NIR Method Research Articles

Water as a probe to understand the traditional Chinese medicine extraction process with near infrared spectroscopy: A case of Danshen (Salvia miltiorrhiza Bge) extraction process

Extraction process is not only a critical manufacturing unit but also the initial process of various extracts and preparations. Taking the most extensive Chinese herbal medicine Danshen (Salvia miltziorrhiza Bge) as an example, salvianolic acid B (Sal B) is its main active pharmaceutical ingredient but lacks accurate characterization of the extraction process. As one of process analytical technologies, near-infrared spectroscopy (NIRS) technology has been widely applied for monitoring pharmaceutical extraction process. In most past studies, water spectral information is often eliminated due to its high absorption. However, this study proposed a method of using water spectrum to understand the whole extraction process and to quickly determine the content of Sal B. Principal component analysis (PCA) was first utilized to investigate the whole extraction process, then the reconstructed spectrum based on PCA was established and analyzed by Aquaphotomics, and finally the partial least squares regression (PLSR) quantitative model of Sal B was established. PCA and Aquaphotomics results showed the whole extraction process could be considered as a dynamic change from structure breaker to structure maker, and the dominance of highly H-bonded water structures increases with the extraction time. Also, the Sal B quantitative model with water spectrum showed higher accuracy and stability than other methods, which parameters (RMSEC, RMSECV, RMSEP, R2c, R2cv, R2p, RPD) were 0.2408mg/mL, 0.2939mg/mL, 0.2584mg/mL, 0.9536, 0.9300, 0.9494, 4.6298, respectively, and the paired t-test showed that Sal B content measured by NIR and HPLC methods had no significant differences (p>0.05). In conclusion, all result indicated that water can be used as a probe to understand the traditional Chinese medicine extraction process with NIRS.

FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples.

Daily consumption of caffeine in coffee, tea, chocolate, cocoa, and soft drinks has gained wide and plentiful public and scientific attention over the past few decades. The concentration of caffeine in vivo is a crucial indicator of some disorders—for example, kidney malfunction, heart disease, increase of blood pressure and alertness—and can cause some severe diseases including type 2 diabetes mellitus (DM), stroke risk, liver disease, and some cancers. In the present study, near-infrared spectroscopy (NIRS) coupled with partial least-squares regression (PLSR) was proposed as an alternative method for the quantification of caffeine in 25 commercially available tea samples consumed in Oman. This method is a fast, complementary technique to wet chemistry procedures as well as to high-performance liquid chromatography (HPLC) methods for the quantitative analysis of caffeine in tea samples because it is reagent-less and needs little or no pre-treatment of samples. In the current study, the partial least-squares (PLS) algorithm was built by using the near-infrared NIR spectra of caffeine standards prepared in tea samples scanned by a Frontier NIR spectrophotometer (L1280034) by PerkinElmer. Spectra were collected in the absorption mode in the wavenumber range of 10,000–4000 cm−1, using a 0.2 mm path length and CaF2 sealed cells with a resolution of 2 cm−1. The NIR results for the contents of caffeine in tea samples were also compared with results obtained by HPLC analysis. Both techniques provided good results for predicting the caffeine contents in commercially available tea samples. The results of the proposed study show that the suggested FT-NIRS coupled with PLS regression algorithun has a high potential to be routinely used for the quick and reproducible analysis of caffeine contents in tea samples. For the NIR method, the limit of quantification (LOQ) was estimated as 10 times the error of calibration (root mean square error of calibration (RMSECV)) of the model; thus, RMSEC was calculated as 0.03 ppm and the LOQ as 0.3 ppm.

