Near-infrared Model Research Articles

Quantification of Amlodipine Maleate Content in Amorphous Solid Dispersions Produced by Fluidized Bed Granulation Using Process Analytical Technology Tools

Background: Active pharmaceutical ingredient (API) content is a critical quality attribute (CQA) of amorphous solid dispersions (ASDs) prepared by spraying a solution of APIs and polymers onto the excipients in fluid bed granulator. This study presents four methods for quantifying API content during ASD preparation. Methods: Raman and three near-infrared (NIR) process analysers were utilized to develop methods for API quantification. Four partial least squares (PLS) models were developed using measurements from three granulation batches, with an additional batch used to evaluate model predictability. Models performance was assessed using metrics such as root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), residual prediction deviation (RPD), and others. Results: Off-line and at-line NIR models were identified as suitable for process control applications. Additionally, at-line Raman measurements effectively predicted the endpoint of the spraying phase. Conclusions: To the best of authors’ knowledge, this is the first study focused on monitoring API content during fluidized bed granulation (FBG) used for ASD preparation. The findings provide novel insights into the application of Raman and NIR process analysers with PLS modelling for monitoring and controlling ASD preparation processes.

Extragalactic globular cluster near-infrared spectroscopy

Context. One way to constrain the evolutionary histories of galaxies is to analyse their stellar populations. In the local Universe, our understanding of the stellar population properties of galaxies has traditionally relied on the study of optical absorption and emission-line features. Aims. In order to overcome limitations intrinsic to this wavelength range, such as the age-metallicity degeneracy and the high sensitivity to dust reddening, we must use wavelength ranges beyond the optical. The near-infrared (NIR) offers a possibility to extract information on spectral signatures that are not as obvious in traditional optical bands. Moreover, with the current and forthcoming generation of instrumentation focusing on the NIR, it is mandatory to explore possibilities within this wavelength range for nearby-Universe galaxies. However, although the NIR shows great potential, we are only beginning to understand it. Widely used techniques such as a full spectral fitting and line strength indices need to be tested on systems that are as close to simple stellar populations as possible, and the result from the techniques need to be compared to the yields from a traditional optical analysis. Methods. We present a NIR spectral survey of extragalactic globular clusters (GCs). The set was composed of 21 GCs from the Centaurus A galaxy that were obtained with SOAR/TripleSpec4, which covered the ∼1.0–2.4 μm range with a spectral resolution (R = λ/Δλ) of 3500. These spectra cover Hβ equivalent widths between 0.98 Å and 4.32 Å, and [MgFe]′ between 0.24 Å and 3.76 Å. Results. This set was ideal for performing absorption band measurements and a full spectral fitting, and it can be used for kinematic studies and age and abundance measurements. With this library, we expect to be able to probe the capabilities of NIR models, as well as to further improve stellar population estimates for the GCs around the Centaurus A galaxy.

Investigation of near infrared and Raman fibre optic process sensors for protein determination in milk protein concentrate

This study investigated the potential of two fibre optic process sensors based on near infrared (NIR) or Raman spectroscopic technology for protein measurement in milk protein concentrate (MPC). Partial least squares (PLS) models were developed using NIR, Raman, and fusion of NIR and Raman spectra data. Calibration models developed were optimized by selecting different spectral pre-treatment methods and spectral regions. Overall, the three optimal models (NIR, Raman, fused NIR and Raman) yielded R2p values >0.9 and RMSEP values in the range of 0.168–0.185 %. The optimised fusion model outperformed all Raman models and had a similar protein prediction accuracy (R2p = 0.911 and RMSEP = 0.178 %) compared to the optimised NIR model (R2p = 0.917 and RMSEP =0.168 %). Results of the study demonstrated that both NIR and Raman process probes can be used as process analytical technology (PAT) tools for inline protein measurements of MPC post membrane filtration.

