cm for all-in-case-out cross-validation and 3.5 cm for stratified cross-validation, which is close to the result achieved by the radar-based method (RMSE of 3 cm), which is considered the most accurate method available. The proposed solution is characterized by a high degree of explainability and generalization.

Quantitative analysis of three ingredients in Salvia miltiorrhiza by near infrared spectroscopy combined with hybrid variable selection strategy

Rosmarinic acid (RA), Tanshinone IIA (Tan IIA), and Salvianolic acid B (Sal B) are crucial compounds found in Salvia miltiorrhiza. Quickly predicting these components can aid in ensuring the quality of S. miltiorrhiza. Spectral preprocessing and variable selection are essential processes in quantitative analysis using near infrared spectroscopy (NIR). A novel hybrid variable selection approach utilizing iVISSA was employed in this study to enhance the quantitative measurement of RA, Tan IIA, and Sal B contents in S. miltiorrhiza. The spectra underwent 108 preprocessing approaches, with the optimal method being determined as orthogonal signal correction (OSC). iVISSA was utilized to identify the intervals (feature bands) that were most pertinent to the target chemical. Various methods such as bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable combination population analysis (VCPA), successive projections algorithm (SPA), iteratively variable subset optimization (IVSO), and iteratively retained informative variables (IRIV) were used to identify significant feature variables. PLSR models were created for comparison using the given variables. The results fully demonstrated that iVISSA-SPA calibration model had the best comprehensive performance for Tan IIA, and iVISSA-BOSS had the best comprehensive performance for RA and Sal B, and correlation coefficients of cross-validation (R2cv), root mean square errors of cross-validation (RMSECV), correlation coefficients of prediction (R2p), and root mean square errors of prediction (RMSEP) were 0.9970, 0.0054, 0.9990 and 0.0033, 0.9992, 0.0016, 0.9961 and 0.0034, 0.9998, 0.0138, 0.9875 and 0.1090, respectively. The results suggest that NIR spectroscopy, along with PLSR and a hybrid variable selection method using iVISSA, can be a valuable tool for quickly quantifying RA, Sal B, and Tan IIA in S. miltiorrhiza.

Machine learning-enhanced drug testing for simultaneous morphine and methadone detection in urinary biofluids

The simultaneous identification of drugs has considerable difficulties due to the intricate interplay of analytes and the interference present in biological matrices. In this study, we introduce an innovative electrochemical sensor that overcomes these hurdles, enabling the precise and simultaneous determination of morphine (MOR), methadone (MET), and uric acid (UA) in urine samples. The sensor harnesses the strategically adapted carbon nanotubes (CNT) modified with graphitic carbon nitride (g-C3N4) nanosheets to ensure exceptional precision and sensitivity for the targeted analytes. Through systematic optimization of pivotal parameters, we attained accurate and quantitative measurements of the analytes within intricate matrices employing the fast Fourier transform (FFT) voltammetry technique. The sensor’s performance was validated using 17 training and 12 test solutions, employing the widely acclaimed machine learning method, partial least squares (PLS), for predictive modeling. The root mean square error of cross-validation (RMSECV) values for morphine, methadone, and uric acid were significantly low, measuring 0.1827 µM, 0.1951 µM, and 0.1584 µM, respectively, with corresponding root mean square error of prediction (RMSEP) values of 0.1925 µM, 0.2035 µM, and 0.1659 µM. These results showcased the robust resiliency and reliability of our predictive model. Our sensor’s efficacy in real urine samples was demonstrated by the narrow range of relative standard deviation (RSD) values, ranging from 3.71 to 5.26%, and recovery percentages from 96 to 106%. This performance underscores the potential of the sensor for practical and clinical applications, offering precise measurements even in complex and variable biological matrices. The successful integration of g-C3N4-CNT nanocomposites and the robust PLS method has driven the evolution of sophisticated electrochemical sensors, initiating a transformative era in drug analysis.

