Combination Of Near-infrared Spectroscopy Research Articles

Combination of near infrared spectroscopy with characteristic interval selection for rapid detection of rice protein content

Protein level significantly influences the nutritional quality of rice. For this reason, this study introduced a method to rapidly measure the rice protein content through a combination of near infrared spectroscopy (NIRS) with characteristic spectral interval (CSI) selection. Using the interval partial least squares (iPLS) concept as a basis, this study integrated genetic simulated annealing algorithm (GSA) with partial least squares (PLS) and support vector machine (SVM) to develop two CSI selection algorithms, namely GSA-iPLS and GSA-iSVM, respectively. The CSI selected by the above algorithms were compared with synergy iPLS and backward iPLS, and quantitative calibration models were established for PLS and SVM, respectively. The study revealed that the PLS calibration model for rice protein content, developed using CSI selected by GSA-iPLS, exhibited the highest regression accuracy. The optimal model achieved determination coefficients of 0.945 and 0.964, relative root mean square errors of 2.598 % and 2.796 %, and residual predictive deviations of 4.265 and 5.023 for the validation and the external test sets, respectively, which met practical detection requirements. The results indicate that the combination NIRS with GSA CSI intelligent search is a reliable approach for the rapid and accurate detection of rice protein content.

Combination of Near-Infrared Spectroscopy and Multisource Data Fusion for the Geographical Origin Authentication of Panax notoginseng

Panax notoginseng (Burk.) F.H. Chen (P. notoginseng), a precious medicinal herb, provides both considerable values. There is confusion in the market regarding the geographical origins of P. notoginseng. It is of paramount importance to authenticate geographical origins of P. notoginseng, which will facilitate the rational utilization of resources and sustainable development. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to investigate the influence of the tissue and data fusion strategy for the authentication of P. notoginseng origins. The PLS-DA model employing the main root spectra demonstrated great potential for authenticating P. notoginseng origin. The PLS-DA training and test sets based on feature-level fusion (VIP) spectral data were 100% accurate, indicating excellent potential in authenticating P. notoginseng. The data fusion strategy improved the authentication. The article presents a reliable and efficacious novel methodology for authenticating P. notoginseng origins and a scientific foundation for its quality control.

Near-Infrared Spectroscopy Combined with Fuzzy Improved Direct Linear Discriminant Analysis for Nondestructive Discrimination of Chrysanthemum Tea Varieties.

The quality of chrysanthemum tea has a great connection with its variety. Different types of chrysanthemum tea have very different efficacies and functions. Moreover, the discrimination of chrysanthemum tea varieties is a significant issue in the tea industry. Therefore, to correctly and non-destructively categorize chrysanthemum tea samples, this study attempted to design a novel feature extraction method based on the fuzzy set theory and improved direct linear discriminant analysis (IDLDA), called fuzzy IDLDA (FIDLDA), for extracting the discriminant features from the near-infrared (NIR) spectral data of chrysanthemum tea. To start with, a portable NIR spectrometer was used to collect NIR data for five varieties of chrysanthemum tea, totaling 400 samples. Secondly, the raw NIR spectra were processed by four different pretreatment methods to reduce noise and redundant data. Thirdly, NIR data dimensionality reduction was performed by principal component analysis (PCA). Fourthly, feature extraction from the NIR spectra was performed by linear discriminant analysis (LDA), IDLDA, and FIDLDA. Finally, the K-nearest neighbor (KNN) algorithm was applied to evaluate the classification accuracy of the discrimination system. The experimental results show that the discrimination accuracies of LDA, IDLDA, and FIDLDA could reach 87.2%, 94.4%, and 99.2%, respectively. Therefore, the combination of near-infrared spectroscopy and FIDLDA has great application potential and prospects in the field of nondestructive discrimination of chrysanthemum tea varieties.

