Near-infrared Analysis Research Articles

A Switchable Near‐Infrared‐Absorbing Dye Based on Redox‐Bistable Benzitetraazaporphyrin

AbstractActivatable near‐infrared (NIR) dyes responsive to external stimuli are used in medical and other applications. Here, we describe the design and synthesis of bench‐stable 18π‐ and 20π‐electron benzitetraazaporphyrins (BzTAPs) possessing redox‐switchable NIR properties. X‐Ray, NMR, and UV/Visible‐NIR analyses revealed that 20π‐electron BzTAP 1 exhibits NIR absorption and antiaromaticity with a paratropic ring‐current, while 18π‐electron BzTAP 2 shows weakly aromatic character with NIR inertness. Notably, the NIR‐silent BzTAP 2 was readily converted to the NIR‐active BzTAP 1 in the presence of mild reducing agents such as amine. The intense NIR absorption band of BzTAP 1 is in sharp contrast to the very weak absorption bands of previously reported antiaromatic porphyrinoids. Molecular orbital analysis revealed that symmetry‐lowering perturbation of the 20π‐electron porphyrinoid skeleton enables the HOMO–LUMO transition of 1 to be electric‐dipole‐allowed. BzTAPs are expected to be useful for constructing activatable NIR probes working in reductive environments.

Adaptation of the rapid near-infrared (NIR) spectroscopy technique to determine the mass fraction of protein and moisture in gluten-free bakery products

Analysis of protein in food products is typically performed using a time-consuming and labor-intensive Kjeldahl method. The NIR method is allowed to determine the mass fraction of not only protein, but also moisture, fat, and sugar in one sample within 2 min. The NIR method is indirect; therefore, appropriate calibration models should be developed for each group of analyzed products. We performed a series of laboratory baking tests for gluten-free bakery products; the protein content in the products varied and depended on the ingredients included into the formula: rice, buckwheat and corn flour, starch and soy isolate. Continuous spectra were recorded using a NIR analyzer that operates in the wavelength range from 1400 to 2500 nm. It was shown that after validation for a specific device, the developed calibration models yield the root-mean-square deviation of 0.29 when determining the protein mass fraction and that of 0.46 when determining the moisture mass fraction.

Evaluation of Polysaccharide Content in Shiitake Culinary-Medicinal Mushroom, Lentinula edodes (Agaricomycetes), via Near-Infrared Spectroscopy Integrated with Deep Learning.

Polysaccharide is one of the bioactive ingredients extracted from the fruiting body of Lentinula edodes (=L. edodes), which has many medicinal functions. While the content of polysaccharide can be measured by near-infrared (NIR) spectroscopy, the NIR analytical models established previously only covered L. edodes from very limited sources, and thus could not achieve high accuracy for large samples from more varied sources. Strictly, there is a nonlinear relationship between NIR spectral data and chemical label values, and traditional modeling methods for NIR data analysis have problems such as insufficient feature learning ability and difficulty in training. The deep learning model has excellent nonlinear modeling ability and generalization capacity, which is very suitable for analyzing larger samples. In this study, we constructed a novel framework with deep learning techniques on the NIR analysis of the content of polysaccharide in L. edodes. The siPLS model was established based on the combination of the bands 4797-3995 cm-1 and 6401-5600 cm-1, while the one-dimensional convolutional neural network (1D-CNN) model was established with improved feature in the treatment of the spectral data. The comparative experimental results showed that the 1D-CNN model (R2pre = 95.50%; RMSEP =0.1875) outperformed the siPLS model (R2pre = 87.89%, RMSEP = 0.6221). As such, this work has demonstrated that NIR spectroscopy with the integration of deep learning can provide more accurate quantification of polysaccharide in L. edodes. Such method can be very useful for nutritional grading and quality control of diverse L. edodes in the market.

Application of Wavelength Selection Combined with DS Algorithm for Model Transfer between NIR Instruments

This study aims to realize the sharing of near-infrared analysis models of lignin and holocellulose content in pulp wood on two different batches of spectrometers and proposes a combined algorithm of SPA-DS, MCUVE-DS and SiPLS-DS. The Successive Projection Algorithm (SPA), the Monte-Carlo of Uninformative Variable Elimination (MCUVE) and the Synergy Interval Partial Least Squares (SiPLS) algorithms are respectively used to reduce the adverse effects of redundant information in the transmission process of the full spectrum DS algorithm model. These three algorithms can improve model transfer accuracy and efficiency and reduce the manpower and material consumption required for modeling. These results show that the modeling effects of the characteristic wavelengths screened by the SPA, MCUVE and SiPLS algorithms are all greatly improved compared with the full-spectrum modeling, in which the SPA-PLS result in the best prediction with RPDs above 6.5 for both components. The three wavelength selection methods combined with the DS algorithm are used to transfer the models of the two instruments. Among them, the MCUVE combined with the DS algorithm has the best transfer effect. After the model transfer, the RMSEP of lignin is 0.701, and the RMSEP of holocellulose is 0.839, which was improved significantly than the full-spectrum model transfer of 0.759 and 0.918.

