Root Mean Square Error Of Calibration Research Articles

Non-destructive Detection of Fatty Acid Content of Camellia Seed Based on Hyperspectral.

As a unique traditional vegetable oil in China, camellia seed oil has very high edible value. Camellia seed kernel is mainly composed of fatty acids, which not only determines the oil yield of camellia seed, but also exert an important impact on the storage performance of camellia seed. In order to quickly and accurately determine the fatty acid content of camellia seed, this paper took camellia seed as the research object, used hyperspectral technology to determine the fatty acid content of camellia seed, and establishes a spectral model. 8 pretreatment methods, such as Savitzky-Golay smoothing, normalization, baseline correction, multivariate scattering correction, standard normal variable transformation, detrending algorithm, first derivative and second derivative, were adopted in this paper. The spectral prediction model of fatty acid content in camellia seed was established by combining 4 modeling methods: principal components regression (PCR), partial least square regression (PLSR), back propagation neural network (BP), radial basis function neural network (RBF). The optimal prediction model was selected by comparing the coefficient of determination (R2) and root mean square error (RMSE) of various models. The results showed that the spectral sensitive bands with high correlation coefficients (r) were 410-420 nm, 450-460 nm, 490-510 nm, 545-580 nm, 845-870 nm and 905-925 nm, respectively. The r obtained by MSC pretreatment of spectral data was the largest. The data obtained by 8 different pretreatment methods combined with RBF neural network model was the best, in which the average value of coefficient of determination (RC2) in the calibration set was 0.8654, and the root mean square error of calibration (RMSEC) was 0.0777; the average value of coefficient of determination (R2P) and root mean square error of prediction (RMSEP) in the prediction set model were 0.8437 and 0.0827, respectively. It could be seen that the best accuracy could be achieved by MSC pretreatment combined with RBF neural network modeling. This paper can provide reference for rapid nondestructive detection of fatty acid content in camellia seed by hyperspectral technology.

Predicting soil carbon in granitic soils using Fourier-transform mid-infrared (FT-MIR) spectroscopy: the value of database disaggregation

Soil carbon (C) is an important component in quality assessments and efficient models are required to estimate C rapidly. Accurate C assessments are valuable in monitoring land-use changes. Fourier-transform mid-infrared (FT-MIR) spectroscopy has proved to be a powerful tool for assessing C. The potential of FT-MIR spectroscopy to estimate C was evaluated using the following techniques: (1) three algorithms [partial least squares (PLS)], principal component regression (PCR), and classical least squares (CLS); and (2) disaggregating the dataset into subgroups based on soil depth and texture. The C contents of samples collected in the Johannesburg Granite Dome were determined by dry combustion for comparison. The soils ranged considerably in C (0.123–2.650%), clay (2.80–41.20%), and silt content (8.56–23.75%). Using standard normal variant (SNV), Savitzky-Golay smoothing and PLS, the best-performing model was the horizons subgroup which provided values of root mean square error for prediction (RMSEP) between 0.079 and 0.095%, root mean square error for calibration (RMSEC) = 0.041 − 0.092, r 2 pre = 0.6174–0.8459, r 2 cal = 0.6599–0.9778, residual prediction variation (RPD) = 2.404–2.753, and ratio of performance to interquartile range (RPIQ) = 2.667–3.454. The pronounced accuracy of FT-MIR spectroscopy coupled with PLS, pre-processing techniques, and textural subgroups confirms the potential of infrared spectroscopy as an efficient tool for estimating C content. Future studies should investigate the combined effects of FT-MIR spectroscopy and subgroups grouped according to soil types and land-uses when predicting C.

Infrared Spectroscopy-Based Chemometric Analysis for Lard Differentiation in Meat Samples

One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness. The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary, for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis. Fourier Transform Infrared Spectroscopy (FTIR) is used in this work to identify lard adulteration in cow, lamb, and chicken samples. A simplified extraction method was implied to obtain the lipids from pure and adulterated meat. Adulterated samples were obtained by mixing lard with chicken, lamb, and beef with different concentrations (10%–50% v/v). Principal component analysis (PCA) and partial least square (PLS) were used to develop a calibration model at 800–3500 cm⁻¹. Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken, lamb, and beef samples. The corresponding FTIR peaks for the lard have been observed at 1159.6, 1743.4, 2853.1, and 2922.5 cm⁻¹, which differentiate chicken, lamb, and beef samples. The wavenumbers offer the highest determination coefficient R² value of 0.846 and lowest root mean square error of calibration (RMSEC) and root mean square error prediction (RMSEP) with an accuracy of 84.6%. Even the tiniest fat adulteration up to 10% can be reliably discovered using this methodology.

