Near-infrared spectroscopy and chemometric modelling for rapid diagnosis of kidney disease

Rapid diagnosis is important for efficient treatment in clinical medicine. This study aimed at development of a method for rapid and reliable diagnosis using near-infrared (NIR) spectra of human serum samples with the help of chemometric modelling. The NIR spectra of sera from 48 healthy individuals and 16 patients with suspected kidney disease were analyzed. Discrete wavelet transform (DWT) and variable selection were adopted to extract the useful information from the spectra. Principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLSDA) were used for discrimination of the samples. Classification of the two-class sera was obtained using LDA and PLSDA with the help of DWT and variable selection. DWT-LDA produced 93.8% and 83.3% of the recognition rates for the validation samples of the two classes, and 100% recognition rates were obtained using DWT-PLSDA. The results demonstrated that the tiny differences between the spectra of the sera were effectively explored using DWT and variable selection, and the differences can be used for discrimination of the sera from healthy and possible patients. NIR spectroscopy and chemometrics may be a potential technique for fast diagnosis of kidney disease.     

Discrimination of Ganoderma lucidum according to geographical origin with near infrared diffuse reflectance spectroscopy and pattern recognition techniques

A rapid and nondestructive near infrared (NIR) method combined with chemometrics was used to discriminate Ganoderma lucidum according to cultivation area. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of G. lucidum samples were also investigated to find out the difference between samples from six varied origins. It could be found that the amount of polysaccharides and triterpenoid saponins in G. lucidum samples was considerably different based on cultivation area. These differences make NIR spectroscopic method viable. Principal component analysis (PCA), discriminant partial least-squares (DPLS) and discriminant analysis (DA) were applied to classify the geographical origins of those samples. The results showed that excellent classification could be obtained after optimizing spectral pre-treatment. For the discriminating of samples from three different provinces, DPLS provided 100% correct classifications. Moreover, for samples from six different locations, the correct classifications of the calibration as well as the validation data set were 96.6% using the DA method after the SNV first derivative spectral pre-treatment. Overall, NIR diffuse reflectance spectroscopy using pattern recognition was shown to have significant potential as a rapid and accurate method for the identification of herbal medicines.     

Evaluation and comparison of mid-infrared,Raman and near-infrared spectroscopies for characterization and determination of the compositions in fully biodegradable poly(lactic acid)/poly(propylene carbonate)/poly(butylene adipate-co-terephthalate) blends combined with chemometrics

In this work, mid-infrared (MIR), Raman and near-infrared (NIR) spectroscopies were evaluated and compared for characterization and determination of the compositions in poly(lactic acid)/poly(propylene carbonate)/poly(butylene adipate-co-terephthalate) (PLA/PPC/PBAT) blends via chemometrics. Qualitative analysis of MIR, Raman, and NIR spectra of the three compositions was performed. Partial least squares (PLS) models were developed based on each spectroscopy for quantitative determination of the concentrations. The data suggested that MIR and Raman have an advantage over NIR in terms of qualitative recognition of the three compositions. The data also showed that Raman and NIR succeeded in determining the concentrations, while the concentration determined via MIR was inaccurate. Hence, Raman is the optimal analytical tool for qualitative characterization and quantitative determination of the compositions in fully biodegradable PLA/PPC/PBAT blends. The characteristic bands in the Raman spectra clearly identify PLA, PPC, and PBAT to be 392 cm^?1 (δ CCO), 948 cm^?1 (ν C?O?C) and 1600 cm^?1 (ν C ? C in benzene ring), respectively. The optimal calibration models based on Raman for PLA, PPC, and PBAT exhibited root mean square error of prediction (RMSEP) values of 3.140%, 3.576%, and 2.538%, respectively.     

