Squared Error Of Prediction Research Articles

Predicting methane emissions of individual grazing dairy cows from spectral analyses of their milk samples

Data on the enteric methane emissions of individual cows are useful not just in assisting management decisions and calculating herd inventories but also as inputs for animal genetic evaluations. Data generation for many animal characteristics, including enteric methane emissions, can be expensive and time consuming, so being able to extract as much information as possible from available samples or data sources is worthy of investigation. The objective of the present study was to attempt to predict individual cow methane emissions from the information contained within milk samples, specifically the spectrum of light transmittance across different wavelengths of the mid-infrared (MIR) region of the electromagnetic spectrum. A total of 93,888 individual spot measures of methane (i.e., individual samples of an animal's breath when using the GreenFeed technology) from 384 lactations on 277 grazing dairy cows were collapsed into weekly averages expressed as grams per day; each weekly average coincided with a MIR spectral analysis of a morning or evening individual cow milk sample. Associations between the spectra and enteric methane measures were performed separately using partial least squares regression or neural networks with different tuning parameters evaluated. Several alternative definitions of the enteric methane phenotype (i.e., average enteric methane in the 6 d preceding or 6 d following taking the milk sample or the average of the 6 d before and after the milk sample, all of which also included the enteric methane emitted on the day of milk sampling), the candidate model features (e.g., milk yield, milk composition, and milk MIR) as well as validation strategy (i.e., cross-validation or leave-one-experimental treatment-out) were evaluated. Irrespective of the validation method, the prediction accuracy was best when the average of the milk MIR from the morning and evening milk sample was used and the prediction model was developed using neural networks; concurrently including milk yield and days in milk in the prediction model generated superior predictions relative to just the spectral information alone. Furthermore, prediction accuracy was best when the enteric methane phenotype was the average of at least 20 methane spot measures across a 6-d period flanking each side of the milk sample with associated spectral data. Based on the strategy that achieved the best accuracy of prediction, the correlation between the actual and predicted daily methane emissions when based on 4-fold cross-validation varied per validation stratum from 0.68 to 0.75; the corresponding range when validated on each of the 8 different experimental treatments focusing on alternative pasture grazing systems represented in the dataset varied from 0.55 to 0.71. The root mean square error of prediction across the 4-folds of cross-validation was 37.46 g/d, whereas the root mean square error averaged across all folds of leave-one-treatment-out was 37.50 g/d. Results suggest that even with the likely measurement errors contained within the MIR spectrum and gold standard enteric methane phenotype, enteric methane can be reasonably well predicted from the infrared spectrum of milk samples. What is yet to be established, however, is whether (a) genetic variation exists in this predicted enteric methane phenotype and (b) selection on estimates of genetic merit for this phenotype translate to actual phenotypic differences in enteric methane emissions.

Authentication analysis of milkfish oil treated with activated charcoal and bentonite using Fourier Transform Infrared Spectroscopy and Multivariate Data Analysis

Adulteration issues are important not only for fats and oils consumers but also for producers and regulatory bodies, therefore authentication analytical methods are continuously developed. The objective of this study was to highlight the employment of attenuated total Fourier transform infrared (FTIR) spectroscopy and chemometrics for the authentication of milkfish oil (MFO) from pumpkin seed oil (PSO). In this study, ATRFTIR spectra of MFO, PSO and its binary mixtures were scanned, and the information obtained was used for quantitative analysis of adulterants and classification between authentic MFO and MFO adulterated with PSO. Two multivariate calibrations of partial least square calibration (PLSR) and principal component regression (PCR) were compared for their performance during quantitative analysis and linear discriminant analysis was applied for discrimination between authentic MFO and MFO adulterated with PSO. The results revealed that PLSR using 2nd derivative spectra at wavenumbers of 1500-1000 cm-1 was preferred for simultaneous quantitative analysis of MFO-treated with activated charcoal, while PCR using 2nd derivative spectra at wavenumbers of 1500-1000 cm-1 was suitable for analysis of MFO-treated with bentonite providing the acceptable values of R2 , root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). Linear discriminant Analysis using normal FTIR spectra at the whole wavenumber region was successful for the discrimination of MFO treated with activated charcoal and bentonite with accuracy levels of 100%. It can be concluded that the chemometrics approach for ATR-FTIR spectra could be a simple and effective tool for the authentication of MFO.