Rapid determination of viscosity and viscosity index of lube base oil based on near-infrared spectroscopy and new transformation formula

Viscosity and viscosity index are the key product properties in lubricating oil production process. Rapid and even online analysis of viscosity and viscosity index through near-infrared (NIR) spectroscopy combined with chemometrics is helpful to optimize the production process. However, due to the nonlinear effect, the commonly used linear multivariate correction method is not effective. In this work, the feasibility of four existing viscosity linear transformation formulas for establishing NIR models was studied, and a new viscosity linear transformation formula was developed based on the viscosity-gravity constant. The experimental results showed that three of the four existing viscosity linear transformation formulas made some improvement on the viscosity prediction of base oil, but not as good as the newly established viscosity linear transformation formula. For viscosity index, the accuracy of modeling with reference viscosity index directly was much better than calculating by prediction viscosity value. Both of the viscosity and viscosity index prediction results of NIR analysis were in good agreement with the results of reference method, indicating that the determination can meet the needs of rapid and on-line analysis in industrial field.

Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 1: Generalised theory of sampling

Non-representative sampling of materials, lots and processes intended for near infrared (NIR) analysis is often contributing hidden additions to the full Measurement Uncertainty (MUtotal = TSE + TAENIR). The Total Sampling Error (TSE) can dominate over the Total Analytical Error (TAENIR) by factors ranging from 5 to 10 to even 25 times, depending on material heterogeneity and the specific sampling procedures employed to produce the minuscule aliquot, which is the only material analysed. This review (Parts 1 and 2), extensively referenced with easily available complementing literature, presents a brief of all sampling uncertainty elements in the “lot-to-aliquot” pathway, which must be identified and correctly managed (eliminated or maximally reduced) in order to achieve, and to be able to document, fully minimised MUtotal. The more irregular and pervasive the heterogeneity, the higher the number of increments needed to reach ‘fit-for-purpose representativity’. A particular focus is necessary regarding the sampling bias, which is fundamentally different from the well-known analytical bias. Whereas the latter can easily be subjected to bias correction, the sampling bias is non-correctable by any posteori means, notably not by chemometrics, nor statistics. Instead, all sampling operations must be designed to exclude the so-called Incorrect Sampling Errors (ISE), which are the hidden bias-generating agents. The key element in this endeavour is representative sampling and sub-sampling before analysis, as laid out by the Theory of Sampling (TOS), which is presented here in a novel compact fashion along with a complement of selected examples and demonstrations. TOS includes a safeguard facility, termed the Replication Experiment (RE), which enables estimation of the total sampling- plus-analysis uncertainty level (MUtotal) associated with NIR analysis (the RE is, for practical and logistical reasons, found in Part 2). Neglecting the TSE effects from the before-analysis domain is lack of due diligence. TOS to the fore!

Rapid and Nondestructive Detection of Proline in Serum Using Near-Infrared Spectroscopy and Partial Least Squares.

Proline is an important amino acid that widely affects life activities. It plays an important role in the occurrence and development of diseases. It is of great significance to monitor the metabolism of the machine. With the great advantages of deep learning in feature extraction, near-infrared analysis technology has great potential and has been widely used in various fields. This study explored the potential application of near-infrared spectroscopy in the detection of serum proline. We collected blood samples from clinical sources, separated the serum, established a quantitative model, and determined the changes in proline. Four algorithms of SMLR, PLS, iPLS, and SA were used to model proline in serum. The root mean square errors of prediction were 0.00111, 0.00150, 0.000770, and 0.000449, and the correlation coefficients (Rp) were 0.84, 0.67, 0.91, and 0.97, respectively. The experimental results show that the model is relatively robust and has certain guiding significance for the clinical monitoring of proline. This method is expected to replace the current mainstream but time-consuming HPLC, or it can be applied to rapid online monitoring at the bedside.

