Calibration Correlation Research Articles

Calibration and volunteer testing of a prototype contactless respiratory motion detection system based on laser tracking.

The goal of this study was to assess the feasibility of a cost-effective prototype of a laser-based respiratory motion detection system utilizing a Leuze LDS for breath monitoring through calibration and volunteer tests. This study was performed using the Anzai AZ-773V and computerized imaging reference systems (CIRS) motion phantoms for calibration tests. The calibration of the laser-based respiratory motion detection system involved spatial accuracy testing, amplitude calibration, and temporal accuracy. Volunteer testing was conducted on eight volunteers at the inferior end of the sternum and the abdomen area. The accuracy of the data recorded by the laser-based respiratory motion detection system was validated against established clinical reference tracking systems namely real-time position management (RPM) and Anzai AZ-733V system. Calibration with an Anzai AZ-773V and CIRS phantoms demonstrated an average error of 1.17%±0.64% and an average amplitude calibration correlation coefficient of 0.975±0.004. Volunteer tests, compared to the Anzai AZ-733V clinical system and RPM system, revealed average correlation coefficients for deep inspiration breath-hold are 0.931±0.02 and 0.936±0.03, respectively, and for free breathing are 0.85±0.07 and 0.77±0.1, respectively. Overall, the data suggest that the in-house laser-based respiratory motion detection system performed well, with an error percentage below 10%. A reasonably good correlation coefficient was obtained, indicating that the readings obtained from the laser system are consistent with those set on the phantom and clinical respiratory motion detection systems. Although promising through the calibration process and volunteer tests, further studies are required to generate trigger data linked directly to computerized tomography and linear accelerator facilities, thereby advancing the clinical viability of this innovative laser-based respiratory motion detection system.

Analysis of Total Flavonoid Variation and Other Functional Substances in RILs of Tartary Buckwheat, with Near-Infrared Model Construction for Rapid Non-Destructive Detection

According to the requirements of Tartary buckwheat breeding, it is necessary to develop a method for the rapid detection of functional substances in seeds. To ensure a diverse sample pool, we utilized the stable recombinant inbred lines (RILs) of Tartary buckwheat. The coefficients of variation of the total flavonoid, vitamin E (VE), and GABA contents of the RIL population were 15.06, 16.53, and 36.93, respectively. Subsequently, we established prediction models for the functional substance contents in Tartary buckwheat using near-infrared spectroscopy (NIRS) combined with chemometrics. The Kennard–Stone algorithm divided the dataset into training and test sets, employing six different methods for preprocessing spectra. The Competitive Adaptive Reweighted Sampling algorithm extracted the characteristic spectra. The best models for total flavonoid and VE were normalized using the first derivative. The calibration correlation coefficient (Rc) and prediction correlation coefficient (Rp) of the total flavonoid and VE prediction models were greater than 0.94. The optimal GABA prediction model underwent preprocessing via normalization combined with the standard normal variate, and the Rc and Rp values were greater than 0.93. The results demonstrated that the NIRS-based prediction model could satisfy the requirements for the rapid determination of total flavonoids, VE, and GABA in Tartary buckwheat seeds.

Variation Analysis of Starch Properties in Tartary Buckwheat and Construction of Near-Infrared Models for Rapid Non-Destructive Detection.

