Prediction Performance and Economic Efficiency of Soft Sensors for in-Line Water Content Monitoring in Fluidized Bed Granulation: PP-Based Model vs. NIRS-Based Model.

Soft sensors play a crucial role as process analytical technology (PAT) tools. They are classified into physical models, statistical models, and their hybrid models. In general, statistical models are better estimators than physical models. In this study, two types of standard statistical models using process parameters (PPs) and near-infrared spectroscopy (NIRS) were investigated in terms of prediction accuracy and development cost. Locally weighted partial least squares regression (LW-PLSR), a type of nonlinear regression method, was utilized. Development cost was defined as the cost of goods required to construct an accurate model of commercial-scale equipment. Eleven granulation lots consisting of three laboratory-scale, two pilot-scale, and six commercial-scale lots were prepared. Three commercial-scale granulation lots were selected as a validation dataset, and the remaining eight granulation lots were utilized as calibration datasets. The results demonstrated that the PP-based and NIRS-based LW-PLSR models achieved high prediction accuracy without using the commercial-scale data in the calibration dataset. This practical case study clarified that the construction of accurate LW-PLSR models requires the calibration samples with the following two features: 1) located near the validation samples on the subspace spanned by principal components (PCs), and 2) having a wide range of variations in PC scores. In addition, it was confirmed that the reduction in cost and mass fraction of active pharmaceutical ingredient (API) made the PP-based models more cost-effective than the NIRS-based models. The present work supports to build accurate models efficiently and save the development cost of PAT.

Cost-Effectiveness of Vioptix versus Clinical Examination for Flap Monitoring of Autologous Free Tissue Breast Reconstruction.

BACKGROUND: Vioptix is a near-infrared spectroscopy tissue oximetry technology that allows for noninvasive monitoring of flap perfusion. Despite the reported benefits of Vioptix, the cost-effectiveness of this flap monitoring technology has not been compared to clinical examination alone. METHODS: A cost-effectiveness model, from the patient perspective, was constructed with two treatment arms: clinical examination versus clinical examination combined with Vioptix for flap monitoring after autologous, free flap breast reconstruction. Costs, utilities, and other model inputs were identified from the literature. One-way and probabilistic sensitivity analyses were performed. Gamma distributions were created for cost variables, and beta distributions were created for probability variables. An incremental cost-effectiveness ratio under $50,000 per quality-adjusted life-year (QALY) was considered cost-effective. All analyses were performed using TreeAge Pro (Williamstown, Mass.). RESULTS: Mean cost of autologous free tissue transfer breast reconstruction with clinical examination-based flap monitoring was found to be $37,561 with an effectiveness of 0.79, whereas the mean cost of clinical examination with Vioptix for flap monitoring was $39,361 with effectiveness of 0.82. This yielded an incremental cost-effectiveness ratio of $60,507 for clinical examination combined with Vioptix for flap monitoring. One-way sensitivity analysis revealed that clinical examination with Vioptix became cost-effective when the cost of Vioptix was less than $1487. Probabilistic sensitivity analysis found that clinical examination was cost-effective in 86.5 percent of cases. CONCLUSION: Although clinical examination combined with Vioptix is minimally more effective for flap monitoring after autologous, free flap breast reconstruction, clinical examination alone is the more cost-effective flap monitoring option.

Process Monitoring of Moisture Content and Mass Transfer Rate in a Fluidised Bed with a Low Cost Inline MEMS NIR Sensor.