Shelf-Life Prediction and Critical Value of Quality Index of Sichuan Sauerkraut Based on Kinetic Model and Principal Component Analysis

Kinetic models and accelerated shelf-life testing were employed to estimate the shelf-life of Sichuan sauerkraut. The texture, color, total acid, microbe, near-infrared analysis, volatile components, taste, and sensory evaluation of Sichuan sauerkraut stored at 25, 35, and 45 °C were determined. Principal component analysis (PCA) and Fisher discriminant analysis (FDA) were used to analyze the e-tongue data. According to the above analysis, Sichuan sauerkraut with different storage times can be divided into three types: completely acceptable period, acceptable period, and unacceptable period. The model was found to be useful to determine the critical values of various quality indicators. Furthermore, the zero-order kinetic reaction model (R2, 0.8699–0.9895) was fitted better than the first-order kinetic reaction model. The Arrhenius model (Ea value was 47.23–72.09 kJ/mol, kref value was 1.076 × 106–9.220 × 1010 d−1) exhibited a higher fitting degree than the Eyring model. Based on the analysis of physical properties, the shelf-life of Sichuan sauerkraut was more accurately predicted by the combination of the zero-order kinetic reaction model and the Arrhenius model, while the error back propagation artificial neural network (BP-ANN) model could better predict the chemical properties. It is a better choice for dealers and consumers to judge the shelf life and edibility of food by shelf-life model.

Impact of Freeze-Drying on the Determination of the Geographical Origin of Almonds (Prunus dulcisMill.) by Near-Infrared (NIR) Spectroscopy

Near-infrared (NIR) spectroscopy is a proven tool for the determination of food authenticity, mainly because of good classification results and the possibility of industrial use due to its easy and fast application. Since water shows broad absorption bands, the water content of a sample should be as low as possible. Freeze-drying is a commonly used preparatory step for this to reduce the water content in the sample. However, freeze-drying, also known as lyophilization, is very time-consuming impeding the widespread usage of NIR analysis as a rapid method for incoming goods inspections. We used a sample set of 72 almond samples from six economically relevant almond-producing countries to investigate the question of how important lyophilization is to obtain a well-performing classification model. For this approach, the samples were ground and lyophilized for 3 h, 24 h, and 48 h and compared to non-freeze-dried samples. Karl-Fischer titration of non-lyophilized samples showed that water contents ranged from 3.0 to 10.5% and remained constant at 0.36 ± 0.13% after a freeze-drying period of 24 h. The non-freeze-dried samples showed a classification accuracy of 93.9 ± 6.4%, which was in the same range as the samples which were freeze-dried for 3 h (94.2 ± 7.8%), 24 h (92.5 ± 8.7%), and 48 h (95.0 ± 9.0%). Feature selection was performed using the Boruta algorithm, which showed that signals from lipids and proteins are relevant for the origin determination. The presented study showed that samples with low water content, especially nuts, can be analyzed without the time-consuming preparation step of freeze-drying to obtain robust and fast results, which are especially required for incoming goods inspection.

Study on outlier detection method of the near infrared spectroscopy analysis by probability metric

Due to the high dimensionality and non-linearity of the near infrared (NIR) spectra data result the difficulty of the outlier measure. This paper proposed a probability based outlier detection method, which adopted the distribution probability of the spectra data to identify outliers at each wavelength by using of copula function. The negative logarithmic function was also used to quantify the overall variation of the joint distribution for the outliers. This method not only enlarges the difference of the spectra between typical samples and outliers, but also can be adapted to multi-type of outliers. Moreover, the jump degree in statistics was introduced for the automated determination of threshold for the outliers, which avoids the threshold setting problem in empirical way and the misjudgment of the outliers. In order to investigate the effectiveness of the algorithm, the recognition of different cases and types of outliers were applied, and compared with the commonly used PCA-Mahalanobis distance, spectral residual (SR) and leverage methods. The experimental results showed that the probability based outlier detection method effectively improved the performance of outlier identification and calibration for NIR analysis.

All that and a bag of chips

This column has invited two world-renowned experts in near infrared (NIR) spectroscopy to let the world benefit from decades of leading-edge experience, especially regarding sampling for quantitative NIR analysis.

Reflectance evaluation of eye fundus structures with a visible and near-infrared multispectral camera.

We examined the spectral reflectance of fundus structures in the visible and near-infrared (400-1300 nm) range for contributing to the medical diagnosis of fundus diseases. Spectral images of healthy eye fundus and other ocular diseases were acquired using a novel multispectral fundus camera. Reflectance metrics were computed based on contrast to analyze the spectral features. Significant differences were observed among the structures in healthy and diseased eye fundus. Specifically, near-infrared analysis allows imaging of deeper layers, such as the choroid, which, to date, has not been retrieved using traditional color fundus cameras. Pathological structures, which were hardly observable in color fundus images owing to metamerism, were also revealed by the developed multispectral fundus camera.

Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives.

The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.

In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle

Precision livestock farming technologies are used to monitor animal health and welfare parameters continuously and in real time in order to optimize nutrition and productivity and to detect health issues at an early stage. The possibility of predicting blood metabolites from milk samples obtained during routine milking by means of infrared spectroscopy has become increasingly attractive. We developed, for the first time, prediction equations for a set of blood metabolites using diverse machine learning methods and milk near-infrared spectra collected by the AfiLab instrument. Our dataset was obtained from 385 Holstein Friesian dairy cows. Stacking ensemble and multi-layer feedforward artificial neural network outperformed the other machine learning methods tested, with a reduction in the root mean square error of between 3 and 6% in most blood parameters. We obtained moderate correlations (r) between the observed and predicted phenotypes for γ-glutamyl transferase (r = 0.58), alkaline phosphatase (0.54), haptoglobin (0.66), globulins (0.61), total reactive oxygen metabolites (0.60) and thiol groups (0.57). The AfiLab instrument has strong potential but may not yet be ready to predict the metabolic stress of dairy cows in practice. Further research is needed to find out methods that allow an improvement in accuracy of prediction equations.

Estimation of crude protein and amino acid contents in whole, ground and defatted ground soybeans by different types of near-infrared (NIR) reflectance spectroscopy

This study was designed to examine the ability of near-infrared (NIR) spectroscopy to determine amino acid and crude protein concentrations in soybean and explore the effects of particle size, fat content and types of NIR spectroscopy instruments on the predictive ability of the NIR models. Whole, ground and defatted ground soybean were scanned by a diode array NIR analyzer and defatted ground soybean were scanned by a Fourier transformed NIR analyzer. Samples were analyzed for crude protein and amino acid contents using reference wet chemistry methods. The NIR calibration models of crude protein and most amino acids showed acceptable coefficient of determinations (R2c = 0.605–0.952), while the NIR models for Cys, Met and Trp were less accurate (R2c = 0.498–0.667 for Cys and R2c = 0.482–0.615 for Met and R2c = 0.406–0.481 for Trp). The R2c values in NIR calibration were found to relate to the correlation between amino acids and crude protein. The grinding process and lipid extraction were found to improve the NIR spectroscopy in crude protein and amino acids prediction. The Fourier transformed NIR analyzer was found to perform similar to the diode array NIR analyzer for defatted ground soybean analysis. No significant differences were found between crude protein and amino acid contents predicted by these two NIR spectroscopy instruments (P > 0.05).

Rapid Identification of Apple Maturity Based on Multispectral Sensor Combined with Spectral Shape Features

The rapid and convenient detection of maturity is of great significance to determine the harvest time and postharvest storage conditions of apples. In this study, a portable visible and near-infrared (VIS/NIR) analysis device prototype was developed based on a multispectral sensor and applied to ‘Fuji’ apple maturity detection. The multispectral data of apples with maturity variation was measured, and the prediction model was established by a least-square support vector machine and linear discriminant analysis. Due to the low resolution of the multispectral data, regular preprocessing methods cannot improve the prediction accuracy. Instead, the spectral shape features (spectral ratio, spectral difference, and normalized spectral intensity difference) were used for preprocessing and model establishment, and the combination of the three features effectively improved the model performance with a prediction accuracy of 88.46%. In addition, the validation accuracy of the optimal model was 84.72%, and the area under curve (AUC) value of each maturity level was higher than 0.8972. The results show that the multispectral sensor is an appliable choice for the development of the portable detection device of apple maturity, and the data processing method proposed in this study provides a potential solution to improve the detection accuracy for multispectral sensors.

Evolving Patterns of Hyperfluorescent Fundus Autofluorescence Accompany Retinal Atrophy in the Rat and Mimic Atrophic Age-Related Macular Degeneration