An approach to quantify natural durability of Eucalyptus bosistoana by near infrared spectroscopy for genetic selection

Natural durability within a timber species can be variable. Hence efficient assessments of natural durability are required to ensure quality either through tree breeding or segregation during production. In this study, first the relationship between extractive content and mass loss of Eucalyptus bosistoana heartwood caused by a white and a brown-rot fungus was validated. Then the ability of NIR spectroscopy as a high-throughput method to evaluate heartwood decay resistance was examined. Finally the NIR method was applied to a tree breeding trial. A correlation between extractive content and mass loss against the white-rot fungus (Perenniporia tephropora) and the brown-rot fungus (Coniophora olivacea) were found. Analysis of NIR spectra indicated that this relationship is causal with shared bands for mass loss and extractive content models at 6650, 6017, 5265 and 4659 cm−1. Partial least squares regression (PLSR), supplemented with spectra normalisation and variable selection, allowed prediction of mass loss with a residual mean square error (RMSE) of 7.48 % and 5.76 % for the white-rot and brown-rot, respectively. This level of precision allowed the characterisation of a E. bosistoana resource which showed a range of mass loss from 0 to 60 %. Genetic control was found for mass loss by the white-rot ( h2 = 0.70 and 0.24) and the brown-rot ( h2 = 0.15 and 0.13) at two sites in New Zealand. The rankings were correlated between sites, with genetic correlations (Rg2) of 0.69 and 0.63 for white-rot and brown-rot, respectively, as well as to the predicted extractive content (0.82 to 0.92). However, the study indicated a significant site effect on the decay resistance of the E. bosistoana heartwood. In summary, this study has shown that the decay resistance could be assessed rapidly and efficiently using NIR technology for genetic selection.

Content of Polyphenolic Compounds and Antioxidant Potential of Some Bulgarian Red Grape Varieties and Red Wines, Determined by HPLC, UV, and NIR Spectroscopy

Today, good food criteria also include healthy capacity. So, the wine on our table should not only have good organoleptic qualities, but should be characterized by a high healthy potential. For the first time, extensive research was conducted on commercial red wine grape varieties cultivated in different Bulgarian regions in two consecutive years. Antioxidants, including trans-resveratrol, quercetin, and total phenolic content and antioxidant potential in wine grapes and wines were determined by HPLC, UV, and NIR methods. The results obtained showed similar concentration levels compared to the same varieties, produced in other countries. Trans-resveratrol showed the greatest contribution to the radical scavenging capacity. The factor with largest impact on the content of the tested substances was definitely the variety. Among agro-meteorological condition, temperature amplitude, rain fall, and UV irradiation before ripening had strong influences. Maintaining the balance between the level of synthesized and degraded and captured antioxidants during the wine making process was crucial to preserving the antioxidant properties of the final wine product. NIR spectroscopy showed very good accuracy of determination of trans-resveratrol, quercetin, total phenolic content, and the antioxidant activity of tested grape varieties and red wines. It could be a promising technique in the quantification of their antioxidant parameters.

Spectral analysis as an extra method to soil type discrimination

Abstract. The purpose of the study was to test near infrared soil spectra as an extra method for three soil types (Fluvisols, Vertisols and Solonchaks) discrimination from different regions of South Bulgaria. The diffuse reflectance spectra of 177 soil samples (from the 0-20cm layers): 50 samples of Fluvisols soil type, 78 samples of Vertisols soil type and 48 samples of Solonchaks soil type were obtained using a Spectrum NIRQuest (OceanOptics, Inc.) working within the range from 900 to 1700 nm. Soft independent modelling of class analogy (SIMCA) was performed to classify samples according to their taxonomic classes. The results obtained showed that the soil samples are separated accurately according to their soil type based on their spectral information. All this could be used in the future studies related to the application of the NIRS method as a qualitative or quantitative method for soil analysis and also for the purposes of precision farming.

Rapid Determination of Total Phenol in Leaf Extracts of a Medicinal Plant using Infrared Spectroscopy and Chemometric Methods

Infrared spectroscopy combined with chemometrics has been applied for simple and rapid determination of total phenol in leaf extracts of medicinal plants. Phenols were extracted from leaves of medicinal plants by ultrasonication and maceration methods. Fourier transform infrared and near infrared spectra of selected leaf extracts of medicinal plants were correlated with total phenolic content using chemometrics. The chemometric methods used for calibration analysis were partial least square (PLS), principal component regression, and support vector machines regression and for classification analysis linear discriminant analysis (LDA), soft independent modelling of class analogies, and support vector machines classification were applied. The results have shown that the PLS method gives the best calibration model with the coefficient of determination (R2) of 0.9920, whereas the root mean square error of calibration is 2.0049. In addition, R2 of leave-one-out cross validation and 2-fold-cross-validation were 0.9939 and 0.9834, respectively. Linear discriminant analysis showed good results of classification analysis with 100% accuracy. The PLS and LDA models were then applicated to real samples. The paired samples t-test showed that total phenol content measured by NIR and UV-Vis spectroscopy methods gave no significant differences (p > 0.05).

