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We investigated the near-infrared spectrum of piperine using quantum mechanical calculations. We evaluated two efficient approaches, DVPT2//PM6 and DVPT2//ONIOM [PM6:B3LYP/6-311++G(2df, 2pd)] that yielded a simulated spectrum with varying accuracy versus computing time factor. We performed vibrational assignments and unveiled complex nature of the near-infrared spectrum of piperine, resulting from a high level of band convolution. The most meaningful contribution to the near-infrared absorption of piperine results from binary combination bands. With the available detailed near-infrared assignment of piperine, we interpreted the properties of partial least square regression models constructed in our earlier study to describe the piperine content in black pepper samples. Two models were compared with spectral data sets obtained with a benchtop and a miniaturized spectrometer. The two spectrometers implement distinct technology which leads to a profound instrumental difference and discrepancy in the predictive performance when analyzing piperine content. We concluded that the sensitivity of the two instruments to certain types of piperine vibrations is different and that the benchtop spectrometer unveiled higher selectivity. Such difference in obtaining chemical information from a sample can be one of the reasons why the benchtop spectrometer performs better in analyzing the piperine content of black pepper. This evidenced direct correspondence between the features critical for applied near-infrared spectroscopic routine and the underlying vibrational properties of the analyzed constituent in a complex sample., Graphical Abstract

This paper describes a data preprocessing algorithm that can be used to mitigate the effects of interfering spectral components when the goal is to detect the spectrum of unknown components in a mixture of known components or to verify the presence of suspected components in the spectrum of a mixture of known components. The algorithm is both relatively simple and applicable to a wide range of problems in spectroscopy. The range of applicability can be increased by combining the method with other data preprocessing methods, for example derivative spectra, and can also accommodate variability in the spectra of one or more of the known components. Examples of the application of the algorithm to real problems are given for near-infrared analysis of antibiotic drug formulations inside gelatin capsules and mid-infrared analysis of atmospheric pollutants.

Dynamic spectra (DS) can greatly reduce the influence of individual differences and the measurement environment by extracting the absorbance of pulsating blood at multiple wavelengths, and it is expected to achieve noninvasive detection of blood components. Extracting high-quality DS is the prerequisite for improving detection accuracy. This paper proposed an optimizing differential extraction method in view of the deficiency of existing extraction methods. In the proposed method, the sub-dynamic spectrum (sDS) is composed by sequentially extracting the absolute differences of two sample points corresponding to the height of the half peak on the two sides of the lowest point in each period of the logarithm photoplethysmography signal. The study was based on clinical trial data from 231 volunteers. Single-trial extraction method, original differential extraction method, and optimizing differential extraction method were used to extract DS from the volunteers’ experimental data. Partial least squares regression (PLSR) and radial basis function (RBF) neural network were used for modeling. According to the effect of PLSR modeling, by extracting DS using the proposed method, the correlation coefficient of prediction set ( Rp) has been improved by 17.33% and the root mean square error of prediction set has been reduced by 7.10% compared with the original differential extraction method. Compared with the single-trial extraction method, the correlation coefficient of calibration set ( Rc) has increased from 0.747659 to 0.8244, with an increase of 10.26%, while the correlation coefficient of prediction set ( Rp) decreased slightly by 3.22%, much lower than the increase of correction set. The result of the RBF neural network modeling also shows that the accuracy of the optimizing differential method is better than the other two methods both in calibration set and prediction set. In general, the optimizing differential extraction method improves the data utilization and credibility compared with the existing extraction methods, and the modeling effect is better than the other two methods.

A nondestructive system for measuring the soluble solids content (SSC) and firmness of wax apples was developed using 670, 850, 880, 940, and 980 nm visible–shortwave–near-infrared (Vis-SW-NIR) light-emitting diode (LED) light sources and a silicon (Si) photodetector. These specified wavelengths are highly correlated with the SSC and the firmness of fruit. An LED light source was incident onto the fruit as parallel-polarized waves (P-wave) at the Brewster angle (θB) to minimize the interfacial reflection and maximize the C–H and O–H bonds absorption signals from the fruit. Partial least squares (PLS) regression is used to build calibration modes and analyze the prediction of the correlation ([Formula: see text]) and the root mean square error for prediction (RMSEP) of the reflected optical signals with SSC and firmness. This resulted in [Formula: see text] and RMSEP values of 0.87 and 0.66 °Bx, respectively, in SSC measurements and 0.80 and 1.16 N/cm2, respectively, in firmness measurements. Therefore, the result shows [Formula: see text] of SSC and firmness are 6.4% and 9% higher and the RMSEP are 14% and 20% lower, respectively, than those obtained using non-polarized LED light sources.

A rapid tool to discriminate rhino horn and ivory samples from different mammalian species based on the combination of near-infrared reflection (NIR) spectroscopy and chemometrics was evaluated. In this study, samples from the Australian Museum mammalogy collection were scanned between 950 nm and 1650 nm using a handheld spectrophotometer and analyzed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). An overall correct classification rate of 73.5% was obtained for the classification of all samples. This study demonstrates the potential of NIR spectroscopy coupled with chemometrics as a means of a rapid, nondestructive classification technique of horn and ivory samples sourced from a museum. Near-infrared spectroscopy can be used as an alternative or complementary method in the detection of horn and ivory assisting in the combat of illegal trade and aiding the preservation of at-risk species.

The present study has investigated the transformation of sesame oil kept at low temperature during a definite period of time for refinement (called winterization) as an inactive drug ingredient by using two-dimensional difference spectra (2D-DS) analysis of spectra collected using a near-infrared (NIR) and mid-infrared (MIR) dual-wavelength spectrometer (NIR–MIR-DWS). The NIR and MIR spectra were measured nearly simultaneously from samples of sesame oil before and after winterization. The difference spectrum analysis of the obtained NIR–MIR data elucidated that, after the winterization process, the absorbances at peaks attributed to C=O, C=C, and OH groups decrease while the absorbances arising from the main chain (CH2) increase. The result indicated the removal of lignan and the fatty acids with relatively short main chains. Moreover, sesame oil unwinterized was cooled from room temperature to near 1 ℃ and subsequently warmed to room temperature. And the cycle was repeated two times. Real-time monitoring during the cooling and warming processes were carried out using the NIR-MIR-DWS. The prediction results obtained from partial least square calibration model for the temperature suggests that there are subtle differences in the oil composition between the first cooling process and after the warming and cooling cycle. For the more detailed analysis, the 2D-DS method is proposed. The results of the analyses using 2D-DS revealed that the starting point of the transformation is around 15 ℃. It can be estimated that sesame oil is mainly transformed by the first cooling down. Moreover, it was implied that the structure of methylene (CH2) was significantly related to the modifications in sesame oil with temperature change. A series of experimental results elucidated that the winterization of sesame oil removed its impurities and stabilized its conditions. These results are probably the first report on the effect of the winterization process on sesame oil.

