Potential of NIR spectroscopy for predicting cellulose nanofibril quality in commercial bleached Kraft pulp of Eucalyptus.

Multivariate models were developed to classify cellulose nanofibril (CNF) fibrillation by a quality index from near infrared (NIR) spectra. Commercial pulps of Eucalyptus spp. were used to produce cellulose nanofibrils by means of a fibrillator mill. After each of the five passes through the mill, samples were collected and analyzed for energy consumption and fiber classification. As a standard, pulps were oxidized with TEMPO reagent followed by a single pass through the mill to compare the resulting quality of CNFs produced by each method. NIR spectra of CNFs were associated with quality indices determined by conventional laboratory analyses that included morphology, turbidity, mechanical properties, X-ray diffraction and quality index measurements. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied to the spectral and experimental data. Fibrillator milling to obtain CNFs was efficient and resulted in gel formation following the third pass through the mill. NIR spectroscopy combined with PLS-DA was used successfully to create a model to classify quality of CNFs with 96 % certainty in 3 wt% solutions. These findings suggest that NIR spectroscopy holds promise for estimating CNF quality in suspension, particularly in real-time industrial applications where reliable estimates are crucial.

Assessing Polymorphic Purity of Rifampicin in Double and Triple-Drug Fixed-Dose Combination Products.

First-line tuberculostatic agents, Rifampicin (RIF), Isoniazid (ISH), Ethambutol (ETB), and Pyrazinamide (PZA) are generally administered as a fixed-dose combination (FDC) for improving patient adherence. The major quality challenge of these FDC products is their variable bioavailability, where RIF and its solid state are key factors. In this work, the analysis of the impact of the polymorphism in the performance of RIF in RIF-ISH and PZA-RIF-ISH combined products was carried out by an overall approach that included the development and validation of two methodologies combining near-infrared (NIR) spectroscopy and partial least squares (PLS) to the further evaluation of commercial products. For NIR-PLS methods, training and validation sets were prepared with mixtures of Form I/Form II of RIF, and the appropriate amount of ISH (for double associations) or ISH-PZA (for triple associations). The corresponding matrix of the excipients was added to the mixture of APIs to simulate the environment of each FDC product. Four PLS factors, reduced spectral range, and the combination of standard normal variate and Savitzky-Golay 1st derivative (SNV-D') were selected as optimum data pre-treatment for both methods, yielding satisfactory recoveries during the analysis of validation sets (98.5±2.0%, and 98.7±1.8% for double- and triple-FDC products, respectively). The NIR-PLS model for RIF-ISH successfully estimated the polymorphic purity of Form II in double-FDC capsules (1.02 ± 0.02w/w). On the other hand, the NIR-PLS model for RIF-ISH-PZA detected a low purity of Form II in triple FDC tablets (0.800 ± 0.021w/w), these results were confirmed by X-ray powder diffraction. Nevertheless, the triple-FDC tablets showed good performance in the dissolution test (Q=99-102%), implying a Form II purity about of 80% is not low enough to affect the safety and efficacy of the product.

Review on the Discovery of Water Absorbance Spectral Pattern in Aquaphotomics based on Chemometrics Analytical Tools.

Aquaphotomics, as a new discipline is a powerful tool for exploring the relationship between the structure of water and the function of matter by analyzing the interaction between water and light of various frequencies. However, chemometric tools, especially the Water Absorbance Spectral Pattern (WASP) determinations, are essential in this kind of data mining. In this review, different state-of-the-art chemometrics methods were introduced to determine the WASP of aqueous systems. We elucidate the methods used for identifying activated water bands in three aspects, namely: 1) improving spectral resolution; the complexity of water species in aqueous systems leads to a serious overlap of NIR spectral signals, therefore, we need to obtain reliable information hidden in spectra, 2) extracting spectral features; sometimes, certain spectral information cannot be revealed by simple data processing, it is necessary to extract deep data information, 3) overlapping peak separation; since the spectral signal is produced by multiple factors, overlapping peak separation can be used to facilitate the extraction of spectral components. The combined use of various methods can characterize the changes of different water species in the system with disturbance and can determine the WASP. WASPs of research systems vary from each other, and it is visually displayed in the form of the aquagram. As a new omics family member, aquaphotomics could be applied as a holistic marker in multidisciplinary fields.