NIR and MIR spectral feature information fusion strategy for multivariate quantitative analysis of tobacco components

The determination of total nicotine, total sugar, reducing sugar and total nitrogen contents in tobacco is of great significance to tobacco quality evaluation and formulation design. To quickly detect the content of 4 components of tobacco, using near-infrared (NIR) and mid-infrared (MIR) spectral data from 129 solid samples of tobacco powder provided by Shanghai Tobacco Group Co., Ltd., Two NIR-MIR spectral fusion techniques are studied, that is, fusion technology 1 is to establish a model by fusing feature variables after variable selection of each spectrum. The fusion technology 2 is to first fuse the NIR-MIR spectral data and then select the variables to establish the model. Both fusion technologies use successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), backward interval PLS (biPLS), forward interval PLS (fiPLS), synergy interval PLS (siPLS), and interval interaction moving window partial least squares (iMWPLS) algorithms to filter wavelength variables. The results showed that for total nicotine and total sugar, the PLSR model established by fusion technology method 2 combined with iMWPLS algorithm is the best, and its RMSEP decreases from 0.2314 to 1.3225 to 0.0821 and 0.8079 respectively compared with the full spectrum fusion method, which is superior to the single NIR and MIR models and NIR-MIR fusion technology 1. For reducing sugars, the simple full-spectrum fusion model has the best analytical ability and the lowest RMSEP, which is superior to the single NIR-MIR models and all models established by two spectral fusion techniques combined with six wavelength selection algorithms. For total nitrogen, the prediction effect of fusion technology 1 combined with iMWPLS algorithm model was significantly improved compared with single NIR and MIR models and NIR-MIR fusion technology 2, and its RMSEP was 0.0634. The results showed that the two NIR-MIR spectral fusion techniques made full use of the complementary information provided by NIR and MIR spectroscopy, and successfully applied them to the rapid detection of total nicotine, total sugar, reducing sugar and total nitrogen content in tobacco, which provided a new method and idea for the rapid detection of tobacco components.

Determination of oil quality during crispy pork rind frying: Near infrared spectra and color values as predictors

This study provides a comprehensive analysis of oil degradation during the frying of various semi-finished pork rinds, including high-fat (HF), non-fat (NF), streaky (SK), and thin (TN) types. By quantitatively assessing parameters such as peroxide, acid, anisidine, and Totox values, we sought to elucidate the complex interactions and effects on oil quality during the frying process. The oil samples underwent evaluation utilizing a near-infrared (NIR) spectrometer and the color variations in the frying oil during the process. The resulting data highlighted significant correlations between color parameters and oil quality. The NIR models exhibited an impressive and robust predictive proficiency with high correlation coefficients values (R > 0.9 for all measured parameters) and low root mean square errors of cross-validation (RMSECVs). They promise rapid, non-destructive, and accurate evaluation of oil quality during frying processes. Additionally, the study identified oil color as a viable, low-cost alternative for predicting oil quality parameters where sophisticated spectroscopic equipment is unavailable.

NIR-based models for the evaluation of basic density, moisture and higher heating value of industrial wood chips

ABSTRACT The quick and efficient classification of wood is crucial for optimizing processes in forestry industries, which highlights the use of near infrared (NIR) spectroscopy. This study investigated the application of NIR spectroscopy for estimating the basic density, moisture content and calorific value of wood chips of Eucalyptus urophylla × E. grandis hybrids directly in the company yard. Using two lots of industrial chips collected in the company’s yard (Lots A and B), the NIR signatures were correlated with the results obtained by standard methods via Partial Least Squares Regression (PLS-R). The predictive models presented coefficients of determination (R²) of 0.83 for basic density (Lot A), 0.81 to 0.90 for moisture content (Lots A and B) and 0.74 for calorific value (Lot A). Variations in the moisture content of wood chips impacted the information quality of spectral signatures and thus the model accuracy, highlighting the difficulty of applying this technology in field conditions. However, the NIR models can be used as a solution for fast estimation of wood chips quality in the company yard, optimizing industrial processes.

Raman and NIR spectroscopy-based real-time monitoring of the membrane filtration process of a recombinant protein for the diagnosis of SARS-CoV-2

This research shows the detailed comparison of Raman and near-infrared (NIR) spectroscopy as Process Analytical Technology tools for the real-time monitoring of a protein purification process. A comprehensive investigation of the application and model development of Raman and NIR spectroscopy was carried out for the real-time monitoring of a process-related impurity, imidazole, during the tangential flow filtration of Receptor-Binding Domain (RBD) of the SARS-CoV-2 Spike protein. The fast development of Raman and NIR spectroscopy-based calibration models was achieved using offline calibration data, resulting in low calibration and cross-validation errors. Raman model had an RMSEC of 1.53 mM, and an RMSECV of 1.78 mM, and the NIR model had an RMSEC of 1.87 mM and an RMSECV of 2.97 mM. Furthermore, Raman models had good robustness when applied in an inline measurement system, but on the contrary NIR spectroscopy was sensitive to the changes in the measurement environment. By utilizing the developed models, inline Raman and NIR spectroscopy were successfully applied for the real-time monitoring of a process-related impurity during the membrane filtration of a recombinant protein. The results enhance the importance of implementing real-time monitoring approaches for the broader field of diagnostic and therapeutic protein purification and underscore its potential to revolutionize the rapid development of biological products.