Raman and near-infrared (NIR) chemical mapping are widely used methods in the pharmaceutical industry to understand the distribution of components within a drug product. Recent advancements in instrumentation have enabled the rapid acquisition of high-resolution images. The comparison of these techniques for the analysis of pharmaceutical tablets has not recently been explored and thus the relative performance of each technique is not currently well defined. Here, the differences in the chemical images obtained by each method are assessed and compared with scanning electron microscopy with energy dispersive X-ray microanalysis (SEM-EDX), as an alternative surface imaging technique to understand the ability of each technique to acquire a chemical image representative of the sample surface. It was found that the Raman data showed the best agreement with the spatial distribution of components observed in the SEM-EDX images. Quantitative and qualitative comparison of the Raman and NIR images revealed a very different spatial distribution of components with regards to domain size and shape. The Raman image exhibited sharper and better discriminated domains of each component, whereas the NIR image was heavily dominated by large pixelated domains. This study demonstrated the superiority of using Raman chemical mapping compared with NIR chemical mapping to produce a chemical image representative of the sample surface using routinely available instrumentation to obtain a better approximation of domain size and shape. This is fundamental for understanding knowledge gaps in current manufacturing processes; particularly relating the relationship between components in the formulation, processing condition, and final characteristics. By providing a means to more accurately visualize the components within a tablet matrix, these areas can all be further understood.

Recently, feed frame-based process analytical technology measurements used to assure product quality during continuous manufacturing processes have received significant attention. These measurements are able to accurately determine uniformity of the powder blend before compression, and in these applications, it is necessary to understand the interrogated sample volume per measurement. This understanding ensures that the blend measurement can be indicative of the uniformity of the final dosage form. A scientifically sound approach is proposed here to estimate sample mass for a continuous manufacturing process that utilizes either near infrared or Raman spectroscopy. A wide range of commercially available probes with varying spot diameters are considered. By comparing near infrared and Raman spectroscopy, an optimal range of probe spot diameters was identified in order to reach an estimated sample mass between 50 and 500 mg for pharmaceutical blends per measurement, which is equivalent to common tablet weight ranges for solid oral dosage forms currently on the market.

Polyurethane (PU) foams are finding increasingly wider applications ranging from memory foams and mattresses to cushions and insulation materials. They are prepared by reactions between multifunctional isocyanates and polyols as the two main building blocks, along with other additives, including the blowing agents. A non-contact near-infrared (NIR) hyperspectral imaging (HSI) camera was used in this study to monitor PU foaming reactions between a polymeric methylene diphenyl diisocyanate, polyol, and water. Five foams were prepared with three process variables: water content, mixing time, and catalyst levels. Spectral changes characteristic of the PU reactions were observed and clear difference in kinetics could be effectively extracted from such NIR HSI results. The NIR HSI technology offers two substantial advantages over the conventional Fourier transform- (FT-) NIR systems: (i) faster spectral acquisition time and (ii) higher spatial resolution of line images rather than the point measurement. Examples are provided to illustrate these two advantages. The potential to acquire chemical images of PU foams is also demonstrated.

Characteristics of five subpopulation leukocytes in single-cell levels based on partial principal component analysis coupled with Raman spectroscopy were proposed to recognize the biochemical features of five subpopulation leukocytes. Using wavelet transform, the reconstructed spectra of the low-frequency wavelet coefficients were used to perform multiple principal component analysis based on segmented spectral data wreathing cover at 720–800 cm–1, 840–994 cm–1, and 1010–1070 cm–1 wavenumbers, respectively. Our approach is promising since it enables to establish a better understanding of the underlying molecular difference between the subtypes of leukocytes in a label-free manner and to estimate the source of infection.

Power spectra are a powerful tool provided by two-dimensional correlation analysis. However, this tool is seldom used in practice. This work shows selected examples of using of the power spectra for the study of various kinds of samples with the aim to promote more common use of this tool. By examination of the power spectrum of specific sample, one can estimate the sensitivity of different molecular fragments on a given perturbation. Determination of the power spectra for smaller data subsets provides information on the dynamics of perturbation-induced spectral changes. If the experimental spectra of different samples in the same perturbation window are recorded, the comparison of the power spectra yields information on differences in the sensitivity of various samples on common perturbation. This possibility is particularly useful for studies of the spectra-structure correlations, interactions, and molecular dynamics. A comparison of the power spectra obtained by using different reference spectra provides information on the nature of spectral changes at different wavenumbers.

Content uniformity is a critical attribute for potent and low-dosage formulations of active pharmaceutical ingredient (API) that, in addition to the formulation parameters, plays pivotal role during pharmaceutical development and production. However, when API content is low, implementing a vibrational spectroscopic analytical tool to monitor the content and blend uniformity remains a challenging task. The aim of this study was to showcase the potentials of mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopy for quantitative analysis of alprazolam (ALZ) in a low-content powder blends with lactose, which is used as a common diluent for tablets produced by direct compression. The offered approach might be further scaled up and exploited for potential application in the process analytical technology (PAT). Partial least square and orthogonal PLS (OPLS) methodologies were employed to build the calibration models from raw and processed spectral data (standard normal variate, first and second derivatives). The models were further compared regarding their main statistical indicators: correlation coefficients, predictivity, root mean square error of estimation (RMSEE), and root mean square error of cross-validation (RMSEEcv). All statistical models presented high regression and predictivity coefficients. The RMSEEcv for the optimal models was 1.118, 0.08, and 0.059% for MIR, NIR, and Raman spectroscopy, respectively. The scarce information content extracted from the ALZ NIR spectra and the major band overlapping with those from lactose monohydrate was the main culprit of poor accuracy in the NIR model, whereas the subsampling instrumental setup (resulting in a non-representative spectral acquisition of the sample) was regarded as a main limitation for the MIR-based calibration model. The OPLS models of the Raman spectra of the powder blends manifested favorable statistical indicators for the accuracy of the calibration model, probably due to the distinctive ALZ Raman pattern resulting in the largest number of predictive spectral points that were used for the mathematical modeling. Furthermore, the Raman scattering calibration model was optimized in narrower scanning range (1700–700 cm−1) and its prediction power was evaluated (root mean square error of prediction, RMSEP = 0.03%). Thus, the Raman spectroscopy presented the most favorable statistical indicators in this comparative study and therefore should be further considered as a PAT for the quantitative determination of ALZ in low-content powder blends.

Developing a rapid and stable method for analyzing the quality parameters of rice is important. Near-infrared (NIR) spectroscopy combined with chemometric techniques have been used to predict the critical contents of rice and shown its accuracy and stability. To further improve the predictive ability, we combine the derivative method of fractional order Savitzky–Golay derivation (FOSGD) with the wavelength selection method of competitive adaptive reweighted sampling (CARS). Compared with the traditional integer order Savitzky–Golay derivation (IOSGD), the FOSGD could improve the resolution ratio of the raw spectra more effectively. The wavelength selection method, CARS, could further extract the informative variables from the processed spectra. Four key contents of rice samples, including moisture, amylose, chalkiness degree, and gel consistency, were utilized to validate this method. The prediction results indicated that partial least squares (PLS) models optimized with FOSGD-CARS own higher accuracy and stability with smaller the root mean squared error of cross validations (RMSECVs) and root mean squared error of predictions (RMSEPs). The proposed method is convenient and provides a practical alternative for rice analysis.