Development of a Portable Near-Infrared Spectroscopy Tool for Detecting Freshness of Commercial Packaged Pork.

Real-time monitoring of meat quality requires fast, accurate, low-cost, and non-destructive analytical methods that can be used throughout the entire production chain, including the packaged product. The aim of this work was to evaluate the potential of a portable near-infrared (NIR) spectroscopy tool for the on-site detection of freshness of pork loin fillets in modified atmosphere packaging (MAP) stored on display counters. Pork loin slices were sealed in MAP trays under two proportions of O2/CO2/N2: High-Ox-MAP (30/40/30) and Low-Ox-MAP (5/20/75). Changes in pH, color, thiobarbituric acid reactive substances (TBARS), Warner−Bratzler shear force (WBSF), and microbiology (total viable counts, Enteriobacteriaceae, and lactic acid bacteria) were monitored over 15 days post-mortem at 4 °C. VIS-NIR spectra were collected from pork fillets before (through the film cover) and after opening the trays (directly on the meat surface) with a portable LABSPEC 5000 NIR system in diffuse reflectance mode (350−2500 nm). Quantitative NIR models by partial least squares regression (PLSR) showed a promising prediction ability for meat color (L*, a*, C*, and h*) and microbiological variables (R2VAL > 0.72 and RPDVAL > 2). In addition, qualitative models using PLS discriminant analysis obtained good accuracy (over 90%) for classifying pork samples as fresh (acceptable for consumption) or spoiled (not acceptable) based on their microbiological counts. VIS-NIR spectroscopy allows rapid evaluation of product quality and shelf life and could be used for on-site control of pork quality.

Comparison Between Pure Component Modeling Approaches for Monitoring Pharmaceutical Powder Blends with Near-Infrared Spectroscopy in Continuous Manufacturing Schemes.

Near-infrared (NIR) spectroscopy has become an important process analytical technology (PAT) for monitoring and implementing control in continuous manufacturing (CM) schemes. However, NIR requires complex multivariate models to properly extract the relevant information and the traditional model of choice, partial least squares, can be unfavorable on account of its high material and time investments for generating calibrations. To account for this, pure component-based approaches have been gaining attention due to their higher flexibility and ease of development. In the present study, the application of two pure component approaches, classical least squares (CLS) models and iterative optimization technology (IOT) algorithms, to pharmaceutical powder blends in a continuous feed frame was considered. The approaches were compared from both a model performance and practical implementation perspective. IOT were found to demonstrate superior performance in predicting drug content compared to CLS. The practical implementation of each modelling approach was also given consideration.

Measurement of water-holding capacity in fermented milk using near-infrared spectroscopy combined with chemometric methods.

We investigated the use of near-infrared spectroscopy (NIR) for measuring water-holding capacity (WHC) in fermented milk. Increased WHC ensures improved texture and decreased syneresis in fermented dairy products and also improves cheese yield. NIR combined with partial least-squares-discriminant analysis (PLS-DA) was found to be a promising rapid and non-invasive method with no pretreatment of the samples for prediction of WHC in fermented milk samples. Analysis of the chemical bonds in the region 10 700-4500 cm-1 (935-2200 nm) of the electromagnetic spectrum was able to distinguish between samples with high vs. low WHC. This technique was successfully used to screen different strains of lactic acid bacteria for their ability to provide fermented milk with increased WHC, which is of great importance for use in various dairy products.

Analysing the water spectral pattern by near-infrared spectroscopy and chemometrics as a dynamic multidimensional biomarker in preservation: rice germ storage monitoring.

Water activity is an important phenomenon not yet explained in terms of water molecular structure. This paper aims to find the relationship between the water activity and water molecular structure of the rice germ, based on its spectral pattern which can be measured using non-destructive technology. Aquaphotomics near-infrared spectroscopy was used to study rice germ stored at different levels of water activity and atmosphere. The findings show that state of the rice germ is governed by the water activity upon storage, which is defined by the structure of water within germ matrix. The structure of water can be described solely by the absorbance spectral pattern at the following absorbance bands: proton hydrates, hydration shells and water vapor (1364, 1375 and 1382 nm), trapped water (1392 nm), free water (1410 nm), hydration water (1425 nm), adsorbed water (1455 nm), non-bonded hydroxyl (1436 nm) and bound water (1520 nm).