Near-Infrared Spectroscopy Analysis of the Phytic Acid Content in Fuzzy Cottonseed Based on Machine Learning Algorithms.

Cottonseed is rich in oil and protein. However, its antinutritional factor content, of phytic acid (PA), has limited its utilization. Near-infrared (NIR) spectroscopy, combined with chemometrics, is an efficient and eco-friendly analytical technique for crop quality analysis. Despite its potential, there are currently no established NIR models for measuring the PA content in fuzzy cottonseeds. In this research, a total of 456 samples of fuzzy cottonseed were used as the experimental materials. Spectral pre-treatments, including first derivative (1D) and standard normal variable transformation (SNV), were applied, and the linear partial least squares (PLS), nonlinear support vector machine (SVM), and random forest (RF) methods were utilized to develop accurate calibration models for predicting the content of PA in fuzzy cottonseed. The results showed that the spectral pre-treatment significantly improved the prediction performance of the models, with the RF model exhibiting the best prediction performance. The RF model had a coefficient of determination in prediction (R2p) of 0.9114, and its residual predictive deviation (RPD) was 3.9828, which indicates its high accuracy in measuring the PA content in fuzzy cottonseed. Additionally, this method avoids the costly and time-consuming delinting and crushing of cottonseeds, making it an economical and environmentally friendly alternative.

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction.

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R2) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

Developing multifruit global near‐infrared model to predict dry matter based on just‐in‐time modeling

AbstractModeling near‐infrared (NIR) spectral data to predict fresh fruit properties is a challenging task. The difficulty lies in creating generalized models that can work on fruits of different cultivars, seasons, and even multiple commodities of fruit. Due to intrinsic differences in spectral properties, NIR models often fail in testing, resulting in high bias and prediction errors. One current solution for achieving generalized models is to use large calibration sets measured over multiple cultivars and harvest seasons. However, current practice primarily focuses on calibration sets for single fruit commodities, disregarding the rich information available from other fruit commodities. This study aims to demonstrate the potential of locally weighted partial least‐squares an example of just‐in‐time (JIT) modeling to develop real‐time models based on calibration sets consisting of multiple fruit commodities. The study also explores JIT modeling for leveraging relevant information from other fruit commodities or adapting the model based on new samples. The application demonstrated here predicts the dry matter in fresh fruit using portable NIR spectroscopy. The results show that JIT modeling is particularly effective for multiple fruit commodities in a single calibration set. The JIT models achieved a root mean squared error of prediction (RMSEP) of 0.69% fresh weight (FW), while the traditional partial least squares (PLS) modeling RMSEP was 0.93% FW. JIT modeling can be particularly beneficial when the user has multiple fruit datasets and wants to combine them into a single dataset to utilize all the relevant information available.

Online Determination on the Properties of Naphtha as the Ethylene Feedstock Using Near-Infrared Spectroscopy

Providing real-time information on the properties of naphtha as the ethylene feedstock within the minimal time is significant for improvement of the process simulation, control, and real-time optimization. To develop models predicting naphtha properties for different pre-processing methods, an online full transmittance near-infrared (NIR) spectrum measurement system has been used along with the principal component regression and partial least squares (PLS) methods. The results show that the Savitzky-Golay smoothing combined with the first-derivative pre-processing provides the best denoising effect compared to other methods. The predicted relative errors of the NIR models developed by PLS, especially for the cutting temperature points of the test set, basically make 1‒5% indicating it can be used to create good NIR prediction models for the on-line determination of naphtha properties.

