Mean Squared Prediction Error Research Articles

Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms

Apples are usually bagged during the growing process, which can effectively improve the quality. Establishing an in situ nondestructive testing model for in-tree apples is very important for fruit companies in selecting raw apple materials for valuation. Low-maturity apples and high-maturity apples were acquired separately by a handheld tester for the internal quality assessment of apples developed by our group, and the effects of the two maturity levels on the soluble solids content (SSC) detection of apples were compared. Four feature selection algorithms, like ant colony optimization (ACO), were used to reduce the spectral complexity and improve the apple SSC detection accuracy. The comparison showed that the diffuse reflectance spectra of high-maturity apples better reflected the internal SSC information of the apples. The diffuse reflectance spectra of the high-maturity apples combined with the ACO algorithm achieved the best results for SSC prediction, with a prediction correlation coefficient (Rp) of 0.88, a root mean square error of prediction (RMSEP) of 0.5678 °Brix, and a residual prediction deviation (RPD) value of 2.466. Additionally, the fruit maturity was predicted using PLS-LDA based on color data, achieveing accuracies of 99.03% and 99.35% for low- and high-maturity fruits, respectively. These results suggest that in-tree apple in situ detection has great potential to enable improved robustness and accuracy in modeling apple quality.

Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting.

Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain. The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry. Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints. The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.

Determination of butylated hydroxytoluene in edible oil using near-infrared spectroscopy combined with multivariate analysis

Edible oil is a fundamental component of human daily diet and has a wide impact on global agriculture and economy. The issue of additives in edible oil has always been a concern for its quality and safety, as excessive use of additives can be harmful to human health. This study suggests a quantitative detection technique for determining the concentration of butylated hydroxytoluene (BHT) in edible oil using near-infrared spectroscopy (NIRS) technology. Firstly, the spectral characteristics of edible oil samples with different concentrations of BHT are characterized using a near-infrared spectrometer. Then, particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and fox optimizer (FOX) are employed to optimize the preprocessed spectral wavelengths. Finally, a detection model based on the optimized wavelengths is established using backpropagation neural network (BP-ANN) to achieve quantitative detection of BHT content in edible oil. The results show that the model based on the feature wavelengths optimized by the FOX algorithm has the best detection performance. This model takes 7 features as inputs, with a root mean square error of prediction (RMSEP) of 1.6116 g·kg−1 and a prediction determination coefficient (RP) of 0.9900 on the prediction set. The results demonstrate that near-infrared spectroscopy technology can accurately predict the BHT content in edible oil.

Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments

The Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observations from a real physical/field experiment and correct for any modeling bias (second GP) when predicting under new field conditions (design inputs). There are well-established methods for placement of design inputs for data-efficient planning of a simulation campaign in isolation, that is, without field data: space-filling, or via criterion like minimum integrated mean-squared prediction error (IMSPE). Analogues within the coupled GP KOH framework are mostly absent from the literature. Here we derive a closed form IMSPE criterion for sequentially acquiring new simulator data for KOH. We illustrate how acquisitions space-fill in design space, but concentrate in calibration space. Closed form IMSPE precipitates a closed-form gradient for efficient numerical optimization. We demonstrate that our KOH-IMSPE strategy leads to a more efficient simulation campaign on benchmark problems, and conclude with a showcase on an application to equilibrium concentrations of rare earth elements for a liquid–liquid extraction reaction.

A Critique and Examination of the Polysocial Risk Score Approach: Predicting Cognition in the Health and Retirement Study.

Polysocial risk scores were recently proposed as a strategy to improve clinical relevance of knowledge about social determinants of health. The objective of this paper was to assess if the polysocial risk score model improves prediction of cognition and all-cause mortality in middle-aged and older adults beyond simpler models including a smaller set of key social determinants of health. We used a sample of 13,773 individuals aged 50+ at baseline from the 2006 to 2018 waves of the Health and Retirement Study, a US population-based longitudinal cohort. Four linear mixed models were compared: two simple models including a priori selected covariates and two polysocial risk score models which used LASSO regularization to select covariates among 9 or 21 candidate social predictors. All models included age. Predictive accuracy was assessed via R-squared and root mean-squared prediction error (RMSPE) using training/test split and cross-validation. For predicting cognition, the simple model including age, race, gender, and education had an R-squared of 0.31 and an RMSPE of 0.880. Compared with this, the most complex polysocial risk score selected 12 predictors (R-squared=0.35 and RMSPE=0.858; 2.2% improvement). For all-cause mortality, the simple model including age, race, gender, and education had an AUROC of 0.747, while the most complex polysocial risk score did not demonstrate improved performance (AUROC = 0.745). Models built on a smaller set of key social determinants performed comparably to models built on a more complex set of social "risk factors".