Due to the requirements for quality testing and breeding Tartary buckwheat (Fagopyrum tartaricum Gaerth), it is necessary to find a method for the rapid detection of starch content in Tartary buckwheat. To obtain samples with a continuously distributed chemical value, stable Tartary buckwheat recombinant inbred lines were used. After scanning the near-infrared spectra of whole grains, we employed conventional methods to analyze the contents of Tartary buckwheat. The results showed that the contents of total starch, amylose, amylopectin, and resistant starch were 532.1-741.5 mg/g, 176.8-280.2 mg/g, 318.8-497.0 mg/g, and 45.1-105.2 mg/g, respectively. The prediction model for the different starch contents in Tartary buckwheat was established using near-infrared spectroscopy (NIRS) in combination with chemometrics. The Kennard-Stone algorithm was used to split the training set and the test set. Six different methods were used to preprocess the spectra in the wavenumber range of 4000-12,000 cm-1. The Competitive Adaptive Reweighted Sampling algorithm was then used to extract the characteristic spectra, and the prediction model was built using the partial least squares method. Through a comprehensive analysis of each parameter of the model, the best model for the prediction of each nutrient was determined. The correlation coefficient of calibration (Rc) and the correlation coefficient of prediction (Rp) of the best models for total starch and amylose were greater than 0.95, and the Rc and Rp of the best models for amylopectin and resistant starch were also greater than 0.93. The results showed that the NIRS-based prediction model fulfilled the requirement for the rapid determination of Tartary buckwheat starch, thus providing an effective technical approach for the rapid and non-destructive testing of starch content in the food science and agricultural industry.

Assessing the Impact of Groundwater Recharge on Underground Reservoir Replenishment in Eastern Uttar Pradesh, India

Groundwater is considered a fresh resource of water and its uses have tremendously increased in the recent past due to an increase in population, rapid urbanization, and industrialization. In India, the groundwater level is declining in some parts of the country due to over-exploitation, low or negligible recharge of aquifer systems, and unsustainable development of groundwater resources. The groundwater modeling is an important tool for studying the past and present groundwater behavior and in the development of future strategies for sustainable groundwater management plans. To study the Impact of groundwater recharge on the replenishment of underground reservoir, Ballia district of Uttar Pradesh has been selected which is one of the districts of the most populous state of India, Uttar Pradesh. An attempt has been made to develop a groundwater model using Modflow software to simulate the groundwater trends and predict future groundwater heads. The calibration and validation of the model were done for 5 years and 3 years respectively. The correlation coefficient for calibration and validation was found 0.85 and root mean square errors vary from 2.89 to 3.2m variation in future trends of groundwater heads. The results of the study show that the developed model can be effectively used to predict the future groundwater heads. The groundwater flow was observed from the northwest to southeast direction. It was predicted from the study that groundwater draft will increase by 10% with a decrease in groundwater level by approximately 0.24 m in the north-west direction by the year 2025. However, no impact was observed in the south side of the district and it was predicted that the groundwater level would remain the same in this zone during the next 3 years.

Prediction of antioxidant enzyme activity in tomato leaves based on microhyperspectral imaging technique

Antioxidant enzymes play an important role in defending of tomato leaves against damage under different types of adversity stresses. In this paper, hyperspectral imaging (HSI) technology was used to predict the peroxidase (POD), catalase (CAT) and superoxide dismutase (SOD) activities in tomato leaves. Spectra was preprocessed using Baseline, Smoothing and Normalize methods. Characteristic wavelengths were selected using competitive adaptive reweighted sampling (CARS), interval random frog (IRF) and genetic algorithm-combined partial least squares (GAPLS). Macroscopic prediction models for antioxidant enzyme activity were developed using partial least squares regression (PLSR), principal components regression (PCR) and least square support vector machines (LSSVM) algorithms. To establish microscopic prediction models for microzone cell image and spectral detection, the antioxidant enzyme activity in the observed leaf area was migrated to the microzone using the macroscopic prediction model and the ratio of microzone area to sliced leaf area. As results of model evaluation, the correlation coefficients of calibration (Rc) and prediction (Rp) were 0.853 and 0.832 for POD activity, 0.790 and 0.715 for CAT activity, and 0.975 and 0.937 for SOD activity, respectively. The results indicated the possibility of transfer learning between macroscopic and microscopic models in quantitatively predicting antioxidant enzymes in tomato leaves by HSI. The findings in this study provided a reference data in quantitatively detection of trace substances in tomato leaves and suggested macroscopic to microscopic modelling approaches for the future HSI applications.