Continuous manufacturing of pharmaceutical products: A density-insensitive near infrared method for the in-line monitoring of continuous powder streams

Near infrared (NIR) spectroscopy is a valuable analytical technique for monitoring chemical composition of powder blends in continuous pharmaceutical processes. However, the variation in density captured by NIR during spectral collection of dynamic powder streams at different flow rates often reduces the performance and robustness of NIR models. To overcome this challenge, quantitative NIR measurements are commonly collected across all potential manufacturing conditions, including multiple flow rates to account for the physical variations. The utility of this approach is limited by the considerable quantity of resources required to run and analyze an extensive calibration design at variable flow rates in a continuous manufacturing (CM) process. It is hypothesized that the primary variation introduced to NIR spectra from changing flow rates is a change in the density of the powder from which NIR spectra are collected. In this work, powder stream density was used as an efficient surrogate for flow rate in developing a quantitative NIR method with enhanced robustness against process rate variation. A density design space of two process parameters was generated to determine the conditions required to encompass the apparent density and spectral variance from increases in process rate. This apparent density variance was included in calibration at a constant low flow rate to enable the development of a density-insensitive NIR quantitative model with limited consumption of materials. The density-insensitive NIR model demonstrated comparable prediction performance and flow rate robustness to a traditional NIR model including flow rate variation (“gold standard” model) when applied to monitoring drug content in continuous runs at varying flow rates. The proposed platform for the development of in-line density-insensitive NIR methods is expected to facilitate robust analytical model performance across variable continuous manufacturing production scales while improving the material efficiency over traditional robust modeling approaches for calibration development.

Near-infrared imaging of in vivo performance of orally administered solid forms to rats: Feasibility study with indocyanine green

This study demonstrates the applicability of near-infrared (NIR) imaging to evaluating in vivo oral formulation performance. As a NIR probe and model drug, indocyanine green (ICG) and acetaminophen (ACE) were selected, respectively. The fluorescence intensity of ICG greatly increased upon dissolution, with the dissolved ICG passing through the gastrointestinal tract over time. Both compounds (0.05 mg of ICG and 0.5 mg of ACE) were encapsulated in gelatin and hydroxypropyl methylcellulose (HPMC) capsules in the solid form. In vitro, the HPMC capsules showed a disintegration lag time, a feature that was not observed for the gelatin capsules. After oral administration of each capsule to rats, blood samples were collected, followed by fluorescent imaging of the abdominal region. At 0.25 h after HPMC capsule administration, the fluorescence area and intensity were significantly small and relatively weak compared to that of the gelatin capsule. These tendencies resulted from the difference in capsule disintegration times, leading to a change in gastric emptying, which corresponded well with the initial time profile of the plasma concentration of ACE. These results indicate that possibility of NIR imaging with ICG to evaluate in vivo performance of orally administered formulations.

The research on the molecular spectroscopic recognition mechanism of microplastics in typical agricultural media

To study the identification mechanism of microplastics in agricultural environmental media by molecular spectroscopy, two typical media, soil and fishmeal, were selected for this study. Three common microplastics, PE, PP, and PS, were used as research objects. Near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometric methods were used to explore microplastics' identification and analysis effects in different agricultural media and reveal their identification mechanisms. PCA analysis revealed that different soil types would affect the identification results of microplastics. PLS-DA discriminant analysis showed that the accuracy of NIR spectroscopy technology in identifying microplastics in different types of soil decreased in the order of sand > loam > clay. In contrast, the rule of MIR spectroscopy technology was the opposite. The sensitivity and specificity of the three microplastics in the NIR model of sand and the infrared spectroscopy model of fishmeal reached 1.000. NIR spectroscopy technology is suitable for identifying microplastics in soil, while infrared spectroscopy technology is more suitable for identifying microplastics in fish meal. Furthermore, based on the VIP values of each wavelength point in the spectrum, the characteristic bands that have essential contributions to identifying microplastics in soil were screened out. The NIR spectra of 4500–4300 cm−1, 4300–3900 cm−1 and 7100–5800 cm−1 are the most essential characteristic bands for identifying microplastics in clay, loam, and sand, respectively. The MIR of 3000–2900 cm−1 and 700–650 cm−1 were the most essential characteristic bands for identifying microplastics in soils, and the overlap of the characteristic spectra of the three soils reached 59.45%. The NIR spectra of 6050–5600 cm−1, 4700–4000 cm−1 and the MIR spectra of 2300–1900 cm−1 and 800–400 cm−1 are the most essential characteristic bands for the identifying microplastics in fishmeal. This study provides a more suitable technical "solution" for identifying microplastics in environmental media, which is of great significance for improving the accuracy of molecular spectroscopic identification in environmental media and clarifying the interaction between microplastics and environmental media.