Usage of Near-Infrared Spectroscopy for Inline Monitoring the Degree of Curing in RTM Processes.

Near-infrared spectroscopy (NIR) was implemented in the resin transfer molding (RTM) process to inline monitor the degree of curing of a bio-based epoxy resin, which consists of epoxidized linseed oil (resin) and citric acid (hardener), respectively. A NIR micro-spectrometer was used for the development of robust calibration models using partial least squares (PLS) regression. Since the micro-spectrometer offers a smaller wavelength range compared with conventional NIR devices, and typical absorbance peaks are not directly involved in the captured data range, the results show new insights for the utilization of this technology. Different pre-treatments of the spectroscopic data have been tested, starting with different reference spectra, i.e., uncured resin and polytetrafluorethylene (PTFE), and followed by chemometrical algorithms. As a reference method for the degree of curing, direct current (DC) supported by differential scanning calorimetry (DSC) was used. The results show the potential of these cost-efficient and compact NIR micro-spectrometers for the intended inline monitoring purpose to gain relevant information feedback during the process.

Potential of Near Infrared Spectroscopy as a Rapid Method to Discriminate OTA and Non-OTA-Producing Mould Species in a Dry-Cured Ham Model System.

The ripening process of dry-cured meat products is characterised by the development of fungi on the product's surface. This population plays a beneficial role, but, uncontrolled moulds represent a health risk, since some of them may produce mycotoxins, such as ochratoxin A (OTA). The aim of the present work is to assess the potential of near-infrared spectroscopy (NIRS) for the detection of OTA-producing mould species on dry-cured ham-based agar. The collected spectra were used to develop Support Vector Machines-Discriminant Analysis (SVM-DA) models by a hierarchical approach. Firstly, an SVM-DA model was tested to discriminate OTA and non-OTA producers; then, two models were tested to discriminate species among the OTA producers and the non-OTA producers. OTA and non-OTA-producing moulds were discriminated with 85% sensitivity and 86% specificity in the prediction. Furthermore, the SVM-DA model could differentiate non-OTA-producing species with a 95% sensitivity and specificity. Promising results were obtained for the prediction of the four OTA-producing species tested, with a 69% and 90% sensitivity and specificity, respectively. The preliminary approach demonstrated the high potential of NIR spectroscopy, coupled with Chemometrics, to be used as a real-time automated routine monitorization of dry-cured ham surfaces.

Spectroscopic evaluation on pseudopolymorphs of sodium naproxen.

This paper demonstrates the assessment of vibrational spectroscopy methods such as middle infrared, near infrared and Raman spectroscopy (FTIR, FT-NIR, Raman) for the identification of pseudopolymorphic forms of a model active pharmaceutical ingredient (API) - sodium naproxen (NpxNa). NpxNa, in the form of three different pseudopolymorphs, was investigated by methods dedicated for solid state characterization: DSC (differential scanning calorimetry), XPRD (powder X-ray diffraction), SEM (scanning electron microscopy) and Karl Fischer titration. Novelty in the results sourced in the usage of the method not applied so far to identify pseudopolymorphic forms of NpxNa, that is, FTIR and FT-NIR. Based on the obtained reproduceable results, various pseudopolymorphic forms were successfully evaluated. Spectroscopic data were correlated with DSC and XPRD results. It was concluded that the combination of band's variations visible on the spectra of pseudopolymorphic forms will allow to interpretate the results unequivocally in case of crucial stability tests of medicinal substance or during on-line pharmaceutical process development by FTIR, FT-NIR and Raman spectroscopy.

Polymer types ingested by northern fulmars (Fulmarus glacialis) and southern hemisphere relatives.