Hologram quantitative structure-activity relationship on the gas chromatographic retention index of plant essential oil constituents

The gas chromatography retention index (RI) is an important parameter for the identification of different types of compounds in the field of chromatographic analysis; however, the experimental collection of RI values is a extremely cumbersome process. Thus, there is an urgent need for the establishment of a simple, efficient, and accurate model for the prediction of the RI values of compounds. In this study, first, the experimental RI values for 60 plant essential oil constituents were obtained. Next, a model describing the hologram quantitative structure-activity relationship (HQSAR) between the structural properties of the essential oil constituents and their RI values was investigated and constructed. The optimal HQSAR model was established by setting the model parameters "fragment size", "fragment distinction", "hologram length" and "principal components" to "1-4", "C, Ch", "199", and "4", respectively. Finally, the predictive ability of the model was verified using external test set validation and leave-one-out cross-validation (LOO-CV). The experimental results were as follows, the root mean square error of prediction (RMSEP), predictive determination coefficient ([Formula: see text]), concordance correlation coefficient (CCC), and mean relative error (MRE) for external test set validation were 40.45, 0.984, 0.968, and 2.20%, respectively. Meanwhile, the root mean square error of cross validation (RMSECV) and MRE for LOO-CV were 72.56 and 4.17%, respectively. These findings demonstrate that the established HQSAR model has a good predictive ability and can accurately predict the RI values of plant essential oil constituents. In addition, the molecular contribution maps of the HQSAR model revealed that the RI values of aromatic compounds increase when hydroxyl groups are connected to their alkyl chains. Aliphatic compounds feature long chain alkyl groups, which can lead to an increase in RI values. The above phenomena highlight the promising application prospects of HQSAR for studying the RI values of plant essential oil constituents. Therefore, this study provides a reliable theoretical basis for predicting the RI values of other essential oil constituents.

Determining Moisture Content of Basil Using Handheld Near-Infrared Spectroscopy

Accurate and rapid determination of moisture content is essential in crop production and decision-making for irrigation. Near-infrared (NIR) spectroscopy has been shown to be a promising method for determining moisture content in various agricultural products, including herbs and vegetables. This study tested the hypothesis that NIR spectroscopy is effective in accurately measuring the moisture content of Genovese basil (Ocimum basilicum L.), with the objective of developing a respective calibration model. Spectral data were obtained from a total of 120 basil leaf samples over a period of six days. These included freshly harvested and detached leaves, as well as those left in ambient air for 1–6 days. Five spectra were taken from each leaf using a handheld NIR spectrophotometer, which covers the first and second overtones of the NIR spectral region: 950–1650 nm. After the spectral acquisition, the leaves were weighed for fresh mass and then put in an oven for 72 h at 80 °C to determine the dry weight and calculate the reference moisture content. The calibration model was developed using multivariate analysis in MATLAB, including preprocessing and regression modeling. The data obtained from 75% of the samples were used for model training and 25% for validation. The final model demonstrates strong performance metrics. The root mean square error of calibration (RMSEC) is 2.9908, the root mean square error of cross-validation (RMSECV) is 3.2368, and the root mean square error of prediction (RMSEP) reaches 2.4675. The coefficients of determination for calibration (R2C) and cross-validation (R2CV) are consistent, with values of 0.829 and 0.80, respectively. The model’s predictive ability is indicated by a coefficient of determination for prediction (R2P) of 0.86. The range error ratio (RER) stands at 11.045—highlighting its predictive performance. Our investigation, using handheld NIR spectrophotometry, confirms NIR’s usefulness in basil moisture determination. The rapid determination offers valuable insights for irrigation and crop management.

Rapid Quality Assessment of Polygoni Multiflori Radix Based on Near-Infrared Spectroscopy

The precise and prompt determination of quality control indicators such as moisture, stilbene glycosides, and anthraquinone glycosides is crucial in assessing the quality of Polygoni Multiflori Radix. Near-infrared spectroscopy is a nondestructive analytical technique that offers a more desirable approach than traditional methods for assessing content levels. In this study, various spectral preprocessing techniques were used to preprocess the raw spectral data. The spectral data were correlated with the determination of three-component contents using the partial least squares regression (PLSR) method. Then different algorithms, such as competitive adaptive weighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE), and random frog hopping (RF), were used for model simplification and feature selection. The data suggest that the first-order deconvolution derivative (1st Dev.) processing of the spectral data is superior to other methods in all three model evaluation metrics. The PLSR model for moisture, stilbene glycosides, and anthraquinone glycosides produced the calibration coefficient of determination (R2C) of 0.82, 0.52, and 0.58, the root mean square error of cross validation (RMSECV) of 0.91%, 0.77%, and 0.69%, the prediction coefficient of determination (R2P) of 0.72, 0.28, and 0.54, the root mean square error of prediction (RMSEP) of 0.65%, 0.81%, and 0.75%, and relative percentage differences (RPDs) of 1.7, 1.0, and 0.8. After optimizing the model using CARS, R2C increased by 0.15%, 0.41%, and 0.34%, RMSECV decreased by 0.53%, 0.32%, and 0.24%, R2P increased by 0.21%, 0.63%, and 0.35%, RMSEP decreased by 0.36%, 0.41%, and 0.31%, and RPD increased by 1.1, 0.9, and 0.6, significantly improving the predictive capacity of the model. This research provides a feasible method for rapid compliance testing of Polygoni Multiflori Radix. To further improve the model’s performance and applicability, it is necessary to continuously expand the sample set with different varieties and locations for wide variation.