The combination of near-infrared spectroscopy with chemometrics in achieving rapid and accurate determination of rice mildew

The combination of near-infrared spectroscopy with chemometrics in achieving rapid and accurate determination of rice mildew

Combination of near-infrared spectroscopy with Wasserstein generative adversarial networks for rapidly detecting raw material quality for formula products.

Near-infrared spectroscopy (NIRS) has emerged as a key technique for rapid quality detection owing to its fast, non-destructive, and eco-friendly characteristics. However, its practical implementation within the formulation industry is challenging owing to insufficient data, which renders model fitting difficult. The complexity of acquiring spectra and spectral reference values results in limited spectral data, aggravating the problem of low generalization, which diminishes model performance. To address this problem, we introduce what we believe to be a novel approach combining NIRS with Wasserstein generative adversarial networks (WGANs). Specifically, spectral data are collected from representative samples of raw material provided by a formula enterprise. Then, the WGAN augments the database by generating synthetic data resembling the raw spectral data. Finally, we establish various prediction models using the PLSR, SVR, LightGBM, and XGBoost algorithms. Experimental results show the NIRS-WGAN method significantly improves the performance of prediction models, with R2 and RMSE of 0.949 and 1.415 for the chemical components of sugar, respectively, and 0.922 and 0.243 for nicotine. The proposed framework effectively enhances the predictive capabilities of various models, addressing the issue caused by limited training data in NIRS prediction tasks.

A rapid determination of wheat flours components based on near infrared spectroscopy and chemometrics

In this work, a rapid and simple analytical method for the quantitative determination of moisture, protein, wet gluten, starch and sedimentation index in the wheat flour was established by the combination of near infrared spectroscopy and chemometrics. The spectra of the 229 wheat flour samples were collected by a portable near infrared fast analyzer. The contents of these components were determined according to the relevant Chinese National Standards, and were taken as the corresponding reference database. Seven spectral pretreatment methods were employed to eliminate the optical interference from background and other noise information. The best result was obtained with FD+SG(15, 3)+MC method for moisture, protein, wet gluten and sedimentation index, FD+SG(15, 2)+MC method was more suitable for starch. The principal component numbers (PCs) were also optimized to obtain a superior model effect. Furthermore, partial least squares (PLS) and multiple linear regression (MLR) modeling methods were used to quantify the content of the components. When using FD+SG(15, 3)+MC pretreatment, all the PLS model parameters were significantly better than the MLR model. Both the predicted values and the reference values showed superior linear relationship within the calibration range. Moreover, the absolute error of the predicted values and their corresponding reference values in the PLS model were within their confidence intervals, respectively. The relative errors for moisture, protein, wet gluten and starch fluctuated little, only sedimentation index fluctuated greatly. The actual prediction correct rate of moisture, protein, wet gluten, starch and sedimentation index were 96.8%, 96.8%, 90.3%, 100.0% and 80.6%, respectively, which indicated the prediction was excellent.

Combination of near-infrared spectroscopy and a transient flow method for efficient kinetic analysis of the Claisen rearrangement

An automated flow reactor with a rapid inline monitoring using a compact near-infrared spectrometer and a flow rate ramp control enabled high-density kinetic data acquisition.

Near infrared spectroscopy combined with chemometrics as tool to monitor starch hydrolysis

The objective of this study was to evaluate the feasibility of using near infrared (NIR) spectroscopy combined with principal component analysis (PCA) and partial least squares (PLS) regression to monitor the in vitro hydrolysis of different starch substrates. Potato and rice starches, and pre-gelatinised corn starch were used, where samples collected at different time points (5 to 120 min) during the in vitro hydrolysis and analysed using a Fourier transform NIR instrument with a gold-coated integrating sphere (diffuse reflection). PLS regression models between the spectra and reference data yield a coefficient of determination in cross validation (R2CV) and standard error in cross validation (SECV) of 0.94 and 1105. 8 μg mL−1; 0.81 and 440.81 μg mL−1; 0.45 and 338 μg mL−1; 0.70 and 276 μg mL−1; 0.75 and 296. 2 μg mL−1 for the prediction of the concentration of maltose using all samples, rice and potato combined, and pre-gelatinised corn, potato and rice starches analysed separately, respectively. It was concluded that the combination of NIR spectroscopy with both PCA and PLS regression might provide with a rapid and efficient tool to rapidly monitor changes that occur during the in vitro hydrolysis of starch.