Partial Least Squares Method for the Multicomponent Analysis of Antibacterial Mixture

This study's objective is to assess how well UV spectrophotometry can be used in conjunction with multivariate calibration based on partial least squares (PLS) regression for concurrent quantitative analysis of antibacterial mixture (Levofloxacin (LIV), Metronidazole (MET), Rifampicin (RIF) and Sulfamethoxazole (SUL)) in their artificial mixtures and pharmaceutical formulations. The experimental calibration and validation matrixes were created using 42 and 39 samples, respectively. The concentration range taken into account was 0-17 μg/mL for all components. The calibration standards' absorbance measurements were made between 210 and 350 nm, with intervals of 0.2 nm. The associated parameters were examined in order to develop the optimal calibration model. The cross-validation method was used to determine the ideal number of components. The coefficient of determination (R2) and the root mean square error of calibration (RMSEC) are used to evaluate the calibration model. The relation between the LEV, MET, RIF, and SUL actual values and predicted values had a coefficient of determination that was higher than 0.997, showing very good accuracy of the devised approach. The obtained RMSEC values, 0.181056465 (LEV), 0.180375418 (MET), 0.142767171 (RIF), and 0.17157454 (SUL), show an analytical procedure with adequate precision. The suggested technique for quantitative analysis of the quaternary mixture of LEV, MET, RIF, and SUL have been applied successfully in different pharmaceutical preparations. The UV spectrophotometry assisted with chemometric-PLS without prior treatment, be utilised to resolve multicomponent mixtures successfully.

Development of a screening method for adulteration detection in coconut milk via mid-infrared spectroscopy: A study of linear and nonlinear regression method

In the present study, we developed a screening method for detection of adulteration in coconut milk via mid-infrared spectroscopy. Linear and nonlinear regression methods (principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR)) were employed and compared to achieve an optimal screening method. Spectral data were scanned using the FTIR benchtop with a wavelength range of 4000–16702 nm. The calibration models of the linear and nonlinear regression methods were developed using the leave-one-out cross-validation method before testing using predictive data that had been prepared. Furthermore, five spectral data treatment techniques were employed to improve the accuracy of the proposed calibration model. The results obtained show that the SVMR method is better than PCR and PLSR for the detection of adulteration in coconut milk by mid-infrared data spectroscopy. The coefficient of determination for calibration (R2c) and prediction (R2p), the root mean square error of calibration (RMSEC) and prediction (RMSEP) and the ratio of prediction to deviation (RPD) using the SVMR method were 100%, 0.81, 98.40%, 0.87 and 7.86, respectively. Furthermore, based on RPD analysis, it is known that the SVMR model can be used to perform excellent quality control of water-adulterated coconut milk.

Real-Time Infrared Spectroscopic Measurement of Natural Moisturizing Factor

Real-Time Infrared Spectroscopic Measurement of Natural Moisturizing Factor

Instant quantification of sugars in milk tablets using near-infrared spectroscopy and chemometric tools

Milk tablets are a popular dairy product in many Asian countries. This research aimed to develop an instant and rapid method for determining sucrose and lactose contents in milk tablets using near-infrared (NIR) spectroscopy. For the quantitative analysis, a training set composed of laboratory-scale milk samples was generated based on a central composite design (CCD) and used to establish partial least squares (PLS) regression for the predictions of sucrose and lactose contents resulting in R2 values of 0.9749 and 0.9987 with the corresponding root mean square error of calibration (RMSEC) values of 1.69 and 0.35. However, the physical difference between the laboratory-scale powder and the final product milk tablet samples resulted in spectral deviations that dramatically affected the predictive performance of the PLS models. Therefore, calibration transfer methods called direct standardization (DS) and piecewise direct standardization (PDS) were used to adjust the NIR spectra from the real milk tablet samples before the quantitative prediction. Using high-performance liquid chromatography (HPLC) as a reference method, the developed NIR-chemometric model could be used to instantly predict the sugar contents in real milk tablets by producing root mean square error of prediction (RMSEP) values for sucrose and lactose of 5.04 and 4.22 with Q2 values of 0.7973 and 0.9411, respectively, after the PDS transformation.