Comparison of near-infrared and mid-infrared spectroscopy for the determination of distillation property of kerosene

Near-infrared (NIR) and mid-infrared (MIR) spectroscopy have been compared and evaluated for the determination of the distillation property of kerosene with the use of partial least squares (PLS) regression. Since kerosene is a complex mixture of similar hydrocarbons, both spectroscopic methods will be best evaluated with this complex sample matrix. PLS calibration models for each percent recovery temperature have been developed by using both NIR and MIR spectra without spectral pretreatment. Both methods have shown good correlation with the corresponding reference method, however NIR provided better calibration performance over MIR. To rationalize the improved calibration performance of NIR, spectra of the same kerosene sample were continuously collected and the corresponding spectral reproducibility was evaluated. The greater spectral reproducibility including signal-to-noise ratio of NIR led to the improved calibration performance, even though MIR spectroscopy provided more qualitative spectral information. The reproducibility of measurement, signal-to-noise ratio, and richness of qualitative information should be simultaneously considered for proper selection of a spectroscopic method for quantitative analysis.     

Study on hydration of poly(N-vinylcaprolactam) microgels by near-IR and mid-IR spectroscopy

Hydration of poly(N-vinylcaprolactam) microgels was investigated by near-infrared (NIR) and mid-infrared (MIR) spectroscopy. The thermosensitive microgels were prepared by emulsion polymerization, and turbidity, dynamic light scattering, and differential scanning calorimetry measurements were carried out. In MIR spectra, carbonyl bands consist of three components due to double, single, and zero hydrogen-bonding carbonyl groups as verified by density functional theory calculations. The relative intensities changed critically at the volume phase transition temperature upon heating. In NIR spectra, two absorbance peaks around 5,900?cm^?1 were observed, which can be assigned to the first overtone of C–H bands. Both of them undergo red shifts during the phase transition in a similar way to that of fundamental bands in MIR spectra. The result suggests that NIR spectroscopy may be a new general method that can provide new information for research on hydration of thermosensitive microgels.     

Chemometrical strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification

The possibility provided by Chemometrics to extract and combine (fusion) information contained in NIR and MIR spectra in order to discriminate monovarietal extra virgin olive oils according to olive cultivar (Casaliva, Leccino, Frantoio) has been investigated.Linear discriminant analysis (LDA) was applied as a classification technique on these multivariate and non-specific spectral data both separately and jointly (NIR and MIR data together).In order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers), LDA was preceded either by feature selection or variable compression. For feature selection, the SELECT algorithm was used while a wavelet transform was applied for data compression.Correct classification rates obtained by cross-validation varied between 60% and 90% depending on the followed procedure. Most accurate results were obtained using the fused NIR and MIR data, with either feature selection or data compression.Chemometrical strategies applied to fused NIR and MIR spectra represent an effective method for classification of extra virgin olive oils on the basis of the olive cultivar.     

Tracing the origin of extra virgin olive oils by infrared spectroscopy and chemometrics: A case study

In this paper, the potential of coupling mid- and near-infrared spectroscopic fingerprinting techniques and chemometric classification methods for the traceability of extra virgin olive oil samples from the PDO Sabina was investigated. To this purpose, two different pattern recognition algorithm representative of the discriminant (PLS-DA) and modeling (SIMCA) approach to classification were employed. Results obtained after processing the spectroscopic data by PLS-DA evidenced a rather high classification accuracy, NIR providing better predictions than MIR (as evaluated both in cross-validation and on an external test set). SIMCA confirmed these results and showed how the category models for the class Sabina can be rather sensitive and highly specific. Lastly, as samples from two harvesting years (2009 and 2010) were investigated, it was possible to evidence that the different production year can have a relevant effect on the spectroscopic fingerprint. Notwithstanding this, it was still possible to build models that are transferable from one year to another with good accuracy.     