Evaluation of Tibial Hemodynamic Response to Glucose Tolerance Test in Young Healthy Males and Females.

The relationship between glucose metabolism and bone health remains underexplored despite its clinical relevance. This study utilized the oral glucose tolerance test (OGTT) and near-infrared spectroscopy (NIRS) to probe gender-specific disparities in tibial hemodynamic responses among young healthy adults. Twenty-eight healthy participants (14 males) aged 18-28 years old were recruited for this study. After ingesting a 75 g glucose solution, tibial hemodynamic responses were captured using NIRS in combination with a 5 min ischemic reperfusion technique, both before and at 30 min intervals for two hours post-glucose ingestion. Parameters measured included oxidative metabolic rate (via tissue saturation index [TSI]), immediate recovery slope after occlusion release (TSI10), and total recovery magnitude (ΔTSI). Post-glucose ingestion, both genders demonstrated a surge in blood glucose concentrations at every time point compared to baseline (p < 0.001, 0.002, 0.009, and 0.039 for males; p < 0.001, < 0.001, = 0.002, and 0.017 for females). Baseline tibial metabolic rate, TSI10, and ΔTSI did not significantly differ between males and females (p = 0.734, 0.839, and 0.164, respectively), with no discernible temporal effects in any hemodynamic parameters within each gender (p = 0.864, 0.308, and 0.399, respectively, for males; p = 0.973, 0.453, and 0.137, respectively, for females). We found comparable tibial hemodynamic responses to OGTT between genders. This study demonstrated the utility of NIRS in evaluating tibial hemodynamic responses to glucose ingestion through OGTT, enriching our understanding of the body's metabolic responses to glucose intake.

Geographical origin identification of ginseng using near-infrared spectroscopy coupled with subspace-based ensemble classifiers

Ginseng is a well-known traditional herbal medicine and the ginseng available on the market may not actually be produced in a certain place as claimed. Traditional methods of identifying the geographical origin of Ginseng are subjective, time-consuming or destructive. A more efficient approach is desirable. The feasibility of combining near-infrared (NIR) spectroscopy with ensemble learning for discriminating ginseng producing area was explored. A total of 270 samples were collected and evenly partitioned into the training and test sets. Random subspace ensemble (RSE) that uses linear discriminant classifier (LDA) as weak learner (abbreviated RSE-LDA) was used to construct predictive models. Two parameters including the size of subspace and the number of learners in ensemble were optimized. Classic partial least algorithm (PLS) was applied to build the reference model. The sensitivity, specificity, and total accuracy of final RSE-LDA and PLS models were 97.8 %, 100 %, 99.3 %, and 93.3 %, 96.7 %, 95.6 %, respectively. In order to study the impact of training set composition on the results, the samples were randomly divided 200 times and the algorithm was run repeatedly to statistically analyze the sensitivity and specificity on the test set. Similar results were obtained. The effect of training set size was also investigated. It indicates that the combination of NIR spectroscopy with the RSE algorithm is a potential tool of discriminating the origin of Ginseng.