PURPOSE: The current trend for continuous drug product manufacturing requires new, affordable process analytical techniques (PAT) to ensure control of processing. This work evaluates whether property models based on spectral data from recent Fabry-Pérot Interferometer based NIR sensors can generate a high-resolution moisture signal suitable for process control. METHODS: Spectral data and offline moisture content were recorded for 14 fluid bed dryer batches of pharmaceutical granules. A PLS moisture model was constructed resulting in a high resolution moisture signal, used to demonstrate (i) endpoint determination and (ii) evaluation of mass transfer performance. RESULTS: The sensors appear robust with respect to vibration and ambient temperature changes, and the accuracy of water content predictions (±13 % ) is similar to those reported for high specification NIR sensors. Fusion of temperature and moisture content signal allowed monitoring of water transport rates in the fluidised bed and highlighted the importance water transport within the solid phase at low moisture levels. The NIR data was also successfully used with PCA-based MSPC models for endpoint detection. CONCLUSIONS: The spectral quality of the small form factor NIR sensor and its robustness is clearly sufficient for the construction and application of PLS models as well as PCA-based MSPC moisture models. The resulting high resolution moisture content signal was successfully used for endpoint detection and monitoring the mass transfer rate.

Evaluation of a low-cost portable near-infrared spectrophotometer for in situ cocaine profiling.

The potential of a low-cost (∼US$ 1000) portable near-infrared (NIR) spectrophotometer for in situ characterization of seized cocaine samples was evaluated. A set of 240 samples of cocaine seized in several regions and cities across Brazil by its federal police was employed in this study. These samples were previously analyzed by chromatography at the Forensic Chemistry Laboratory of the National Institute of Criminalistics in Brasília-DF for the contents of several constituents to chemically characterize the samples. A low-cost NanoNIR spectrophotometer (Texas instruments) was used to acquire the NIR spectra of the samples in the range 900-1700 nm. The spectra set was treated by the second derivative to construct and validate multivariate regression (Partial Least Square - PLS) and classification (software independent modeling of class analogy - SIMCA) models aiming to characterize the samples. Consequently, an informative toll for objective decision making could be used by the police agents to produce immediate answers to forensic questions raised at the point of seizing. Among those questions the most relevant are: does the seized sample contain cocaine? what is the cocaine form? what is its content? is the sample adulterated and/or diluted? what is the content of adulterant? is the sample significantly adulterated and/or diluted? what is the degree of oxidation of the cocaine? The results of this work allow to propose a NIR/chemometrics based analytical protocol providing fast answers to these questions with satisfactory confidence level for the purpose of reliably screen the seized samples.

Rapid and non-destructive analysis for the identification of multi-grain rice seeds with near-infrared spectroscopy.

The rapid and non-destructive discriminant analysis of rice seeds has great significance for large-scale agriculture. Using near-infrared (NIR) diffuse-reflectance spectroscopy with partial least squares-discriminant analysis (PLS-DA), a variety identification method of multi-grain rice seeds was developed. The equidistant combination method was adopted for large-range wavelength screening. A step-by-step phase-out method was proposed to eliminate interference wavelengths and improve the predicted effect. The optimal wavelength model was a combination of 54 wavelengths within 808-974 nm of the short-NIR region. One type of pure rice variety (Y Liangyou 900) was used for identification (negative). Positive samples included the other four pure varieties and contamination of Y Liangyou 900 by the above four varieties. The recognition-accuracy rates for positive, negative and total validation samples reached 93.1%, 95.1%, and 94.3%, respectively. In the long-NIR region, the local optimal wavelength model was a combination of 49 wavelengths within 1188-1650 nm, and the recognition-accuracy rates for positive, negative and total validation samples were 90.3%, 94.1%, and 92.5%, respectively. Results confirmed the feasibility of NIR spectroscopy for variety identification of multi-grain rice seeds. The proposed two discrete-wavelength models located in the short- and long-NIR regions can provide valuable reference to a dedicated spectrometer.

Rapid analysis of the Tanreqing injection by near-infrared spectroscopy combined with least squares support vector machine and Gaussian process modeling techniques.