Near infrared spectroscopic imaging assessment of cartilage composition: Validation with mid infrared imaging spectroscopy

Disease or injury to articular cartilage results in loss of extracellular matrix components which can lead to the development of osteoarthritis (OA). To better understand the process of disease development, there is a need for evaluation of changes in cartilage composition without the requirement of extensive sample preparation. Near infrared (NIR) spectroscopy is a chemical investigative technique based on molecular vibrations that is increasingly used as an assessment tool for studying cartilage composition. However, the assignment of specific molecular vibrations to absorbance bands in the NIR spectrum of cartilage, which arise from overtones and combinations of primary absorbances in the mid infrared (MIR) spectral region, has been challenging. In contrast, MIR spectroscopic assessment of cartilage is well-established, with many studies validating the assignment of specific bands present in MIR spectra to specific molecular vibrations. In the current study, NIR imaging spectroscopic data were obtained for compositional analysis of tissues that served as an in vitro model of OA. MIR spectroscopic data obtained from the identical tissue regions were used as the gold-standard for collagen and proteoglycan (PG) content. MIR spectroscopy in transmittance mode typically requires a much shorter pathlength through the sample (≤10 microns thick) compared to NIR spectroscopy (millimeters). Thus, this study first addressed the linearity of small absorbance bands in the MIR region with increasing tissue thickness, suitable for obtaining a signal in both the MIR and NIR regions. It was found that the linearity of specific, small MIR absorbance bands attributable to the collagen and PG components of cartilage (at 1336 and 856 cm⁻¹, respectively) are maintained through a thickness of 60 μm, which was also suitable for NIR data collection. MIR and NIR spectral data were then collected from 60 μm thick samples of cartilage degraded with chondroitinase ABC as a model of OA. Partial least squares (PLS) regression using NIR spectra as input predicted the MIR-determined compositional parameters of PG/collagen within 6% of actual values. These results indicate that NIR spectral data can be used to assess molecular changes that occur with cartilage degradation, and further, the data provide a foundation for future clinical studies where NIR fiber optic probes can be used to assess the progression of cartilage degradation.     

Discrimination of transgenic tomatoes based on visible/near-infrared spectra

VIS-NIR spectroscopy combined with multivariate analysis after the appropriate spectral data pre-treatment has been proved to be a very powerful tool for judgment of the relative pattern of the objects that have very similar properties. In this study, seventy transgenic tomatoes with antisense LeETR2 and 94 of their parents, non-transgenic ones were measured in VIS-NIR diffuse reflectance mode. Principal component analysis (PCA), discriminant analysis (DA) and partial least-squares discriminant analysis (PLSDA) were applied to classify tomatoes with different genes into two groups. Calibrations were developed using PLS regression with the leave-one-out cross-validation technique. The results show that differences between transgenic and non-transgenic tomatoes do exist and excellent classification can be obtained after optimizing spectral pre-treatment. The correct classifications for transgenic and non-transgenic tomatoes were both 100% using PLSDA after derivative spectral pre-treatment. The raw spectra with PLSDA model after the second derivative pre-treatment had the best satisfactory calibration and prediction abilities, with r_c = 0.97964, root mean square error of calibration (RMSEC) = 0.099, r_cv = 0.97963, root mean square error of cross-validation (RMSECV) = 0.0993 and a factor. The results in the present study show VIS-NIR spectroscopy together with chemometrics techniques could be used to differentiate transgenic tomato, which offers the benefit of avoiding time-consuming, costly and laborious chemical and sensory analysis.     

Anharmonicity and Spectra–Structure Correlations in MIR and NIR Spectra of Crystalline Menadione (Vitamin K3)

Mid-infrared (MIR) and near-infrared (NIR) spectra of crystalline menadione (vitamin K₃) were measured and analyzed with aid of quantum chemical calculations. The calculations were carried out using the harmonic approach for the periodic model of crystal lattice and the anharmonic DVPT2 calculations applied for the single molecule model. The theoretical spectra accurately reconstructed the experimental ones permitting for reliable assignment of the MIR and NIR bands. For the first time, a detailed analysis of the NIR spectrum of a molecular system based on a naphthoquinone moiety was performed to elucidate the relationship between the chemical structure of menadione and the origin of the overtones and combination bands. In addition, the importance of these bands during interpretation of the MIR spectrum was demonstrated. The overtones and combination bands contribute to 46.4% of the total intensity of menadione in the range of 3600–2600 cm⁻¹. Evidently, these bands play a key role in shaping of the C-H stretching region of MIR spectrum. We have shown also that the spectral regions without fundamentals may provide valuable structural information. For example, the theoretical calculations reliably reconstructed numerous overtones and combination bands in the 4000–3600 and 2800–1800 cm⁻¹ ranges. These results, provide a comprehensive origin of the fundamentals, overtones and combination bands in the NIR and MIR spectra of menadione, and the relationship of these spectral features with the molecular structure.     