Near-Infrared spectroscopy combined with machine learning methods for distinguishment of the storage years of rice

Rice is one of the most important food crops that provide essential nutrients, micronutrients and daily energy for humans. The freshness of rice determines the quality and nutrition supply property, but the freshness decreases along with the storage time. A simple, nondestructive and rapid detection technology is needed to estimate the time of storage rice as for a fast evaluation of the rice quality. To accomplish this objective, near-infrared spectroscopy (NIRS) is employed in combination with three machine learning methods, including least square support vector machine (LSSVM), random forest (RF) and principal component-neural network (PC-NN). With specific design on grid search of the relevant parameters, the LSSVM model optimally performed classification with the highest accuracy of 95.7% in the distinguishment of three labeled storage years, the RF model and PC-NN models have close accuracies in model training and optimization processes. In comparison to the PLS method, which is the typical chemometric method in NIRS data analysis, the three presented machine learning methods all perform excellent over the PLS model for model training and for model testing. Especially the RF and PC-NN model were optimized by hyperparameter training, to obtain 90% of testing accuracy and reduced the error differences to ∼5.0% between model training and testing. This study indicated the potential of NIRS in combination with machine learning methods as practical chemometric tools for discrimination of the rice storage freshness by distinguishing their storage years. The design of adaptive tuning on hyperparameters provide a valuable approach to improve the model prediction abilities.

Rapid and simultaneous quantification of phenolic compounds in peanut (Arachis hypogaea L.) seeds using NIR spectroscopy coupled with multivariate calibration

Phytochemically, peanuts provide significant nutritional value to humans, animals, and the food industry as a whole. This study was conducted to explore the potential for near-infrared (NIR) spectroscopy and effective variable selection algorithms to quantify phenolic compounds in peanut seeds. The phenolics were extracted and then identified and quantified using high-performance liquid chromatography (HPLC). The spectroscopic data were acquired from the peanut samples using a tabletop NIR spectrometer with a wavelength range of 10,000–4000 cm–1. The acquired spectra were preprocessed using a synergistic effect of first- and second-order derivative (FOD and SOD) preprocessing techniques, and multivariate algorithms were used, examined, and evaluated using correlation coefficients of the validation set (Rp), root mean square error of prediction (RMSEP), and residual predictive deviations (RPDs). The competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model produced optimum performance for chlorogenic acid (Rp = 0.933, RPD = 2.77), kaempferol (Rp = 0.928, RPD = 2.68), p-coumaric acid (Rp = 0.900, RPD = 2.32), and quercetin (Rp = 0.932, RPD = 2.88), respectively. Therefore, this study proved that NIR spectroscopy in combination with CARS-PLS was capable of nondestructively predicting phenolic content in peanut seeds.

Non-Destructive Classification of Chrysanthemum Tea Using Near-Infrared Spectroscopy (NIRS) and Fuzzy Improved Pseudoinverse Linear Discriminant Analysis (FIPLDA)

Chrysanthemum tea is popular because of its health benefits, although different varieties vary in effectiveness and role. The rapid identification of the variety is critical for consumers and the tea industry. This study employed near-infrared (NIR) spectroscopy in combination with feature extraction methods to characterize chrysanthemum tea varieties. To improve the recognition accuracy, a fuzzy improved pseudoinverse linear discriminant analysis (FIPLDA) algorithm was employed to extract discriminant information from the NIR spectra of chrysanthemum tea that were preprocessed using the Savitzky-Golay (SG) algorithm. Next, the dimensionality of the data was reduced by principal component analysis (PCA). The spectral features were extracted by pseudoinverse linear discriminant analysis (PLDA), improved pseudoinverse linear discriminant analysis (IPLDA), and FIPLDA. Five chrysanthemum tea samples were classified by k-nearest neighbor (KNN) and support vector machines (SVM), respectively. The results show that KNN performed better than SVM in classification, and the classification accuracy of FIPLDA-KNN reached 98.33%. Hence, the combination of NIR spectroscopy with FIPLDA suitable for distinguishing chrysanthemum tea.

