Root Mean Square Error Of Calibration Research Articles

Evaluating Moisture Content in Immersion Vacuum-Cooled Sausages with Citrus Peel Extracts Using Hyperspectral Imaging.

The moisture content of immersion vacuum-cooled sausages with modified casings containing citrus fruit extracts under different storage conditions was studied using hyperspectral imaging (HSI) associated with chemometrics. Different pre-processing combinations were applied to improve the robustness of the model. The partial least squares regression model, employing the full reflectance spectrum with pre-treatment of the standard normal variate, showed calibration coefficients of determination (Rc2) of 0.6160 and a root mean square error of calibration (RMSEC) of 2.8130%. For the first time, prediction maps developed via HSI visualized the distribution of moisture content in the immersion vacuum-cooled sausages with unique modified casings in response to fluctuating storage conditions. The prediction maps showed exact parts with high water content, which will help us to monitor and prevent mold growth. The combination of HSI with multivariate analysis not only quantifies changes in moisture content but also visually represents them in response to various casing treatments under different storage conditions, illustrating the significant potential for real-time inspection and early mold detection in sausages within the processed meat industry.

Authentication of oats and discrimination from their gluten-containing adulterants using NIR diffuse reflectance spectroscopy and multivariate analysis

Oat (Avena sativa L.) is recognized for its nutritional value and gluten-free status, however, ensuring its authenticity and purity is crucial, given the potential for contamination with gluten-containing grains. In this study, near-infrared spectroscopy coupled with chemometrics were employed to authenticate oat flour (from different forms of oat; oat groats, steel-cut and rolled oats) and distinguish it from common gluten-containing adulterants including wheat, farro, triticale, barley, rye, and ryegrass. Both unsupervised and supervised chemometric methodologies, encompassing PCA, SIMCA, and OPLS-DA, were applied. Both SIMCA and OPLS-DA models displayed 100% sensitivity, enabling reliable identification of oat flour and detection of potential adulteration with these gluten-containing grains with specificity of 97.78% and 100%, respectively. In the SIMCA model for oat groats and adulterated mixtures, samples with 1% and 2% adulteration were incorrectly classified as oat groats, yet successful discrimination from deliberately-adulterated mixtures was accomplished through the OPLS-DA model with 100% specificity. Moreover, PLS regression analysis was employed to precisely quantify the levels of adulterants in oat groats flour. The models exhibited reliable performance, as reflected in Root Mean Square Error of Calibration (RMSEC) values. Validation of these models using test samples provided further confirmation of their efficacy in detecting sample adulteration and ensuring product authenticity, all while maintaining a high sample throughput rate.

Quality study of animal-derived traditional Chinese medicinal materials based on spectral technology: Calculus bovis as a case.

Calculus bovis (C. bovis) is a typical traditional Chinese medicine (TCM) derived from animals, which has a remarkable curative effect and high price. Rapid identification of C. bovis from different types was realized based on spectral technology, and a rapid quantitative analysis method for the main quality control indicator bilirubin was established. We conducted a supervised and unsupervised pattern recognition study on 44 batches of different types of C. bovis by five spectral pretreatment methods. Three variable selection methods were used to extract the essential information, and the partial least squares regression (PLSR) quantitative model of bilirubin by near-infrared (NIR) spectroscopy was constructed. The partial least squares discriminant analysis (PLS-DA) model could achieve 100% accuracy in identifying different types of C. bovis. The R2 of the NIR quantitative model was 0.979, which is close to 1, and the root mean square error of calibration (RMSEC) was 2.3515, indicating the good prediction ability of the model. The study was carried out to further improve the basic data of quality control of C. bovis and help the high-quality development of TCM derived from animals.