Raman spectroscopy applied to online monitoring of a bioreactor: Tackling the limit of detection

An in-situ monitoring model of alcoholic fermentation based on Raman spectroscopy was developed in this study. The optimized acquisition parameters were an 80 s exposure time with three accumulations. Standard solutions were prepared and used to populate a learning database. Two groups of mixed solutions were prepared for a validation database to simulate fermentation at different conditions. First, all spectra of the standards were evaluated by principal component analysis (PCA) to identify the spectral features of the target substances and observe their distribution and outliers. Second, three multivariate calibration models for prediction were developed using the partial least squares (PLS) method, either on the whole learning database or subsets. The limit of detection (LOD) of each model was estimated by using the root mean square error of cross validation (RMSECV), and the prediction ability was further tested with both validation datasets. As a result, improved LODs were obtained: 0.42 and 1.55 g·L−1 for ethanol and glucose using a sub-learning dataset with a concentration range of 0.5 to 10 g·L−1. An interesting prediction result was obtained from a cross-mixed validation set, which had a root mean square error of prediction (RMSEP) for ethanol and glucose of only 3.21 and 1.69, even with large differences in mixture concentrations. This result not only indicates that a model based on standard solutions can predict the concentration of a mixed solution in a complex matrix but also offers good prospects for applying the model in real bioreactors.

Determination of water-soluble carbohydrates by near-infrared spectroscopy for canola, maize, and sorghum stem fractions

Near infrared spectroscopy (NIRS) was evaluated as a rapid and non-destructive method for determining the concentration of water-soluble carbohydrates (WSC) in stem fractions for winter canola (Brassica napus L.), maize (Zea mays L.) and sorghum (Sorghum bicolor L. Moench) crops. For each crop at different growth stages, stem WSC concentration was determined using NIRS, and benchmarked against the anthrone reagent method, chemical lab analysis. Partial least squares regression was implemented to associate the WSC predicted via NIRS relative to those obtained by laboratory analysis. Spectral regions between 1100 and 1480 nm were critical for WSC determination. The predictive models resulted in coefficient of determinations of 0.93, 0.94, and 0.95, and a Root Mean Square Error of prediction of 10, 20, and 17 for winter-canola, maize and sorghum crops, respectively. The NIRS spectroscopy is a reliable method for WSC determination in stem tissues on these major field crops.

Detection of Toxic Contaminants in Alcohol-Based Hand Sanitizers Using Infrared Spectroscopy

Recent fatalities due to methanol and 1-propanol toxicity in alcohol-based hand sanitizers has led the U.S. Food and Drug Administration (FDA) to ban 594 different hand sanitizer brands. The FDA also introduced methanol testing in alcohol-based hand sanitizers before allowing the products to enter the United States This requirement creates a need for inexpensive, rapid, and portable testing methods to measure methanol and 1-propanol concentrations in alcohol-based hand sanitizers. Here we study the performance of infrared (IR) spectroscopy for measuring methanol and 1-propanol concentrations in an alcohol-based hand sanitizer, and compare the performance of two portable spectrometers, Texas Instruments near-infrared (NIR) spectrometer (TI NIR) and NeoSpectra mid-infrared (MIR) spectrometer. The IR absorbance spectra were measured in transmission mode at different path lengths for 52 different hand sanitizer samples spiked with 0%–1% v/v concentrations of methanol and 1-propanol. A partial least-squares regression analysis shows ability to detect contaminant concentrations with a correlation coefficient of determination ( r2) up to 0.99 and root mean square error of prediction as low as 0.34% v/v.

The splenic evanescent trauma identification and the injured time estimation in decomposing cadavers based on ATR-FTIR and chemometrics