Results and analysis of internal reflection spectral absorbance experiments are reported for near-infrared (NIR) spectra obtained using an optical fiber sensor system. We present a preliminary study to diagnose the efficacy of our fiber optic system to observe and distinguish various phases of water, i.e., ice, liquid, and adsorbed. This study was motivated by the need for a technique capable of obtaining soil water content measurements in real time and in situ, at low humidity conditions for simulation studies of planetary bodies such as Mars. Spectral signatures were observed for the solid, liquid, and adsorbed phases of water. For all phases, peak absorbance at λ ≈1.45 and 1.94 μm was observed despite slight peak shifting due to dispersion effects. Dispersion effects commonly obscure spectra obtained with internal reflection spectroscopy for particular spectral regions. Here we report a spectral region with minimal distortions. Internal reflection spectra were compared directly to transmission spectra with only slight variations. Spectral matching was performed to determine sample penetration depths for unknown incidence angles. In general, relative absorbance and spectral shifting can distinguish spectra of the various phases of water.

The variation of temperature is one of the main interference factors that affect the detection accuracy of near-infrared (NIR) diffuse reflectance. In this paper, a measurement method based on temperature-insensitive radial distance was proposed, and its feasibility and effectiveness were verified in Intralipid solutions. First, the possibility of temperature-insensitive radial distance was deduced based on the analytic solution of the steady-state diffusion equation in an infinite media, and the temperature-insensitive radial distances of 3% Intralipid solution in the wavelength range of 1000–1600 nm was calculated. Second, a detection system was designed to measure the diffuse reflectance of 3% Intralipid solutions at multiple radial distances with different glucose concentration (0–100 mM) and different temperatures (35–40 ℃). Both theoretical calculations and experimental results demonstrated the existence of temperature-insensitive radial distances in the range of 1000–1340 nm and 1440–1600 nm, and the distances were hardly affected by glucose variations. Finally, the glucose information extracted from the diffuse reflectance of Intralipid solutions at different radial distances under random temperature variations and constant temperature were compared. The result showed that the correlation between the glucose concentration and the diffuse reflectance obtained at the temperature-insensitive radial distance was significantly better than that of other radial distances, which was almost close to the situation of constant temperature. Therefore, the measurement method based on temperature-insensitive radial distance can effectively reduce the influence of temperature variations on NIR diffuse reflectance, and it is expected to improve the accuracy of diffuse reflectance in human body components detection and industrial field analysis.

This research was conducted using a spectral database comprising 346 samples of processed animal proteins (PAPs) with a range of compositions, analyzed using a Fourier transform near-infrared spectroscopy multichannel instrument (Matrix-F, Bruker Optics) coupled to a 100 m fiber optic cable. Using both its static and dynamic operating modes (on a conveyor belt), simulating the movement of the product in the plant, the predictive capabilities of both modes of analysis were assessed and compared, for the purposes of predicting moisture, protein, and ashes. The results show that both exhibit highly similar degrees of precision and accuracy for predicting these parameters. This research provides a foundation of scientific-technical knowledge, hitherto unknown, regarding the “on-line” incorporation of an instrument (equipped with a 100 m fiber optic cable) into a processing plant of by-products of animal origin.

The fault detection problem of the oil desalting process is investigated in this paper. Different from the traditional fault detection approaches based on measurable process variables, near-infrared (NIR) spectroscopy is applied to acquire the process fault information from the molecular vibrational signal. With the molecular spectra data, principal component analysis was explored to calculate the Hotelling T2 and squared prediction error, which act as fault indicators. Compared with the traditional fault detection approach based on measurable process variables, NIR spectra-based fault detection illustrates more sensitivity to early failure because of the fact that the changes in the molecular level can be identified earlier than the physical appearances on the process. The application results show that the detection time of the proposed method is earlier than the traditional method by about 200 min.

This study assesses the capacity of a Fourier transform near-infrared (FT-NIR) spectrometer operating in the range 4500–12 000 cm−1(833.33–2222.22 nm) to provide quantitative predictions for the parameters of acidity (AV), p-anisidine (pAV), total polar materials (TPM), peroxide value (PV), and oxidative stability index (OSI). 562 samples of frying oil were analyzed from 14 distinct types of oil. The calibrations obtained accounted for 96%, 95%, 99%, 92%, and 91% of the AV, pAV, TPM, PV, and OSI variations in the study set and the similarity between the standard error of laboratory (RMSEP) values and the reference method errors (RMSEL), enabling the authors to conclude that NIR technology has the capacity to replace traditional methods in thermo-oxidative degradation studies in frying oils.

We present a rapid switching system between a newly developed near-infrared (NIR) spectrometer and its imaging system to select the spot size of a diffuse reflectance (DR) probe. In a previous study, we developed a portable NIR imaging system, known as D-NIRs, which has significant advantages over other systems. Its high speed, high spectral resolution, and portability are particularly useful in the process of monitoring pharmaceutical tablets. However, the spectral accuracies relating to the changes in the formulation of the pharmaceutical tablets have not been fully discussed. Therefore, we improved the rapid optical switching system and present a new model of D-NIRs (ND-NIRs) here. This system can automatically switch the optical paths of the DR and NIR imaging probes, greatly contributing to the simultaneous measurement of both the imaging and spot. The NIR spectra of the model tablets, including 0–10% ascorbic acid, were measured and simultaneous NIR images of the tablets were obtained. The predicted results using spot sizes for the DR probe of 1 and 5 mm diameter, resulted in concentrations of R2 = 0.79 and 0.94, with root mean square errors (RMSE) of 1.78 and 0.89, respectively. For tablets with a high concentration of ascorbic acid, the NIR imaging results showed inhomogeneity in concentration. However, the predicted values for the low concentration samples appeared higher than the known concentration of the tablets, although the homogeneity of the concentration was confirmed. In addition, the optimal spot size using NIR imaging data was estimated to be 5–7 mm. The results obtained in this study show that the spot size of the fiber probe, attached to a spectrometer, is important in developing a highly reliable model to determine the component concentration of a tablet.

The spectral reflection (specular and diffuse) and transmission of Columbia Resin 39 (CR-39) were measured for incoherent light with wavelengths in the range of 200–2500 nm. These results will be of use for the optical characterization of CR-39, as well as in investigations of the chemical modifications of the polymer caused by ultraviolet (UV) exposure. A Varian Cary 5000 was used to perform spectroscopy on several different thicknesses of CR-39. With proper analysis for the interdependence of reflectance and transmittance, results are consistent across all samples. The reflectivity from each CR-39–air boundary reveals an increase in the index of refraction in the near-UV. Absorption observations are consistent with the Beer–Lambert law. Strong absorption of UV light of wavelength shorter than 350 nm suggests an optical band gap of 3.5 eV, although the standard analysis is not conclusive. Absorption features observed in the near infrared are assigned to molecular vibrations, including some that are new to the literature.

Effect of the chain length on mid-infrared (MIR) and near-infrared (NIR) spectra of aliphatic 1-alcohols from methanol to 1-decanol was examined in detail. Of particular interest were the spectra-structure correlations in the NIR region and the correlation between MIR and NIR spectra of 1-alcohols. An application of two-dimensional correlation analysis (2D-COS) and chemometric methods provided comprehensive information on spectral changes in the data set. Principal component analysis (PCA) and cluster analysis evidenced that the spectra of methanol, ethanol, and 1-propanol are noticeably different from the spectra of higher 1-alcohols. The similarity between the spectra increases with an increase in the chain length. Hence, the most similar are the spectra of 1-nonanol and 1-decanol. Two-dimensional hetero-correlation analysis is very helpful for identification of the origin of bands and may guide selection of the best spectral ranges for the chemometric analysis. As shown, normalization of the spectra pronounces the intensity changes in various spectral regions and provides information not accessible from the raw data. The spectra of alcohols cannot be represented as a sum of the CH3, CH2, and OH group spectra since the OH group is involved in the hydrogen bonding. As a result, the spectral changes of this group are nonlinear and its spectral profile cannot be properly resolved. Finally, this work provides a lot of evidence that the degree of self-association of 1-alcohols decreases with the increase in chain length because of the growing meaning of the hydrophobic interactions. For butyl alcohol and higher 1-alcohols the hydrophobic interactions are more important than the OH OH interactions. Therefore, methanol, ethanol, and 1-propanol have unlimited miscibility with water, whereas 1-butanol and higher 1-alcohols have limited miscibility with water.