Quantitative prediction of AFB1 in various types of edible oil based on absorption, scattering and fluorescence signals at dual wavelengths

This study aims to address the challenge of matrix interference of various types of edible oils on intrinsic fluorescence of aflatoxin B1 (AFB1) by developing a novel solution. Considering the fluorescence internal filtering effect, the absorption (μa) and reduced scattering (μ’s) coefficients at dual wavelengths (excitation: 375 nm, emission: 450 nm) were obtained by using integrating sphere technique, and were used to improve the quantitative prediction results for AFB1 contents in six different kinds of edible oils. A research process of “Monte Carlo (MC) simulation - phantom verification - actual sample validation” was conducted. The MC simulation was used to determine interference rule and correction parameters for fluorescence, the results indicated that the escaped fluorescence flux nonlinearly decreased with the μa, μ’s at emission wavelength (μa,em, μ’s,em) and μa at excitation wavelength (μa,ex), however increased with the μ’s at excitation wavelength (μ’s,ex). And the required optical parameters to eliminate the interference of matrix on fluorescence intensity are: effective attenuation coefficients at excitation and emission wavelengths (μeff,ex, μeff,em) and μ’s,ex. Phantom verification was conducted to explore the feasibility of fluorescence correction based on the identified parameters by MC simulation, and determine the optimal machine learning method. The modelling results showed that least squares support vector regression (LSSVR) model could reach the best performance. Three kinds of edible oil (peanut, rapeseed, corn), each with two brands were used to prepare oil samples with different AFB1 contamination. The LSSVR model for AFB1 based on μeff,ex, μeff,em, μ’s,ex and fluorescence intensity at 450 nm was calibrated, both correlation coefficients for calibration (Rc) and the validation (Rv) sets could reach 1.000, root mean square errors for calibration (RMSEC) and the validation (RMSEV) sets were as low as 0.038 and 0.099 respectively. This study proposed a novel method which is based solely on the absorption, scattering, and fluorescence characteristics at excitation and emission wavelengths to achieve accurate prediction of AFB1 content in different types of vegetable oils.

Predicting ruminal degradability and chemical composition of corn silage using near-infrared spectroscopy and multivariate regression.

The aim of this study was to develop and validate regression models to predict the chemical composition and ruminal degradation parameters of corn silage by near-infrared spectroscopy (NIR). Ninety-four samples were used to develop and validate the models to predict corn silage composition. A subset of 23 samples was used to develop and validate models to predict ruminal degradation parameters of corn silage. Wet chemistry methods were used to determine the composition values and ruminal degradation parameters of the corn silage samples. The dried and ground samples had their NIR spectra scanned using a poliSPECNIR 900-1700 model NIR sprectrophotometer (ITPhotonics S.r.l, Breganze, IT.). The models were developed using regression by partial least squares (PLS), and the ordered predictor selection (OPS) method was used. In general, the regression models obtained to predict the corn silage composition (P>0.05), except the model for organic matter (OM), adequately estimated the studied properties. It was not possible to develop prediction models for the potentially degradable fraction in the rumen of OM and crude protein and the degradation rate of OM. The regression models that could be obtained to predict the ruminal degradation parameters showed correlation coefficient of calibration between 0.530 and 0.985. The regression models developed to predict CS composition accurately estimated the CS composition, except the model for OM. The NIR has potential to be used by nutritionists as a rapid prediction tool for ruminal degradation parameters in the field.

FT-MIR-ATR Associated with Chemometrics Methods: A Preliminary Analysis of Deterioration State of Brazil Nut Oil.

Brazil nut oil is highly valued in the food, cosmetic, chemical, and pharmaceutical industries, as well as other sectors of the economy. This work aims to use the Fourier transform infrared (FTIR) technique associated with partial least squares regression (PLSR) and principal component analysis (PCA) to demonstrate that these methods can be used in a prior and rapid analysis in quality control. Natural oils were extracted and stored for chemical analysis. PCA presented two groups regarding the state of degradation, subdivided into super-degraded and partially degraded groups in 99.88% of the explained variance. The applied PLS reported an acidity index (AI) prediction model with root mean square error of calibration (RMSEC) = 1.8564, root mean square error of cross-validation (REMSECV) = 4.2641, root mean square error of prediction (RMSEP) = 2.1491, R2cal (calibration correlation coefficient) equal to 0.9679, R2val (validation correlation coefficient) equal to 0.8474, and R2pred (prediction correlation coefficient) equal to 0, 8468. The peroxide index (PI) prediction model showed RMSEC = 0.0005, REMSECV = 0.0016, RMSEP = 0.00079, calibration R2 equal to 0.9670, cross-validation R2 equal to 0.7149, and R2 of prediction equal to 0.9099. The physical-chemical analyses identified that five samples fit in the food sector and the others fit in other sectors of the economy. In this way, the preliminary monitoring of the state of degradation was reported, and the prediction models of the peroxide and acidity indexes in Brazil nut oil for quality control were determined.