Мultivariate analysis for rapid screening and prediction of solid-state compatibility in pharmaceutical preformulation studies-paving the road for machine learning

Multivariate analysis models were developed to evaluate the results obtained from a compatibility study designed for ibuprofen with a large group of different types of excipients, as a possible approach for rapid screening of the incompatibility between the active pharmaceutical ingredient (API) and excipients. The solid-state characterization of the binary mixtures was performed by Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC). Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) using SIMCA® software were applied for evaluation of the experimentally obtained results. The optimal PCA model for the FTIR spectra explains 96.2 % of the variations in the dataset with good statistical indicators (R2X = 0.960, Q2 = 0.900), which was also the case for the PCA model for the DSC curves (R2X = 0.981, Q2 = 0.866). The applied PLS-DA models have shown similar behaviour to the PCA. Moreover, the main spectral variations in the FTIR spectra and the thermal events in the DSC data were attributed the highest variable importance for the projection (VIP) scores in the corresponding VIP plots, confirming the model capability for predicting ibuprofen interactions. Furthermore, the prediction power of the optimal models for FTIR and DSC experimental data was evaluated by the root mean square error of prediction (RMSEP) of 0.10 and 0.16, respectively. The obtained results demonstrated the potential of multivariate statistical analysis as a machine learning-based technique for screening and prediction of ibuprofen-excipients solid-state compatibility in the preformulation phase of the pharmaceutical development of dosage forms.

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

Cerasus Humilis fruit is susceptible to rapid color changes post-harvest, which degrades its quality. This research utilized hyperspectral imaging technology to detect and visually analyze the soluble solid content (SSC) and firmness of the fruit, aiming to improve quality and achieve optimal pricing. Four maturity stages (color turning stage, coloring stage, maturity stage, and fully ripe stage) of Cerasus Humilis fruit were examined using hyperspectral images (895–1700 nm) alongside data collection on SSC and firmness. These samples were divided into a calibration set and a validation set with a ratio of 3:1 by sample set partitioning based on the joint X-Y distances (SPXY) method. The original spectral data was processed by a spectral preprocessing method. Multiple linear regression (MLR) and nonlinear least squares support vector machine (LS-SVM) detection models were established using feature wavelengths selected by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and two combined downscaling algorithms (UVE-SPA and UVE-CARS), respectively. For SSC and firmness detection, the best models were the SNV-SPA-LS-SVM model with 18 feature wavelengths and the original spectra-UVE-CARS-LS-SVM model with eight feature wavelengths, respectively. For SSC, the correlation coefficient of prediction (Rp) was 0.8526, the root mean square error of prediction (RMSEP) was 0.9703, and the residual prediction deviation (RPD) was 1.9017. For firmness, Rp was 0.7879, RMSEP was 1.1205, and RPD was 2.0221. Furthermore, the optimal model was employed to retrieve the distribution of SSC and firmness within Cerasus Humilis fruit. This retrieved information facilitated visual inspection, enabling a more intuitive and comprehensive assessment of SSC and firmness at each pixel level. These findings demonstrated the effectiveness of hyperspectral imaging technology for determining SSC and firmness in Cerasus Humilis fruit. This paves the way for online monitoring of fruit quality, ultimately facilitating timely harvesting.

Resolution of overlapping spectra using chemometric manipulations of UV-spectrophotometric data for the determination of Atenolol, Losartan, and hydrochlorothiazide in pharmaceutical dosage form

Simultaneous determination of atenolol (ATN), losartan potassium (LOS), and hydrochlorothiazide (HCZ) in presence of HCZ impurity B was conducted by chemometric approaches and radial basis function network (RBFN) using UV-spectrophotometry without preliminary separation. Three chemometric models namely, classical least-squares (CLS), principal component regression (PCR), and partial least-squares (PLS) along with RBFN were utilized using the ternary mixtures of the three drugs. The multivariate calibrations were obtained by measuring the zero-order absorbance of the mixtures from 250 to 270 nm at the interval of 0.2 nm. The models were built covering the concentration range of (4.0 to 20.0), (3.8 to 20.2), and (0.9 to 50.1) μg mL−1 for ATN, LOS, and HCZ, respectively. The regression coefficient was calculated between the actual and predicted concentrations of the 3 drugs using CLS, PCR, PLS and RBFN. The accuracy of the developed models was evaluated using the root mean square error of prediction (RMSEP) giving satisfactory results. The proposed methods were simple, accurate, precise and were applied efficiently for the quantitation of the three components in laboratory-prepared mixtures, and in dosage form showing good recovery values. In addition, the obtained results were compared statistically with each other using ANOVA test showing non-significant difference between them.