Although ingestion of plastic by tubenosed seabirds has been documented regularly, identification of the polymer composition of these plastics has rarely been described. Polymer assessment may assist in identifying sources and may indicate risks from additives occurring in specific types of polymers. Using known test materials, two identification methods Fourier transform infrared spectroscopy and near infrared spectroscopy (FTIR and NIR) were compared. Although both methods were found to be similarly suitable for identification of plastic polymers, a significant difference was observed in identification of natural materials. FTIR frequently misclassified natural materials as being a synthetic polymer. Within our results, an 80% match score threshold functioned best to distinguish between natural items and synthetics. Using NIR, the historical variability of plastics ingested by northern fulmars (Fulmarus glacialis) from the Dutch sector of the North Sea was analysed for three time periods since the 1980s. For the more recent decade, variability between fulmars from different regions in the northeast Atlantic was investigated. Regional variation was further explored by analysing plastics obtained from the stomachs of southern hemisphere relatives of the fulmar (southern fulmar, cape petrel, snow petrel) and Wilson's storm petrel. Results show that proportional abundance of polymer types in these seabirds is closely related to the plastic categories that they ingest (e.g. pellets, foam, fragments). The uptake of different plastic categories and related polymer types most likely reflects spatial and temporal variations in availability rather than ingestion preferences of the birds.

Tackling quantitative polymorphic analysis through fixed-dose combination tablets production. Pyrazinamide polymorphic assessment.

Pyrazinamide (PZA), Rifampicin (RIF), Isoniazid (ISH) and Ethambutol (ETB) form the core for the treatment of Tuberculosis, today a devastating disease in low-income populations around the world. These drugs are usually administrated by fixed-dose combination (FDC) products, to favour the patient compliance and prevent bacterial resistance. PZA exists in four enantiotropically-related polymorphs (Forms α, δ, ß and γ), but only Form α is considered suitable for pharmaceutical products due to its stability and bioavailability properties. The classical approaches to address solid-state (microscopy, X-ray diffraction and calorimetry) shows limitations for quantification of polymorphs in the presence of excipients and other active components, as in the case of FDC tablets. In this work, an overall strategy was developed using near infrared spectroscopy (NIR) coupled to partial least squares regression (PLS) to quantify Form α of PZA in drug substance (raw material) and PZA/RIF/ISH-FDC tablets. For this purpose, two PLS models were constructed, one for drug substance preparing training (n = 30) and validation (n = 18) samples with a ternary composition (Form α/Form δ/Form γ), and other for FDC drug products, also including the appropriate amount of RIF, ISH and the matrix of excipients in order to simulate the environment of PZA/RIF/ISH association. The NIR-PLS models were optimized using a novel smart approach based on radial optimization (full range, 3 L V and MSC-D' and SNV-D' as pre-treatment, for raw material and FDC tablets, respectively). During the validation step, both methods showed no bias or systematic errors and yielded satisfactory recoveries (102.5 ± 3.1 % for drug substance and 98.7 ± 1.5 % for FDC tablets). When commercial drug substance was tested, NIR-PLS was able to predict the content of Form α (0.98 ± 0.01 w/w). The model for FDC tablets allowed estimating polymorphic purity in intact (0.984 ± 0.003 w/w), sectioned (0.986 ± 0.002 w/w), and powered (0.985 ± 0.004 w/w) tablets, showing the methodology could be applied to a different stage of the process (i.e premixed-powders or granulates). The suitability of the method was also verified when Form α was satisfactorily analysed in FDC fortified with Form δ and Form γ to reach 0.78, 0.88 and 0.98 w/w, Form α. This strategy results in an excellent alternative to ensure the polymorphic purity of PZA throughout the overall pharmaceutical manufacturing process.

Prediction of Sensory Parameters of Cured Ham: A Study of the Viability of the Use of NIR Spectroscopy and Artificial Neural Networks.

Dry-cured ham is a high-quality product owing to its organoleptic characteristics. Sensory analysis is an essential part of assessing its quality. However, sensory assessment is a laborious process which implies the availability of a trained tasting panel. The aim of this study was the prediction of dry-ham sensory characteristics by means of an instrumental technique. To do so, an artificial neural network (ANN) model for the prediction of sensory parameters of dry-cured hams based on NIR spectral information was developed and optimized. The NIR spectra were obtained with a fiber-optic probe applied directly to the ham sample. In order to achieve this objective, the neural network was designed using 28 sensory parameters analyzed by a trained panel for sensory profile analysis as output data. A total of 91 samples of dry-cured ham matured for 24 months were analyzed. The hams corresponded to two different breeds (Iberian and Iberian x Duroc) and two different feeding systems (feeding outdoors with acorns or feeding with concentrates). The training algorithm and ANN architecture (the number of neurons in the hidden layer) used for the training were optimized. The parameters of ANN architecture analyzed have been shown to have an effect on the prediction capacity of the network. The Levenberg-Marquardt training algorithm has been shown to be the most suitable for the application of an ANN to sensory parameters.

