Quantitative analysis of a pharmaceutical formulation: Performance comparison of different handheld near-infrared spectrometers

Demonstration of an End-To-End Workflow Using Atmospheric Solids Analysis Probe-Mass Spectrometry (ASAP-MS) With Real-Time Sample Recognition Software for the Identification of Falsified and Substandard Pharmaceutical Tablets

Counterfeit pharmaceuticals are a subclass of falsified and substandard medicines. They are illicit products, purporting to be genuine medicines, that are made and sold by criminal organisations. They represent a significant risk to patient safety, as well as a financial and reputational threat to the companies who make the genuine medicines. It is essential to have analytical methods to determine if suspect samples seized by law enforcement agencies are counterfeit, with mass spectrometry (MS) being a commonly used technique in forensic cases. Speed-to-answer is vital to enable law enforcement agencies to progress investigations, as well as for pharmaceutical companies so that they can notify health authorities of the circulation of counterfeit medicines. In this work, an atmospheric solids analysis probe (ASAP)-MS was assessed as a fast and simple-to-use approach to analyse tablets on a commercially available instrument. Complementing the analytics with real-time sample recognition software demonstrated that the classification of tablets as authentic or counterfeit could be achieved quickly (< 2 min) and without the need for MS interpretation skills. Authentication of five tablets (two authentic pharmaceuticals, one placebo and two counterfeits containing the correct active pharmaceutical ingredient [API] but at lower quantities than in the genuine medicine and with different excipient contents) of unknown origin was achieved with 100% success. This creates the opportunity to deploy the end-to-end workflow as a tool for non-scientists, such as law enforcement officers and border control staff, for use in-territory to obtain fast answers and make data-led decisions to control the illegal trading of medicines.

Measurement errors and implications for preprocessing in miniaturised near-infrared spectrometers: Classification of sweet and bitter almonds as a case of study

Near-infrared (NIR) spectroscopy is a well-established analytical technique that has been used in many applications over the years. Due to the advancements in the semiconductor industry, NIR instruments have evolved from benchtop instruments to miniaturised portable devices. The miniaturised NIR instruments have gained more interest in recent years because of the fast and robust measurements they provide with almost no sample pretreatments. However, due to the very different configurations and characteristics of these instruments, they need a dedicated optimization of the measurement conditions, which is crucial for obtaining reliable results. To comprehensively grasp the capabilities and potentials offered by these sensors, it is imperative to examine errors that can affect the raw data, which is a facet frequently overlooked. In this study, measurement error covariance and correlation matrices were calculated and then visually inspected to gain insight into the error structures associated with the devices, and to find the optimal preprocessing technique that may result in the improvement of the models built. This strategy was applied to the classification of sweet and bitter almonds, which were measured with the three portable low-cost NIR devices (SCiO, FlameNIR+ and NeoSpectra Micro Development Kit) after removing the shelled, since their classification is of utmost importance for the almond industry. The results showed that bitter almonds can be classified from sweet almonds using any of the instruments after selecting the optimal preprocessing, obtained through inspection of covariance and correlation matrices. Measurements obtained with FlameNIR + device provided the best classification models with an accuracy of 98 %. The chosen strategy provides new insight into the performance characterization of the fast-growing miniaturised NIR instruments.

Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview

Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.

Fast identification of the BmNPV infected silkworms by portable NIR spectroscopy and chemometrics

A convenient, low-cost, and rapid detection of BmNPV-infected silkworms is of great significance for the safety of the sericulture industry. In this study, a portable NIR system was used to collect the spectra of normal silkworms and the infected silkworms induced by the administration of Bombyx mori nuclear polyhedrosis virus (BmNPV). Different spectral pretreatment methods were applied, then principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) were used for the classification analysis. The results showed that PCA and LDA were unable to achieve the purpose. For the PLSDA calibration, after the pretreatment of SNV combining 2nd derivative, it had a high identification performance, and obtained low classification errors of 0.023, 0.033, and 0.030 for the calibration set, cross-validation set, and test set, respectively, with higher sensitivity and specificity. Therefore, the BmNPV-infected silkworms can be identified by portable NIR spectroscopy, which will effectively reduce losses for the sericulture industry.