Application Research of Visible Near-Infrared Spectroscopy Technology for Detecting Intracerebral Hematoma

To explore the visible near-infrared spectroscopic (VNIRS) characteristics of intracerebral hematoma, and provide experimental basis for hematoma localization and residual detection in hypertensive intracerebral hemorrhage (HICH) surgery. Using VNIRS, spectral data of cerebral hematoma and cortex were collected during HICH craniotomy, and characteristic spectra were matched with paired-sample T-test. A partial least squares (PLS) quantitative model for cerebral hematoma spectra was established. The reflectance of cerebral hematoma spectra in the 500-800nm band was lower than that of the cortex, and there were statistically significant differences in the 510, 565, and 630nm bands (P < 0.05). The calibration correlation coefficient of the PLS quantitative model for cerebral hematoma spectra was R2= 0.988, the cross-validation correlation coefficient was R2cv=0.982, the root mean square error of calibration was RMSEC=0.101, the root mean square error of cross-validation was RMSEV=0.122, the external validation correlation coefficient was CORRELATION=0.902, and the root mean square error of prediction was RMSEP=0.426, indicating that the model had high fitting degree and good predictive ability. VNIRS as a noninvasive, real-time and portable analysis technology, can be used for real-time detection of hematoma during HICH surgery, and provide reliable basis for hematoma localization and residual detection.

Robust principal component analysis-multivariate adaptive regression splines (rPCA-MARS) model for determining total acid number (TAN) and total base number (TBN) of crude oil samples using attenuated total reflectance fourier transform infrared (ATR-FTIR) spectroscopy

Rapid assessments of Total Acid Number (TAN) and Total Base Number (TBN) in crude oil samples are significant in the oil industry. The analysis of TAN and TBN was conducted using the standard methods of the American Society for Testing and Materials (ASTM). However, the standard methods are costly and require a large number of samples for analysis. Therefore, providing analytical methods for the rapid crude oil analysis is very essential. In the current research, we report an application of Attenuated Total Reflection Fourier-Transform Infrared (ATR-FTIR) spectroscopy, based on robust Principal Component Analysis-Multivariate Adaptive Regression Splines (rPCA-MARS), for crude oil analysis. In the rPCA-MARS model, the ATR-FTIR spectral matrix data was decomposed into PCs scores using the rPCA method as an input data for the MARS model. The multivariate calibration models were applied to the analysis of crude oil samples based on the quantitative determination of total acid number (TAN) and total base number (TBN). An analytical method was developed for determination of TAN and TBN of crude oil samples. The ATR-FTIR spectroscopy coupled with multivariate calibration methods can be applied as a novel analytical method for crude oil samples. The result of partial least square regression (PLS-R), principle component analysis (PCR), Piecewise-Linear rPCA-MARS and Piecewise-cubic rPCA-MARS models were compared for analysis of total acid and base number of crude oil samples. The squared correlation coefficient (R2) and root mean square error (RMSE) for calibration and prediction sets were calculated to evaluate multivariate calibration models. The importance of methods was studied and discussed. The mean square error (MSE) values of Piecewise-Linear rPCA-MARS for TAN and TBN were 1.587 * 10−4 and 0.009, respectively. The Piecewise-Linear rPCA-MARS model can be successfully applied for the analysis of TAN and TBN of crude oil samples based on the obtained results. The fast and easy prediction capability of the proposed Piecewise-Linear rPCA-MARS model, as compared to standard methods, for determining TAN and TBN without any sample preparation step is important in the oil industry