In this research paper, a fast, quantitative, analytical model for magnesium oxide (MgO) content in medicinal mineral talcum was explored based on near-infrared (NIR) spectroscopy. MgO content in each sample was determined by ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy, and then a variety of processing methods of spectra data were compared to establish a good NIR spectroscopy model. To start, 50 batches of talcum samples were categorized into training set and test set using the Kennard–Stone (K-S) algorithm. In a partial least squares regression (PLSR) model, both leave-one-out cross-validation (LOOCV) and training set validation (TSV) were used to screen spectrum preprocessing methods from multiplicative scatter correction (MSC), and finally the standard normal variate transformation (SNV) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLSR method, and the characteristic spectrum ranges were determined as 11995–10664, 7991–6661, and 4326–3999 cm−1, with four optimal ranks. In the support vector machine (SVM) model, the radical basis function (RBF) kernel function was used. Moreover, the full spectrum data of samples pretreated with SNV, the characteristic spectrum data screened using synergy interval partial least squares (SiPLS), and the scoring data of the first four ranks obtained by a partial least squares (PLS) dimension reduction of characteristic spectrum were taken as input variables of SVM, and the MgO content reference values of various sample were taken as output values. In addition, the SVM model internal parameters were optimized using the grid optimization method (GRID), particle swarm optimization (PSO), and genetic algorithm (GA) so that the optimal C and g-values were determined and the validation model was established. By comprehensively comparing the validation effects of different models, it can be concluded that the scoring data of the first four ranks obtained by PLS dimension reduction of characteristic spectrum were taken as input variables of SVM, and the PLS-SVM regression model established using GRID was the optimal NIR spectroscopy quantitative model of talc. This PLS-SVM regression model (rank = 4) measured that the MgO content of talcum was in the range of 17.42–33.22%, with root mean square error of cross validation (RMSECV) of 2.2127%, root mean square error of calibration (RMSEC) of 0.6057%, and root mean square error of prediction (RMSEP) of 1.2901%. This model showed high accuracy and strong prediction capacity, which can be used for rapid prediction of MgO content in talcum.

The production of active pharmaceutical ingredients (APIs) is currently undergoing its biggest transformation in a century. The changes are based on the rapid and dramatic introduction of protein- and macromolecule-based drugs (collectively known as biopharmaceuticals) and can be traced back to the huge investment in biomedical science (in particular in genomics and proteomics) that has been ongoing since the 1970s. Biopharmaceuticals (or biologics) are manufactured using biological-expression systems (such as mammalian, bacterial, insect cells, etc.) and have spawned a large (>E35 billion sales annually in Europe) and growing biopharmaceutical industry (BioPharma). The structural and chemical complexity of biologics, combined with the intricacy of cell-based manufacturing, imposes a huge analytical burden to correctly characterize and quantify both processes (upstream) and products (downstream). In small molecule manufacturing, advances in analytical and computational methods have been extensively exploited to generate process analytical technologies (PAT) that are now used for routine process control, leading to more efficient processes and safer medicines. In the analytical domain, biologic manufacturing is considerably behind and there is both a huge scope and need to produce relevant PAT tools with which to better control processes, and better characterize product macromolecules. Raman spectroscopy, a vibrational spectroscopy with a number of useful properties (nondestructive, non-contact, robustness) has significant potential advantages in BioPharma. Key among them are intrinsically high molecular specificity, the ability to measure in water, the requirement for minimal (or no) sample pre-treatment, the flexibility of sampling configurations, and suitability for automation. Here, we review and discuss a representative selection of the more important Raman applications in BioPharma (with particular emphasis on mammalian cell culture). The review shows that the properties of Raman have been successfully exploited to deliver unique and useful analytical solutions, particularly for online process monitoring. However, it also shows that its inherent susceptibility to fluorescence interference and the weakness of the Raman effect mean that it can never be a panacea. In particular, Raman-based methods are intrinsically limited by the chemical complexity and wide analyte-concentration-profiles of cell culture media/bioprocessing broths which limit their use for quantitative analysis. Nevertheless, with appropriate foreknowledge of these limitations and good experimental design, robust analytical methods can be produced. In addition, new technological developments such as time-resolved detectors, advanced lasers, and plasmonics offer potential of new Raman-based methods to resolve existing limitations and/or provide new analytical insights. KB is a Government of Ireland Postdoctoral Fellow and would like to thank the Irish Research Council for their support (Grant no.: GOIPD/2015/88). Some support for KB from the Synthesis and Solid State Pharmaceutical Centre, funded by Science Foundation Ireland and industry partners (Grant no.: 12/RC/2275) is also acknowledged. peer-reviewed

Due to the complex nature of near-infrared (NIR) spectra, it is usually very difficult to provide quantitative interpretations of spectral data. As a consequence, careful building and validation of calibration models are of fundamental importance prior to development of useful applications of NIR technologies. For this reason, this work presents a statistical study about the NIR spectroscopy, analyzing the NIR behavior when the experimental conditions are changed. Near-infrared spectra were measured at different temperatures and stirring velocities for systems containing a pure solvent and a suspension of polymer powder in order to perform the error analysis. Then, mixtures of xylene and toluene were analyzed through NIR at different temperatures and stirring velocities and the obtained data were used to build calibration models with multivariate techniques. The results showed that the precision of the NIR measurements depends on the analytical conditions and that unavoidable fluctuations of spectral data (or spectral data variability) are strongly correlated, leading to full covariance matrices of spectral fluctuations, which has been surprisingly neglected during quantitative analyses. In particular, modeling of the xylene/toluene NIR data performed with different multivariate techniques revealed that the principal directions are not preserved when the real covariance matrix of measurement errors is taken into account.

Investigation of a sample covered by an interfering layer is required in many fields, e.g., for process control, biochemical analysis, and many other applications. This study is based on the analysis of spectra collected by near-infrared (NIR) diffuse reflectance spectroscopy. Each spectrum is a composition of a useful, target spectrum and a spectrum of an interfering layer. To recover the target spectrum, we suggest using a new phenomenological approach, which employs the multivariate curve resolution (MCR) method. In general terms, the problem is very complex. We start with a specific problem of analyzing a system, which consists of several layers of polyethylene (PE) film and underlayer samples with known spectral properties. To separate information originating from PE layers and the target, we modify the system versus both the number of the PE layers as well as the reflectance properties of the target sample. We consider that the interfering spectrum of the layer can be modeled using three components, which can be tentatively called transmission, absorption, and scattering contributions. The novelty of our approach is that we do not remove the reflectance and scattering effects from the spectra, but study them in detail aiming to use this information to recover the target spectrum.