Machine Learning-Assisted Near- and Mid-Infrared spectroscopy for rapid discrimination of wild and farmed Mediterranean mussels (Mytilus galloprovincialis)

The objective of this study was to investigate the ability to discriminate between wild and farmed Mediterranean mussels (Mytilus galloprovincialis) using machine learning-assisted near-infrared (NIR) and mid-infrared (MIR) spectroscopy. Mussels are of significant global importance in aquaculture due to their nutritional characteristics, encompassing a rich source of protein, essential fatty acids, various vitamins, and abundant minerals. Additionally, their ease of farming adds to their value as a desirable aquaculture species. The mussels' capacity to reflect environmental quality attributes makes them valuable as biomonitoring agents. However, differences in nutritional composition may arise between wild mussels harvested from natural marine hard-bottoms and those farmed in open artificial systems in the sea. In this study aimed at distinguishing between the two types of mussels, the classification models were created, and the most accurate results were achieved using the FT-MIR spectral data extracted from the interior part of the mussels, while the performance of FT-MIR data obtained from the mussels' shells was slightly lower, with the accuracy of 92% and R2 of 0.87. Still, the accuracies of all the classification models were over 90%. The Ensemble model, trained using FT-MIR spectra from the interior part of the mussel, achieved an accuracy of 98.4%, surpassing the performance of other variable sets. In both NIR and MIR models, spectra from the mussels' interior provide better discrimination than spectra from the outer shell.

Improving transferability and service life of the calibration model of total plant alkaloids in tobacco leaves on seven NIR spectroscopy devices by multi-step wavelength selection methods

The rapid determination of total plant alkaloids (TPA) content in tobacco products by means of near infrared (NIR) spectroscopy is becoming conventional detection method in tobacco industry. An established NIR model of TPA is anticipated to be shared by other devices as long as possible not needing to update the model. Present study developed a three-step wavelength selection method SISCW (selecting important and stable characteristic wavelengths）to build a robust NIR calibration model of TPA to improve its transferability and to prolong its use period at many devices. For this, 292 flue-cured tobacco leaf samples from more than 10 regions of China, which were harvested in 2011–2013 and tested on the primary NIR device, were used to build the model. 77 samples harvested from 2011 to 2013 were applied to verify the transferability of the model on six secondary NIR devices. 180 samples harvested from 2014 to 2020 that were divided into 7 groups according to their growing years, were used to examine the long-term application capacity of the model on the seven NIR devices. The SISCW method integrates an image processing method of scale invariant feature transformation (SIFT) with analyzing standard deviation of the sample spectra (SDSS) and water absorption coefficients to select important and stable characteristic wavelengths (SISCW). The wavelengths selected by the SISCW were recorded as Uisc. The results indicated that the partial least square (PLS) calibration model of TPA based on Uisc (SISCW-PLS) performed well on both primary and the six secondary devices when it was employed to predict TPA of the 77 samples of 2011–2013. The model has run 7 years on the primary and 3 of the secondary devices from 2014 to 2020. This emphasizes the SISCW method has great potential for improving transferability and service-life of TPA calibration model.

Quantitative analysis of moisture content and particle size in a fluidized bed granulation process using near infrared spectroscopy and acoustic emission combined with data fusion strategies

Monitoring granule property is essential for fluidization maintenance and product quality control in fluidized bed granulation (FBG). In this study, two non-invasive techniques, near-infrared (NIR) spectroscopy and acoustic emission (AE), were applied for quantitative analysis of moisture content (MC) and median particle size (D50) in a FBG process, combined with chemometrics and data fusion strategies. Partial least squares (PLS) and support vector machine (SVM) regression models were established based on NIR and AE spectral data. The optimal quantitative models were identified considering the effect of spectra preprocessing and variable selection. In the comparison study, the best separate models for MC and D50 quantification were based on NIR and AE, respectively. The NIR model exhibited the better prediction ability with the determination coefficient of validation set (R2v) of 0.9815, root mean square error of validation set (RMSEv) of 0.2226 %, and residual predictive deviation (RPD) of 7.4674 for MC. Meanwhile, the AE model presented the better prediction performance with R2v of 0.9710, RMSEv of 18.2643 μm, and RPD of 5.9740 for D50. Furthermore, among three data fusion strategies, the high-level fusion model achieved the best overall performance on D50 quantification with R2v of 0.9863, RMSEv of 12.5707 μm, and RPD of 8.6798. The results indicated that both NIR and AE are effective monitoring tools for MC and D50 analysis in fluidized bed granulation process. In addition, a more accurate and reliable analysis of particle size can be achieved by combining NIR and AE technology with high-level data fusion.