Non-destructive prediction of ready-to-eat kiwifruit firmness based on Fourier transform near-infrared spectroscopy

There is a growing demand for ready-to-eat kiwifruit in the world. However, ready-to-eat kiwifruit has a rather narrow range of firmness (e.g. 10–30 N), and it remains challenging to predict this firmness in a non-destructive manner. Here, we report a strategy for non-destructive prediction of kiwifruit firmness based on Fourier transform near-infrared (FT-NIR) spectroscopy. The radial basis function (RBF) model displayed superior performance, with a coefficient of determination (Rc2) of 0.83, a cross-validation coefficient of determination (Rp2) of 0.73, a root mean square error of calibration (RMSEC) of 0.58, a root mean square error of prediction (RMSEP) of 0.72, and a ratio of performance to deviation (RPD) of 1.92. To enhance the accuracy of kiwifruit firmness prediction, we optimized the FT-NIR algorithm through data preprocessing, feature selection, and dimensionality reduction. The results showed that the FD-CARS-SVR (RBF) algorithm exhibited the best performance in predicting kiwifruit firmness during the shelf life with impressive values of Rc2 (0.99), Rp2 (0.92), RMSEC (0.15), RMSEP (0.40), and RPD (3.48). To further evaluate the applicability of the FT-NIR model, we compared the data predicted by the model and acquired from the Kiwifirm™ and penetrometer GY-4. The results revealed pronounced superiority of the FT-NIR model for the firmness ranging from 10 to 40 N to replace Kiwifirm™, providing a new non-destructive model for the prediction of the firmness of ready-to-eat kiwifruit.

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.

Near infrared spectroscopy for determination of moisture content in lyophilized formulation

A non-invasive near infrared (NIR) spectroscopic method was developed for the quantitative moisture determination in a lyophilized injection formulation. The calibration samples were prepared by exposing lyophilized samples at different temperatures and relative humidity. The samples from different scales and different process parameters were considered for adding robustness to the model. The NIR spectra were collected using a Fourier-transform (FT) NIR with a diffuse reflectance probe and the same samples were further analyzed by the Karl Fisher (KF) method for moisture content. The pre-treated NIR spectra were used for quantitative method development for moisture content. Partial least squares regression was used to develop calibrations in the 5600-4950 cm−1 region with calibration coefficient of determination (R2) of 0.96 and root mean square error of calibration (RMSEC) of 0.149. The model was cross-validated internally using the Kernel algorithm with r2 = 0.96 and RMSECV = 0.15. The accuracy of the NIR method against the KF method, precision, and reproducibility were good and the model was robust in predicting different external validation samples. This work allowed NIR as an alternative measurement for moisture analysis as well as facilitate 100% monitoring before packaging and save the cost of sample and time of KF analysis.

Determination of Trace Levels of Fenbendazole in Milk, Yogurt and Curd Using Surface Enhanced Raman Spectroscopy (SERS)

A simple, rapid and sensitive surface enhanced Raman spectroscopy (SERS) method is described for the determination of fenbendazole (FBZ) residues in milk and fermented dairy products using yogurt and curd as examples. An extraction method was used to purify the samples from interfering components of the matrix. 5-Nitrothiabendazole, which has similar physicochemical properties, was used as an internal standard. The detection limits for milk were 1.8 nM, 8.8 nM for yogurt, and 13.5 nM for curd. Using partial least squares (PLS), a calibration model was built for the determination of fenbendazole from 100 to 1000 nM. The root-mean square error of calibration (RMSEC) and the root-mean square error of prediction (RMSEP) were 39.4 and 46.5 nM, respectively. The recovery rate was from 97% to 120% and the relative standard deviation was less than 5.0%. The difference between this technique from the widely used liquid chromatography–mass spectrometry (LC–MS) method is in the reduction in time for preliminary sample preparation and significantly lower costs for reagents and consumables. The article examines the influence of order when mixing reagents in a SERS experiment, which is an important tool for optimizing the sensitivity and reproducibility of the method. It was shown for the first time that when various alkaline earth metal chlorides were used for agglomeration of silver nanoparticles (AgNPs), a change in the ratio of peak intensities of the two analytes in the solution was observed. This phenomenon may be used to increase the selectivity of SERS analysis of multicomponent samples.