Wavelength modulation spectroscopy (WMS) combined with a multipass absorption cell has been used to measure a weak absorption line of carbon monoxide (CO) at 1.578 µm. A 0.95m Herriott-type cell provides an effective absorption path length of 55.1 m. The WMS signals from the first and second harmonic output of a lock-in amplifier (WMS-1 f and 2 f, respectively) agree with the Beer–Lambert law, especially at low concentrations. After boxcar averaging, the minimum detection limit achieved is 4.3 ppm for a measurement time of 0.125 s. The corresponding normalized detection limit is 84 ppm m Hz–1/2. If the integrated time is increased to 88 s, the minimum detectable limit of CO can reach to 0.29 ppm based on an Allan variation analysis. The pressure-dependent relationship is validated after accounting for the pressure factor in data processing. Finally, a linear correlation between the WMS-2 f amplitudes and gas concentrations is obtained at concentration ratios less than 15.5%, and the accuracy is better than 92% at total pressure less than 62.7 Torr.

Distribution of substandard and falsified (SF) medicines is on the rise, and its impact on public health, particularly in low-resource countries, is becoming increasingly significant. Portable, nondestructive screening devices can support regulatory authorities in their defense against the spread of SF medicines. Vibrational spectroscopy is an ideal candidate due to its sampling ease and speed. In this work, five portable, among which four are considered low-cost, spectroscopic devices based on near-infrared (NIR), Raman, and mid-infrared (MIR) were evaluated to quantify active pharmaceutical ingredients (APIs) and formulation accuracy within simulated authentic, falsified, and substandard medicines. Binary sample mixtures containing a typical API in antimalarial, antiretroviral, or anti-tuberculosis medicines were assessed. In both univariate and multivariate analyses, the API quantification performance of the digital light processing (DLP) NIR spectrometer and a handheld Raman device consistently matched or exceeded that of the other NIR spectrometers and a scientific grade MIR spectrometer. In the formulation accuracy tests, data from all devices, other than the silicon photodiode array NIR spectrometer, were able to create regression models with less than 6% error. From this exploratory study, we conclude that certain portable NIR devices hold significant promise as cost-effective screening tools for falsified and potentially substandard medicines, and they warrant further investigation and development.

Glass is a unique material that is often encountered in chemical and biological studies as a convenient sample holder (vial or microscope slide in particular). If the sample is probed with light in fluorescence and Raman spectroscopic experiments, the contribution from glass is often present and can obscure the spectra from the analyte of interest. It is important to understand the nature of glass photoemission properties to control this potential interference. The Raman spectrum of glass is dominated by peaks around 500 and 1000 cm–1 at the excitation with UV and visible light. A strong broad emission band centered at 880 nm appears when glass is irradiated with near-infrared light, a popular 785 nm laser light in particular. We proved experimentally in this study that this broad band is due to glass photoluminescence and not Raman scattering. In addition, three narrow components were found to contribute to this band, which have different excitation profiles indicating that they originate from three different species or the same species experiencing three different types of local environments. It has been hypothesized that these peaks could be due to the presence of rare earth impurities in the glass. Further study is necessary to identify these luminescent centers.

Process analytical technology (PAT) has shown great potential for in-line tableting process monitoring. The study focuses on the development and validation of an in-line near-infrared (NIR) spectroscopic method for the determination of content uniformity of blends in a tablet feed frame. An in-line NIR method was developed after careful evaluation of the impact of potential experimental factors on the robustness and model accuracy and precision. The NIR method was validated according to the principles outlined in International Conference on Harmonization–Q2 for validation of analytical procedures and was demonstrated to be suitable for monitoring blend content for the formulation under evaluation. Reliable measurements of blend homogeneity rely on representative sampling. To reach the appropriate scale of scrutiny for a unit dose, the study assessed factors that influence the effective sample size measured by NIR. Spectral averaging, integration time, and feed frame paddle wheel speed were found to influence the effective sample size measured by the NIR probe. The effective sampling size was also estimated by comparing the distribution of predicted values with the reference values. The development of a robust, in-line PAT method was facilitated by thorough understanding of the sensitivity of PAT sensors to factors affecting pharmaceutical processes and products.

As a key, yet difficult, issue currently in the quantitative remote sensing analysis of soil, the accurate and stable monitoring of soil salinity content (SSC) in situ should be studied and improved. The purpose of this study is to explore the method of fusing spectra outdoors with spectra indoors and improve the estimation precision of SSC based on near-infrared (NIR) reflectance hyper-spectra. First, samples of saline soil from the Yellow River delta of China were collected and analyzed. We measured three groups of sample spectra using a spectrometer: (1) situ-spectra, measured at sampling points in situ; (2) out-spectra, measured outdoors on air-dried samples; and, (3) lab-spectra, measured in a dark laboratory with the above air-dried samples. Second, four algorithms (multiplicative update, alternating least-squares, sparse affine non-negative matrix factorization (NMF), and gradient projection algorithms) of NMF were used to fuse the situ-spectra or out-spectra with the lab-spectra for the calibration of SSC. Finally, estimation models of SSC were built using the multiple linear regression method based on the first derivatives of the un-fused and fused spectra. The results indicate that using the NMF method to fuse the situ-spectra or out-spectra with the lab-spectra can heighten the correlation between SSC and the outdoor spectra in most wavelength ranges and improve the accuracy of the prediction model. The gradient projection algorithm shows the best performance with fewer variables and highest accuracy of the SSC model based on the NIR spectra.

The main objective of this communication is to compare the performance of a miniaturized handheld near-infrared (NIR) spectrometer with a benchtop Fourier transform near-infrared (FT-NIR) spectrometer. Generally, NIR spectroscopy is an extremely powerful analytical tool to study hydrogen-bonding changes of amide functionalities in solid and liquid materials and therefore variable temperature NIR measurements of polyamide II (PAII) have been selected as a case study. The information content of the measurement data has been further enhanced by exploiting the potential of two-dimensional correlation spectroscopy (2D-COS) and the perturbation correlation moving window two-dimensional (PCMW2D) evaluation technique. The data provide valuable insights not only into the changes of the hydrogen-bonding structure and the recrystallization of the hydrocarbon segments of the investigated PAII but also in their sequential order. Furthermore, it has been demonstrated that the 2D-COS and PCMW2D results derived from the spectra measured with the miniaturized NIR instrument are equivalent to the information extracted from the data obtained with the high-performance FT-NIR instrument.

In this work, we study the detection of acetylene (C2H2), carbon dioxide (CO2) and water vapor (H2O) gases in the near-infrared (NIR) range using an on-chip silicon micro-electro-mechanical system (MEMS) Fourier transform infrared (FT-IR) spectrometer in the wavelength range 1300–2500 nm (4000–7692 cm−1). The spectrometer core engine is a scanning Michelson interferometer micro-fabricated using a deep-etching technology producing self-aligned components. The light is free-space propagating in-plane with respect to the silicon chip substrate. The moving mirror of the interferometer is driven by a relatively large stroke electrostatic comb-drive actuator corresponding to about 30 cm−1 resolution. Multi-mode optical fibers are used to connect light between the wideband light source, the interferometer, the 10 cm gas cell, and the optical detector. A wide dynamic range of gas concentration down to 2000 parts per million (ppm) in only 10 cm length gas cell is demonstrated. Extending the wavelength range to the mid-infrared (MIR) range up to 4200 nm (2380 cm−1) is also experimentally demonstrated, for the first time, using a bulk micro-machined on-chip MEMS FT-IR spectrometer. The obtained results open the door for an on-chip optical gas sensor for many applications including environmental sensing and industrial process control in the NIR/MIR spectral ranges.