Generalizability of polygenic prediction models: how is theR2defined on test data?

Polygenic risk scores (PRS) quantify an individual's genetic predisposition for different traits and are expected to play an increasingly important role in personalized medicine. A crucial challenge in clinical practice is the generalizability and transferability of PRS models to populations with different ancestries. When assessing the generalizability of PRS models for continuous traits, the is a commonly used measure to evaluate prediction accuracy. While the is a well-defined goodness-of-fit measure for statistical linear models, there exist different definitions for its application on test data, which complicates interpretation and comparison of results. Based on large-scale genotype data from the UK Biobank, we compare three definitions of the on test data for evaluating the generalizability of PRS models to different populations. Polygenic models for several phenotypes, including height, BMI and lipoproteinA, are derived based on training data with European ancestry using state-of-the-art regression methods and are evaluated on various test populations with different ancestries. Our analysis shows that the choice of the definition can lead to considerably different results on test data, making the comparison of values from the literature problematic. While the definition as the squared correlation between predicted and observed phenotypes solely addresses the discriminative performance and always yields values between 0 and 1, definitions of the based on the mean squared prediction error(MSPE) with reference to intercept-only models assess both discrimination and calibration. These MSPE-based definitions can yield negative values indicating miscalibrated predictions for out-of-target populations. We argue that the choice of the most appropriate definition depends on the aim of PRS analysis - whether it primarily serves for risk stratification or also for individual phenotype prediction. Moreover, both correlation-based and MSPE-based definitions of can provide valuable complementary information. Awareness of the different definitions of the on test data is necessary to facilitate the reporting and interpretation of results on PRS generalizability. It is recommended to explicitly state which definition was used when reporting values on test data. Further research is warranted to develop and evaluate well-calibrated polygenic models for diverse populations.

Atmospheric Chemistry Surrogate Modeling With Sparse Identification of Nonlinear Dynamics

AbstractModeling atmospheric chemistry is computationally expensive and limits the widespread use of chemical transport models. This computational cost arises from solving high‐dimensional systems of stiff differential equations. Previous work has demonstrated the promise of machine learning (ML) to accelerate air quality model simulations but has suffered from numerical instability during long‐term simulations. This may be because previous ML‐based efforts have relied on explicit Euler time integration—which is known to be unstable for stiff systems—and have used neural networks which are prone to overfitting. We hypothesize that parsimonious models combined with modern numerical integration techniques can overcome this limitation. Using a small‐scale mechanism to explore the potential of these methods, we have created a machine‐learned surrogate by (a) reducing dimensionality using singular value decomposition to create an interpretably‐compressed low‐dimensional latent space and (b) using Sparse Identification of Nonlinear Dynamics (SINDy) to create a differential‐equation‐based representation of the underlying dynamics in the compressed latent space with reduced stiffness. The root mean square error of ML model prediction for ozone concentration over 9 days is 37.8% of the root mean square concentration across all simulations in our testing data set. The surrogate model is 10× faster with 12× fewer integration timesteps compared to reference model and is numerically stable in all tested simulations. Overall, we find that SINDy can be used to create fast, stable, and accurate surrogates of a simple photochemical mechanism. In future work, we will explore the application of this method to more detailed mechanisms and their use in large‐scale simulations.