Near-infrared spectroscopy (NIRS) combined with chemometrics was used to analyze the main active ingredients including chlorogenic acid, caffeic acid, luteoloside, baicalin, ursodesoxycholic acid, and chenodeoxycholic acid in the Tanreqing injection. In this paper, first, two hundred samples collected in the product line were divided into the calibration set and prediction set, and the reference values were determined by the High Performance Liquid Chromatography- Diode Array Detector/Evaporative Light Scattering Detector (HPLC-DAD/ELSD) method. Partial least squares (PLS) analysis was implemented as a linear method for models calibrated with different preprocessing means. Wavelet transformation (WT) was introduced as a variable selection technique by means of multiscale decomposition, and wavelet coefficients were employed as the input for modeling. Furthermore, two nonlinear approaches, least squares support vector machine (LS-SVM) and Gaussian process (GP), were applied to exploit the complicated relationship between the spectra and active ingredients. The optimal models for each ingredient were obtained by LS-SVM and GP methods. The performance of the final models was evaluated by the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (R). All of the models in the paper give a good calibration ability with an R value above 0.92, and the prediction ability is also satisfactory, with an R value higher than 0.85. The overall results demonstrate that nonlinear models are more stable and predictable than linear ones, and they will be more suitable for the CHM system when high accuracy analysis is required. It can be concluded that NIRS with the LS-SVM and GP modeling methods is promising for the implementation of process analytical technology (PAT) in the pharmaceutical industry of Chinese herbal injections (CHIs).

Rapid quantification of polysaccharide and the main onosaccharides in Dendrobium huoshanense by near-infrared attenuated total reflectance spectroscopy.

A rapid, green, low cost and nondestructive attenuated total reflection near infrared (ATR NIR) method was developed to quantify the total polysaccharide and the main monosaccharides mannose and glucose in Dendrobium huoshanense. Total 100 D. huoshanense samples from different places were analyzed using ATR NIR method. Potential outlying samples were initially removed from the collected NIR data using the PCA-Mahalanobis distance method. Spectral data preprocessing was studied in the construction of a partial least squares (PLS) model and six different signal pretreatment methods, including multiplicative scattering correction (MSC), standard normal transformation (SNV), first and second derivatives, the combination of MSC with the first derivative, and the combination of SNV with the first derivative, were compared. The results showed that the best signal pretreatment method was the spectral data pretreated by SNV combined with the first derivative due to it showed the lowest root-mean-square error of cross-validation (RMSECV), highest R2 for both the polysaccharide and its main monosaccharides. In order to improve the performance of the model, the pretreated full spectrum was calculated by different wavelength selection method. The results showed that the optional wavelength selection model was the one simultaneously selecting the NIR wavelength ranges 7500-5750 cm-1, 5250-4700 cm-1, 4450-4300 cm-1 and 4200-4100 cm-1 because of the lowest RMSECV and the highest R2 among the ten wavelength selection models. The external validation and the complete external validation confirmed the robustness and reliability of the developed NIR model. The contents of the total polysaccharide and the main monosaccharides are the essential quality assessment criterion for plant medicines while their traditional quantification methods involved sample destruction, tedious sample processing and non-environmentally friendly pretreatment, therefore, our study might provide an efficient technique tool for the rapid, green and nondestructive quantification of the total polysaccharide and the main monosaccharides for D. huoshanense and other rich-in-polysaccharide plant medicines.

Use of chemometrics to compare NIR and HPLC for the simultaneous determination of drug levels in fixed-dose combination tablets employed in tuberculosis treatment.