Prediction of chicken meatball quality through NIR spectroscopy and multivariate analysis

Near Infrared (NIR) Spectroscopy leads a great opportunity to replace the expensive and time-consuming chemical conventional analysis for determination of the quality of meat products. This study was conducted aiming to evaluate the feasibility of NIRS and to establish a rapid assessment method to easily predict the quality of chicken meatball. Samples of meatball (n=123) were collected from Golden Harvest Company of Bangladesh. After collecting sample, spectra were obtained prior to analysis and a total of 369 NIRs were collected and stored in computer by DLP NIR scan Nano Software. To generate reference data 123 meatball samples were analyzed for proximate components, instrumental color CIE L*, a*, b*, and pH of meatball. After that a partial least square regression model for calibration and cross validation were developed for data analysis using The Unscrambler X software. Accuracies of the calibration models were evaluated using the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), coefficient of calibration (R²C) and coefficient of cross validation (R2 CV). Calibration equations were developed from reference data using partial least squares regressions. The standard deviation is 2.41, 0.14, 2.1, 0.41, 1.31, 0.31, 1.26, 0.38, and 0.38 for L*, a*, b*, pH, DM, moisture, CP, EE and ash respectively which indicates that all values are adequate for analytical purposes. Predictive ability of the models was assessed by coefficient of determination of cross-validation (R2 CV) and root mean square error of cross-validation. Predictions were good (R2 CV=0.84) for lightness (L*), (R2 CV=0.72) for redness (a*), (R2 CV=0.77) for yellowness (b*), (R2 CV=0.78) for pH, (R2 CV=0.73) for CP, (R2 CV=0.83) for EE (R2 CV=0.72) for moisture, (R2 CV=0.72) for DM and (R2 CV=0.74) for ash. From the results, it can be concluded that NIRS can be used for the rapid assessment of physico-chemical traits of chicken meatball.

Application of Deep-Learning Algorithm Driven Intelligent Raman Spectroscopy Methodology to Quality Control in the Manufacturing Process of Guanxinning Tablets.

Coupled with the convolutional neural network (CNN), an intelligent Raman spectroscopy methodology for rapid quantitative analysis of four pharmacodynamic substances and soluble solid in the manufacture process of Guanxinning tablets was established. Raman spectra of 330 real samples were collected by a portable Raman spectrometer. The contents of danshensu, ferulic acid, rosmarinic acid, and salvianolic acid B were determined with high-performance liquid chromatography-diode array detection (HPLC-DAD), while the content of soluble solid was determined by using an oven-drying method. In the establishing of the CNN calibration model, the spectral characteristic bands were screened out by a competitive adaptive reweighted sampling (CARS) algorithm. The performance of the CNN model is evaluated by root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), coefficient of determination of calibration (), coefficient of determination of cross-validation (), and coefficient of determination of validation (). The values for soluble solid, salvianolic acid B, danshensu, ferulic acid, and rosmarinic acid are 0.9415, 0.9246, 0.8458, 0.8667, and 0.8491, respectively. The established model was used for the analysis of three batches of unknown samples from the manufacturing process of Guanxinning tablets. As the results show, Raman spectroscopy is faster and more convenient than that of conventional methods, which is helpful for the implementation of process analysis technology (PAT) in the manufacturing process of Guanxinning tablets.

Evaluation of natural ageing responses on Burmese amber durability by FTIR spectroscopy with PLSR and ANN models

Amber ageing is an inevitable process, which is very important in precious organic gemstone relics protection. In order to explore the mechanism of amber ageing and estimate the durability of Burmese amber, this research investigates the changing spectral features of Burmese ageing amber via Fourier Transform Infrared Spectroscopy (FTIR) and solid 13C Nuclear Magnetic Resonance spectroscopy (NMR) and develops the regression models for its micro-hardness by micro-FTIR spectra. The Partial Least Squares Regression (PLSR) and Artificial Neural Networks (ANN) methods as well as Competitive Adaptive Reweighted Sampling (CARS) algorithm for wavelength variables selection have been applied to predict and assess the Vickers hardness of amber samples with different ageing degrees. As a result, the FTIR and the solid 13C NMR spectra reveal that the contents of CO groups (of esters) increase substantially, and which of the other oxygenic groups (CO (of acids), COC, COCC) increase modestly in amber ageing. When comparing with the results of four different models (PLSR, ANN, CARS-PLSR and CARS-ANN), the CARS-PLSR model obtained the optimal results as follows: the squared correlation coefficient of calibration(R2cal) is 0.9230 and the root mean square error of calibration (RMSEC) is 1.2977 HV; the squared correlation coefficient of prediction (R2pre) is 0.7762 and the root mean square error of prediction (RMSEP) is 2.2208 HV. The overall results sufficiently demonstrate that FTIR spectroscopy technique coupled with appropriate chemometrics methods are very promising tools to estimate and predict the hardness property of Burmese ageing amber.