Towards Point-of-Care Manufacturing and Analysis of Immediate-Release 3D Printed Hydrocortisone Tablets for The Treatment of Congenital Adrenal Hyperplasia

Hydrocortisone (HC) is the preferred drug in children with congenital adrenal hyperplasia due to its lower potency as well as fewer reports of side effects. Fused deposition modelling (FDM) 3D printing holds the potential to produce low-cost personalised doses for children at the point of care. However, the compatibility of the thermal process to produce immediate-release bespoke tablets for this thermally labile active is yet to be established. This work aims to develop immediate-release HC tablets using FDM 3D printing and assess drug contents as a critical quality attribute (CQA) using a compact, low-cost near-infrared (NIR) spectroscopy as a process analytical technology (PAT). The FDM 3D printing temperature (140 °C) and drug concentration in the filament (10%-15% w/w) were critical parameters to meet the compendial criteria for drug contents and impurities. Using a compact low-cost NIR spectral device over a wavelength of 900-1700 nm, the drug contents of 3D printed tablets were assessed. Partial least squares (PLS) regression was used to develop individual calibration models to detect HC content in 3D printed tablets of lower drug contents, small caplet design, and relatively complex formula. The models demonstrated the ability to predict HC concentrations over a wide concentration range (0-15% w/w), which was confirmed by HPLC as a reference method. Ultimately, the capability of the NIR model had preceding dose verification performance on HC tablets, with linearity (R² = 0.981) and accuracy (RMSECV = 0.46%). In the future, the integration of 3DP technology with non-destructive PAT techniques will accelerate the adoption of on-demand, individualised dosing in a clinical setting.

Chemometric Differentiation of Sole and Plaice Fish Fillets Using Three Near-Infrared Instruments

Fish species substitution is one of the most common forms of fraud all over the world, as fish identification can be very challenging for both consumers and experienced inspectors in the case of fish sold as fillets. The difficulties in distinguishing among different species may generate a “grey area” in which mislabelling can occur. Thus, the development of fast and reliable tools able to detect such frauds in the field is of crucial importance. In this study, we focused on the distinction between two flatfish species largely available on the market, namely the Guinean sole (Synaptura cadenati) and European plaice (Pleuronectes platessa), which are very similar looking. Fifty fillets of each species were analysed using three near-infrared (NIR) instruments: the handheld SCiO (Consumer Physics), the portable MicroNIR (VIAVI), and the benchtop MPA (Bruker). PLS-DA classification models were built using the spectral datasets, and all three instruments provided very good results, showing high accuracy: 94.1% for the SCiO and MicroNIR portable instruments, and 90.1% for the MPA benchtop spectrometer. The good classification results of the approach combining NIR spectroscopy, and simple chemometric classification methods suggest great applicability directly in the context of real-world marketplaces, as well as in official control plans.

Establishment of instrument operation qualification and routine performance qualification procedures for handheld near-infrared spectrometers used at different locations within a laboratory network

Quality assurance of finished pharmaceuticals is a necessity in ensuring the safety of consumers. There is a need for low-cost and portable rapid screening methods of pharmaceuticals in resource limited areas. Recent advances in technology have made handheld and low-cost diffuse reflectance spectrometers available to the public. While these handheld spectrometers offer advantages over benchtop spectrometers, the accuracy and repeatability must be assessed before these instruments can be used for quality assurance screening. Here, five handheld spectrometers of the same model were purchased, where an in-house installation qualification and operational qualification (IQOQ) was subsequently established for the instruments. Wavelength and photometric accuracy (and repeatability), spectroscopic noise, stray light, and bandpass were assessed between instruments. Results were found to be consistent between the spectrometers, passing IQOQ procedures, and were determined to be ready for field use. Once the handheld spectrometer's performance was verified, a practical and low-cost daily performance verification was established using common high density polyethylene vial caps on location in South Africa, Thailand, and the United States. A Mahalanobis distance-based classifier found the five spectrometers to be in agreement.