NIR spectroscopy application for determination caffeine content of Arabica green bean coffee

Caffeine is considered as an important quality indicators of coffee. Caffeine content in green coffee bean influence the flavour and price of coffee. Commonly, percentage of caffeine content is determined by chemical method, which is known time consuming, destructive and expensive, so it is not suitable for a real time coffee content prediction system. The objective of this study was to develop a PLS model based on NIR-Spectroscopy to predict caffeine content of Indonesia coffee beans nondestructively. The wavelength used in this study was 1000-2500 nm and the caffeine content of samples were determined by LCMS method. Several data pretreatments such as multiple scatter correction (MSC), first derivative (dg1), combination of dg1+MSC, with PLS factors variation were applied to obtain the best prediction of caffeine content by NIR. The best prediction was obtained using MSC and 6 PLS factors indicated by a high coefficient correlation value which was >0.9. NIR method can be used for predicting the caffeine content of green bean coffee nondestructively.

Estimation of Harumanis (Mangifera indica L.) Sweetness using Near-Infrared (NIR) Spectroscopy

Harumanis mango quality demanded by consumers is depending on the sweetness level of the fruit. The sweetness is evaluated by brix percentage using refractometer as a representative factor correlated with near-infrared (NIR) spectroscopy spectral absorbance. NIR spectroscopy method of sampling have been tested to overcome the time consuming, complex chemical analysis more importantly invasive sampling methods in order to determine the sugar content in mangoes. Spectral absorbance data from range 941 nm to 1685 nm of mango skin is correlated with Brix reading then tested through five pre-processing techniques. Data calibration and prediction of both data is evaluated using Partial Least Square Regression (PLSR) model. In the final analysis, Unit vector normalization (UVN) technique has achieved as a best pre-processing technique for predicting results, with the coefficient of determination (R2) values of 0.9836 and root mean square error (RMSE) values of 0.3131. Overall, the correlation of NIRS absorbance data and Brix data can be obtained using PLSR model with UVN pre-processing technique. Henceforth, we can conclude that the NIRS method of sampling can be used to identify sugar content in Harumanis mango by using time saving, non-invasive and less laborious method of sampling.

Prediction of drug dissolution from Toremifene 80 mg tablets by NIR spectroscopy

The aim of our study was to justify substitution of dissolution analysis for NIR measurement of Toremifene 80mg tablets. We studied implementation of a NIRS method by integrating the method development to discrimination power of the dissolution method. Hence, we analyzed 20 DoE tablet batches and studied which of the critical formulation factors affecting dissolution were statistically significant. To study if these factors can be detected by NIRS, PLS calibration models were developed. Finally, PLS model was built to correlate NIR data with the actual dissolution results to predict the released amount of toremifene in 30min. To obtain the data the tablet batches were measured by NIR using diffuse reflectance technique and multivariate analysis tool was used to calibrate the NIRS models. Correlations between the critical formulation factors and the NIR spectra of Toremifene 80mg tablet were shown and it was thus justified to develop a NIRS prediction model for dissolution. Variance (R2), standard error of estimate (SEE) and standard error of prediction (SEP) of the model were 90.0%, 4.3% and 5.9%, respectively. It was thus shown that multi-phased and time consuming dissolution procedure could be substituted for fast non-invasive NIRS method.