Near-infrared spectroscopy as a rapid and non-destructive analytical technique offers great advantages for pharmaceutical raw material identification (RMID) to fulfill the quality and safety requirements in pharmaceutical industry. In this study, we demonstrated the use of portable miniature near-infrared (MicroNIR) spectrometers for NIR-based pharmaceutical RMID and solved two challenges in this area, model transferability and large-scale classification, with the aid of support vector machine (SVM) modeling. We used a set of 19 pharmaceutical compounds including various active pharmaceutical ingredients (APIs) and excipients and six MicroNIR spectrometers to test model transferability. For the test of large-scale classification, we used another set of 253 pharmaceutical compounds comprised of both chemically and physically different APIs and excipients. We compared SVM with conventional chemometric modeling techniques, including soft independent modeling of class analogy, partial least squares discriminant analysis, linear discriminant analysis, and quadratic discriminant analysis. Support vector machine modeling using a linear kernel, especially when combined with a hierarchical scheme, exhibited excellent performance in both model transferability and large-scale classification. Hence, ultra-compact, portable and robust MicroNIR spectrometers coupled with SVM modeling can make on-site and in situ pharmaceutical RMID for large-volume applications highly achievable.

The change in custody of fuel shipments at depots, pipelines, and ports could benefit from an analyzer that could rapidly verify that properties are within specifications. To meet this need, the design requirements for a fuel analyzer based on near-infrared (NIR) spectroscopy, such as spectral region and resolution, were examined. It was found that the 1000 to 1600 nm region, containing the second CH overtone and combination vibrational modes of hydrocarbons, provided the best near-infrared to fuel property correlations when path length was taken into account, whereas 4 cm−1 resolution provided only a modest improvement compared to 16 cm−1 resolution when four or more latent variables were used. Based on these results, a field-portable near-infrared fuel analyzer was built that employed an incandescent light source, sample compartment optics to hold 2 mL glass sample vials with ∼1 cm path length, a transmission grating, and a 256 channel InGaAs detector that measured the above stated wavelength range with 5–6 nm (∼32 cm−1) resolution. The analyzer produced high signal-to-noise ratio (SNR) spectra of samples in 5 s. Twenty-two property correlation models were developed for diesel, gasoline, and jet fuels with root mean squared error of correlation – cross-validated values that compared favorably to corresponding ASTM reproducibility values. The standard deviations of predicted properties for repeat measurements at 4, 24, and 38℃ were often better than ASTM documented repeatability values. The analyzer and diesel property models were tested by measuring seven diesel samples at a local ASTM certification laboratory. The standard deviations between the analyzer determined values and the ASTM measured values for these samples were generally better than the model root mean squared error of correlation—cross-validated values for each property.

A key quality and processing parameter for cotton fiber is micronaire, which is a function of the fiber's maturity and fineness. Near-infrared (NIR) spectroscopy has previously shown the ability to measure micronaire, primarily in the laboratory and using large, research-grade laboratory NIR instrumentation. International interest has been expressed by the industry in the measurement of fiber micronaire using small, portable NIR spectroscopy instruments for both laboratory and outside the laboratory (e.g., field or greenhouse) locations. New, very small NIR micro-spectrometers have been commercialized that offer the potential advantages of smaller size and lower weight, lower cost, and increased portability over current portable units. A program was implemented to determine the feasibility of a small NIR micro-spectrometer to measure fiber micronaire both in the laboratory and outside the laboratory, with initial emphasis on laboratory measurements prior to moving to field evaluations. In the laboratory, distinct spectral differences with increasing micronaire were observed. Optimal sampling and instrumental procedures and protocols for two units (different spectral wavelength capabilities) were established. Comparative evaluations established very good method micronaire agreement between the micro-spectrometer and a standard portable spectrometer, with high Regression (R) value, low residuals, and few outliers (less than 20%). The NIR micro-spectrometer measurements were fast (

This study describes changes observed in the near-infrared (NIR) diffuse reflectance (DR) spectra of pharmaceutical tablets after these tablets were subjected to different levels of strain (exposure to shear) during the mixing process. Powder shearing is important in the mixing of powders that are cohesive. Shear stress is created in a system by moving one surface over another causing displacements in the direction of the moving surface and is part of the mixing dynamics of particulates in many industries including the pharmaceutical industry. In continuous mixing, shear strain is developed within the process when powder particles are in constant movement and can affect the quality attributes of the final product such as dissolution. These changes in the NIR spectra could affect results obtained from NIR calibration models. The aim of the study was to understand changes in the NIR diffuse reflectance spectra that can be associated with different levels of strain developed during blend shearing of laboratory samples. Shear was applied using a Couette cell and tablets were produced using a tablet press emulator. Tablets with different shear levels were measured using NIR spectroscopy in the diffuse reflectance mode. The NIR spectra were baseline corrected to maintain the scattering effect associated with the physical properties of the tablet surface. Principal component analysis was used to establish the principal sources of variation within the samples. The angular dependence of elastic light scattering shows that the shear treatment reduces the size of particles and produces their uniform and highly isotropic distribution. Tablet compaction further reduces the diffuse component of scattering due to realignment of particles.

The influence of subcutaneous adipose tissue (ATT) and oxygen (O2) delivery has been poorly defined in frequency domain (FD) near-infrared spectroscopy (NIRS). Therefore, the aim of this study was to investigate the possible influence of these variables on all FD NIRS responses using a reliable protocol. Moreover, these influences were also investigated when using relative oxy- and deoxyhemoglobin and -myoglobin (oxy[Hb + Mb] and deoxy[Hb + Mb]) values (in %). A regression analysis was carried out for ATT and maximal–minimum oxy[Hb + Mb], deoxy[Hb + Mb], oxygen saturation (SmO2), and total hemoglobin (totHb) amplitudes during an incremental cyclic contraction protocol (ICCP) in a group of 45 participants. Moreover, the same analysis was carried out between subcutaneous ATT and the relative oxy- and deoxy[Hb + Mb] values (in %). In the second part of this study, a regression analysis was performed for peak forearm blood flow (FBF) during ICCP and the absolute and relative NIRS values in a group of 37 participants. Significant exponential correlation coefficients were found between ATT and deoxy[Hb + Mb] (r = 0.53; P 2 amplitudes (r = 0.57; P 2 (r = −0.267; P = 0.111). Significant linear correlation coefficients were found between FBF and deoxy[Hb + Mb] (r = 0.51; P = 0.001), oxy[Hb + Mb] (r = −0.50; P = 0.001), SmO2 (r = −0.54; P = 0.001), and totHb amplitude (r = 0.61; P 2 delivery significantly influence NIRS measurements. To eliminate these influences, use of relative deoxy[Hb + Mb] is advised, especially in clinical settings or in people with a higher subcutaneous ATT layer.