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.

On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow

Abstract. In northern cold-temperate countries, a large portion of annual streamflow is produced by spring snowmelt, which often triggers floods. It is important to have spatial information about snow variables such as snow water equivalent (SWE), which can be incorporated into hydrological models, making them more efficient tools for improved decision-making. The present research implements a unique spatial pattern metric in a multi-objective framework for calibration of hydrological models and attempts to determine whether raw SNODAS (SNOw Data Assimilation System) data can be utilized for hydrological model calibration. The spatial efficiency (SPAEF) metric is explored for spatially calibrating SWE. Different calibration experiments are performed combining Nash–Sutcliffe efficiency (NSE) for streamflow and root-mean-square error (RMSE) and SPAEF for SWE, using the Dynamically Dimensioned Search (DDS) and Pareto Archived Dynamically Dimensioned Search multi-objective optimization (PADDS) algorithms. Results of the study demonstrate that multi-objective calibration outperforms sequential calibration in terms of model performance (SWE and discharge simulations). Traditional model calibration involving only streamflow produced slightly higher NSE values; however, the spatial distribution of SWE could not be adequately maintained. This study indicates that utilizing SPAEF for spatial calibration of snow parameters improved streamflow prediction compared to the conventional practice of using RMSE for calibration. SPAEF is further implied to be a more effective metric than RMSE for both sequential and multi-objective calibration. During validation, the calibration experiment incorporating multi-objective SPAEF exhibits enhanced performance in terms of NSE and Kling–Gupta efficiency (KGE) compared to calibration experiment solely based on NSE. This observation supports the notion that incorporating SPAEF computed on raw SNODAS data within the calibration framework results in a more robust hydrological model. The novelty of this study is the implementation of SPAEF with respect to spatially distributed SWE for calibrating a distributed hydrological model.

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.

Sustainable chemometric methods boosted by Latin hypercube technique for quantifying the recently FDA-approved combination of bupivacaine and meloxicam in the presence of bupivacaine carcinogenic impurity: Comprehensive greenness, blueness, and whiteness assessments

Addressing sustainability requires developing analytical methods that minimize hazards, waste, and energy utilization per the principles of green and white chemistry. Herein, we present a sustainable spectrophotometric chemometrics technique for quantifying the recently approved Bupivacaine (BUP) and Meloxicam (MEL) combination and the potential BUP carcinogen, 2,6-dimethylaniline (DMA). Our models, including partial least squares (PLS), principal component regression (PCR), genetic algorithm-PLS (GA-PLS), and GA-PCR, were established through a comprehensive experimental design involving 25 mixtures as the calibration set. A key innovation is the utilization of the Latin Hypercube Sampling (LHS) technique, which enables the creation of a robust validation set for evaluating the performance and generalizability of these models. The GA-PLS model demonstrated excellent accuracy, with recovery percentages (R%) from 98 to 102% for all analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.097, 0.050, and 0.112) and (0.119, 0.044 and 0.131) for BUP, DMA, and MEL, respectively. The model also showed a negligible bias-corrected mean square error of prediction (BCMSEP) of (-0.014, −0.003, and 0.018), with relative root mean square error of prediction (RRMSEP) reaching (0.992, 0.752, and 0.659), and limits of detection (LOD) reaching (0.153, 0.081, and 0227) for BUP, DMA, and MEL, respectively. Comprehensive greenness, blueness, and whiteness assessments were performed and compared between the suggested and reported methods. This research pioneers a green–blue-white alternative to conventional approaches, serving as a model for developing sustainable techniques that reduce the usage of resources and chemical waste while meeting the global appeal for environmentally accountable solutions.