PurposeComplex two-dimensional (2D) patterns of hyperfluorescent short-wave fundus autofluorescence (FAF) at the border of geographic atrophy (GA) can predict its expansion in patients with late non-exudative “dry” AMD. However, preclinical models do not phenocopy this important feature of disease. We sought to describe the spatiotemporal changes in hyperfluorescent FAF patterns that occur following acute oxidative stress, potentially in association with GA expansion.MethodsSprague Dawley rats (n = 54) received systemic sodium iodate (25–45 mg/kg, n = 90 eyes) or saline (n = 18 eyes) and underwent serial full fundus imaging by confocal scanning laser ophthalmoscopy, including blue FAF and delayed near-infrared analysis. Composite images of the fundus were assembled, and the 2D patterns were described qualitatively and quantitatively. A subset of eyes underwent tissue analysis, and four underwent optical coherence tomography (OCT) imaging.ResultsReproducibly changing, complex patterns of hyperfluorescent FAF emerge at the borders of toxin-induced damage; however, in the absence of GA expansion, they percolate inward within the region of retinal pigment epithelium loss, evolving, maturing, and senescing in situ over time. Unexpectedly, the late FAF patterns most closely resemble the diffuse tricking form of clinical disease. A five-stage classification system is presented.ConclusionsLongitudinal, full-fundus imaging of outer retinal atrophy in the rat eye identifies evolving, complex patterns of hyperfluorescent FAF that phenocopy aspects of disease.Translational RelevanceThis work provides a novel tool to assess hyperfluorescent FAF in association with progressive retinal atrophy, a therapeutic target in late AMD.

Near-infrared analysis of nanofibrillated cellulose aerogel manufacturing

Biomaterial aerogel fabrication by freeze-drying must be further improved to reduce the costs of lengthy freeze-drying cycles and to avoid the formation of spongy cryogels and collapse of the aerogel structures. Residual water content is a critical quality attribute of the freeze-dried product, which can be monitored in-line with near-infrared (NIR) spectroscopy. Predictive models of NIR have not been previously applied for biomaterials and the models were mostly focused on the prediction of only one formulation at a time. We recorded NIR spectra of different nanofibrillated cellulose (NFC) hydrogel formulations during the secondary drying and set up a partial least square regression model to predict their residual water contents. The model can be generalized to measure residual water of formulations with different NFC concentrations and the excipients, and the NFC fiber concentrations and excipients can be separated with the principal component analysis. Our results provide valuable information about the freeze-drying of biomaterials and aerogel fabrication, and how NIR spectroscopy can be utilized in the optimization of residual water content.

A calibration friendly approach to identify drugs of abuse mixtures with a portable near-infrared analyzer.

Both the increasing number and diversity of illicit‐drug seizures complicate forensic drug identification. Traditionally, colorimetric tests are performed on‐site, followed by transport to a laboratory for confirmatory analysis. Higher caseloads increase laboratory workload and associated transport and chain‐of‐evidence assurance performed by police officers. Colorimetric tests are specific only for a small set of drugs. The rise of new psychoactive substances therefore introduces risks for erroneous results. Near‐infrared (NIR)‐based analyzers may overcome these encumbrances by their compound‐specific spectral selectivity and broad applicability. This work introduces a portable NIR analyzer that combines a broad wavelength range (1300–2600 nm) with a chemometric model developed specifically for forensic samples. The application requires only a limited set of reference spectra for time‐efficient model training. This calibration‐light approach thus eliminates the need of extensive training sets including mixtures. Performance was demonstrated with 520 casework samples resulting in a 99.6% true negative and 97.6% true positive rate for cocaine. Similar results were obtained for MDMA, methamphetamine, ketamine, and heroin. Additionally, 236 samples were analyzed by scanning directly through their plastic packaging. Also here, a >97% true positive rate was obtained. This allows for non‐invasive, operator‐safe chemical identification of potentially potent drugs of abuse. Our results demonstrate the applicability for multiple drug‐related substances. Ideally, the combination of this NIR approach with other portable techniques, such as Raman and IR spectroscopy and electrochemical tests, may eventually eliminate the need for subsequent laboratory analysis; therefore, saving tremendous resources in the overall forensic process of confirmatory illicit drug identification.

Novel automatic model construction method for the rapid characterization of petroleum properties from near-infrared spectroscopy

Petroleum fuels play an important role in economic society, and near-infrared analysis has been widely used in the characterization of petroleum fuels due to its effectiveness and efficiency. However, near-infrared spectra are high dimensional data that require high computational cost, thereby complicating the practical application. Previous model construction methods are also restricted to pre-designed model structure, thus limiting the model construction ability. To solve these problems, this paper proposes an automatic model construction algorithm that increases the diversity of model structure with linear-in-parameter representation and optimizes the model structure with genetic programming. The proposed automatic construction algorithm is verified on datasets from literature and experiments with real petroleum products compared with Partial Least Square (PLS) and Support Vector Machine Regression (SVR). The proposed method outperforms PLS and SVR in overall predictive accuracy and reduces the involved variable number by at least 70% during the model construction process. Good performance in prediction accuracy, model complexity, and interpretability on the rapid characterization of different petroleum products can assist refinery plant in product control and management during production.