Yem Fabrikalarında Uygulanan Teknolojik İşlemlerin Karma Yem Besin Maddesi İçeriklerine Etkisinin Belirlenmesi ve Kalite Kritik Noktaları Oluşturulması

Bu çalışmada, karma yem fabrikalarında hazırlanan yemlerin kalitesinin üretimden önce belirlenmesi için kalite kritik noktalarının oluşturulması amaçlanmıştır. Bunun için, karma yem üretiminde kalite kontrol noktalarının oluşturulması ve bu noktaların sürekli denetlenmesi gerekmektedir. Yaptığımız çalışma ile, karma yem üretiminde kullandığımız hammaddelerin kalitesini (yem besin maddesi içeriklerini) belirleyip daha sonra yem fabrikalarında karma yemde önemli teknolojik değişime sebep olan; değirmen, mikser, melasiyer, tavlama, pelet presi, soğutucu ve paketleme bölümlerinde kalite kritik noktaları oluşturulmuştur. Söz konusu bölgelerden 4 farklı karma yemden (bıldırcın yemi, yumurtacı, süt sığırı ve besi karma yemi) numune alınmıştır. Çalışma özel bir yem fabrikasında gerçekleştirilmiştir. Öncelikle bu karma yemlere ait bilgisayarda formülasyon programı ile rasyonlar hazırlanmıştır. Daha sonra belirlenen noktalardan numuneler alınmış ve besin madde içerikleri (kuru madde, ham protein, ham yağ, ham selüloz, ham kül, nişasta, şeker, metabolik enerji değeri) kimyasal ve NIRS yöntemi ile analiz edilmiştir. Değirmen çıkışından alınan numuneler, kimyasal ve NIRS yöntemi ile yapılan analiz sonuçları karma yemlerin formülasyon değerlerinden çok farklı olmuştur. Diğer gerçekleştirilen kalite kritik noktalarından (değirmen, mikser melasiyer, tavlama, pelet pres girişi, pelet pres çıkışı, soğutucu ve paketleme) alınan değerler ise büyük oranda formülasyon verileri ile benzer özellik göstermiştir. Böylece, yem fabrikalarında değirmen hariç, söz konusu belirlenen noktalardan örnekler alınarak, karma yemin kalitesi tahmin edilebilecektir. Araştırma sonucunda elde ettiğimiz bulgulara göre, mikser altı bunkerden alınan numunesinden elde edilen sonuçlar, karma yemlerin formülasyon değerlerinden yakın değeri göstermiş ve en uygun kalite kritik noktası olabileceği sonucuna varılmıştır.

Determination of Oxygen Tension in Peripheral Neuropathy and Diabetes using Nirs Method

According to WHO, India had 69.2 million people living with diabetes in 2015 and is expected to rise by more than a 5th from 406 million in 2018 to 511 million in 2030. Diabetes currently affects over 425 million people worldwide. Chronic Blood sugar and diabetes may cause severe nerve damage known as Peripheral neuropathy. Approximately 15% of patients with diabetes are affected with diabetes. Various studies have indicated that peripheral vascular disease is related to tissue hypoxia and clinical device monitors uses pulse oximetry principle to measure changes of light absorption in oxygenated and deoxygenated blood. In this project we intend to develop a portable device to assess oxygen tension near foot ulcerate area using NIRS (Near infrared spectroscopy) method in diabetic subjects. The results can be visualized in the LCD and Visual display. The main purpose of oxygen tension estimation is to have a better understanding in wound healing potential, assessment of success in revascularization, and predictability of amputation level and the study the need of hyperbaric oxygen in therapy.

Real-time monitoring of particle size distribution in a continuous granulation and drying process by near infrared spectroscopy