Near-infrared spectra of liquid and gas samples by diffuse reflectance employing benchtop and handheld spectrophotometers

This paper describes a new method to obtain NIR spectra of liquid and gas samples by diffuse reflectance, which is especially suitable for handheld spectrophotometers, since most of these instruments are designed to acquire spectrum using this geometry. The core of the method is a diffuse reflectance cell, which consists of a vial containing a mixture of the liquid or gas sample (rare medium) and a powder (dense medium). Using this strategy, no adaptation is required to measure spectra with most portable NIR spectrometers. This new method was used to obtain NIR spectra of several liquids and gases, which were compared with traditional transmittance spectra. As a proof of concept, measurements of biodiesel/vegetable oil/diesel blends were used to build multivariate calibrations to predict the contents of biodiesel and vegetable oil in diesel blends using benchtop and handheld FT-NIR spectrophotometers. This low-cost method was demonstrated to be suitable for overcoming problems related to the handling of viscous samples and expand the applications with portable NIR instruments.

Real-time concentration monitoring using a compact composite sensor array for in situ quality control of aqueous formulations

Recent advancements have demonstrated the feasibility of refrigerator-sized pharmaceutical manufacturing platforms (PMPs) for integrated end-to-end manufacturing of active pharmaceutical ingredients (APIs) into formulated drug products. Unlike typical laboratory- or industrial-scale setups, PMPs present unique requirements for process analytical technology (PAT) with respect to versatility, flexibility, and physical size to fit into the PMP space constraints. In this proof of principle study, a novel compact composite sensor array (CCSA) combining ultraviolet (UV) and near infrared (NIR) features at four different wavelengths (280, 340, 600, 860 nm) with temperature measuring capability in a 380 × 30 mm housing (length x diameter, 7 mm diameter at the probe head), were evaluated. The results indicate that the CCSA prototype is capable of measuring the solution and suspension concentrations in aqueous formulations of four model APIs (warfarin sodium isopropanol solvate, lidocaine hydrochloride monohydrate, 6-mercaptopurine monohydrate, acetaminophen) in situ and in real-time with similar accuracy as an established Raman spectrometer commonly applied for method development.

Fast monitoring total acids and total polyphenol contents in fermentation broth of mulberry vinegar using MEMS and optical fiber near-infrared spectrometers

This study evaluated the potential of Micro-Electro-Mechanical System (MEMS) and optical fiber Near-Infrared (NIR) spectrometers as a rapid method for monitoring total acids (TA) and total polyphenol content (TPC) in the fermentation process of mulberry vinegar. The NIR spectrometers Digital Light Processing (DLP) NIRscan Nano EVM (MEMS instrument) were used for this purpose, and another NIR spectrometer NIRQuest 512 was used for comparison. The standard Normal Variate (SNV) was selected as the best method used to pre-process spectra, and the competitive adaptive reweighted sampling (CARS) was applied to optimize wavelength variables, which were then subjected to partial least squares (PLS) regression. The results showed that TA and TPC could be determined. For TA, NIRQuest 512 provided a good predictive performance with the root-mean-square error of prediction set (RMSEP) of 0.22% and the R-squared of prediction set (R²_P) of 0.977, whiles DLP NIRscan Nano EVM obtained the RMSEP of 0.32% and R²p of 0.950. For TPC, the NIRQuest 512 provided a predictive performance with the RMSEP of 8.11 mgGAE/L (mg gallic acid equivalent/L) and R²_P of 0.820, and DLP NIRscan Nano EVM had the RMSEP of 8.22 mg GAE/L and R²p of 0.800. In conclusion, the low-price MEMS NIR instruments DLP NIRscan Nano EVM can be applied to monitor TA and TPC in the fermentation broth of mulberry vinegar.