Trauma is one of the most prevalent disorders in the biomedical field. Abdominal trauma is widely encountered in accidents and somecriminal situations, and it has a significant influence on the investigation or conviction of violent crimes. With about 33% of cases involving blunt abdominal trauma, the spleen is the organ that suffers the most and is associated with a significant risk of morbidity and mortality, particularly in patients who have accumulated multiple or compound injuries. “Evanescent trauma” describes an injury that becomes invisible as time passes, decomposition occurs, and other factors involved.In this study, we have found methods to identify splenic evanescent trauma and estimate the injured time in postmortem cadavers by FTIR combination with chemometrics. The PLSDA model to identify splenic evanescent trauma in decomposing cadavers' specificity of externally validation was 0.917, sensitivity was 0.958, and accuracy was 0.950. The PLSDA model to identify DSR and SR in decomposing cadavers' specificity was 0.833, sensitivity was 0.889, and accuracy was 0.875.To eliminate the impact cause by the cadaver's autolysis and putrefaction, and the redundancy of full-spectral data modeling, we performed variables selection with the methods of CARS, GA, SPA. The performance of CARS-PLS was superior to GA-PLS. The SPA-CARS-PLS regression model for the injured time estimation in decomposing cadavers at PMI 2d and 10d had the better performance and better interpretability of spectral. The SPA-CARS-PLS regression model to estimate the injured time at PMI 2d was constructed with 6 latent variables, the R2CV was 0.911 and the RMSECV was 1.477 d. For external validation, the R2Pred value was 0.884. The root mean square error of prediction(RMSEP) was 2.404 d. The SPA-CARS-PLS regression model to estimate the injured time at PMI 10d was built with 6 latent variables, the R2CV was 0.858 and the RMSECV was 1.868 d. For external validation, the prediction model's R2Pred value was 0.930, the RMSEP was 1.487 d. FTIR combine with chemometrics is useful in identifying the splenic evanescent trauma and estimating the injured time in postmortem decomposed cadavers.

The effect of reinsurance treaties on the cedent loss reserving

PurposeThe usual, simple and computationally expensive recovery payment method for a given reinsurance treaty, besides the total run-off triangle, builds a new run-off triangle, say recovery run-off triangle, for the reinsurer’s contribution and predicts the reinsurer’s contribution to the total loss reserves. This paper, without building a recovery run-off triangle, uses the available prior knowledge about a reinsurance treaty to predict the cedent’s loss reserve under five reinsurance treaties.Design/methodology/approachThe authors propose a new solution to the problem of how to consider reserving issues when there is a reinsurance treaty for a portfolio of general insurance policies. Considering this when determining pricing or making capital decisions is very important.FindingsIn particular, it considers the quota share (QS) treaty, surplus (SPL) treaty, excess-of-loss (XL) treaty, largest claims reinsurance (LCR) treaty and excédent du coût moyen relatif (ECOMOR) treaty. Then, it develops a theoretical foundation for predicting the cedent’s loss reserve and evaluating such prediction using the mean square error of prediction (MSEP). The impact of such reinsurance treaties on the variability of the cedent’s loss reserve has been investigated through a simulation study.Originality/valueThis paper, without building a recovery run-off triangle, uses the available prior knowledge about a reinsurance treaty to predict the cedent’s loss reserve under five reinsurance treaties. In particular, it considers the QS treaty, SPL treaty, XL treaty, LCR treaty and ECOMOR treaty. Then, it develops a theoretical foundation for predicting the cedent’s loss reserve and evaluating such prediction using the MSEP. The impact of such reinsurance treaties on the variability of the cedent’s loss reserve has been investigated through a simulation study.

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.

Assessment of bioactive compounds in faba bean using infrared spectroscopy

AbstractFaba bean (Vicia faba) is growing in popularity in Australia, partly due to its higher levels of health‐benefiting compounds compared to other grain crops. This study investigated infrared spectroscopy for predicting levels of bioactive compounds such as antioxidants and phenolics in faba bean flour. Calibration models were performed on 60 samples of faba bean, comprising 10 varieties grown across two field locations in 1 year. For model validation, an independent test set comprising the same varieties grown in a different year was utilised. Near‐infrared spectroscopy (NIRS) showed promise for the prediction of total phenolic content, with an R2pred of 0.66 and root mean square error of prediction (RMSEP) of 76 mg/100 g. Similarly, prediction of ferric reducing antioxidant power, a measure of antioxidant activity, gave an R2pred of 0.59 and RMSEP of 87 mg/100 g. Additionally, moving window optimisation was used to determine the most important wavelength region for the prediction of these analytes. Fourier transform infrared spectroscopy did not yield any suitable models for the analytes investigated. Although the NIRS models developed were not capable of exactly quantifying phenolic or antioxidant content, infrared spectroscopy appears useful for rapidly discriminating between samples containing high and low levels of phenolics or antioxidant compounds. With further refinement, this technique could potentially be applied for the quality assurance of phenolic content or antioxidant capacity in faba bean seeds.