Accurate analysis of coal calorific value using NIRS-XRF: Utilizing RF classification and PLSR subtype modeling

Rapid and precise measurement of the calorific value of coal is crucial for coal chemical enterprises. However, due to the wide variety of coal sources and the diverse types of coal, the matrix effect significantly reduces the accuracy of spectroscopic rapid detection techniques for coal quality. To address this issue, this study conducts research on a coal calorific value detection method based on the combined use of near-infrared spectroscopy (NIRS) and X-ray fluorescence spectroscopy (XRF), exploring the feasibility of identifying coal types first and then predicting calorific value based on corresponding subtype models. In the aspect of spectral preprocessing, fusion of the dual spectra is achieved using techniques such as truncation, smoothing, Standard Normal Variate (SNV), and normalization. For coal type identification, the identification performance of five machine learning algorithms is compared, and the random forest (RF) algorithm is used to improve the identification accuracy to 98.43 %. In terms of prediction modeling, a comparison is made between traditional holistic models based on Partial Least Squares Regression (PLSR) and subtype models built according to coal types, revealing that the latter demonstrating stronger generalization ability and robustness. Accordingly, a precise prediction strategy for coal calorific value based on the combined use of NIRS and XRF in complex coal types is proposed: first, the RF algorithm is used to automatically identify coal types based on the fusion spectrum of NIRS and XRF, and then the calorific value of the corresponding coal type is accurately predicted using the PLSR subtype model selected according to the identification results. Industrial test results indicate that the average absolute error (AAE), root mean square error of prediction (RMSEP), average relative error (ARE), and standard deviation (SD) for coal calorific value prediction are 0.20 MJ/kg, 0.24 MJ/kg, 0.79 %, and 0.04 MJ/kg, respectively, meeting the requirements of enterprises. The research findings provide a practical technical method for timely and accurate acquisition of coal calorific value data, addressing the interference problem of matrix effects in complex coal type scenarios and holding broad application prospects in the coal-based energy industries.

Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques.

Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible-near-infrared (Vis-NIR) and NIR) were utilized to acquire separate HSI data, which were then fused and analyzed toforecast the starch and alcohol contents during the fermentation of lees. Five preprocessing methods were first used to preprocess the Vis-NIR, NIR, and the fused Vis-NIR and NIR data, after which partial least squares regression models were established to determine the best preprocessing method. Following, competitive adaptive reweighted sampling, successive projection algorithm, and principal component analysis algorithms were used to extract the characteristic wavelengths to accurately predict the starch and alcohol levels. Finally, support vector machine (SVM)-AdaBoost and XGBoost models were built based on the low-level fusion (LLF) and intermediate-level fusion (ILF) of single Vis-NIR and NIR as well as the fused data. The results showed that the SVM-AdaBoost model built using the LLF data afterpreprocessing by standard normalized variable was most accurate for predicting the starch content, with an of 0.9976 and a root mean square error of prediction (RMSEP) of 0.0992. The XGBoost model built using ILF data was most accurate for predicting the alcohol content, with an of 0.9969 and an RMSEP of 0.0605. In conclusion, the analysis of fused data from distinct HSI technologies facilitates rapid and precise determination of the starch and alcohol contents in fermented grains.

Predicting grass proportion in fresh alfalfa: Grass mixtures using a hand‐held near‐infrared spectrometer

AbstractTechnological advancements have made hand‐held near infrared (NIR) spectrometers more affordable and more accurate, creating interest in on‐farm application for forage management. The objective of this study was to evaluate the ability of a hand‐held NIR spectrometer to predict grass percentage within fresh alfalfa (Medicago sativa L.):grass mixtures. Forage samples were collected at a range of maturities and varieties during the 2021 and 2022 growing seasons from multiple locations in New York. Fresh forage samples were chopped, and pure species were combined into known proportions on a dry matter basis, resulting in 534 samples. Analysis was carried out on NIR spectra collected from a hand‐held NeoSpectra spectrometer using stationary and sliding scanning techniques. Development of calibration models was completed using partial least squares regression with cross validation. The best performing calibration model using absorbance was from the sliding scanning technique with preprocessing consisting of mean‐centering (R2 = 0.89, root mean square error of prediction [RMSEP] = 13.7%, and ratio of prediction to deviation = 2.53). A total of 84% of the samples were correctly classified when the grass component was lower than 40%. For samples with the grass component above 40%, a total of 94% of the samples were correctly classified. Correct sample classification is critical considering that the extension recommendation in New York is to reseed alfalfa fields when the grass component exceeds 40% of the sward on a botanical composition basis. This research demonstrates that NIR technology has potential to provide the agricultural industry with rapid, non‐destructive, and affordable information to allow farmers and consultants to predict grass proportion within alfalfa:grass fresh forage mixtures in real time.