Detection of Al, Mg, Ca, and Zn in copper slag by LIBS combined with calibration curve and PLSR methods

The precise measurement of Al, Mg, Ca, and Zn composition in copper slag is crucial for effective process control of copper pyrometallurgy. In this study, a remote laser-induced breakdown spectroscopy (LIBS) system was utilized for the spectral analysis of copper slag samples at a distance of 2.5 m. The composition of copper slag was then analyzed using both the calibration curve (CC) method and the partial least squares regression (PLSR) analysis method based on the characteristic spectral intensity ratio. The performance of the two analysis methods was gauged through the determination coefficient (R 2), average relative error (ARE), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). The results demonstrate that the PLSR method significantly improved both R 2 for the calibration and test sets while reducing ARE, RMSEC, and RMSEP by 50% compared to the CC method. The results suggest that the combination of LIBS and PLSR is a viable approach for effectively detecting the elemental concentration in copper slag and holds potential for online detection of the elemental composition of high-temperature molten copper slag.

Development of FTIR Spectroscopy and multivariate calibration for authentication beef meatballs from pork meat

In several countries, especially Indonesia, meatballs are one of the favorite foods made from meat. The high price of beef is a reason for adulterated beef meat with pork to gain a significant profit. All food products must not contain pig derivatives such as pork in Muslim societies. This study aimed to develop Fourier Transform Infrared (FTIR) spectroscopy in combination with linear discriminant analysis (LDA) for classifying pork in beef meatballs and multivariate calibration for qualitative and quantitative analysis methods using partial least square regression (PLSR) and principal component regression (PCR). The lipid component from meatballs containing pork and beef in different concentrations was obtained by employing three lipid extraction methods, namely BlighDyer, Folch, and Soxhlet methods, and subjected to attenuated total reflectance (ATR) spectral measured at wavenumbers of 4000-650 cm-1 . LDA at 3800-800 cm-1 was able to differentiate between beef meatballs and meatballs containing pork meat (PM). Additionally, PLSR using second derivative FTIR spectra at wavenumbers of 3100-900 cm-1 delivered great region for predicting pork levels in beef meatballs extracted using Folch methods with coefficient determination (R2 ) values was > 0.99 and 0.02% for root mean square error of calibration (RSMEC) and root mean square error of prediction (RSMEP) values, respectively. PCR using absorbance values of 1st derivative FTIR spectra at wavenumbers 1400-800 cm-1 for Bligh-Dyer method and 3800-800 cm-1 for Soxhlet was offered the R2 value was> 0.99 with low RSMEC and RSMEP values for prediction of pork fat in beef meatballs, respectively. It can be concluded that FTIR spectroscopy on the Savitzy-Golay derivatization method in combination with chemometrics was an accurate and quick approach for authenticating pork adulteration in beef meatballs.

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.

Simultaneous determination of tartrazine and auramine O using FTIR spectroscopy and partial least square

Nowadays, the use of colouring agents are needed to increase customers’ acceptability of foods. However, the development of food technology averts a big opportunity to use prohibited food colouring agents. Tartrazine (TAR) and Auramine O (AO) are two colouring agents may be used as food colorants. TAR is allowed, while AO is not allowed to be used in food colorants, respectively. For this study, the goal was to validate and apply Fourier transform infrared (FTIR) combined with multivariate calibrations for quantitative analysis of TAR and AO in beverages. An attenuated total reflectance FTIR scan of samples was performed at wavenumbers ranging from 4000-650 cm-1 . Quantitative analysis using FTIR spectroscopy was made by plotting the actual levels of TAR and AO obtained using HPLC and the prediction levels facilitated by partial least square (PLS) from the sample series. Internal cross-validation is performed on the model using the leave-one-out method. The root mean square error of prediction (RMSEP), the root mean square error of cross validation (RMSECV), the root mean square error of calibration (RMSEC), as well as the coefficient of determination (R2 ) become validation criteria. The results exhibited that calibration model was gained using variables of FTIR spectra at wavenumbers of 1730-1264 cm-1 . Correlation coefficients (r) of 0.9437 (TAR) and 0.9381 (FTIR) are present in the calibration model for the correlation of actual and predicted values. The method for analyzing tartrazine and auramine O in powdered beverages was validated using multivariate PLS FTIR calibration spectroscopy. FTIR spectroscopy could be further developed as an alternative technique for analysis of TAR and AO in beverages.