Statistical comparison of predictive models for quantitative analysis and classification in the framework of LIBS spectroscopy: A tutorial

Laser-Induced Breakdown Spectroscopy (LIBS) is a widely accepted technique used for both classification and quantification purposes considering complex and heterogeous samples. Based on a set of training spectra acquired from diverse and representative samples within a specific application domain, it becomes possible to apply various data processing techniques and modeling methods to construct the predictive model in question. Naturally the complexity of both the laser-matter and the laser-plasma interactions and the heterogeneity of natural samples often requires the development of various predictive models, which are then compared based on figures of merit such as the RMSEP (Root Mean Square Error of Prediction) value for quantification or the classification rate for qualitative analysis. Our ultimate goal is, of course, to select the model that appears to be the most accurate, which ultimately boils down to searching for the lowest RMSEP value or the highest classification rate. This is precisely where the whole problem lies because even if we observe a different level of error for two models, for example, this difference is not necessarily statistically significant. In such a case, we are therefore not allowed to say that the lower error indicates the best predictive model to consider. The purpose of this article is to provide a tutorial on introducing a statistical model comparison procedure, whether they are quantitative or qualitative. Two LIBS data sets have been used to illustrate the principles of the proposed method.

Comparison of Si-GA-PLS and Si-CARS-PLS build algorithms for quantitation of total polyphenols in black tea using the spectral analytical system.

The objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si-PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables. The collected spectral data were divided into 19 sub-interval selections totaling 246 variables, yielding the lowest root mean square error of cross-validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (RC ), prediction (RP ), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si-GA-PLS model produced the following results: PCs = 9; RC = 0.915; RMSECV = 1.39; RP = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si-CARS-PLS model was noted to be best at PCs = 10, while RC = 0.9723, RMSECV = 0.81, RP = 0.9114, RMSEP = 1.45 and RPD = 2.59. The build model's prediction ability was amended in the order PLS < Si-PLS < CARS-PLS when full spectroscopic data were used and Si-PLS < Si-GA-PLS < Si-CARS-PLS when interval selection was performed with the Si-PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry.

Quantitative analysis of blood glucose by FT‐Raman spectroscopy and multivariate statistical analysis

AbstractDiabetes is one of the common chronic diseases in worldwide and increasing among young children. Testing glycated hemoglobin levels as well as blood glucose levels is a method for detecting diabetes. In this article, with multivariate statistical analysis was used to monitor the blood glucose levels by the Fourier Transform Raman spectroscopy. Principal component regression with direct orthogonal signal correction is developed and applied to analyze the glucose concentration with the blood Raman spectroscopy. The root means square error of calibration is 3.0824 mg/dL (0.1712 mmol/L), the root means square error of prediction is 3.2688 mg/dL (0.1816 mmol/L), the correlation coefficients (R2) is .9946 and ratio of performance to deviation is 6.4789 and, respectively. The results show that this method can be used as a strong potential diagnostic tool for diabetes mellitus.

Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics.

In the process of storage and cold chain logistics, apples are prone to physical bumps or microbial infection, which easily leads to spoilage in the micro-environment, resulting in widespread infection and serious post-harvest economic losses. Thus, development of methods for monitoring apple spoilage and providing early warning of spoilage has become the focus for post-harvest loss reduction. Thus, in this study, a spoilage monitoring and early warning system was developed by measuring volatile component production during apple spoilage combined with chemometric analysis. An apple spoilage monitoring prototype was designed to include a gas monitoring array capable of measuring volatile organic compounds, such as CO2, O2 and C2H4, integrated with the temperature and humidity sensor. The sensor information from a simulated apple warehouse was obtained by the prototype, and a multi-factor fusion early warning model of apple spoilage was established based on various modeling methods. Simulated annealing-partial least squares (SA-PLS) was the optimal model with the correlation coefficient of prediction set (Rp) and root mean square error of prediction (RMSEP) of 0.936 and 0.828, respectively. The real-time evaluation of the spoilage was successfully obtained by loading an optimal monitoring and warning model into the microcontroller. An apple remote monitoring and early warning platform was built to visualize the apple warehouse's sensors data and spoilage level. The results demonstrated that the prototype based on characteristic gas sensor array could effectively monitor and warn apple spoilage.