127 Utilizing predictive models for bone mineral content and bone mineral density as a tool to assess dietary phosphorous

Abstract Dietary phosphorous is a necessary nutrient for bone development in pigs at all stages of its growing. Deficiencies in phosphorous decrease bone mineral content (BMC) and bone mineral density (BMD) and may be a causative of lameness. However, the normal range expected in whole body (WB) and individual bones for BMC and BMD in commercial pigs has been poorly defined. Therefore, our objectives were to 1) model normal WB bone density at multiple timepoints in wean to finish pigs, and 2) determine the extent to which individual bone (femur and 3rd metatarsal) BMC and BMD could predict WB BMC and BMD. Mixed sex pigs [Camborough (1050) x 337, PIC] were utilized and included freshly weaned ~21 d (n = 8), ~44 d of age (n = 24), ~84 d of age (n = 24) and ~154 d of age (n = 24). Pigs were individually penned. Excluding weaning age, at each age group pigs were randomly assigned to diets adequate, marginally deficient, or deficient in available phosphorus (avP) and fed for 3, 4, and 6 wk (age group 44, 84, 154, respectively). Within each age group, diets were formulated to meet nutrient and energy requirements except for avP and pigs fed ad libitum. On-test and off-test body weight (BW) and feed intake and gain to feed ratio were assessed for each age group. At the end of each feeding period, all pigs were euthanized and WB, femur (F) and 3rd metatarsal (M) BMC and BMD determined by dual X-ray absorptiometry (DXA). Data were analyzed using linear and generalized additive models that predicted response variables (BMC or BMD) as a function of BW or age. Only pigs fed adequate avP diets were used in the model fit. Pigs fed adequate avP diets increased BMD with age (0.22, 0.42, 0.84 and 1.10 g/cm2, days of age 21, 44, 84 and 154 respectively, P < 0.001). Within day of age 84, marginally and deficient avP diets reduced BMC and BMD by 21 to 41% (P = 0.002) and 12 to 25% (P = 0.006), respectively. Within day of age 154, marginally and deficient avP diets reduced BMC and BMD by 9% (P = 0.05) and 6 to 8% (P = 0.06), respectively. Body weight record and individual bone (F and M) BMC and BMD from pigs fed deficient avP diets were used to predict WB BMC and BMD and the mean square error of prediction was selected as a criterion to evaluate the ability of the models to detect these inadequately fed pigs. Using the selected models, pigs fed nutritionally deficient diets were detected due to 2- to 7-fold increase in MSE of predicted WBMD or WBMC compared with pigs fed adequate diets. These models can be used to assess bone development and strength in growing pigs and to infer the adequacy of avP level supplied in their diets.

Near-infrared spectroscopy in resin transfer molding—determination of the degree of cure

AbstractNear-infrared (NIR) spectroscopy is employed to directly monitor the degree of cure of composite material during the curing phase in the resin transfer molding (RTM) process. The composite material consists of natural fibers and an epoxy-amine resin system. The quantitative determination of the degree of cure, α, from the NIR spectra is realized by partial least square (PLS) regression in conjunction with various pre-treatments of the spectral data. As a reference, the degree of cure is determined by isothermal differential scanning calorimetry (DSC) experiments. The best PLS model that could be obtained for α is characterized by a determination coefficient of prediction (R²P) of 0.980 and root mean square error of prediction (RMSEP) of 4.4 %. While the spectra change during curing according to the literature for each RTM trial, significant differences can be observed between the spectra of different RTM trials. The reasons for this are discussed in detail. The findings show the potential of inline NIR spectroscopy for monitoring α in the RTM process.

Rapid determination of residual pefloxacin in mutton based on hyperspectral imaging and data fusion

Non-destructive detection of veterinary drug residues in meat products is critical for ensuring food safety and human health. The feasibility of using visible near-infrared (Vis-NIR) and near-infrared (NIR) hyperspectral imaging (HSI) system combined with data fusion for the prediction of pefloxacin residues in mutton was investigated in this study. A partial least squares regression optimization model was developed by multivariate data processing methods. The results showed that the low-level fusion (LLF) produced better results with R2P = 0.907 and RMSEP = 0.462 as compared to individual data blocks. Furthermore, intermediate-level fusion (ILF) based on iterative retention of informative variables algorithm showed the best results with R2P = 0.940 and RMSEP = 0.375. Finally, the visualized distributions of Vis-NIR and NIR based on the optimal model combination were mapped. The results have demonstrated that a combination of HSI with data fusion may be applied to rapidly and non-destructively detect pefloxacin residues in mutton.