A sample selection method specific to unknown test samples for calibration and validation sets based on spectra similarity

As is known to all, the construction of calibration and validation sets is of great importance for how to select representative samples into subsets so that the calibration model can be built, evaluated and predicted effectively for model development. In this study, a method was proposed for the calibration and validation sets constructed by selecting samples maximally similar to the test samples based on the spectra data. Both the Euclidean distance and Mahalanobis distance were attempted to estimate the spectra similarity. The method to select samples for calibration is more suitable and specific to unknown test samples in practical applications, thus improving the measurement accuracy. In addition, the optimization of calibration set size was carried out to avoid the influence of unnecessary samples. Two data sets of Salvia miltiorrhiza (S. miltiorrhiza) and corn by near infrared spectroscopy (NIR) were used to test the performance of the proposed method compared with two typical sample-selection algorithms, Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). The experimental results indicated that the proposed method could select a more targeted set of samples for the unknown test samples and had the superior predictive performance to the KS and SPXY methods.

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.

Simultaneous Quantification of 14 Compounds in Achillea millefolium by GC-MS Analysis and Near-Infrared Spectroscopy Combined with Multivariate Techniques

The proposed work is focused on the simultaneous quantification of 14 compounds in the medicinal plant Achillea millefolium based on Near-Infrared Spectroscopy (NIR). The regression model of single-compound models (SCMs) and multicompound model (MCM) were created by partial least-squares regression (PLSR). Also, these models were calibrated by gas chromatographic mass spectroscopy (GC-MS). The results showed that the averaged standard errors of prediction (SEP) for the SCMs and MCM were 0.49 and 0.62, respectively, and most of the 14 compounds were significantly correlated. 43 correlations were significant at the 0.01 level (47.25% of the total), and 11 correlations were significant at the 0.05 level (12.09% of the total). The first three principal components (PCs) of principal component analysis (PCA) can explain >78% of the total variance. According to the component matrix and the communality table, octadecanoic acid has the largest influence on PC 1 (extraction squared = 46.72%), whose extraction was 0.932. The communality of neophytadiene, Z,Z,Z-9,12,15-octadecatrienoic acid, and oleic acid was also found to be large, whose extractions were 0.955, 0.937, and 0.859, respectively. These results indicate that if one compound shows a linear relationship with the NIR absorbance signal (SCM) also, an MCM can be created due to the close interrelations of these compounds. In this context, the present work highlights a suitable sample preparation technique to perform NIR analysis of raw plant material to benefit from robust and precise calibrations. To sum up, this NIR spectroscopic approach offers a precise, rapid, and cost-effective high-throughput analytical technique to simultaneously and noninvasively perform quantitative analysis of raw plant materials.

Rapid analysis of wheat flour by different handheld near-infrared spectrometers: A discussion of calibration model maintenance and performance comparison

With the commercial availability of handheld near-infrared (NIR) spectrometers at significantly reduced costs, novel applications for qualitative and quantitative in-the-field and on-site analysis of such instruments for new user communities are currently reported. In the present study, the feasibility of rapid quantitative analysis of the essential quality parameters protein, wet gluten, moisture, ash, and sedimentation of wheat flour samples with three handheld NIR spectrometers based on different monochromator principles (Linear Variable Filter, Fourier-Transform Technique and Fabry-Perot Tunable Filter) has been investigated. In view of the long-term (two years) measurement and calibration schedule with reference sample batches of significantly varying parameter values, the necessary countermeasures for the important issue of calibration maintenance have been addressed and discussed in detail. Due to the variation of available reference values for the different flour parameters, the total number of reference samples varied between 124 (for sedimentation) and 235 (for moisture). These samples were then split up in a 3/1 calibration/test set ratio. Based on the detailed analysis of the statistical calibration parameters and the prediction accuracy for the test sample sets of the different flour parameters, the performance of the spectrometers under investigation has been compared. Despite instrumental and sampling deficiencies, such as hardware changes and significantly varying sample batches, respectively, during the long-term measurement schedule, the results proved the general suitability of the different handheld NIR spectrometers for the rapid quantitative on-site determination of the essential wheat flour parameters.