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

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

Non-destructive detection of polysaccharides and moisture in Ganoderma lucidum using near-infrared spectroscopy and machine learning algorithm

Ganoderma lucidum, the fruiting body of the Poraceae fungi G. lucidum or Ganoderma sinense, has a long history of use in promoting health and longevity in Asian countries. However, traditional methods for detecting polysaccharides and moisture in G. lucidum are complicated, time-consuming, and damaging (to the sample). In this study, rapid and nondestructive near-infrared (NIR) spectroscopy (700–2500 nm) was uesd to directly scan the back of the G. lucidum cap without powdering. Thereafter, we used synergy interval partial least squares to select the performing band and the ant lion optimization (ALO) algorithm to optimize the least squares support vector machine (LSSVM) model for these two components. The results showed that the ALO-LSSVM model could predict the total polysaccharide and moisture content with high accuracy. The correlation coefficient for calibration were both >0.9 and their ratio of prediction to deviation (RPD) values of prediction were 2.6 and 3.6, respectively, indicating the non-destructive determination of polysaccharides and moisture in G. lucidum will provide great convenience for on-site testing by procurement personnel. This shows the combination of NIR spectroscopy and the ALO-LSSVM algorithm has potential applications for the rapid and nondestructive analysis of natural products.

Development and Field Testing of an Online Monitoring System for Atmospheric Particle-Bound Reactive Oxygen Species (ROS)

Excessive accumulation of reactive oxygen species (ROS) in the body can lead to a redox imbalance and result in cellular and tissue damage. Since ROS are highly reactive, traditional offline methods may underestimate their true concentration. In this study, we developed an online monitoring system for particle-bound ROS based on the fluorescent probe 2′,7′-dichlorofluorescin (DCFH), which consists of an Aerosol Collector and a Fluorescence Detector. The performance of the system was evaluated in terms of collection efficiency, instrument calibration, and comparison with offline methods. The results demonstrate that the collection efficiency of the system is over 93%, the calibration correlation coefficient (R2) is 99.75%, and the online system reduces ROS loss due to offline methods by more than 60%. The system has a temporal resolution of 20 min and the limit of detection of the system was 1.9 nmol H2O2/m3. Field observations revealed that particle-bound ROS exhibited similar diurnal variations to O3, and photochemical reactions were the main factors affecting its diurnal variation.

Rapid Detection of Tea Polyphenols in Fresh Tea Leaves Based on Fusion of Visible/Short-Wave and Long-Wave near Infrared Spectroscopy and Its Device Development

Tea polyphenols are considered as an important indicator of tea quality. Rapid detection of tea polyphenol content plays a valuable role for tea breeding and quality inspection during tea production. In this work, a portable rapid non-destructive detection device of tea polyphenols in fresh tea leaves was developed, which integrated the fusion technology of visible/short-wave (400–1050 nm) and long-wave (1000–1650 nm) near-infrared spectroscopy (Vis/NIR). Experimental results indicated that the spectra within the overlapping region (1000–1050 nm) were assembled by applying the spectral data fusing method. Followed by spectral data preprocessing with the Savitzky–Golay smoothing (SG) method, least squares support vector regression (LS–SVR) models were established for detecting the tea polyphenol content of fresh tea leaves. Based on the fused Vis/NIR spectra (dual-band), the correlation coefficient of calibration (RC), root mean square error of calibration (RMSEC), correlation coefficient of prediction (RP), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) reached 0.976, 0.679%, 0.893, 0.897%, and 2.230, respectively, which were better than the visible/short-wave or long-wave near infrared spectral data (single-band). The sensitive spectral wavebands of tea polyphenols extracted using the random frog (RF) algorithm were distributed in 402–448 nm, 555–600 nm, 810–1042 nm, 1056–1103 nm, 1219–1323 nm, 1406–1416 nm, and 1499–1511 nm. This demonstrated that the prediction of tea polyphenol content using fused spectral data combined with the LS–SVR model depended on various functional groups such as auxochromes, chromogenic groups, and hydrogen-containing groups. The proposed device is capable of non-destructive detection of tea polyphenol content in fresh tea leaves, which can provide effective technical support for tea breeding and tea leaf quality control.