In continuous granulation, it can be important to control granules particle size distribution (PSD), as it may affect final product quality. Near infrared spectroscopy (NIRS) is already a routine analytical procedure within pharmaceutical continuous manufacturing for the in-line analysis of chemical material-characteristics. Consequently, the extraction of additional information related to granules’ physical properties like particle size distribution is tempting, as it would enhance process knowledge without the need for new capital investments.Three in-line NIRS methods were developed via partial least squares regression, to predict dried granules PSD-fractions X10, X50, and X90 within a GMP-qualified continuous twin-screw wet granulation and fluid-bed drying process. Methods were developed for the size range of 20–234 µm (X10), 98–1017 µm (X50), and 748–2297 µm (X90) and assessed with one internal and three external validation datasets in agreement with current guidelines on NIRS. Internal validation indicated root mean square error of predictions (RMSEPs) of 17 µm, 97 µm, and 174 µm, for PSD X10, X50, and X90 respectively, with acceptable linearity, slope, and bias. Furthermore, the ratio of prediction to deviation (RPD), the ratio of prediction error to laboratory error (PRL), and the range error ratio (RER) were evaluated, with all values within the acceptance range for adequate to good NIR methods (1.75 > RPD < 3, PRL ≤ 2, RER ≥ 10). Methods applicability to in-line processes and their robustness towards water content and active pharmaceutical ingredient content was further demonstrated with three independent in-line datasets in real-time, showing good agreement between predicted and reference values. In summary, methods demonstrated to be sufficient for their intended purpose to monitor trends and sudden changes in dried granules PSD during continuous granulation and drying. Because of their fast response time, they are unique tools to characterize the dynamic behavior and navigate the agglomeration state of the material in static and transient process conditions during continuous granulation and drying.

Variographic analysis: A new methodology for quality assurance of pharmaceutical blending processes

Analytical methods for real time monitoring of pharmaceutical blending processes are thoroughly validated but not the sampling methods. Variographic analysis was investigated as a method to determine the sampling and analytical errors when the drug concentration of pharmaceutical powder blends is determined by near infrared spectroscopy. Variograms provide an estimate of the true process variance and the minimum possible error (MPE) defined as the sum of the total sampling error and the total analytical error under the specific sampling scenario. In this study, the MPE was 50–186 times greater for the 97.00 [%w/w] blends and 75–342 times higher for the 95.00 [% w/w] blends than the short-term precision of the analytical NIR method. This study shows that variographic analysis may be used along with concentration vs. time profiles to evaluate a blending process, and also to discriminate between the sources of sampling, analytical and process errors.

A comparison study of five different methods to measure carotenoids in biofortified yellow cassava (Manihot esculenta)

The most commonly used method for measuring carotenoid concentration is high-performance liquid chromatography (HPLC). Nevertheless, easier, quicker, and less costly proxy methods exist. We aimed to determine the diagnostic performance of several proxy methods: the spectrophotometer, iCheck Carotene, and near-infrared spectroscopy using both a desktop (dNIRS) and a portable (pNIRS) device for the measurement of total carotenoid concentration (TCC) and all-trans-β-carotene concentration (trans-BC) in 30 fresh cassava (Manihot esculenta Crantz) storage roots in comparison with HPLC. The spectrophotometer presented the highest predictability for TCC, followed by iCheck, dNIRS, and pNIRS. The dNIRS showed the highest predictability and agreement for trans-BC. The pNIRS showed the poorest repeatability and greatest underestimations compared with HPLC. The agreement between all methods was lower for higher carotenoid concentration, with the exception of the spectrophotometer. According to our results, and for screening purposes, the measurement of carotenoids in fresh cassava roots can be carried out by spectrophotometer, iCheck Carotene and NIRS methods depending on the availability of equipment.

A New Statistical Approach to Describe the Quality of Extra Virgin Olive Oils Using Near Infrared Spectroscopy (NIR) and Traditional Analytical Parameters