Four chemometric models enhanced by Latin hypercube sampling design for quantification of anti-COVID drugs: sustainability profiling through multiple greenness, carbon footprint, blueness, and whiteness metrics

Montelukast sodium (MLK) and Levocetirizine dihydrochloride (LCZ) are widely prescribed medications with promising therapeutic potential against COVID-19. However, existing analytical methods for their quantification are unsustainable, relying on toxic solvents and expensive instrumentation. Herein, we pioneer a green, cost-effective chemometrics approach for MLK and LCZ analysis using UV spectroscopy and intelligent multivariate calibration. Following a multilevel multifactor experimental design, UV spectral data was acquired for 25 synthetic mixtures and modeled via classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), and genetic algorithm-PLS (GA-PLS) techniques. Latin hypercube sampling (LHS) strategically constructed an optimal validation set of 13 mixtures for unbiased predictive performance assessment. Following optimization of the models regarding latent variables (LVs) and wavelength region, the optimum root mean square error of cross-validation (RMSECV) was attained at 2 LVs for the 210–400 nm spectral range (191 data points). The GA-PLS model demonstrated superb accuracy, with recovery percentages (R%) from 98 to 102% for both analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.0943, 0.1872) and (0.1926, 0.1779) for MLK and LCZ, respectively, as well bias-corrected mean square error of prediction (BCMSEP) of -0.0029 and 0.0176, relative root mean square error of prediction (RRMSEP) reaching 0.7516 and 0.6585, and limits of detection (LOD) reaching 0.0813 and 0.2273 for MLK and LCZ respectively. Practical pharmaceutical sample analysis was successfully confirmed via standard additions. We further conducted pioneering multidimensional sustainability evaluations using state-of-the-art greenness, blueness, and whiteness tools. The method demonstrated favorable environmental metrics across all assessment tools. The obtained Green National Environmental Method Index (NEMI), and Complementary Green Analytical Procedure Index (ComplexGAPI) quadrants affirmed green analytical principles. Additionally, the method had a high Analytical Greenness Metric (AGREE) score (0.90) and a low carbon footprint (0.021), indicating environmental friendliness. We also applied blueness and whiteness assessments using the high Blue Applicability Grade Index (BAGI) and Red–Green–Blue 12 (RGB 12) algorithms. The high BAGI (90) and RGB 12 (90.8) scores confirmed the method's strong applicability, cost-effectiveness, and sustainability. This work puts forward an optimal, economically viable green chemistry paradigm for pharmaceutical quality control aligned with sustainable development goals.

Traceability of Microplastic Fragments from Waste Plastic Express Packages Using Near-Infrared Spectroscopy Combined with Chemometrics.