Vis-NIR spectroscopic discriminant analysis of aflatoxin B1 excessive standard in peanut meal as feedstuff materials

A fast, simple and reagent-free detection method for aflatoxin B1 (AFB1) is of great significance to food safety and human health. Visible and near-infrared (Vis-NIR) spectroscopy was applied to the discriminant analysis of AFB1 excessive standard of peanut meal as feedstuff materials. Two types of excessive standard discriminant models based on spectral quantitative analysis with partial least squares (PLS) and direct pattern recognition with partial least squares-discrimination analysis (PLS-DA) were established, respectively. Multi-parameter optimization of Norris derivative filtering (NDF) was used for spectral preprocessing; the two-stage wavelength screening method based on equidistant combination-wavelength step-by-step phase-out (EC-WSP) was used for wavelength optimization. A rigorous sample experimental design of calibration-prediction-validation was utilized. The calibration and prediction samples were used for modeling and parameter optimization, and the selected model was validated using the independent validation samples. For quantitative analysis-based, the positive, negative and total recognition-accuracy rates in validation (RARV+, RARV-, and RARV) were 84.8 %, 74.6 % and 79.8 %, respectively; but, the relative root mean square error of prediction was as high as 51.0 %. For pattern recognition-based, the RARV+, RARV-, and RARV were 93.3 %, 90.5 % and 91.9 %, respectively. Moreover, the number of wavelengths N was drastically reduced to 17, and the discrete wavelength combination was in NIR overtone frequency region. The results indicated that, the EC-WSP-PLS-DA model achieved significantly better discrimination effect. Thus demonstrated that Vis-NIR spectroscopy has feasibility for the excessive standard discrimination of aflatoxin B1 in feedstuff materials.

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.

Species-Specific quantification of bioactive boswellic acids in Boswellia resin using NIR spectroscopy, HPLC and Multivariate data analysis

The bioactive compounds Acetyl-11-keto-β-boswellic acid (AKBA) and 11-keto-β-boswellic acid (KBA), found in the resin of the Boswellia tree, exhibit anti-inflammatory properties, rendering Boswellia resin an intriguing natural medicinal products. However, the content of boswellic acids varies across different Boswellia species and proper knowledge of its species-dependent nature, as well as alternatives to the resource- and time-intensive HPLC analysis, are lacking. Here we present a comprehensive investigation into the boswellic acid content of seven Boswellia species from ten countries and introduce a novel and non-destructive Near-Infrared spectroscopy method for predicting boswellic acid concentrations in solid resin samples. The HPLC-UV reference analysis revealed AKBA concentrations of up to 7.27 % (w/w) with KBA concentrations reaching up to 1.28 % (w/w). Principal Component Analysis of the HPLC and NIR spectroscopy data unveiled species-specific variations, facilitating differentiation based on boswellic acid content, characteristic chromatograms and NIR spectra. Using the HPLC-UV quantification as reference, we developed a Partial Least Squares regression model based on NIR spectra of the resin samples. This model demonstrated highly satisfactory predictive capabilities for AKBA content, achieving a root mean square error of prediction of 0.74 % (w/w) and an R2val of 0.79 in independent test set validation. Although the model was less effective for predicting KBA content, it still offered valuable estimates. The spectroscopic method introduced in this study provides a cost-effective and solvent-free approach for predicting boswellic acid content, demonstrating the potential for application in non-laboratory settings through the use of miniaturized NIR spectrometers. Consequently, this method aligns well with the principles of green chemistry and addresses the growing demand for alternative analytical techniques.

Confocal controlled laser-induced breakdown spectroscopy for quantitative detection of cadmium in soil

Soil, one of the most precious natural resources on Earth, gradually accumulates heavy metals, inevitably causing significant damage to the ecological environment. Here, we introduce confocal controlled laser induced breakdown spectroscopy (CCLIBS) technology for the quantitative analysis of the heavy metal cadmium in soil for the first time. CCLIBS offers better spatial consistency and stable plasma temperature during sample ablation compared to traditional LIBS, thereby reducing matrix effects to improve the accuracy of the quantitative results. The fluctuation of the spectrum and limit of detection are reduced by 0.6 times and 0.39 times, respectively. An effective prediction model was established using the partial least squares method, with a determination coefficient increased to 0.96. The root mean square error of prediction and average relative error are reduced to 67.67 and 0.20, respectively. These results indicate that CCLIBS provides consistent ablation conditions for elemental quantification and yields reliable test results, which is significant for monitoring heavy metals in the ecological environment and effectively intervene and mitigate environmental contamination.