Challenging handheld NIR spectrometers with moisture analysis in plant matrices: Performance of PLSR vs. GPR vs. ANN modelling

The global demand for natural products grows rapidly, intensifying the request for the development of high-throughput, fast, non-invasive tools for quality control applicable on-site. Moisture content is one of the most important quality parameters of natural products. It determines their market suitability, stability and shelf life and should preferably be constantly monitored. Miniaturized near-infrared (NIR) spectroscopy is a powerful method for on-site analysis, potentially fulfilling this requirement. Here, a feasibility study for applicability and analytical performance of three miniaturized NIR spectrometers and two benchtop instruments was evaluated in that scenario. The case study involved 192 dried plant extracts composed of five different plants harvested in different countries at various times within two years. The reference analysis by Karl Fischer titration determined the water content in this sample set between 1.36% and 6.47%. For the spectroscopic analysis half of the samples were laced with a drying agent to comply with the industry standard. The performance of various calibration models for NIR analysis was evaluated on the basis of root-mean square error of prediction (RMSEP) determined for an independent test set. Partial least squares regression (PLSR), Gaussian process regression (GPR) and artificial neural network (ANN) models were constructed for the spectral sets from each instrument. GPR and ANN models performed superior for all samples measured by handheld spectrometers and for native ones analyzed by benchtop instruments. Moreover, the accuracy penalty when analyzing native samples was lower for GPR and ANN prediction as well. With GPR or ANN calibration, miniaturized spectrometers offered the prediction performance at the level of the benchtop instruments. Therefore, in this analytical application miniaturized spectrometers can be used on-site with no penalty to the performance vs. laboratory-based NIR analysis.

Principles and Applications of Miniaturized Near-Infrared (NIR) Spectrometers

This review article focuses on the principles and applications of miniaturized near-infrared (NIR) spectrometers. This technology and its applicability has advanced considerably over the last few years and revolutionized several fields of application. What is particularly remarkable is that the applications have a distinctly diverse nature, ranging from agriculture and the food sector, through to materials science, industry and environmental studies. Unlike a rather uniform design of a mature benchtop FTNIR spectrometer, miniaturized instruments employ diverse technological solutions, which have an impact on their operational characteristics. Continuous progress leads to new instruments appearing on the market. The current focus in analytical NIR spectroscopy is on the evaluation of the devices and associated methods, and to systematic characterization of their performance profiles.

Monitoring the Processing of Dry Fermented Sausages with a Portable NIRS Device

This work studies the ability of a MicroNIR (VIAVI, Santa Rosa, CA) device to monitor the dry fermented sausage process with the use of multivariate data analysis. Thirty sausages were made and subjected to dry fermentation, which was divided into four main stages. Physicochemical (weight lost, pH, moisture content, water activity, color, hardness, and thiobarbiruric reactive substances analysis) and sensory (quantitative descriptive analysis) characterizations of samples on different steps of the ripening process were performed. Near-infrared (NIR) spectra (950-1650 nm) were taken throughout the process at three points of the samples. Physicochemical data were explored by distance to K-Nearest Neighbor (K-NN) cluster analysis, while NIR spectra were studied by partial least square-discriminant analysis; before these models, Principal Component Analysis (PCA) was performed in both databases. The results of multivariate data analysis showed the ability to monitor and classify the different stages of ripening process (mainly the fermentation and drying steps). This study showed that a portable NIR device (MicroNIR) is a nondestructive, simple, noninvasive, fast, and cost-effective tool with the ability to monitor the dry fermented sausage processing and to classify samples as a function of the stage, constituting a feasible decision method for sausages to progress to the following processing stage.

Effect of Polymers and Storage Relative Humidity on Amorphous Rebamipide and Its Solid Dispersion Transformation: Multiple Spectra Chemometrics of Powder X-Ray Diffraction and Near-Infrared Spectroscopy

This study aimed to investigate the effect of polymers and storage relative humidity on amorphous rebamipide (RB) and its solid dispersion phase transformation using chemometrics based on multiple datasets. The amorphous RB was prepared using particle mixture and grinding methods with hydroxypropyl cellulose, polyvinylpyrrolidone, and sodium dodecyl sulfate. Prepared amorphous RB and solid dispersion samples were stored under a relative humidity of 30% and 75% for four weeks. Infrared spectra of the dispersion samples suggested that the hydrogen bond network was constructed among quinolinone, carbonyl acid, and amide of RB and other polymers. The dataset combining near-infrared (NIR) spectra and powder X-ray diffractograms were applied to principal component analysis (PCA). The relationship between diffractograms and NIR spectra was evaluated using loadings and the PCA score. The multiple spectra analysis is useful for evaluating model amorphous active pharmaceutical ingredients without a standard sample.