Feasibility of a portable, low-cost near-infrared spectrophotometer for the quality screening of omega-3 dietary supplements.

The quality of omega-3 supplements, commercialized at substantial high prices and supplied by several manufacturers, must be assessed. The existing reference methods to attest the quality of omega-3 supplements are based on chromatography, which requires expensive equipment, a cumbersome analytical protocol to determine the contents of the active components, and operates ex-situ. This work evaluates, comprehensively, the feasibility of a low-cost near-infrared spectrophotometer and simple chemometrics to achieve fast and robust characterization of omega-3 supplements. The necessary attention, very often neglected, to the performance of the low-cost portable equipment is highlighted, and an appropriate sample measurement protocol is established. A non-conventional way to construct multivariate regression models based on partial least square regression to evaluated the omega-3 content in the supplements was proposed using few references values and completing the data set with the label values selected using the X-Y relation outliers plots. The results showed that the simple, fast and inexpensive approach proposed could deliver in situ relevant information related to the quality of the supplements such as identification several raw materials employed in its fabrication, screening for the content of the active components, identify frauds/non-conformities, and report on unscrupulous marketing practices.

Early Diagnosis of Type 2 Diabetes Based on Near-Infrared Spectroscopy Combined With Machine Learning and Aquaphotomics.

Early diagnosis is important to reduce the incidence and mortality rate of diabetes. The feasibility of early diagnosis of diabetes was studied via near-infrared spectra (NIRS) combined with a support vector machine (SVM) and aquaphotomics. Firstly, the NIRS of entire blood samples from the population of healthy, pre-diabetic, and diabetic patients were obtained. The spectral data of the entire spectra in the visible and near-infrared region (400-2,500 nm) were used as the research object of the qualitative analysis. Secondly, several preprocessing steps including multiple scattering correction, variable standardization, and first derivative and second derivative steps were performed and the best pretreatment method was selected. Finally, for the early diagnosis of diabetes, models were established using SVM. The first overtone of water (1,300-1,600 nm) was used as the research object for an aquaphotomics model, and the aquagram of the healthy group, pre-diabetes, and diabetes groups were drawn using 12 water absorption patterns for the early diagnosis of diabetes. The results of SVM showed that the highest accuracy was 97.22% and the specificity and sensitivity were 95.65 and 100%, respectively when the pretreatment method of the first derivative was used, and the best model parameters were c = 18.76 and g = 0.008583.The results of the aquaphotomics model showed clear differences in the 1,400-1,500 nm region, and the number of hydrogen bonds in water species (1,408, 1,416, 1,462, and 1,522 nm) was evidently correlated with the occurrence and development of diabetes. The number of hydrogen bonds was the smallest in the healthy group and the largest in the diabetes group. The suggested reason is that the water matrix of blood changes with the worsening of blood glucose metabolic dysfunction. The number of hydrogen bonds could be used as biomarkers for the early diagnosis of diabetes. The result show that it is effective and feasible to establish an accurate and rapid early diagnosis model of diabetes via NIRS combined with SVM and aquaphotomics.

Multi-block classification of Italian semolina based on Near Infrared Spectroscopy (NIR) analysis and alveographic indices.

Durum wheat (Triticum turgidum ssp. durum) is widely grown in the Mediterranean area. The semolina obtained by this grain is used to prepare pasta, couscous, and baked products all over the world. The growing area affects the characteristics of Durum wheat; consequently, it is relevant to trace this product. The present study aims at developing an analytical methodology which would allow tracing durum semolina harvested in 7 different Italian macro-areas. In order to achieve this goal, 597 samples of semolina have been analysed by Near Infrared Spectroscopy, and by measuring alveographic parameters. Eventually, the information collected have been handled by a multi-block classifier (SO-PLS-LDA) in order to predict the origin of samples. The proposed approach provided extremely satisfactory results (in external validation, on a test set of 140 objects), correctly classifying all samples according to their growing area, confirming it represents a suitable solution for tracing durum wheat semolina.