Detection of encapsulant addition in butterfly-pea (Clitoria ternatea L.) extract powder using visible–near-infrared spectroscopy and chemometrics analysis

Abstract Butterfly-pea (Clitoria ternatea L.) extract powder is a functional product with numerous benefits obtained by extraction followed by the drying process. During drying, encapsulations can be added to protect the color and antioxidants of the samples. Using visible-near-infrared (Vis–NIR) spectroscopy, this research aimed to detect maltodextrin and soybean protein isolate (SPI) added as encapsulants to butterfly-pea extract powder. Butterfly-pea extract powder were added with 10, 20, 30, 40, and 50% concentrations of maltodextrin and SPI. Spectral data were acquired using a Vis–NIR fiber optic spectrometer at 350–1,000 nm. The chemometric methods used were principal component analysis (PCA), PCA-discriminant analysis (PCA–DA), partial least square regression (PLSR), and partial least square discriminant analysis (PLS-DA). The results showed that PCA can discriminate pure and maltodextrin- and SPI-added samples using low principal components. PCA-DA determined the accuracy levels of 88% for maltodextrin and 94.67% for SPIs. The PLSR models predicted the addition of maltodextrin with the following variables: coefficient of determination of calibration (R c 2), 0.98; coefficient of determination of prediction (R p 2), 0.98; root mean square error of calibration (RMSEC), 2.1%; and root mean square error of prediction (RMSEP), 4.02%. The values for the addition of SPI were R c 2 of 0.97, R p 2 of 0.97, RMSEC of 2.72%, and RMSEP of 2.83%. The PLS-DA models resulted in an accuracy of 98 and 91% for the identification of maltodextrin and SPI, respectively. In conclusion, this research showed the potency of Vis–NIR spectroscopy combined with a proper chemometric analysis to detect additives in butterfly-pea extract powders.

Hyperspectral imaging combined with chemometrics for rapid detection of talcum powder adulterated in wheat flour

Detection of illegal additives in wheat flour is of crucial practical significance to ensure the market order and people's health. In this study, the potential of hyperspectral imaging in 900–1700 nm for the rapid detection of talcum powder adulterated in wheat flour was investigated. The raw spectral information within the hyperspectral images of samples were extracted and preprocessed with standard normal variate (SNV), baseline correction (BC), multiplicative scatter correction (MSC) and Gaussian filter smoothing (GFS), respectively. Partial least square (PLS) quantitative models with the preprocessed spectra were constructed and assessed to predict the talcum powder concentration in wheat flour. The results showed that the SNV-PLS model had best performance, with correlation coefficient (r) of 0.98 for both calibration and prediction, residual predictive deviation (RPD) of 4.69, and root mean square error of calibration (RMSEC), cross-validation (RMSECV) and prediction (RMSEP) of 2.86%, 2.99%, 3.13%, respectively. The characteristic wavelengths were then respectively selected by regression coefficient (RC), successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithm to simplify the SNV-PLS model. Based on the four characteristic wavelengths (907.135, 1339.866, 1392.573 and 1394.22 nm) selected by CARS method, the SNV-CARS-PLS model was constructed and had best performance in predicting talcum powder content, leading to rP of 0.98, RMSEP of 2.88% and RPD of 5.09. The whole study indicated that the hyperspectral imaging in 900–1700 nm range combined with CARS method could be used for further developing a portable detection equipment to realize the rapid and accurate talcum detection in wheat flour.

Application of Fourier Transform Infrared (FT-IR) Spectroscopy, Multispectral Imaging (MSI) and Electronic Nose (E-Nose) for the Rapid Evaluation of the Microbiological Quality of Gilthead Sea Bream Fillets.