Theae nigrae folium: Comparing the analytical performance of benchtop and handheld near-infrared spectrometers

We investigated caffeine and l-theanine, quality characteristics for camellia sinensis, in milled and ground black tea samples with near-infrared (NIR) spectroscopy giving a direct comparison between the performances of benchtop and handheld NIR spectrometers. The constructed partial least squares regression (PLSR) models for all spectrometers were validated by test-set-validation and according to the obtained root mean square errors of prediction (RMSEP). The performances of the spectrometers were as follows: The benchtop spectrometer NIRFlex N-500 (Büchi, Flawil, Switzerland) showed the best results for milled samples with a RMSEP of 0.14% for caffeine and 0.12% for l-theanine. For the ground samples, a RMSEP of 0.17% for caffeine and 0.12% for l-theanine was gained. While the handheld spectrometers MicroNIR 2200 (Viavi Solutions (former: JDS Uniphase Corporation), Milpitas, USA) and the microPHAZIR (Thermo Fisher Scientific, Waltham, USA) both provided good results for the prediction of caffeine in milled samples (RMSEP of 0.22% and 0.26%), only the microPHAZIR was able to satisfactorily determine the caffeine content in ground samples (RMSEP of 0.28%). The investigation of l-theanine with handheld spectrometers did not lead to convincing results, since R² was 0.75 for milled samples while ground samples could not be calculated. Decisive differences were concluded in how different NIR instruments capture the chemical information on caffeine vs. l-theanine. The handheld spectrometers manifested limited applicability to l-theanine. Deeper insight was obtained through the detailed NIR band assignments of caffeine and l-theanine derived from quantum mechanical simulation. Narrow working spectral region of handhelds omits the characteristic absorption bands of l-theanine. Therefore, information on l-theanine content measured by the evaluated miniaturized spectrometers is insufficient to enable its effective quantification. In contrast, the most characteristic NIR absorption of caffeine matches the working spectral regions of the handheld NIR spectrometers, hence their performance is comparable with the benchtop device.

Handheld near-infrared spectrometers: Where are we heading?

During NIR 2019 conference, Gold Coast, Australia, a presentation upon a critical review of instrumentation and applications of handheld spectrometers was delivered during the plenary session held on Thursday morning, 19 September. Following the conference presentation, a vivid discussion flared up among the audience that equally involved academic scholars, industry representatives, as well as professionals who carry out every day in-the-field applications. Various aspects were raised connected with the emerged new generation of near-infrared instrumentation, with many individuals expressing their point-of-view on the merits and pitfalls of the miniaturized spectrometers. This vigorous dispute and exchange of impressions indicated that the community remains concerned about the applicability of such devices. That concern reflects the still relatively shallowly explored miniaturization versus performance factor, which can only be dismissed by focused feasibility studies with comparative analyses carried out on scientific-grade benchtop spectrometers. It is the aim of the present manuscript to summarize the discussed scientific content and to share the developed point-of-view with addition of our remarks.

Rapid determination of the content of digestible energy and metabolizable energy in sorghum fed to growing pigs by near-infrared reflectance spectroscopy1