NIR prediction models are developed for the determination of relevant parameters to evaluate olive oil quality such as acidity (FFA), peroxide value (PV), UV‐absorption at 232 and at 268/270 nm, p‐anisidine value (AnV), isomeric diacylglycerols (DG), and pyropheophytin A (PPP). In addition a new NIR method to estimate the age of olive oil is presented. The relevant wavenumbers are given for the calculation of the parameters and the precision data are presented in comparison to the chemical reference methods. The calibration and validation of the methods are executed with independent data sets (test in test instead of cross‐validation) to cover the wide range of variability of the analytical parameters including the corresponding accurate analytical results from the reference chemical methods. The correctness and accuracy of the developed NIR methods are verified by analyzing certified materials. Finally, a simple statistical approach has been developed to describe the quality of olive oils using the parameters FFA, PV, K232 and K270, DG and PPP. The probability of presence of a sensory defect (100% ≙ 1; 0% ≙ 0) is calculated using the following equation: Pred (BIN) = 1/(1 + exp(−(−9 + 37 × FFA‐0.9 × PV‐2.9 × K232 + 14 × K270 + 3.7 × PPP‐0.17 × DG))).Practical Applications: The use of NIR allows the analysis of different parameters relevant for the quality of olive oil in one run in comparison to the individual chemical reference methods. That allows saving time and money, and a skilled operator is not necessary, making the method interesting for routine analysis. The use of a simple equation developed from the logistic regression using FFA, PV, K‐values, DG, and PPP measured by NIR as variables enables for the first time to describe the sensory quality of olive oils without making sensory testing. This criterion without setting limits for the individual parameters estimates the probability of the presence of sensory defects on basis of chemical parameters. This tool can easily be used to differentiate the two categories extra virgin and virgin olive oils both using the traditional laboratory methods and the corresponding NIR‐methods.In the present study, NIR prediction models are developed for the determination of free fatty acids, peroxide value, UV‐absorption at 232 and at 268/270 nm, p‐anisidine value, isomeric di‐acyl‐glycerols, and pyropheophytin A to evaluate olive oil quality. On basis of NIR, a new method is developed to estimate the age of olive oil. The parameters FFA, PV, K232 and K270, DG and PPP have been used to describe the quality of olive oils by a simple statistical approach.

Preliminary results on direct quantitative determination of cocaine in impregnated materials by infrared spectroscopy

Partial least squares models were built for the direct determination of cocaine in seized impregnated smuggled materials. Measurements are based on the attenuated total reflectance middle infrared spectra (ATR-MIR) and diffuse reflectance spectra in the near range (DR-NIR) obtained directly from the surface of the impregnated materials. The aforementioned procedures offer fast, cheap and environmentally friendly green alternatives to the reference method based on the extraction of the drug and its quantification by gas chromatography. Additionally it has been verified that results found are statistically comparable with those obtained by the reference method with root mean square errors of prediction (RMSEP) of 0.60% w/w and 0.88% w/w for MIR and NIR methods, and ratio of performance to deviation (RPD) values of 13.20 and 7.54, respectively. Examples of application for the quantification of cocaine in impregnated clothes, pulp paper and foam have been also provided.

Rapid and Simultaneous Determination of Three Active Components in Raw and Processed Root Samples of Scutellaria baicalensis by Near-infrared Spectroscopy.

Motivated by the wide use of Scutellariae Radix (SR) in the food and pharmaceutical industries, a rapid and non-destructive near-infrared spectroscopy (NIRS) method was developed for the simultaneous analysis of three main active components in raw SR and SR processed by stir-frying with wine. From seven geographical areas, 58 samples were collected. The reference contents for the SR components baicalin, baicalein, and wogonin were determined by high-performance liquid chromatography. Two multivariate analysis methods, partial least-squares (PLS) regression as a linear regression method and artificial neural networks (ANN) as a nonlinear regression method, were applied to the NIR data, and their results were compared. In the PLS model, different model parameters (i.e., 11 spectral pre-treatment methods), spectral region, and latent variables were investigated to optimize the calibration model; additionally, the ANN model was applied with five different spectral pre-treatment methods and six algorithms. For the optimal model parameters, the correlation coefficients of the calibration set for baicalin, baicalein, and wogonin were 0.9979, 0.9786, and 0.9773, respectively; the correlation coefficients of the prediction set were 0.9756, 0.9843, and 0.9592, respectively; the root mean square error of validation values were 0.215, 0.321, and 0.174, respectively. The optimal NIR models were then employed to analyze the effects of processing and geographical regions on analyte contents. The established NIR methods were robust, accurate, and reproducible. NIRS may be a promising approach for the routine screening and quality control of traditional Chinese medicines.