The potential of Fourier transform infrared (FT-IR) spectroscopy, multispectral imaging (MSI), and electronic nose (E-nose) was explored in order to determine the microbiological quality of gilthead sea bream (Sparus aurata) fillets. Fish fillets were maintained at four temperatures (0, 4, 8, and 12 °C) under aerobic conditions and modified atmosphere packaging (MAP) (33% CO2, 19% O2, 48% N2) for up to 330 and 773 h, respectively, for the determination of the population of total viable counts (TVC). In parallel, spectral data were acquired by means of FT-IR and MSI techniques, whereas the volatile profile of the samples was monitored using an E-nose. Thereafter, the collected data were correlated to microbiological counts to estimate the TVC during fish fillet storage. The obtained results demonstrated that the partial least squares regression (PLS-R) models developed on FT-IR data provided satisfactory performance in the estimation of TVC for both aerobic and MAP conditions, with coefficients of determination (R2) for calibration of 0.98 and 0.94, and root mean squared error of calibration (RMSEC) values of 0.43 and 0.87 log CFU/g, respectively. However, the performance of the PLS-R models developed on MSI data was less accurate with R2 values of 0.79 and 0.77, and RMSEC values of 0.78 and 0.72 for aerobic and MAP storage, respectively. Finally, the least satisfactory performance was observed for the E-nose with the lowest R2 (0.34 and 0.17) and the highest RMSEC (1.77 and 1.43 log CFU/g) values for aerobic and MAP conditions, respectively. The results of this work confirm the effectiveness of FT-IR spectroscopy for the rapid evaluation of the microbiological quality of gilthead sea bream fillets.

Development of optimized regression model for Raman spectral analysis of pharmaceutical drugs cefixime and sitagliptin

To build a robust model for the quantification of pharmaceutical drugs, different regression algorithms and preprocessing techniques are employed. In this study, Raman spectroscopy is used for the analysis of solid dosage formulations of two drugs, cefixime and sitagliptin. Using the different amounts of excipients and active pure ingredients (API), the different concentrations of cefixime and sitagliptin are made. The principal component regression (PCA), partial least-squares regression (PLSR), and PLSR based on feature selection by variable importance in projection (VIP) scores are used to regress the model to quantify the API concentration of cefixime and sitagliptin in a mixture of excipients. These models are built on both normalized and unnormalized data of cefixime and sitagliptin to check the performance of the constructed models. By comparing errors of all trained models, it is observed that the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were minimum in the case of PLSR model construction on unnormalized data of both cefixime and sitagliptin.

RhB@MOF-5 Composite Film as a Fluorescence Sensor for Detection of Chilled Pork Freshness.

This study presents a novel composite thin film based on rhodamine B encapsulated into MOF-5 (Metal Organic Frameworks) as a fluorescence sensor for the real-time detection of the freshness of chilled pork. The composite film can adsorb and respond to the volatile amines produced by the quality deterioration of pork during storage at 4 °C, with the fluorescence intensity of RhB decreasing over time. The quantitative model used for predicting the freshness indicator (total volatile base nitrogen) of pork was built using the fluorescence spectra (excited at 340 nm) of the RhB@MOF-5 composite film combined with the partial least squares (PLS) algorithm, providing Rc2 and Rp2 values of 0.908 and 0.821 and RMSEC (root mean square error of calibration) and RMSEP (root mean square error of prediction) values of 3.435 mg/100 g and 3.647 mg/100 g, respectively. The qualitative model established by the partial least squares discriminant analysis (PLS-DA) algorithm was able to accurately classify pork samples as fresh, acceptable or spoiled, and the accuracy was 86.67%.

Application of principal component analysis‐multivariate adaptive regression splines for the determination of surface area in zeolites

AbstractIn this study, the principal component analysis‐multivariate adaptive regression splines (PCA‐MARS) technique was applied to efficiently predict the surface area in zeolites. Before building the MARS model, the data matrix obtained from infrared spectroscopy was subjected to robust principal component analysis (rPCA) and six principal component (PC) scores with the explained validation variance 97.983% were used as inputs to the MARS algorithm. The training set (72 × 6) was used to develop the rPCA‐MARS model, and the efficiency of the proposed method was evaluated in terms of coefficient of determination (R2), R2 estimated by generalized cross‐validation (R2GCV), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and mean absolute error (MAE). R2, RMSEC, RMSEP, MAE, and R2GCV in the piecewise‐linear rPCA‐MARS model were 0.9976, 1.4166, 1.8370, 1.0120, and 0.9955, respectively. We proposed also to use varimax (Var) rotated of the significant principal components as input to the MARS algorithm (R2 = 0.9964, RMSEC = 1.4214, RMSEP = 1.9031, MAE = 1.0511, and R2GCV = 0.9951). Upon viewing the PCA‐Var‐MARS results, we concluded that the obtained results are comparable with rPCA‐MARS results. In this study, principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR) methods were also used for the quantitative determination of the surface area of zeolites. The RMSEP for PCR, PLSR, and SVMR was 11.5636, 8.7111, and 7.2426, respectively. Based on the results obtained, the MARS model is more reliable than commonly used regression models. It can be concluded that the rPCA‐MARS method is an appropriate method for analyzing spectral data to determine the textural properties of zeolite.