Determination of fumonisin content in maize using near-infrared hyperspectral imaging (NIR-HSI) technology and chemometric methods.

Maize (Zea mays L.) is of socioeconomic importance as an essential food for human and animal nutrition. However, cereals are susceptible to attack by mycotoxin-producing fungi, which can damage health. The methods most commonly used to detect and quantify mycotoxins are expensive and time-consuming. Therefore, alternative non-destructive methods are required urgently. The present study aimed to use near-infrared spectroscopy with hyperspectral imaging (NIR-HSI) and multivariate image analysis to develop a rapid and accurate method for quantifying fumonisins in whole grains of six naturally contaminated maize cultivars. Fifty-eight samples, each containing 40 grains, were subjected to NIR-HSI. These were subsequently divided into calibration (38 samples) and prediction sets (20 samples) based on the multispectral data obtained. The averaged spectra were subjected to various pre-processing techniques (standard normal variate (SNV), first derivative, or second derivative). The most effective pre-treatment performed on the spectra was SNV. Partial least squares (PLS) models were developed to quantify the fumonisin content. The final model presented a correlation coefficient (R2) of 0.98 and root mean square error of calibration (RMSEC) of 508 µg.kg-1 for the calibration set, an R2 of 0.95 and root mean square error of prediction (RMSEP) of 508 µg.kg-1 for the test validation set and a ratio of performance to deviation of 4.7. It was concluded that NIR-HSI with partial least square regression is a rapid, effective, and non-destructive method to determine the fumonisin content in whole maize grains.

The Application of FTIR Spectroscopy Combined Chemometrics for Analysis of Keting Fish Oil in Binary Mixture with Patin Fish Oil and Palm Oil

Keting fish (Mystus gulio) is a local fish from Indonesia. The quality of keting fish oil needs to be maintained to avoid counterfeiting. The use of Fourier transform infrared (FTIR) spectroscopy can be developed for quantitative analysis of keting fish oil (KFO) in binary mixtures with palm oil (PO) and patin fish oil (PFO). In this present research, PLS and PCR models were used to construct a multivariate calibration for the KFO content in the binary mixtures analyzed at fingerprint region frequencies of 1500-1000 cm-1. The results showed that PLS with second derivative FTIR spectra was well suited for quantitative analysis of KFO-PFO with a value of the coefficient of determination (R2) = 0.997 and the error expressed as root mean square error of calibration (RMSEC) = 0.0204. Meanwhile, R2 0.999 and RMSEC values obtained for KFO in a mixture with PO were and 0.0133. In general, FTIR spectroscopy serves as a suitable technique for the determination of KFO in mixtures with other oils.

Rapid assessment of residual solvent content in the TEGDN dual-base propellants by near-infrared reflectance spectroscopy

Excessive residual solvent (RS) levels in triethyleneglycol dinitrate (TEGDN) dual-base propellants can significantly impair combustion performance. This work aimed to develop a rapid and accurate model for detecting the RS content in the TEGDN dual-base propellants using near-infrared (NIR) spectroscopy in the reflectance mode. The optimal wavelength range for modelling, spanning from 1124.9−1230.2 nm and 1335.5–1527.5 nm, was identified based on absorption peaks characteristic of TEGDN dual-base propellant samples and RS. To enhance the quality of the data, we determined optimal window sizes for pre-processing methods: derivative pre-processing and Savitzky-Golay (S-G) smoothing pre-processing. After evaluating the performance of different pre-treatment methods, we found that the model employing multiple scattering corrections (MSC) in conjunction with first-order derivative (FD) pre-processing demonstrated superior results. The partial least squares (PLS) method was used to build the RS model with an optimal number of factors of 6. For the developed RS model, the root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) were 0.019 and 0.024, respectively. The determination coefficient of calibration (Rc2) and the determination coefficient of cross-validation (Rcv2) were 0.968 and 0.952, respectively. In assessing the validation set using the developed model, we observed a root mean square error of prediction (RMSEP) of 0.025 and a determination coefficient of prediction (Rp2) of 0.958. Importantly, the relative error between the predicted values obtained through the NIR method and the measured values from the reference method consistently remained below 2 % under all circumstances. Consequently, the NIR-based RS model developed in this study offers a rapid and efficient means of detecting RS content in TEGDN dual-base propellants, facilitating judgment regarding the qualification of RS content.