Mango quality prediction based on near-infrared spectroscopy using multi-predictor local polynomial regression modeling

Background: pH and total soluble solids (TSS) are important quality parameters of mangoes; they represent the acidity and sweetness of the fruit, respectively. This study predicts the pH and TSS of intact mangoes based on near-infrared (NIR) spectroscopy using multi-predictor local polynomial regression (MLPR) modeling. Herein, the prediction performance of kernel partial least square regression (KPLSR), support vector machine regression (SVMR), and MLPR is compared. Methods: For this purpose, 186 intact mango samples at three different maturity stages are used. Prediction models are built using MLPR, KPLSR, and SVMR based on untreated and treated spectra. The best regression model for predicting pH is MLPR based on Gaussian filter smoothing spectra. Moreover, the TSS value is more accurately predicted using MLPR based on Savitzky–Golay smoothing. Results: The findings reveal that MLPR is highly accurate in estimating the pH and TSS of mangoes, with mean absolute percentage error (MAPE) values less than 10 %. In addition, the MLPR model has the best predictive performance with the lowest Mean Squared error (MSE) and root mean squared error (RMSE) values and the highest R2 value. Conclusions: The use of NIR spectroscopy in combination with multi-predictor local polynomial regression could provide a quick and non-destructive technique for predicting mango quality. Thus, the results of this study help support sustainable production as a sustainable development goal.

Non-Invasive Single-Grain Screening of Proteins and Other Features by Combination of Near-Infrared Spectroscopy and Nuclear Magnetic Resonance

The non-invasive analysis of seeds is of great interest to experimental biologists and breeders. To reach a high varietal identity and purity of seed material, it is often necessary to access features of individual seeds via the screening of mutant populations. While near-infrared spectroscopy (NIRS) and time-domain nuclear-magnetic-resonance spectroscopy (TD-NMR) are well-known in seed research and industry for bulk seed measurements, their application for individual seeds is challenging. Here we demonstrate how to overcome this limitation using a practical approach to cereal grains using oat (Avena sp.) as a model. For this, we generated a representative collection of oat seeds from the ex situ German federal gene bank, which includes wide variation in grain size, shape, and coloration. Next, we established a short experimental pipeline to exemplify how to improve the procedure for individual seed measurements. In its current state, the method is ready to use for the high-accuracy estimation of nitrogen (protein) content (R2 = 0.877), water content (R2 = 0.715), and seed weight (R2 = 0.897) of individual oat grains. This work introduces the combination of NIRS and TD-NMR as an efficient, precise, and, most importantly, non-destructive analytic platform for a high throughput analysis of individual intact seeds.

Combination of near-infrared spectroscopy and key wavelength-based screening algorithm for rapid determination of rice protein content

Rice taste quality is one of the most important factors influencing rice marketing and distribution, and rice with a high taste quality is more popular with consumers and has a higher price. Accurate and rapid determination of rice protein content helps to assess the rice taste quality and aids in marketing. In this study, NIR spectra of 84 rice samples combined with partial least squares regression (PLSR) were used to model protein content, and different selection algorithms based on key wavelengths (competitive adaptive reweighted sampling, CARS; Monte-Carlo uninformative wavelength elimination, MC-UVE; random frog, RF) were used to understand the accuracy of NIR spectra in predicting the rice protein content. Our results showed that the R2P and RPD of the original full-spectrum PLSR model were 0.83 and 1.95, respectively. After the second-order derivative preprocessing, the R2P and RPD of the full spectrum were improved to 0.95 and 4.14. Both CARS and MC-UVE increased the R2P of the PLSR model to 0.97 and the RPD to 5.57 and 5.65, respectively. R2C and R2CV in the PLSR model based on CARS algorithm were 0.93 and 0.91, respectively. The CARS algorithm had excellent results in predicting the rice protein content.