How to measure coating thickness of tablets: Method comparison of optical coherence tomography, near-infrared spectroscopy and weight-, height- and diameter gain.

Film coating of pharmaceutical dosage forms, such as tablets and pellets, can be used to tailor the drug release profile. With that regard, a uniform coating thickness of a single tablet (intra-tablet), all tablets (inter-tablet) and subsequent batches (inter-batch) is crucial. We present a method comparison between in-line (optical coherence tomography and near-infrared spectroscopy) and well-established off-line (height-, weight- and diameter-gain) approaches to determining the coating thickness of tablets. We used single tablets drawn during a commercial coating process. Comparing the low intra- and high inter-tablet coating variability indicated that the tablets had a broad distribution of spray zone passes but at a random tablet orientation. Even at the end of the coating process at a mean coating thickness of about 70 µm, the inter-tablet standard deviation was about 9 µm or 13% relative standard deviation. Determining correlations between the methods identified the factors that contribute to the measurement uncertainty and bias for each method. Ultimately, we aimed to establish that in-line methods match or even surpass the conventional off-line reference methods in terms of accuracy and precision of coating thickness measurement.

An innovative multivariate strategy for HSI-NIR images to automatically detect defects in green coffee.

In the present study, an advanced and original multivariate strategy for the processing of hyperspectral images in the near-infrared region is proposed to automatically detect physico-chemical defects in green coffee, which are similar one to each other by naked eye. An object-based approach for the characterization of individual beans, rather than single pixels, was adopted, calculating a series of descriptive parameters characterizing the distribution of scores on the lowest-order principal components. On such parameters, the k-nearest neighbors (k-NN) classification algorithm was applied and the predictive results on the test samples indicate that this approach is able not only to distinguish defective beans from non-defective ones, but also to differentiate the various types of defects. Hyperspectral imaging is demonstrated to be a valid alternative for the sorting of green beans - a crucial phase for coffee import/export.

Application of near infrared spectroscopy (NIR), X-ray fluorescence (XRF) and chemometrics to the differentiation of marmora samples from the Mediterranean basin.

Near-infrared (NIR) and X-ray fluorescence spectra were recorded for 15 different samples of marmora, from the Mediterranean Basin and of different colours. After appropriate pretreatment (SNV transform + second derivative), the results were subjected to principal component analysis (PCA) treatment with a view to differentiating them. The observed differences among the samples were chemically interpreted by highlighting the NIR wavelengths and minerals, respectively, contributing the most to the PCA models. Moreover, a mid-level data fusion protocol allowed integrating the information from the different techniques and, in particular, to correctly identify (based on the distance in the score space) three test samples of known type. Moreover, it should be stressed that positive results on the differentiation and identification of marmora were obtained using two completely non-invasive, non-destructive and relatively inexpensive techniques, which can also be used in situ.

Metabolomics data fusion between near infrared spectroscopy and high-resolution mass spectrometry: A synergetic approach to boost performance or induce confusion.

In general, data fusion can improve the classification performance of the model, but little attention is paid to the influence of the data fusion on the spatial distribution of the modeling samples. In this paper, the effect of data fusion on sample spatial distribution was studied through integrating NIR data and UHPLC-HRMS data for sulfur-fumigated Chinese herb medicine. Twelve samples collected from four different geographical origins were sulfur fumigated in the lab, and then metabolomics analysis was conducted using NIR and UHPLC-LTQ-Orbitrap mass spectrometer. First of all, the discriminating power of each technique was respectively examined based on PCA analysis. Secondly, combining NIR and UHPLC-HRMS data sets together with or without variable selection was parallelly compared. The results demonstrated that the discriminable ability was remarkably improved after data fusion, indicating data fusion could visualize variable selection and enhance group separation. Samples in the margin between two classes of samples may increase the experience error but has positive effect on the separation direction. Besides, an interesting feature extraction could obtain better discriminable effect than common data fusion. This study firstly provided a new path to employ a comprehensive analytical approach for discriminating SF Chinese herb medicines to simultaneously benefit from the advantages of several technologies.