The object of this study was to establish a new method to predict the content of DE and ME in sorghum fed to growing pigs by using near-infrared reflectance spectroscopy (NIRS). A total of 33 sorghum samples from all over China were used in this study. The samples were scanned for their spectra in the range of 12,000 to 4,000 cm-1. Based on principal components analysis of the spectra, the samples were split into a calibration set (n = 24) and a validation set (n = 9) according to the ratio of 3:1. With animal experiment values as calibration reference, the calibration models of DE and ME were established using partial least squares regression algorithm. Different spectral pretreatments were applied on the spectra to reduce the noise level. The best wavenumber ranges were also investigated. Results showed that DE and ME content in sorghum fed to growing pigs ranged from 14.57 to 16.70 MJ/kg DM and 14.31 to 16.35 MJ/kg DM, respectively. The optimal spectral preprocessing method for DE and ME was the combination of first derivative and multiplicative scatter correction. The most informative near-infrared spectral regions were 9,403.9 to 6,094.4 cm-1 and 4,605.5 to 4,242.9 cm-1 for both DE and ME. The best performance for DE and ME calibration models was the coefficient of determination of calibration (R2c) of 0.94 and 0.93, coefficient of determination of cross-external validation (R2cv) of 0.88 and 0.86, residual predictive deviation of cross-external validation (RPDcv) of 2.86 and 2.64, coefficient of determination of external validation (R2v) of 0.90 and 0.81, and residual predictive deviation of external validation (RPDv) of 3.15 and 2.35, respectively. There were no significant differences between the measured and NIRS predicted values for DE and ME (P = 0.895 for DE and P = 0.644 for ME). As the number of calibration samples increased from 24 to 33, the calibration performance of DE and ME models was improved, indicated by increased R2c, R2cv, and RPDcv values. In conclusion, NIRS quantitative models of the available energy in sorghum were established in this study. The results demonstrated that the content of DE and ME in sorghum could be predicted with relatively high accuracy based on NIRS and NIRS showed the superiority of speediness and practicality when compared with previous research methods including animal experiments, regression equations, and computer-controlled simulated digestion system.

Tuned near infrared fluorescent hyaluronic acid conjugates for delivery to pancreatic cancer for intraoperative imaging

The prognosis of pancreatic cancer remains poor. Intraoperative fluorescence imaging of tumors could improve staging and surgical resection, thereby improving prognosis. However, imaging pancreatic cancer with macromolecular delivery systems, is often hampered by nonspecific organ accumulation.

Methods: We describe the rational development of hyaluronic acid (HA) conjugates that vary in molecular weight and are conjugated to near infrared fluorescent (NIRF) dyes that have differences in hydrophilicity, serum protein binding affinity, and clearance mechanism. We systematically investigated the roles of each of these properties on tumor accumulation, relative biodistribution, and the impact of intraoperative imaging of orthotopic, syngeneic pancreatic cancer.

Results: Each HA-NIRF conjugate displayed intrapancreatic tumor enhancement. Regardless of HA molecular weight, Cy7.5 conjugation directed biodistribution to the liver, spleen, and bowels. Conjugation of IRDye800 to 5 and 20 kDa HA resulted in low liver and spleen signal while enhancing the tumor up to 14-fold compared to healthy pancreas, while 100 kDa HA conjugated to IRDye800 resulting in liver and spleen accumulation.

Conclusion: These studies demonstrate that by tuning HA molecular weight and the physicochemical properties of the conjugated moiety, in this case a NIRF probe, peritoneal biodistribution can be substantially altered to achieve optimized delivery to tumors intraoperative abdominal imaging.

Quantification of Water, Protein and Soluble Sugar in Mulberry Leaves Using a Handheld Near-Infrared Spectrometer and Multivariate Analysis

Mulberry (Morus alba L.) leaves are not only used as the main feed for silkworms (Bombyx mori) but also as an added feed for livestock and poultry. In order to rapidly select high-quality mulberry leaves, a hand-held near-infrared (NIR) spectrometer combined with partial least squares (PLS) regression and wavelength optimization methods were used to establish a predictive model for the quantitative determination of water content in fresh mulberry leaves, as well as crude protein and soluble sugar in dried mulberry leaves. For the water content in fresh mulberry leaves, the R-square of the calibration set (R2 C), R-square of the cross-validation set (R2 CV) and R-square of the prediction set (R2 P) are 0.93, 0.90 and 0.91, respectively, the corresponding root mean square error of calibration set (RMSEC), root mean square error of cross-validation set (RMSECV) and root mean square error of prediction set (RMSEP) are 0.96%, 1.13%, and 1.18%, respectively. The R2 C, R2 CV and R2 P of the crude protein prediction model are 0.91, 0.83 and 0.92, respectively, and the corresponding RMSEC, RMSECV and RMSEP are 0.71%, 0.97% and 0.61%, respectively. The soluble sugar prediction model has R2 C, R2 CV, and R2 P of 0.64, 0.51, and 0.71, respectively, and the corresponding RMSEC, RMSECV, and RMSEP are 2.33%, 2.73%, and 2.36%, respectively. Therefore, the use of handheld NIR spectrometers combined with wavelength optimization can fastly detect the water content in fresh mulberry leaves and crude protein in dried mulberry leaves. However, it is a slightly lower predictive performance for soluble sugar in mulberry leaves.