Enhanced Accuracy for the Determination of Chromium in Steel by Laser-Induced Breakdown Spectroscopy (LIBS) and Extreme Random Tree (ERT)

The content of chromium (Cr) affects the mechanical and wear resistance of steel. Hence, the determination of Cr in steel is of great significance to determine the quality. In this study, a new method based on laser-induced breakdown spectroscopy (LIBS) combined with extreme random tree (ERT) is reported to determine Cr in steel. First, the Cr I 425.43 nm line was selected for univariate quantitative analysis. Next, multivariate analysis of Cr was performed using partial least squares regression (PLSR), random forest (RF), and ERT. The abilities of the models for the determination were validated by the fitting coefficient (R 2), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and average relative error (ARE). The R 2, RMSEC, RMSEP, and ARE values of ERT were 0.9984, 0.0274 wt.%, 0.0299 wt.%, and 2.43%, respectively. Compared with the univariate model, PLSR, and RF models, the R 2 of ERT increased by 0.1317, 0.0306, and 0.013, respectively; RMSEC decreased by 0.0987, 0.0955, and 0.0174 wt.%; RMSEP decreased by 0.1463, 0.0876, and 0.002 wt.%; and ARE decreased by 17.88%, 16.98%, and 5.95%, respectively. The results show that combining LIBS with the ERT model for multivariate analysis improves the elemental analysis of steel.

Near-Infrared Spectral Analysis for Assessing Germination Rate of Rapeseed Seeds: An Applied Sciences Approach

Brassica rapa, commonly known as the rapeseed plant, is globally recognized for its nutrient-rich composition and oil-packed seeds, earning its distinction as a substantial oil-seed crop. The seed quality, particularly the germination rate, is instrumental in guaranteeing a high-yield rapeseed crop. Given this, the accurate, quantitative determination and selection of germination rates in seed batches prior to sowing is of paramount importance. However, conventional germination tests, employed to determine the average germination rate of seed batches, are marred by substantial time and cost inefficiencies. This study proposes the use of near-infrared spectral analysis as a proficient, non-invasive approach for assessing germination rates in rapeseed seed batches. The research involved artificial aging of seeds procured from a variety of rapeseed strains, resulting in 228 batches with a broad germination rate spectrum of 15.73% to 99.13%. We recorded near-infrared diffuse reflectance spectra and applied a range of strategies for spectral data preprocessing and feature variable selection. Furthermore, we leveraged support vector regression (SVR) modeling to augment the detection methodology. SVR training and detection were conducted using MATLAB, with selected feature wavelengths undergoing rigorous scrutiny and discussion. The results indicated that employing Savitzky–Golay convolution smoothing for spectral preprocessing, along with Synergy interval Partial Least Squares (SiPLS) in conjunction with Random Frog (RF) for the selection of 50 feature wavelength points, yielded optimal germination rate prediction performance within the SVR model. The coefficients of determination (R2c) for the training set and (R2p) for the testing set were observed to be 0.8559 and 0.8386, respectively, while the Root Mean Square Errors of Calibration (RMSEC) and Prediction (RMSEP) were calculated to be 13.76% and 17.04%. The mechanism of detecting seed vigor through near-infrared spectroscopy was analyzed based on joint variable screening and sensitive variable traceability. Consequently, the SG–SiPLS–RF–SVR model demonstrates its effectiveness in predicting the average germination rate of seed batches, offering a rapid, non-invasive detection method that can be universally applied to various rapeseed strains, thus significantly improving seed production efficiency.