The noninvasive sensing of the blood glucose concentration is usually based on optical, electrical, or acoustical signals induced by blood glucose; these signals are extremely weak and subject to fluctuations caused by the variation in the body or surroundings. Therefore, it is challenging to detect blood glucose noninvasively with high accuracy, and no successful accurate and noninvasive clinical application has been reported. We found that there are two key measurement issues to be addressed: systematic errors, such as the errors induced by the drifts of devices or by variations in body temperature, among others, are too large to guarantee the trueness of measurement at present; and random disturbances in repeated tests, such as disturbances associated with variations in the human–machine interface, pulses, and the thermal noise of the devices, cause larger repeated measurement errors and compromise precision. Recent novel reference measurements based on differential near-infrared (NIR) spectroscopy are considered promising for solving the systematic error issue by establishing matched references, collected at another detection site or at another time, and subsequently differencing to remove the common systematic errors. However, differencing weakens the signal of interest itself and enlarges the effects of the second issue, random disturbances affecting the precision. It is understood that only reference measurements that can meet the precision requirement will be promising for future applications. Therefore, this study quantitatively evaluates the precision of the main differential NIR spectroscopy measurements considering similar conditions and minimized random disturbances. The precision of the measurements under these conditions should represent their optimal precision levels. After the evaluation, noninvasive glucose-sensing methods that hold promise for future clinical application are proposed. Finally, the evaluation criteria could be a reference for the noninvasive detection of other physiological components.

Using partial least square discriminate analysis (PLSDA), we studied the spectroscopic differences between the commonly used filler-binder microcrystalline cellulose (MCC) from five manufactures. These samples had subtle differences in the chemical and physical properties, which are often the cause of differences in excipient performance. Studying these differences allowed us to build and validate a model to classify five manufacturers of MCC using near-infrared (NIR) spectra. The sample training set includes 39 MCC samples collected from five manufactures with regions spanning the United States of America, Japan, Taiwan, Germany, and Brazil. The samples from individual manufacturers include diverse grades that differ in moisture content, particle size, and bulk density. Optimized pretreatment methods were identified as standard normal variate normalization, followed by Savitzky-Golay second derivative, mean centering, and orthogonal signal correction. The model was optimized with cross-validation and validated with an independent sample set comprising nine samples collected from those five manufacturers. The results showed that none of the samples in the independent validation set was misclassified. The score and loading plots revealed that the differences in content of oxidized cellulose group, water content and states, hydrogen bonding, and degree of polymerization of the MCC samples are responsible for the class differentiation. Permutation test demonstrated that the outcome of the PLSDA model was significantly different from that of the randomly generated model. The advantages and limitations of the method in this type of application were discussed.

Identification of different chicken parts using portable equipment could provide useful information for the processing industry and also for authentication purposes. Traditionally, physical–chemical analysis could deal with this task, but some disadvantages arise such as time constraints and requirements of chemicals. Recently, near-infrared (NIR) spectroscopy and machine learning (ML) techniques have been widely used to obtain a rapid, noninvasive, and precise characterization of biological samples. This study aims at classifying chicken parts (breasts, thighs, and drumstick) using portable NIR equipment combined with ML algorithms. Physical and chemical attributes (pH and L*a*b* color features) and chemical composition (protein, fat, moisture, and ash) were determined for each sample. Spectral information was acquired using a portable NIR spectrophotometer within the range 900–1700 nm and principal component analysis was used as screening approach. Support vector machine and random forest algorithms were compared for chicken meat classification. Results confirmed the possibility of differentiating breast samples from thighs and drumstick with 98.8% accuracy. The results showed the potential of using a NIR portable spectrophotometer combined with a ML approach for differentiation of chicken parts in the processing industry.

Unexplained abnormal fatigue is characterized by chronic fatigue persisting for at least six months and not sufficiently explained by any recognized medical condition. In this pilot study, twelve individuals with abnormal fatigue remaining unexplained after thorough screening were investigated using a near-infrared (NIR) spectroscopy handgrip test. Four of them were found to have an abnormal oxygen extraction pattern similar to participants with documented mitochondrial myopathy. In three of the four individuals, diverse mitochondrial abnormalities were documented by spectrophotometric, immunocytological, fluorescent, and morphological analyses performed in skeletal muscle and in cultured skin fibroblasts. Three of the four participants with decreased muscular oxygen extraction were each shown to harbor a different homoplasmic pathogenic mitochondrial DNA point mutation (m.961T > C, m.1555A > G, m.14484T > C). In the fourth participant, the presence of multiple large mitochondrial DNA deletions was suspected in muscle tissue. In contrast, none of the eight abnormally fatigued participants with normal NIR spectroscopy results harbored either a pathogenic mitochondrial DNA point mutation or large deletions ( P

The identification for haploid seeds is an important process in maize haploid breeding. Thanks to the diffuse transmission (DT) technology of near-infrared (NIR) spectroscopy, maize haploid seeds can be selected automatically using NIR spectrum features. However, the NIR spectra of maize seeds contain a large number of redundant features and noise that will degrade the identification performance. We resolved this problem by designing a low dimension and uniform space of seed spectrum features to improve the collected spectra. The zero-phase component analysis (ZCA) method was utilized to uniform the feature space and the partial least squares regression (PLSR) was employed to design the low dimension space. Then, by using the classifier of back propagation neural network (BPNN), a high qualitative identification method was developed for selecting maize haploid seeds. The study results demonstrate that the average accuracy of the proposed method is outstanding (96.16%) with a minor standard deviation (SD) compared with other methods. Therefore, our proposed method is potentially useful for automatically identifying maize haploid seeds.

The technique of moving-window two-dimensional heterospectral (MW2DHetero) correlation spectroscopy is proposed. This computational method is based on the ideas of perturbation-correlation moving-window two-dimensional (PCMW2D) correlation spectroscopy and two-dimensional heterospectral correlation analysis. Not only small spectral variations, but also detailed bands assignments were captured using the analysis. This method was applied to near-infrared (NIR) spectra in the 10 000–4000 cm−1 region and mid-infrared (mid-IR) spectra in the 5000–1200 cm−1 region, which were simultaneously detected using a dual-region spectrometer. Near-infrared and mid-IR spectra collected during an alcoholic fermentation process using a solution containing glucose and fructose were reported. Slight time differences for the consumption of sugars compared with the production of ethanol were found between 50 and 150 min. It was concluded that these slight time differences are evidence for different consumption times between glucose and fructose during the fermentation process. The result proved a possibility of the selective monitoring of the simultaneous reaction processes between productive and consumptive components.

Skin moisturization is an important function of cosmetics in dermatology, and acquisition of two-dimensional information about the water content of facial skin is of great interest. Near-infrared (NIR) imaging using the water OH band centered near 1460 nm has been applied to the evaluation of water in skin. However, detection of small changes in the water content of skin water is difficult using this band because of the low absorption coefficient of water at that wavelength and inadequate optical units. We developed a high-sensitivity water imaging system using strong water bands centered near 1920 nm. This system can be used for the entire face. With the water imaging system, time-dependent changes in the water content of moisturizer-treated skin and hair were visualized with high sensitivity. In this study, we performed a water distribution analysis, with the aim of understanding the water distribution in facial skin under different environmental conditions. The water imaging system combines a diffuse illumination unit and an extended-range indium–gallium arsenide NIR camera with a detection range of 1100–2200 nm. The skin water distributions for multiple subjects with different facial shapes and sizes were compared using averaged NIR image data and a mesh partition analysis using a developed algorithm. Changes in the facial skin water content with season and humidity were visualized by the algorithm. The water content decreased in autumn, especially near the eyes and upper-cheek. Compared to other areas on the face, the water content around the eyes decreased more during an 85 min stay in a room at 10% relative humidity. The proposed method for water distribution analysis provides a powerful tool for facial skin hydration research in dermatological and cosmetics fields.