Development and Robustness Verification of an At-Line Transmission Raman Method for Pharmaceutical Tablets Using Quality by Design (QbD) Principles

The purpose of the present study was to investigate the feasibility of developing a fast non-destructive at-line transmission Raman spectroscopy (TRS) method for core tablet potency and content uniformity (CU) as part of a real-time release testing (RTRt) control strategy. The effects of tablet hardness and weight (thickness), API particle size, and concentration were studied by using a novel experimental design called generalized subset designs (GSDs). A subset of 28 experiments plus three replicate center points were selected for a total of 31 experiments. Matrix effects included tablets with active pharmaceutical ingredient (API) at seven concentration levels (14 to 26%w/w), and API particle size (17 to 71 μm), tablet weight (275 to 328 mg), and tablet hardness (8 to 16 SCU) at two levels. Three calibration models were developed, by using partial least squares (PLS) and different preprocessing conditions (model nos. 1 to 3). In model no. 1, all matrix effects were excluded. This model showed high potency prediction errors (RMSEP of 10.0%). When all matrix variations were included in the multivariate calibration according to the GSD as shown in model nos. 2 and 3, the prediction accuracy was greatly improved (RMSEP 2.56 and 1.74% respectively). The statistical significance of the tablet weight, hardness, and API particle size in the %Recovery (TRS vs. the reference HPLC method) was investigated by using MODDE Pro (Sartorius Stedim Data Analytics). Statistically significant effects were identified if the calculated p value was ≤ 0.05. The main effect hardness, the cross-term hardness×particle size, and the quadratic term cal level×cal level showed to be statistically significant. However, these effects had a very small impact on tablet prediction accuracy (± 1.0%w/w) well within the intermediate precision of the method. A non-destructive TRS method for core tablet potency and CU was fully validated, following ICH Q2 and EMEA NIR guidelines. The applicability of the method to process development batches was demonstrated and compared to a previously developed and validated NIR method.

Near Infra-Red spectroscopy for content uniformity of powder blends – Focus on calibration set development, orthogonality transfer and robustness testing

The aim of this work was to develop and validate a NIR method for the quantification of three active ingredients from powder blends. Calibration set formulations were selected based on a D-optimal experimental design with three factors (ibuprofen, paracetamol, caffeine) and five variation levels (80–90–100–110–120%). NIR spectra were recorded in transmittance mode using a rotating sample configuration. Prior to model development the effect of spectral pre-processing was assessed by evaluating its impact over the transfer of orthogonality from concentration space to spectral space. NIR method was validated on the full calibration range with external prediction sets, using the accuracy profile approach. Robustness testing results showed that the accuracy of predictions for the analyte found in lower concentrations (caffeine) was influenced by relative humidity, while paracetamol/ibuprofen predictions were robust to all factors. Redefinition of interfering factor variation level was beneficial to reduce the bias in caffeine content predictions. Also, alternative solutions are provided for ensuring robustness and successful routine use.

Abstract P-509: THE VALUE OF MONITORING BRAIN OXYGEN SATURATION IN CHILDREN SEPSIS WITH NEAR INFRARED SPECTROSCOPY

Aims & Objectives: To examine the difference of rScO2 in children with sepsis via NIRS Methods We monitored the rScO2 of sepsis children. We tried to find the change rule of rScO2 and explore the relationship between rScO2 and some physiological indicators. And then we studied the relationship between rScO2 and prognosis Results A total of 30 subjects were enrolled into this study, which included 17 septic shock children and 13 non-shock children. The rScO2 of septic shock children is significantly lower than the non-shock children and the control group in the first 4–5 day after admission (58.8% vs 63.5%). There is no statistically difference between non-shock group and the control group in rScO2. The rScO2 is not statistically correlated with mean arterial pressure, heart rate, and the concentration of hemoglobin and lactic acid, but has a positive relationship with ScvO2, the correlation coefficient is about 0.6–0.7. Although there is no statistically significant correlation between rScO2 and GOS, the rScO2 in dead children is lower than that of surviving children (57.6% vs 62.8%) Conclusions The rScO2 has strong positive correlation with ScvO2. Compared with survivors, the rScO2 is lower in dead children. NIRS may be a predictive factor in children sepsis