Raman spectroscopy combined with multiple one-dimensional deep learning models for simultaneous quantification of multiple components in blended olive oil

Blended vegetable oils are highly prized by consumers for their comprehensive nutritional profile. Therefore, there is an urgent need for a rapid and accurate method to identify the true content of blended oils. This study combined Raman spectroscopy with three deep learning models (CNN-LSTM, improved AlexNet, and ResNet) to simultaneously quantify extra virgin olive oil (EVOO), soybean oil, and sunflower oil in olive blended oil. The results demonstrate that all three deep learning models exhibited superior predictive ability compared to traditional chemometric methods. Specifically, the CNN-LSTM model achieved a coefficient of determination (R2p) of over 0.995 for each oil in the quantitative analysis of three-component blended oils, with a mean square error of prediction (RMSEP) of less than 2%. This study presents a novel approach for the simultaneous quantitative analysis of multi-component blended oils, providing a rapid and accurate method for the identification of falsely labeled blended oils.

Nondestructive Detecting Maturity of Pineapples Based on Visible and Near-Infrared Transmittance Spectroscopy Coupled with Machine Learning Methodologies

Pineapple is mainly grown in tropical regions and consumed fresh worldwide due to its attractive flavor and health benefits. With increasing global production and trade volume, there is an urgent need for nondestructive techniques for accurate and efficient detection of the internal quality of pineapples. Therefore, this study is dedicated to developing a nondestructive method for real-time determining the internal quality of pineapples by using VIS/NIR transmittance spectroscopy technique and machine learning methodologies. The VIS/NIR transmittance spectrums ranging in 400–1100 nm of total 195 pineapples were collected from a dynamic experimental platform. The maturity grade and soluble solids content (SSC) of individual pineapples were then measured as indicators of internal quality. The qualitative model for discriminating maturity grades of pineapple achieved a high accuracy of 90.8% by the PLSDA model for unknown samples. Meanwhile, the quantitative model for determining SSC also reached a determination coefficient (RP2) of 0.7596 and a root mean square error of prediction (RMSEP) of 0.7879 °Brix by the ANN-PLS model. Overall, high model performance demonstrated that using VIS/NIR transmittance spectroscopy technique coupled with machine learning methodologies could be a feasible method for nondestructive and real-time detection of the internal quality of pineapples.

Enhancing Wind Turbine Performance: Statistical Detection of Sensor Faults Based on Improved Dynamic Independent Component Analysis

Efficient detection of sensor faults in wind turbines is essential to ensure the reliable operation and performance of these renewable energy systems. This paper presents a novel semi-supervised data-based monitoring technique for fault detection in wind turbines using SCADA (supervisory control and data acquisition) data. Unlike supervised methods, the proposed approach does not require labeled data, making it cost-effective and practical for wind turbine monitoring. The technique builds upon the Independent Component Analysis (ICA) approach, effectively capturing non-Gaussian features. Specifically, a dynamic ICA (DICA) model is employed to account for the temporal dynamics and dependencies in the observed signals affected by sensor faults. The fault detection process integrates fault indicators based on I2d, I2e, and squared prediction error (SPE), enabling the identification of different types of sensor faults. The fault indicators are combined with a Double Exponential Weighted Moving Average (DEWMA) chart, known for its superior performance in detecting faults with small magnitudes. Additionally, the approach incorporates kernel density estimation to establish nonparametric thresholds, increasing flexibility and adaptability to different data types. This study considers various types of sensor faults, including bias sensor faults, precision degradation faults, and freezing sensor faults, for evaluation. The results demonstrate that the proposed approach outperforms PCA and traditional ICA-based methods. It achieves a high detection rate, accurately identifying faults while reducing false alarms. It could be a promising technique for proactive maintenance, optimizing the performance and reliability of wind turbine systems.

Quantification of the adulteration concentration of palm kernel oil in virgin coconut oil using near-infrared hyperspectral imaging

The adulteration concentration of palm kernel oil (PKO) in virgin coconut oil (VCO) was quantified using near-infrared (NIR) hyperspectral imaging. Nowadays, some VCO is adulterated with lower-priced PKO to reduce production costs, which diminishes the quality of the VCO. This study used NIR hyperspectral imaging in the wavelength region 900–1,650 nm to create a quantitative model for the detection of PKO contaminants (0–100%) in VCO and to develop predictive mapping. The prediction equation for the adulteration of VCO with PKO was constructed using the partial least squares regression method. The best predictive model was pre-processed using the standard normal variate method, and the coefficient of determination of prediction was 0.991, the root mean square error of prediction was 2.93%, and the residual prediction deviation was 10.37. The results showed that this model could be applied for quantifying the adulteration concentration of PKO in VCO. The prediction adulteration concentration mapping of VCO with PKO was created from a calibration model that showed the color level according to the adulteration concentration in the range of 0–100%. NIR hyperspectral imaging could be clearly used to quantify the adulteration of VCO with a color level map that provides a quick, accurate, and non-destructive detection method.