The World Health Organization recommends that TB treatment be administered using combination therapy. The methodologies for quantifying simultaneously associated drugs are highly complex, being costly, extremely time consuming and producing chemical residues harmful to the environment. The need to seek alternative techniques that minimize these drawbacks is widely discussed in the pharmaceutical industry. Therefore, the objective of this study was to develop and validate a multivariate calibration model in association with the near infrared spectroscopy technique (NIR) for the simultaneous determination of rifampicin, isoniazid, pyrazinamide and ethambutol. These models allow the quality control of these medicines to be optimized using simple, fast, low-cost techniques that produce no chemical waste. In the NIR - PLS method, spectra readings were acquired in the 10,000-4000cm-1 range using an infrared spectrophotometer (IRPrestige - 21 - Shimadzu) with a resolution of 4cm-1, 20 sweeps, under controlled temperature and humidity. For construction of the model, the central composite experimental design was employed on the program Statistica 13 (StatSoft Inc.). All spectra were treated by computational tools for multivariate analysis using partial least squares regression (PLS) on the software program Pirouette 3.11 (Infometrix, Inc.). Variable selections were performed by the QSAR modeling program. The models developed by NIR in association with multivariate analysis provided good prediction of the APIs for the external samples and were therefore validated. For the tablets, however, the slightly different quantitative compositions of excipients compared to the mixtures prepared for building the models led to results that were not statistically similar, despite having prediction errors considered acceptable in the literature.

Rapid classification of Chinese quince (Chaenomeles speciosa Nakai) fruit provenance by near-infrared spectroscopy and multivariate calibration.

The quality of Chinese quince fruit is a significant factor for medicinal materials, influencing the quality of the medicine. However, it is difficult to distinguish different types of Chinese quince fruit. The main objective of this work was to use near-infrared (NIR) spectroscopy, which is a rapid and non-destructive analysis method, to classify the varieties of Chinese quince fruits. Raw spectra in the range of 1000 to 2500 nm were combined with linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machines (SVMs) for classification. The first three principal component analysis (PCA) scores were used as input variables to build LDA, QDA, and SVM discriminant models. The results indicate that all three of these methods are effective for distinguishing the different types of Chinese quince fruit. The classification accuracies for LDA, QDA, and SVM are 94, 96, and 98 %, respectively. QDA led to high-level classification accuracy of Chinese quince fruit.

Rapid estimation of the energy content of composite foods: the application of the Calorie Answer™.

The estimation of calories in foods is central in the maintenance of body weight and energy regulation. Conventional laboratory analysis using bomb calorimetry to determine calorie content is expensive and time-consuming. There is a need to explore alternative techniques for calorie estimation that requires less processing and resources. The potential of using near infrared spectroscopy for calorie measurements with Calorie Answer™ was evaluated in this study. The caloric content of 105 different foods was measured, and compared against values reported on nutrition labels. The average percentage relative standard deviation for triplicate measurements was 1.7% for all foods. The percentage difference between stated and measured calories was modest, at 4.0% for all foods. Stated and measured calorie contents were significantly and highly correlated (R2=0.98, p<0.001). The use of near infrared spectroscopy, using Calorie Answer™, is a rapid, reproducible and cost-effective way of measuring calorie content in a diverse range of foods. Its application in many parts of Asia Pacific and other emerging nations will generate much needed information on the calorie content of complex foods consumed by people living in these regions.

Rapid determination of major bioactive isoflavonoid compounds during the extraction process of kudzu (Pueraria lobata) by near-infrared transmission spectroscopy.

Near-infrared (NIR) spectroscopy has been developed into an indispensable tool for both academic research and industrial quality control in a wide field of applications. The feasibility of NIR spectroscopy to monitor the concentration of puerarin, daidzin, daidzein and total isoflavonoid (TIF) during the extraction process of kudzu (Pueraria lobata) was verified in this work. NIR spectra were collected in transmission mode and pretreated with smoothing and derivative. Partial least square regression (PLSR) was used to establish calibration models. Three different variable selection methods, including correlation coefficient method, interval partial least squares (iPLS), and successive projections algorithm (SPA) were performed and compared with models based on all of the variables. The results showed that the approach was very efficient and environmentally friendly for rapid determination of the four quality indices (QIs) in the kudzu extraction process. This method established may have the potential to be used as a process analytical technological (PAT) tool in the future.

An innovative approach to near-infrared spectroscopy using a standard mobile device and its clinical application in the real-time visualization of peripheral veins.