Influence of powder stream density on near infrared measurements upon scale-up of a simulated continuous process

Near-infrared (NIR) spectroscopy is a powerful process analytical tool for monitoring chemical constituents in continuous pharmaceutical processes. However, the density variation introduced when quantitative NIR measurements are performed on powder streams at different flow rates is a potential source of a lack of model robustness. Since different flow rates are often required to meet the production requirements (e.g., during scale-up) of a continuous process, the development of efficient strategies to characterize, understand, and mitigate the impact of powder density on NIR measurements is highly desirable. This study focused on assessing the effect of powder physical variation on NIR by enabling the in-line characterization of powder stream density in a simulated continuous system. The in-line measurements of powder stream density were facilitated through a unique analytical interface to a flowing process. Powder streams delivered at various design levels of flow rate and tube angle were monitored simultaneously by NIR diffuse reflectance spectroscopy, live imaging, and dynamic mass characterization. Statistical analysis and multivariate modeling confirmed powder density as a significant source of spectral variability due to flow rate. Besides providing broader process understanding, results elucidated potential mitigation strategies to facilitate effective continuous process scale-up while ensuring NIR model robustness against density.

Evaluation of Multivariate Filters on Vibrational Spectroscopic Fingerprints for the PLS-DA and SIMCA Classification of Argan Oils from Four Moroccan Regions.

This study aimed to develop an analytical method to determine the geographical origin of Moroccan Argan oil through near-infrared (NIR) or mid-infrared (MIR) spectroscopic fingerprints. However, the classification may be problematic due to the spectral similarity of the components in the samples. Therefore, unsupervised and supervised classification methods-including principal component analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA)-were evaluated to distinguish between Argan oils from four regions. The spectra of 93 samples were acquired and preprocessed using both standard preprocessing methods and multivariate filters, such as External Parameter Orthogonalization, Generalized Least Squares Weighting and Orthogonal Signal Correction, to improve the models. Their accuracy, precision, sensitivity, and selectivity were used to evaluate the performance of the models. SIMCA and PLS-DA models generated after standard preprocessing failed to correctly classify all samples. However, successful models were produced after using multivariate filters. The NIR and MIR classification models show an equivalent accuracy. The PLS-DA models outperformed the SIMCA with 100% accuracy, specificity, sensitivity and precision. In conclusion, the studied multivariate filters are applicable on the spectroscopic fingerprints to geographically identify the Argan oils in routine monitoring, significantly reducing analysis costs and time.

Moisture insensitive analysis of polyester/viscose waste textiles using Near-Infrared spectroscopy and Orthogonalization of external parameters algorithm

Near-Infrared (NIR) spectroscopic analyses can be applied in waste textile recycling as a rapid and non-invasive method to provide both qualitative and quantitative results. However, it has been a challenge to enhance the accuracy rate of NIR-based waste textile sorting due to the major influences from water contexts in the samples. Orthogonalization of External Parameters (EPO) has been introduced to reduce the interference from water absorption in NIR spectral signals for better accuracy and reliability in modeling. Here we explore the feasibility of applying EPO strategy with varieties of algorithms, including partial least squares regression (PLS), artificial neural network (ANN), decision tree (DT), random forest (RF), gradient boosting decision tree (GBDT), extreme random tree (Extra-tree), decision tree model based on AdaBoost algorithm (AdaBoost-tree), support Vector machine (SVM), one-dimensional convolutional neural network (1D-CNN), and one-dimensional convolutional neural network with improved Inception structure (1D-Inception-CNN). 216 waste textiles samples from Xinjiang, China, were studied with different moisture levels. Among them, 80 samples were used to develop the EPO algorithm, 112 were used to establish the prediction models, and 24 were used as test datasets. Then, the samples were scanned using a near-infrared spectrometer at different moisture regain rates. Our results showed that the moisture content of waste textiles had strong absorption peaks near 1150 and 1450 nm, leading to a decrease in the near-infrared reflectance of waste textiles. To verify the effectiveness of the EPO algorithm, the decision coefficients (R2 score) and other indicators of the model without the EPO process and the model with EPO process are systematically compared. Our results show that the EPO algorithm preprocessing improves the accuracy of the NIR model (The average decision coefficient (R2 score) of the models was increased by 0.83), especially when the moisture interference level is significant. Therefore, the EPO integrated modeling method is a reliable approach for better accuracy in NIR-based waste textile sorting.