Near-infrared chemical imaging (NIR-CI) combines spectroscopy with digital imaging, enabling spatially resolved analysis and characterization of pharmaceutical samples. Hardness and relative density are critical quality attributes (CQA) that affect tablet performance. Intra-sample density or hardness variability can reveal deficiencies in formulation design or the tableting process. This study was designed to develop NIR-CI methods to predict spatially resolved tablet density and hardness. The method was implemented using a two-step procedure. First, NIR-CI was used to develop a relative density/solid fraction (SF) prediction method for pure microcrystalline cellulose (MCC) compacts only. A partial least squares (PLS) model for predicting SF was generated by regressing the spectra of certain representative pixels selected from each image against the compact SF. Pixel selection was accomplished with a threshold based on the Euclidean distance from the median tablet spectrum. Second, micro-indentation was performed on the calibration compacts to obtain hardness values. A univariate model was developed by relating the empirical hardness values to the NIR-CI predicted SF at the micro-indented pixel locations: this model generated spatially resolved hardness predictions for the entire tablet surface.

Cashmere and wool are two protein fibers with analogous geometrical attributes, but distinct physical properties. Due to its scarcity and unique features, cashmere is a much more expensive fiber than wool. In the textile production, cashmere is often intentionally blended with fine wool in order to reduce the material cost. To identify the fiber contents of a wool–cashmere blend is important to quality control and product classification. The goal of this study is to develop a reliable method for estimating fiber contents in wool–cashmere blends based on near-infrared (NIR) spectroscopy. In this study, we prepared two sets of cashmere–wool blends by using either whole fibers or fiber snippets in 11 different blend ratios of the two fibers and collected the NIR spectra of all the 22 samples. Of the 11 samples in each set, six were used as a subset for calibration and five as a subset for validation. By referencing the NIR band assignment to chemical bonds in protein, we identified six characteristic wavelength bands where the NIR absorbance powers of the two fibers were significantly different. We then performed the chemometric analysis with two multilinear regression (MLR) equations to predict the cashmere content (CC) in a blended sample. The experiment with these samples demonstrated that the predicted CCs from the MLR models were consistent with the CCs given in the preparations of the two sample sets (whole fiber or snippet), and the errors of the predicted CCs could be limited to 0.5% if the testing was performed over at least 25 locations. The MLR models seem to be reliable and accurate enough for estimating the cashmere content in a wool–cashmere blend and have potential to be used for tackling the cashmere adulteration problem.

Consumer demand for quality and healthfulness has led to a higher need for quality assurance in meat production. This requirement has increased interest in near-infrared (NIR) spectroscopy due to the ability for rapid, environmentally friendly, and noninvasive prediction of meat quality or authentication of added-value meat products. This review includes the principles of NIR spectroscopy, pre-processing methods, and multivariate analyses used for quantitative and qualitative purposes in the meat sector. Recent advances in portable NIR spectrometers that enable new online applications in the meat industry are shown and their performance evaluated. Discrepancies between published studies and potential sources of variability are discussed, and further research is encouraged to face the challenges of using NIRS technology in commercial applications, so that its full potential can be achieved.

The building of multivariate calibration models using near-infrared spectroscopy (NIR) and partial least squares (PLS) to estimate the lignin content in different parts of sugarcane genotypes is presented. Laboratory analyses were performed to determine the lignin content using the Klason method. The independent variables were obtained from different materials: dry bagasse, bagasse-with-juice, leaf, and stalk. The NIR spectra in the range of 10 000–4000 cm−1 were obtained directly for each material. The models were built using PLS regression, and different algorithms for variable selection were tested and compared: iPLS, biPLS, genetic algorithm (GA), and the ordered predictors selection method (OPS). The best models were obtained by feature selection with the OPS algorithm. The values of the root mean square error prediction (RMSEP), correlation of prediction ( RP), and ratio of performance to deviation (RPD) were, respectively, for dry bagasse equal to 0.85, 0.97, and 2.87; for bagasse-with-juice equal to 0.65, 0.94, and 2.77; for leaf equal to 0.58, 0.96, and 2.56; for the middle stalk equal to 0.61, 0.95, and 3.24; and for the top stalk equal to 0.58, 0.96, and 2.34. The OPS algorithm selected fewer variables, with greater predictive capacity. All the models are reliable, with high accuracy for predicting lignin in sugarcane, and significantly reduce the time to perform the analysis, the cost and the chemical reagent consumption, thus optimizing the entire process. In general, the future application of these models will have a positive impact on the biofuels industry, where there is a need for rapid decision-making regarding clone production and genetic breeding program.

One of the main challenges in the noninvasive sensing of blood glucose by near-infrared (NIR) spectroscopy is the background variations from light source drift, sweating, and temperature change at the human–machine interface. In this paper, a differential correction method based on the spectra from the floating-reference position and measuring position is proposed to eliminate these spectral variations from background interferences. Its effectiveness was validated by in vitro and in vivo experiments in which the diffuse reflectance of intralipid solutions and human skin was collected at the source distances of 0.6 mm and 2 mm by the custom-built system with six super-luminescent emitting diodes (SLEDs) light source. The results showed that, for the in vitro experiments of intralipid solutions, the coefficients of variations of diffuse reflectance decreased by 20.5% under all the six wavelengths after differential correction. For the in vivo experiments of oral glucose tolerance tests (OGTTs), partial least squares (PLS) regression models between glucose concentrations and the diffuse reflectance from palm skin were built, and the root mean square error of cross validation (RMSECV) decreased by 38.0% on average after the differential correction. Further, the spectra of the oral water tolerance tests (OWTTs) were collected for correlation with glucose concentration in OGTTs, and their correlation coefficients (R) decreased by 35.0% on average after the differential correction. Therefore, this differential correction method based on the spectra from the floating-reference position and measuring position can weaken the influence of background variations on the NIR spectroscopy and has promising potential in in vivo detection, especially for noninvasive blood glucose measurement.

Exposure to unknown, mislabeled, and counterfeit pharmaceutical products is a worldwide problem that presents a serious risk to public health. Near-infrared (NIR) spectroscopy can serve as a useful tool for screening pharmaceuticals in a rapid and cost-effective manner to ensure that drug products are safe and effective. By applying chemometric techniques to NIR spectra from finished products in tablet form, minor spectral differences are discoverable, even in instances where the tablets being evaluated contain the same active pharmaceutical ingredients (APIs). Differences in NIR spectra can occur as a result of various factors including the types and quantities of pharmaceutical excipients used to generate the product and associated manufacturing site process variables. In this study, variability in the NIR spectra of intact tablets with the same API was evaluated using an unsupervised chemometric technique in the form of hierarchical cluster analysis (HCA) on a data set consisting of NIR spectra from more than 800 ciprofloxacin tablets from six manufacturers. Results obtained from HCA and squared Euclidean distance measurements indicate the largest dissimilarities in NIR spectra occur between manufacturers. Based on these findings, a quadratic discriminant analysis (QDA) model was built following dimensionality reduction by principal component analysis for the purpose of predicting the origin of ciprofloxacin tablets. Using QDA, we were able to correctly classify a collection of 907 tablets with greater than 96% accuracy. Chemometric models such as the one developed here could ultimately be employed as part of a large, diversified drug surveillance program.