Rapid Qualitative and Quantitative Characterization of Arnebiae Radix by Near-Infrared Spectroscopy (NIRS) with Partial Least Squares—Discriminant Analysis (PLS-DA)

Near infrared (NIR) spectroscopy with chemometrics was used for rapid source identification and component quantification of Arnebiae Radix. The modeling parameters were determined using the processing trajectory method. The quantitative model of target components was established using the model fusion method. The root mean square error of calibration (RMSEC), root mean square error of validation (RMSEP), determination coefficient of calibration (Rcal 2 ) and validation (Rpre 2 ), and the ratio of standard error of prediction to standard deviation (RPD) were used to evaluate the constructed model. The RMSEC, Rcal 2 , RMSEP, Rpre 2 , and RPD of β-β'-dimethylacryloacanin partial least square (PLS) model established using the step-by-step strategy were 1.3004 mg/g, 0.9453, 1.3833 mg/g, 0.9544, and 4.6650, respectively. The RMSEC, Rcal 2, RMSEP, Rpre 2 , and RPD of total pigment PLS model were 4.5095 mg/g, 0.9533, 4.5679 mg/g, 0.9534, and 3.0509, respectively. The RMSEC, Rcal 2 , RMSEP, Rpre 2 , and RPD of β-β'-dimethylacryloacanin Bagging-PLS model established using the process trajectory were 1.6126 mg/g, 0.9159, 1.2254 mg/g, 0.9649, and 5.2662, respectively. The RMSEC, Rcal 2 , RMSEP, Rpre 2 , and RPD of the total pigment Bagging-PLS model were 4.3499 mg/g, 0.9565, 4.4662 mg/g, 0.9116, and 3.1205, respectively. The accuracy of quantitative model may be improved using the process trajectory and model fusion method. Partial least squares discriminant analysis (PLS-DA) was used to establish the discriminant models of different sources of Arnebiae Radix. The predicted accuracy rate was 100%. The method is simple, rapid, and demonstrated for the discrimination and quantitative analysis of the main active ingredients of Arnebiae Radix.

Detection of calcium homogeneity distribution in magnesia-aluminum spinel using laser-induced breakdown spectroscopy

To further improve the yield of producing magnesium aluminum spinel and detect the distribution of sintering aid in raw materials, the Mg–Al spinel samples were prepared using MgO and Al2O3 as raw materials and CaO as sintering aids. Information on the distribution of the sintering aid Ca element in mixed samples was rapidly obtained by laser-induced breakdown spectroscopy (LIBS) mapping using a picosecond laser and a closed chamber filled with Ar gas. The optimal mixing time and environmental parameters of the raw materials were determined through optimization experiments, and the calibration models of Ca/Al and Ca/Mg were established with the intensities of Al and Mg characteristic lines as the internal standard. The correlation coefficient R2, Root Mean Square Error of Calibration (RMSEC) and Mean Relative Error (MRE) of different calibration curves were evaluated. The feasibility of LIBS technique to detect the uniform distribution of sintering aids in Mg–Al spinel is verified by comparing with the images scanned by X-ray energy dispersive spectroscopy (EDS). Finally, we performed LIBS scans in the depth direction and obtained the spatial distribution of the Ca element. This work opens up a new research route for realizing the application of LIBS analysis in magnesia-aluminum spinel.