The pollution from waste plastic express packages (WPEPs), especially microplastic (MP) fragments, caused by the blowout development of the express delivery industry has attracted widespread attention. On account of the variety of additives, strong complexity, and high diversity of plastic express packages (PEPs), the multi-class classification of WPEPs is a typical large-class-number classification (LCNC). The traceability and identification of microplastic fragments from WPEPs is very challenging. An effective chemometric method for large-class-number classification would be very beneficial for the comprehensive treatment of WPEP pollution through the recycling and reuse of waste plastic express packages, including microplastic fragments and plastic debris. Rather than using the traditional one-against-one (OAO) and one-against-all (OAA) dichotomies, an exhaustive and parallel half-against-half (EPHAH) decomposition, which overcomes the defects of the OAO's classifier learning limitations and the OAA's data proportion imbalance, is proposed for feature selection. EPHAH analysis, combined with partial least squares discriminant analysis (PLS-DA) for large-class-number classification, was performed on 750 microplastic fragments of polyethylene WPEPs from 10 major courier companies using near-infrared (NIR) spectroscopy. After the removal of abnormal samples through robust principal component analysis (RPCA), the root mean square error of cross-validation (RMSECV) value for the model was reduced to 0.01, which was 21.5% lower than that including the abnormal samples. The best models of PLS-DA were obtained using SNV combined with SG-17 smoothing and 2D (SNV+SG-17+2D); the latent variables (LVs), the error rates of Monte Carlo cross-validation (ERMCCVs), and the final classification accuracies were 6.35, 0.155, and 88.67% for OAO-PLSDA; 5.37, 0.103, and 87.33% for OAA-PLSDA; and 3.12, 0.054, and 96.00% for EPHAH-PLSDA. The results showed that the EPHAH strategy can completely learn the complex LCNC decision boundaries for 10 classes, effectively break the tie problem, and greatly improve the voting resolution, thereby demonstrating significant superiority to both the OAO and OAA strategies in terms of classification accuracy. Meanwhile, PLS-DA further maximized the covariance and data interpretation abilities between the potential variables and categories of microplastic debris, thereby establishing an ideal performance identification model with a recognition rate of 96.00%.

Predicting Silicate Glass Geochemistry Using Raman Spectroscopy and Supervised Machine Learning: Partial Least Square Applications to Amorphous Raman Spectra.

Here, Raman spectroscopy is used to develop a univariate partial least squares (PLS) calibration capable of quantifying geochemistry in synthetic and natural silicate glass samples. The calibration yields eight oxide-specific models that allow predictions of silicon dioxide (SiO2), sodium oxide (Na2O), potassium oxide (K2O), calcium oxide (CaO), titanium dioxide (TiO2), aluminum oxide (Al2O3), ferrous oxide (FeOT), and magnesium oxide (MgO) (wt%) in glasses spanning a wide range of compositions, while also providing correlation-coefficient matrices that highlight the importance of specific Raman channels in the regression of a particular oxide. The PLS suite is trained on 48 of the 69 total glasses, and tested against 21 validation samples (i.e., held out of training). Trends in root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP) model accuracy metrics are investigated to uncover the efficacy of utilizing multivariate analysis for such Raman data and are contextualized against recently produced strategies. The technique yields an average root mean of calibration (∼2.4 wt%), cross-validation (∼ 2.9 wt%), prediction (∼ 2.6 wt%), and normalized variance (∼ 28%). Raman band positional shifts are also mapped against underlying chemical variations; with major influences arising primarily as a function of overall oxidation state and silica concentration: via ferric cation (Fe3+)/ferrous cation (Fe2+) ratios and SiO2 (wt%). The algorithm is further validated preliminarily against a separate external set of 11 natural basaltic glasses to unravel the limitations of the synthetic models on natural samples, and to determine the suitability of "universal" Raman-model applications in scenarios where prior chemical contextualization of the target sample is possible. This study represents the first time Raman spectra of amorphous silicates have been paired with PLS, offering a foundation for future improvements utilizing these systems.

Prediction of antioxidant activities of Sidaguri (Sida rhombifolia) with different harvesting times using FTIR spectra and chemometrics

Sidaguri (Sida rhombifolia) is one of the compound plants widely used as herbal components and is reported to contain some bioactive responsible for biological activities including antioxidants. This study aimed to evaluate the antioxidant activities of Sidaguri from different harvesting times and to correlate FTIR spectroscopy in combination with chemometrics for the prediction of antioxidant activities. Sidaguri was planted and cultivated for the third, fourth, and fifth months and then evaluated for its antioxidant activities, phenolic contents, and flavonoid contents. Sidaguri samples were also subjected to FTIR spectral measurement to be correlated with scavenging activity toward 2,2′- diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azinobis-3-ethylbenzothiazoline-6-sulfonic acid. The results showed that the longer harvesting time tends to lower the antioxidant activities which can be associated with the lower bioactive components responsible for antioxidant activities. Partial least square applied to 1st derivative FTIR spectra using absorbances at wavenumbers of 3700-650 cm-1 provide the best modelling for correlation between actual values of antioxidant and predicted values with R2 -calibration and R2 - validation of 0.9995 and 0.9998, with the root mean square error of calibration (RMSEC), the root mean square error of calibration (RMSEP) values of 0.0674 and 0.0827, PRESS and the root-mean-square error of cross-validation (RMSECV) values 0.226 and 0.194, respectively. The chemometrics of principal component analysis (PCA) could separate Sidaguri samples harvested during different months. FTIR spectra in combination with chemometrics could be an alternative method for the prediction of antioxidant activities of Sidaguri with acceptable accuracy and precision.