Synthetic cannabinoid receptor agonists (Spice): the problem they represent and a chemometric solution

Synthetic cannabinoid receptor agonists (SCRAs) are novel psychoactive substances (NPS) that can bind to and trigger endogenous cannabinoid receptors, initially developed as research agents for studying the endocannabinoid system and later for therapeutic purposes. Significant side effects limited their use as therapeutics, but SCRAs were sold to members of the public in the UK as “legal highs” in the early 2000s. Despite the known adverse effects and implemented legislation changes, SCRAs remain in circulation, particularly amongst vulnerable populations, as the sheer diversity of SCRA chemical structures makes it difficult to detect SCRAs in an adulterated product such as tobacco. To overcome this issue, researchers have looked at multivariate techniques such as Raman and near-infrared spectroscopy in combination with chemometric techniques including principal component analysis and partial least-squares regression to detect the diverse range of SCRAs. This review discusses the problem that SCRAs represent, and then the analytical and chemometric techniques that can/could be brought to bear on said problem.

Mechanism of Binding of Polyproline to Ice via Interfacial Water: An Experimental and Theoretical Study.

The molecular mechanism underlying inhibition of ice growth by polyproline (PPro), a minimal antifreeze glycoprotein mimic, remains unclear. In this work, the change in the structure of water during the growth of ice in PPro solutions was investigated using a combination of near-infrared spectroscopy and molecular dynamics (MD) simulations. The results show that only high concentrations of PPro solutions can effectively inhibit ice growth, as indicated by the variation in the spectral intensity of ice with time. When PPro exhibits an antifreeze effect, the spectral intensity of hydrated water associated with PPro in a solution is weakened. The experiments and MD simulations reveal that the quantity of the interfacial water between the ice crystal and the hydrophobic groups of PPro progressively reaches a plateau. Most significantly, we present clear evidence that the stable existence of this interfacial water is critical for the antifreeze activity of PPro.

A new protocol for improving immediate monitoring of skin-island free flap with near-infrared spectroscopy and ultrasound

Postoperative monitoring is essential for detecting early complications and improving the salvage rate of free flaps. We propose a new protocol for free flap monitoring based on the combination of near-infrared spectroscopy (NIRS) and ultrasound. All free flaps with a skin paddle were included and divided into two groups according to the immediate postoperative monitoring method used: ultrasound examination (control group) or those monitored using our protocol (study group). The number of surgical revisions, intraoperative findings, immediate flap failure, sensitivity, and specificity were compared between the two groups. A total of 221 free flaps performed in 209 patients were included. The NIRS automatically detected vascular compromise in 21.8% of cases. A complication was confirmed in half of these cases by ultrasound examination, and surgical reintervention was indicated (10.9%), even in the absence of clinical changes in the skin paddle. In all the surgical revisions, the complication was confirmed, and there was no flap necrosis in the non-revised cases. The salvage rate for revised flaps and the flap survival rate were higher in the study group (salvage rate: 25% vs 72.7%; survival rate: 92.5% vs 97%). A sensitivity of 100% and a specificity of 100% were found for the combination of both monitoring methods. The proposed protocol is a non-invasive and reliable method for early identification of postoperative complications of free flaps that allows higher rates of salvage rate and reduces the need for specific staff with continuous on-site presence for flap monitoring.

Rapid Identification of the Geographical Origin of Black Pepper in Vietnam Using Near-Infrared Spectroscopy and Chemometrics

The aim of this study was to develop a rapid method for determining geographical origin of black pepper based on combination of near-infrared spectroscopy and chemometric algorithms. Near-infrared spectroscopy was used to determine the geographical origin of 111 black pepper samples from three regions in Vietnam: North Central Region, Central Highlands and Southeast Region. A portable NIR spectrometer was used to collect the spectra of intact black pepper. Support vector machine (SVM), linear discriminant analysis (LDA) and K-nearest neighbors (KNN) were established and the identification effect of these models with different pre-processing methods were optimized and compared. By using support vector machine (SVM) classification and standard normal variate (SNV) spectral pre-processing, classification accuracy was 98.7% on the calibration and 100% on the validation sets for the determination of origin of black pepper. This study has demonstrated that portable spectrometers can be effective tools to use in the classification of black pepper samples according to their geographical origin.