At‐line monitoring of industrial corn seed classification quality by digital images analysis using smartphone

This study aimed to evaluate the feasibility of using the PhotoMetrix PRO® application to perform the selection of good quality corn seeds from defective ones, through digital images and the multivariate analysis of color elements. An optimization of the experimental conditions was carried out. Eighteen experiments were performed varying the size of the analysis recipient, the distance of the sample from the smartphone and the size of the region of interest (ROI). A calibration model was built using a partial least squares (PLS) regression model in the determined experimental conditions, and the prediction of external samples was then performed. The conditions that provided the smallest cross-validation error consisted of using the smaller chamber, the largest ROI, and the longest distance from the smartphone to the sample. The coefficient of determination obtained by the model was .976. In addition, the root-mean-square error of calibration (RMSEC) and the root-mean-square error of cross-validation (RMSECV) obtained were 1.2% and 4.8%, respectively. In the prediction, two outliers were observed, and the RMSEP dropped to 2.2% when they were removed. The proposed methodology shows great potential to contribute to the industrial process of corn seed production. It is fast, accurate, and low cost, allowing the analysis to be performed 15 times faster than the current process. Practical Applications Corn is one of the most important commodities in Brazil's economy. During the processing of corn seeds in the industry, those of good quality are selected among the others through parameters, such as color and density, for example. The gravity table and the Color Sorter machine are equipment used for this purpose. However, even if the machine's separation is effective, an important percentage of good seeds are wasted. To reduce the waste of good seeds remaining in the tailings of these machines, the operators carry out a manual inspection of tailings samples, determining the quantity of quality seeds present there for later adjustment of the machines. This visual and manual analysis becomes time-consuming, subjective, and imprecise. Thus, as an alternative, this study proposes the use of digital image and partial least square model built by a mobile app to determine the quantity of quality corn seeds in the machines' wastes.

Determination of aflatoxin B1 level in rice (Oryza sativa L.) through near-infrared spectroscopy and an improved simulated annealing variable selection method

Direct quantification analysis of near-infrared (NIR) spectra is challenging because the number of spectral variables is usually considerably higher than the number of samples. To mitigate the so-called curse of dimensionality, variable selection is often performed before multivariate calibration. There has been much work in this regard, where the developed variable selection method can be categorized as individual variable selection, such as uninformative variable elimination or variable importance in projection, and continuous interval variable selection method such as interval partial least squares or moving window partial least squares. In this study, a new individual variable selection method, modified simulated annealing (MSA), was proposed and used in conjunction with the partial least squares regression (PLSR) model. The interpretability of the selected variables in the determination of aflatoxin B1 levels in white rice was assessed. The results revealed that the PLSR model combined with MSA not only yielded higher accuracy than the full-spectrum PLSR but also successfully shrank the variable space. The developed simplified PLSR model using MSA produced satisfactory performances, with root mean square error of calibration (RMSEC) of 0.11 μg/kg and 0.56 μg/kg, and root mean square error of prediction (RMSEP) of 7.16 μg/kg and 14.42 μg/kg, were obtained for the low-aflatoxin B1-level- and high-aflatoxin-B1-level samples, respectively. Specifically, the MSA-based models yielded improvements of 97.80% (calibration set) and 44.62% (prediction set) as well as 95.85% (calibration set) and 62.57% (prediction set) for both datasets when compared with the full-spectrum PLSR (low aflatoxin: RMSEC = 5.02 μg/kg, RMSEP = 12.93 μg/kg; high aflatoxin: RMSEC = 13.50 μg/kg, RMSEP = 38.53 μg/kg). Compared with the baseline method of simulated annealing (SA) (low aflatoxin: RMSEC = 0.21 μg/kg, RMSEP = 9.78 μg/kg; high aflatoxin: RMSEC = 12.27 μg/kg, RMSEP = 38.53 μg/kg), the MSA significantly improved the predictive performance of the regression models, with the number of selected variables being almost half of that in the SA. A comparison with other commonly used variable selection methods of selectivity ratio (low aflatoxin: RMSEC = 6.09 μg/kg, RMSEP = 13.75 μg/kg; high aflatoxin: RMSEC = 13.74 μg/kg, RMSEP = 41.13 μg/kg), uninformative variable elimination (low aflatoxin: RMSEC = 0.32 μg/kg, RMSEP = 5.11 μg/kg; high aflatoxin: RMSEC = 3.80 μg/kg, RMSEP = 17.76 μg/kg), and variable importance in projection (low aflatoxin: RMSEC = 2.67 μg/kg, RMSEP = 10.71 μg/kg; high aflatoxin: RMSEC = 13.51 μg/kg, RMSEP = 32.53 μg/kg) also indicated the promising efficacy of the proposed MSA.