A disposable optofluidic micro-transmission cell with tailorable length for Fourier-transform infrared spectroscopy of biological fluids.

Mid-infrared Fourier-transform infrared (FT-IR) spectroscopy of liquid biological samples is limited by the high absorption of water in this spectral range, which makes conventional transmission cuvettes unsuitable as their centimeter-scale length is already too big. The most common alternative relies on the use of attenuated total reflection (ATR) accessories, which provide a small interaction path length for light along the interface between the analyte and the expensive ATR crystals. In this work, we address this issue by proposing a disposable and low-cost micro-transmission cell. Its construction relies on a simple technique, which consists of dispersing plastic spherical microparticles in a liquid sample before dispensing it between two pieces of silicon assembled one onto the other and acting as windows for the cell. Consequently, the microparticles act as a spacer of very precise height in-between the two silicon windows. This technique allows easy construction of infrared absorption cells with near-optimum optical interaction path length just by selecting the most appropriate particle size. The concept is demonstrated by measuring the concentration of glucose in aqueous solutions using microspheres of diameter 20 μm then 40 μm and analyzing the corresponding glucose absorption peaks in the wavenumber range 950-1200 cm-1. The performance is compared to that of standard ATR spectroscopy of the same samples. This resulted in a root-mean-square error of cross-validation (RMSECV) of 58.8 mg dl-1 as obtained for transmission measurements by partial least squares (PLS) regression, which is comparable to the RMSECV of 53 mg dl-1 for single-reflection diamond ATR measurements.

Application of near-infrared spectroscopy and chemometrics for the rapid detection of insect protein adulteration from a simulated matrix

The popularity of insect protein as a food and feed supplement is growing. Protein quality, end use and prices vary considerably between different insect species, which may incentivise insect protein adulteration. Here, near-infrared (NIR) spectroscopy and chemometrics were used to detect the presence of cricket, black soldier fly larvae (BSFL) and mealworm proteins in a simulated complex insect protein mixture. Additionally, BSFL protein powders collected from three commercial sources were investigated to determine whether the NIR-based technology can discriminate the proteins obtained from different companies based on their composition. The proximate analysis suggests compositional protein, fat and chitin differences between insect species. A partial least square (PLS) regression model obtained Q2 values ranging from 0.991 to 0.997 for the predictions of the content of protein mixtures containing BSFL, cricket and mealworm powders mixed at various proportions. The root mean square error of cross-validation (RMSCV) values range from 1.8% to 2.9%, and residual prediction deviation (RPD) values from 10.4 to 17.1 for the adulterated insect protein powders. The accuracy of the prediction model (∼2%) for the adulterated percentages varied depending on the insect species. The NIR spectra could differentiate (Q2 = 0.999) the origin of BSFL protein powders from three different companies and two types of processing (whole meal and defatted samples). Overall, this study established a rapid and low-cost insect protein adulteration monitoring pipeline for the three common insect protein powders. We envisage that NIR can be applied to assess insect adulteration, authentication, and quality control in the emerging insect food and feed industries.

Differentiation and prediction of the in vitro antifungal activity of Brazilian propolis against Fonsecaea pedrosoi using chemometrics and diffuse reflectance infrared Fourier Transform spectroscopy

The in vitro antifungal activity of ethanolic extracts of propolis was evaluated at five different concentrations in twelve Fonsecaea pedrosoi strains using agar dilution. Eight propolis extracts were collected from towns located in the central region of the state of Rio Grande do Sul, Brazil. Diffuse reflectance infrared Fourier Transform spectroscopy (DRIFTS) was employed to analyze the spectra of lyophilized propolis extracts. The use of chemometric tools allowed for the development of models with more relevant factors and with fewer variables. Partial least squares (PLS) regression was applied, allowing for the development of PLS-DRIFTS models to predict antifungal activity of all fungal strains with root mean square error of cross validation (RMSECV) lower to 5.00 mm2 and coefficient correlation of cross-validation (R2) upper to 0.99 with five to eight latent variables at 4000-2400 and 2200-600 cm-1. Concluding, DRIFTS models associated with chemometrics are powerful screening tools of propolis samples with fungistatic properties against Fonsecaea pedrosoi.