Evaluation of convolutional neural network for non-destructive detection of imidacloprid and acetamiprid residues in chili pepper (Capsicum frutescens L.) based on visible near-infrared spectroscopy

Consumption of agricultural products with pesticide residue is risky and can negatively affect health. This study proposed a nondestructive method of detecting pesticide residues in chili pepper based on the combination of visible and near-infrared (VIS/NIR) spectroscopy (400–2498 nm) and deep learning modeling. The obtained spectra of chili peppers with two types of pesticide residues (acetamiprid and imidacloprid) were analyzed using a one-dimensional convolutional neural network (1D-CNN). Compared with the commonly used partial least squares regression model, the 1D-CNN approach yielded higher prediction accuracy, with a root mean square error of calibration of 0.23 and 0.28 mg/kg and a root mean square error of prediction of 0.55 and 0.49 mg/kg for the acetamiprid and imidacloprid data sets, respectively. Overall, the results indicate that the combination of the 1D-CNN model and VIS/NIR spectroscopy is a promising nondestructive method of identifying pesticide residues in chili pepper.

Quantitative analysis of aflatoxin B1 of peanut by optimized support vector machine models based on near-infrared spectral features

This study designs a chemometric framework for quantitatively evaluating aflatoxin B1 (AFB1) in peanuts based on near-infrared (NIR) spectroscopy technique. The NIR spectra of peanut samples exhibiting diverse fungal contamination levels were acquired using a portable NIR spectrometer. Subsequently, appropriate pre-processing techniques were employed for data refinement. To streamline the analysis, the iterative variable subset optimization (IVSO) technique was employed to conduct an initial screening of the pre-processed NIR spectra, eliminating numerous irrelevant variables. Building upon this screening process, the beluga whale optimization (BWO) algorithm was utilized to optimize the selected feature variables further. Subsequently, support vector machine (SVM) models were developed using the refined near-infrared spectral features to test AFB1 in peanuts quantitatively. The results indicate that the SVM model significantly improves detection performance and generalization proficiency, particularly after secondary optimization using BWO-IVSO. Among the different models considered, the SVM model established after BWO-IVSO optimization exhibited the most extraordinary level of generalization, with a root mean square error of prediction of 24.6322 μg∙kg−1, a correlation coefficient of 0.9761, and a relative percent deviation of 4.6999. Overall, this investigation highlights the effectiveness of the proposed NIR spectroscopy model based on BWO-IVSO-SVM for quantitatively analyzing AFB1 in peanuts. The study contributes valuable technical and methodological insights that can serve as a reference for rapidly determining mycotoxins in cereal crops.

Liquid-liquid microextraction method using a deep eutectic solvent for the determination of total paraben content by fluorescence spectroscopy and second-order calibration

Concerns regarding the potential harm associated with endocrine dysfunction are increasing, particularly in children, due to the immaturity of their metabolic system and skin barrier. In this work, parabens, which are esters widely used as preservatives in cosmetics, were quantified in commercial samples of diaper rash cream. For this purpose, a green and efficient liquid–liquid microextraction (LLME) method was accomplished using a deep eutectic solvent (DES). To enhance recovery rates, the extraction parameters were optimized using a central composite design (CCD) on blank matrix samples spiked. The quantification was based on excitation-emission fluorescence spectroscopy, combined with parallel factor analysis (PARAFAC) decomposition, which proved be an advantageous tool to solve the overlap of uncalibrated constituents in the complex matrix. The PARAFAC modeling strategy provided satisfactory results within a linear range response of 100–1300 ng g−1, with LOD equal to 23 ng g−1. A validation set, with various proportions of methyl, ethyl, propyl, and butyl paraben standards, was used to validate the calibration model, resulting in root mean square error (RMSE) and relative error of prediction (REP) values of 32 ng g−1 and 4.5%, respectively. Moreover, acceptable root mean square error of prediction (RMSEP) and REP values (105 ng g−1 and 15%, respectively) were obtained, when a set of blank matrix samples spiked was applied. The proposed method was successfully applied to quantify total paraben content in commercial samples of diaper rash cream, yielding recoveries ranging from 83% to 125%. The method proved to be sensitive, accurate, and green, as it significantly reduced the consumption of organic solvents. Furthermore, it could serve as an alternative for the determination of preservatives in complex cosmetic samples.