Effect of spectrum measurement position on detection of Klason lignin content of snow pears by a portable NIR spectrometer

AbstractSnow pears are an important and widespread agricultural product that can relieve respiratory symptoms, constipation, and alcoholism. Lignin content (LC) has a direct and negative role on the fruit texture and taste of snow pears. Here, we studied the effect on the near‐infrared (NIR) spectroscopy determination of the LC in snow pears due to the position at which spectral measurements were obtained. NIR diffuse reflection spectra were collected from nine measurement positions on each sample by a portable NIR spectrometer. Partial least squares regression (PLSR) was used to develop spectrum compensation models of the LC for three local spectrum models, an average spectrum model, and a global spectrum model. The results indicated that the prediction accuracy of the LC was affected by the spectral measurement position. Compared with the local spectrum models and the global spectrum model, the average spectrum model had good prediction results. Next, synergy interval partial least squares, bootstrapping soft shrinkage, competitive adaptive reweighted sampling, genetic algorithm, and an improved variable stability and frequency analysis algorithm (VSFAA) method were used to select the most effective variables to build the PLSR model. The average spectrum calibration model established using the 10 effective variables selected by VSFAA reduced the influence of the variation of the spectral measurement position for LC prediction and achieved more promising results, with the correlation coefficient of calibration and prediction of 0.842 and 0.824, respectively. The root mean square error of cross‐validation and prediction were 0.736 and 0.694, respectively. The overall results showed that the average spectrum model based on the nine spectral measurement positions reduced the sensitivity to the variation of spectral measurement position for predicting the LC and combined the VSFAA variable selection algorithm to improve the accuracy and provide a robust model for prediction of LC in snow pears. Compared with the local spectrum position models and the global spectrum position model, the average spectrum position model combining the nine measurement positions (three stem‐calyx longitudes intersected three latitudes (stem, equator, calyx)) produced good prediction results and reduced the sensitivity to the variation of the spectral measurement position. The effective wavelengths (SNV‐VSFAA‐PLSR)‐average spectrum position model achieved good results, reducing the influence of the variation of the spectral measurement position for LC prediction, and the effective wavelengths selected from the average spectrum position model were helpful for offsetting the influence of the variation of spectral measurement position on the PLSR models based on the spectrum from the equatorial positions alone.

Support vector machine-based rapid detection and quantification of butter yellow adulteration in mustard oil using NIR spectra

Butter yellow (BY; 3′-methyl-4-dimethyl-aminoazobenzene) is a carcinogenic adulterant of edible mustard oil. In this study, Fourier-transform near-infrared (FT-NIR) spectral data was combined with multivariate data analysis to both detect and quantify BY adulteration in mustard oil. Fifteen pure mustard oil and one hundred fifty mustard oil samples with BY (0.1–1% w/v) as the adulterant were evaluated. It was observed that sonication- based processing of adulterated samples provides sensitive fingerprints during FT-NIR analysis when compared to heat- based processing. Principal component analysis (PCA) of spectral regions 6000–6064 cm−1 showed well-defined discrimination of pure and adulterated mustard oil with 100% variability. For quantification of BY both NIPALS (non-linear iterative partial least squares) based partial least square (PLS) and LS-SVM (least squares support vector machine) were carried for full-spectrum as well as selected fingerprints using RCGA (real coded genetic algorithm), a variable selection method. Overall, RCGA-LS-SVM model showed best calibration and prediction model with high precision and accuracy based on low root mean square error of cross validation (RMSECV = 0.0073) and prediction (RMSEP = 0.0060). RCGA has indeed lowered the number of variables to mere 30 variables to predict BY in the range of (0.1–1% w/v) with a correlation coefficient of calibration (RC2 = 0.9997) and prediction (RP2 = 0.9996). The key fingerprints detect CH3 and CH functional groups (1st overtone) of BY. This study demonstrated that FT-NIR spectroscopy, combined with selected 30 variables using RCGA-PLS procedure could be a robust technique for the rapid quantification of butter yellow in mustard oil samples. The study would certainly help in quality control and quality assurance of mustard oil and could be emulated for other edible oils.