Hand-Held Near-Infrared Spectroscopy for Authentication of Fengdous and Quantitative Analysis of Mulberry Fruits

Recently, miniaturization of Raman, mid-infrared (MIR) and near-infrared (NIR) spectrometers have made substantial progress, and marketing companies predict this segment of instrumentation a significant growth rate within the next few years. This increase will be based on a more frequent implementation for industrial quality and process control and a broader adoption of spectrometers for in-the-field testing, on-site measurements, and every-day-life consumer applications. The reduction in size, however, must not lead to compromises in measurement performance and the hand-held instrumentation will only have a real impact if spectra of comparable quality to laboratory spectrometers can be obtained. The present communication will, on the one hand, explain the instrumental reasons why NIR spectroscopy is presently the most advanced technique regarding miniaturization and on the other hand, it will emphasize the impact of NIR spectroscopy for plant analysis by discussing in some detail a qualitative and a quantitative application example.

Breakthrough Potential in Near-Infrared Spectroscopy: Spectra Simulation. A Review of Recent Developments

Near-infrared (12,500–4,000 cm⁻¹; 800–2,500 nm) spectroscopy is the hallmark for one of the most rapidly advancing analytical techniques over the last few decades. Although it is mainly recognized as an analytical tool, near-infrared spectroscopy has also contributed significantly to physical chemistry, e.g., by delivering invaluable data on the anharmonic nature of molecular vibrations or peculiarities of intermolecular interactions. In all these contexts, a major barrier in the form of an intrinsic complexity of near-infrared spectra has been encountered. A large number of overlapping vibrational contributions influenced by anharmonic effects create complex patterns of spectral dependencies, in many cases hindering our comprehension of near-infrared spectra. Quantum mechanical calculations commonly serve as a major support to infrared and Raman studies; conversely, near-infrared spectroscopy has long been hindered in this regard due to practical limitations. Advances in anharmonic theories in hyphenation with ever-growing computer technology have enabled feasible theoretical near-infrared spectroscopy in recent times. Accordingly, a growing number of quantum mechanical investigations aimed at near-infrared region has been witnessed. The present review article summarizes these most recent accomplishments in the emerging field. Applications of generalized approaches, such as vibrational self-consistent field and vibrational second order perturbation theories as well as their derivatives, and dense grid-based studies of vibrational potential, are overviewed. Basic and applied studies are discussed, with special attention paid to the ones which aim at improving analytical spectroscopy. A remarkable potential arises from the growing applicability of anharmonic computations to solving the problems which arise in both basic and analytical near-infrared spectroscopy. This review highlights an increased value of quantum mechanical calculations to near-infrared spectroscopy in relation to other kinds of vibrational spectroscopy.

Investigations into the Performance of a Novel Pocket-Sized Near-Infrared Spectrometer for Cheese Analysis

The performance of a newly developed pocket-sized near-infrared (NIR) spectrometer was investigated by analysing 46 cheese samples for their water and fat content, and comparing results with a benchtop NIR device. Additionally, the automated data analysis of the pocket-sized spectrometer and its cloud-based data analysis software, designed for laypeople, was put to the test by comparing performances to a highly sophisticated multivariate data analysis software. All developed partial least squares regression (PLS-R) models yield a coefficient of determination (R²) of over 0.9, indicating high correlation between spectra and reference data for both spectrometers and all data analysis routes taken. In general, the analysis of grated cheese yields better results than whole pieces of cheese. Additionally, the ratios of performance to deviation (RPDs) and standard errors of prediction (SEPs) suggest that the performance of the pocket-sized spectrometer is comparable to the benchtop device. Small improvements are observable, when using sophisticated data analysis software, instead of automated tools.