BACKGROUND: Excessive venipunctures are a significant problem both in emergency rooms and during hospital stays. Near-infrared (NIR) illumination devices improve venipuncture success rate but their usage is limited by their availability and economic cost. The objectives of this study were to develop a low-cost NIR spectroscopy prototype from a standard mobile device, to evaluate its efficacy and acceptance as an educational tool, and in a clinical setting. METHODS: Through a user-centric design process a prototype device was developed. Its educational efficacy was evaluated through a non-invasive, observational study (20 student clinicians, 25 subjects) and its acceptance was assessed using quantitative and qualitative analysis. A smaller clinical trial was performed by a group of 4 medical professionals over a period of 6 weeks that involved 64 patients. RESULTS: The prototype enables real-time visualization of peripheral veins on a variety of Android-based devices. The prototype was 35.2% more successful in visualizing and locating veins (n = 500 attempts) than the nursing students. The acceptance assessment revealed high perception of usefulness, satisfaction, and ease of use. In the clinical trial, 1.6 (SD 1.3) additional veins per patient were identified compared with the traditional visualization methods. CONCLUSIONS: To the best of our knowledge this is the first study that describes the design, feasibility and application of an NIR spectroscopy prototype developed on a standard mobile device.

Rapid detection of sildenafil analogue in Eurycoma longifolia products using a new two-tier procedure of the near infrared (NIR) spectra database.

A simple and cost-effective two-tier drug screening procedure comprises a 'dedicated' NIR spectral database of common medicines and a 'unified' database was developed to detect the sildenafil analogue in Eurycoma longifolia products. Diffuse reflectance spectra of ten commercial herbal products containing E. longifolia were obtained over the wavelength range of 1100-2500 nm. The spectral search of two products purchased via the internet against a dedicated database of reputable E. longifolia products have resulted in the similarity index of more than 0.1 which indicated significantly different spectra. Further searches against the unified database showed a close match to the spectra of drug containing sildenafil citrate suggesting the presence of a sildenafil analogue. This finding was supported by clustering of these spectra in the PCA score plot within 5% significance level. This approach has alleviated the use of reference product or standard active for direct comparison and has a potential to be used for adulterated food and drugs detection.

Near infrared emission photometer for measuring the oxidative stability of edible oils.

Near infrared emission spectroscopy (NIRES) allows the determination of the induction time (IT) of edible oils in accelerated oxidation experiments by monitoring the emissivity of a band at 2900 nm, which corresponds to the formation of hydroperoxides. In this work, a new near infrared emission photometer dedicated to the determination of oxidative stability is described. The photometer presents several advantages compared to the previously reported NIRES instrument, such as lower cost and extreme simplicity of design and maintenance. The results obtained in the evaluation of the proposed instrument were compared with the official Rancimat method and instrument. The significant advantages include: faster analysis, lower sample consumption and operational simplicity. It is demonstrated that the procedure for determination of oxidative stability of oils can be significantly simplified and performed by measuring the sample emission at only one spectral region centered at 2900 nm. Also, the proposed instrument and method present precision equivalent to the Rancimat method (coefficient of variation=5.0%). A significant correlation between the methods has been found (R(2)=0.81).

Rapid determination of immunoglobulin G concentration in cold ethanol precipitation process of raw plasma with near-infrared spectroscopy.

Near-infrared spectroscopy (NIRS) is known to be a powerful analytical tool in process monitoring. The feasibility of NIRS was investigated for determination of immunoglobulin G (IgG) in raw plasma cold ethanol precipitation process. Partial least squares (PLS) was used to develop regression model for 63 samples between spectra and reference data measured with a UV spectrophotometer. Three different variable selection methods, including correlation coefficient method, interval partial least squares (iPLS) and successive projection algorithm (SPA), were performed and compared with models based on all the variables. The values of Rc and root mean square error of cross validation (RMSECV) produced by the best model for the calibration set were 0.9599 and 0.6135 g/L, respectively. While for the validation set, the values of Rp and root mean square error of prediction (RMSEP) were 0.9577 and 0.4913 g/L, respectively. The results of this paper demonstrated that NIRS could be a feasible alternative approach for rapid determination of IgG in the cold ethanol precipitation process and can be used as a PAT tool in the future.

Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS).

Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non-destructive advantages of NIRS.

Quantitative analysis of cefalexin based on artificial neural networks combined with modified genetic algorithm using short near-infrared spectroscopy.

In this paper, a novel chemometric method was developed for rapid, accurate, and quantitative analysis of cefalexin in samples. The experiments were carried out by using the short near-infrared spectroscopy coupled with artificial neural networks. In order to enhancing the predictive ability of artificial neural networks model, a modified genetic algorithm was used to select fixed number of wavelength.

A spectral transfer procedure for application of a single class-model to spectra recorded by different near-infrared spectrometers for authentication of olives in brine.

Analytical methods for confirmation of food authenticity claims should be rapid, economic, non-destructive and should not require highly skilled personnel for their deployment. All such conditions are satisfied by spectroscopic techniques. In order to be extensively implemented in routine controls, an ideal method should also give a response independent of the particular equipment used. In the present study, near-infrared (NIR) spectroscopy was used for verifying authenticity of commercial olives in brine of cultivar Taggiasca. Samples were analysed in two laboratories with different NIR spectrometers and a mathematical spectral transfer correction - the boxcar signal transfer (BST) - was developed, allowing to minimise the systematic differences existing between signals recorded with the two instruments. Class models for the verification of olive authenticity were built by the unequal dispersed classes (UNEQ) method, after data compression by disjoint principal component analysis (PCA). Models were validated on an external test set.

Robust calibrations on reduced sample sets for API content prediction in tablets: definition of a cost-effective NIR model development strategy.

Owing to spectral variations from other sources than the component of interest, large investments in the NIR model development may be required to obtain satisfactory and robust prediction performance. To make the NIR model development for routine active pharmaceutical ingredient (API) prediction in tablets more cost-effective, alternative modelling strategies were proposed. They used a massive amount of prior spectral information on intra- and inter-batch variation and the pure component spectra to define a clutter, i.e., the detrimental spectral information. This was subsequently used for artificial data augmentation and/or orthogonal projections. The model performance improved statistically significantly, with a 34-40% reduction in RMSEP while needing fewer model latent variables, by applying the following procedure before PLS regression: (1) augmentation of the calibration spectra with the spectral shapes from the clutter, and (2) net analyte pre-processing (NAP). The improved prediction performance was not compromised when reducing the variability in the calibration set, making exhaustive calibration unnecessary. Strong water content variations in the tablets caused frequency shifts of the API absorption signals that could not be included in the clutter. Updating the model for this kind of variation demonstrated that the completeness of the clutter is critical for the performance of these models and that the model will only be more robust for spectral variation that is not co-linear with the one from the property of interest.

A study on the use of near-infrared spectroscopy for the rapid quantification of major compounds in Tanreqing injection.

In this paper we describe the strategy used in the development and validation of a near infrared spectroscopy method for the rapid determination of baicalin, chlorogenic acid, ursodeoxycholic acid (UDCA), chenodeoxycholic acid (CDCA), and the total solid contents (TSCs) in the Tanreqing injection. To increase the representativeness of calibration sample set, a concentrating-diluting method was adopted to artificially prepare samples. Partial least square regression (PLSR) was used to establish calibration models, with which the five quality indicators can be determined with satisfied accuracy and repeatability. In addition, the slope/bias (S/B) method was used for the models transfer between two different types of NIR instruments from the same manufacturer, which is contributing to enlarge the application range of the established models. With the presented method, a great deal of time, effort and money can be saved when large amounts of Tanreqing injection samples need to be analyzed in a relatively short period of time, which is of great significance to the traditional Chinese medicine (TCM) industries.