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.

A comparative study of three chemometrics methods combined with excitation-emission matrix fluorescence for quantification of the bioactive compounds aesculin and aesculetin in Cortex Fraxini.

Cortex Fraxini is an important traditional Chinese herbal medicine with various medical functions. Aesculin and aesculetin are the main effective components of Cortex Fraxini. The fluorescence signals of the two compounds have a high degree of overlap with each other, making quantitative analysis difficult with conventional analytical methods. In the present study, different chemometrics methods, including lasso regression (LAR), interval partial least squares (iPLS), and multidimensional partial least squares (N-PLS) methods, were employed and combined with excitation–emission matrix (EEM) fluorescence for the purpose of accurate quantification of aesculin and aesculetin in Cortex Fraxini samples. The most satisfactory results were obtained by using the N-PLS method based on the EEM spectra without scatterings, with correlation coefficient of calibration and prediction values higher than 0.9972 and 0.9962, respectively, root mean squared errors for calibration and prediction values lower than 0.0304 and 0.1165, respectively, and recovery values in the range of 83.32%–104.62%. The obtained credible models indicated that the N-PLS method combined with EEM spectra has the advantages of being green, low cost, and accurate and it is a good strategy for the determination of active compounds in complex samples. To further confirm the accuracy of the obtained results, the same samples were analyzed by the recognized ultra-performance liquid chromatography method.

Nondestructive detection of kiwifruit textural characteristic based on near infrared hyperspectral imaging technology

ABSTRACT The nondestructive detection of kiwifruit texture has become an important necessity that influences kiwifruit economic efficiency and consumer recognition of the kiwifruit. This study investigated the feasibility of using hyperspectral imaging technology to identify textural characteristics of kiwifruit, and to establish the model with best performance. Firstly, a near-infrared hyperspectral online grading system (1000–2500 nm) was constructed to acquire spectral images. Textural characteristics were measured via three different textural analysis tests: textural profile analysis, puncture test, and shear test. Then, samples were divided into a calibration set and a prediction set based on rank sampling at a ratio of 3:1. Mean centralization, standard normal variate, and multiplicative scatter correction methods were applied to pre-process obtained spectra. Finally, the partial least squares method was used to establish prediction models of kiwifruit textural characteristics. The models of hardness1, chewiness, resilience, peel hardness, average hardness, and corrected hardness achieved good performance. Both the correlation coefficients of calibration (rc) and prediction (rp) values exceeded 0.9. The difference between the root mean square error (RMSE) of calibration and prediction was small, and the ratio of prediction to deviation (RPD) value exceeded 2, which was used to predict kiwifruit quality. However, the model of average shear force had a low accuracy with an RPD of 1.680 and the model of shear force also had an RPD below 1.5 and a low RMSE. The results showed that near-infrared hyperspectral imaging technique can be used as a nondestructive method for the textural characteristics kiwifruit.

Non-Destructive Analysis of Chlorpheniramine Maleate Tablets and Granules by Chemometrics-Assisted Attenuated Total Reflectance Infrared Spectroscopy.

Non-destructive analysis of chlorpheniramine maleate (CPM), pharmaceutical tablets, and granules was conducted by chemometrics-assisted attenuated total reflectance infrared spectroscopy (ATR-IR). For tablets, an optimum PLSR model with eight latent factors was obtained from area-normalized and standard normal variate (SNV) pretreated ATR-IR spectral data with correlation coefficients (R2) of calibration and cross-validation of 0.9716 and 0.9602, respectively. The model capability for the 42 test set samples was proven with R2 between the reference and model prediction values of 0.9632, and a root-mean-square error of prediction (RMSEP) of 1.7786. The successive PLSR model for granules was constructed from SNV and first derivative pretreated ATR-IR spectral data with two latent factors and correlation coefficients (R2) of calibration and cross-validation of 0.9577 and 0.9450, respectively.