Performance Improvement of NIR Spectral Pattern Recognition from Three Compensation Models’ Voting and Multi-Modal Fusion

Inspired by aquaphotomics, the optical path length of measurement was regarded as a perturbation factor. Near-infrared (NIR) spectroscopy with multi-measurement modals was applied to the discriminant analysis of three categories of drinking water. Moving window-k nearest neighbor (MW-kNN) and Norris derivative filter were used for modeling and optimization. Drawing on the idea of game theory, the strategy for two-category priority compensation and three-model voting with multi-modal fusion was proposed. Moving window correlation coefficient (MWCC), inter-category and intra-category MWCC spectra, and k-shortest distances plotting with MW-kNN were proposed to evaluate weak differences between two spectral populations. For three measurement modals (1 mm, 4 mm, and 10 mm), the optimal MW-kNN models, and two-category priority compensation models were determined. The joint models for three compensation models’ voting were established. Comprehensive discrimination effects of joint models were better than their sub-models; multi-modal fusion was better than single-modal fusion. The best joint model was the dual-modal fusion of compensation models of one- and two-category priority (1 mm), one- and three-category priority (10 mm), and two- and three-category priority (1 mm), validation’s total recognition accuracy rate reached 95.5%. It fused long-wave models (1 mm, containing 1450 nm) and short-wave models (10 mm, containing 974 nm). The results showed that compensation models’ voting and multi-modal fusion can effectively improve the performance of NIR spectral pattern recognition.     

Combination of mid- and near-infrared spectroscopy for the determination of the quality properties of beers

The combination of infrared (MIR) and near-infrared (NIR) spectroscopy has been employed for the determination of important quality parameters of beers, such as original and real extract and alcohol content. A population of 43 samples obtained from the Spanish market and including different types of beer, was evaluated. For each technique, spectra were obtained in triplicate. In the case of NIR a 1 mm pathlength quartz flow cell was used, whereas attenuated total reflectance measurements were used in MIR. Cluster hierarchical analysis was employed to select calibration and validation data sets. The calibration set was composed of 15 samples, thus leaving 28 for validation. A critical evaluation of the prediction capability of multivariate methods established from the combination of NIR and MIR spectra was made. Partial least squares (PLS) and artificial neural networks (ANN) were evaluated for the treatment of data obtained in each individual technique and the combination of both. Different parameters of each methodology were optimized. A slightly better predictive performance was obtained for NIR-MIR combined spectra, and in all the cases ANN performs better than PLS, which may be interpreted from the existence of some non-linearity in the data. The root-mean-sqare-error of prediction (RMSEP) values obtained for the combined NIR-MIR spectra for the determination of real extract, original extract and ethanol were 0.076% w/w, 0.14% w/w and 0.091% v/v.     

Coffee varietal differentiation based on near infrared spectroscopy

Near infrared spectroscopy (NIRS) was used to discriminate between arabica and robusta pure coffee varieties and blends of varied varietal composition. Direct orthogonal signal correction (DOSC) pre-processing method was applied on a set of 191 roasted coffee NIR spectra from both pure varieties and blends varying the final robusta content from 0 to 60% (w/w) in order to remove information unrelated to the actual varietal composition of samples. The corrected NIR spectra, as well as raw NIR spectra, were used to develop separate classification models using the potential functions method as class-modelling technique, exploring several options more or less restrictive according to the final number of considered categories. All constructed classification models were compared to evaluate their respective qualities and to show the suitability of applying DOSC method as pre-processing step for developing improved classification models for coffee varietal identification purposes.     

Vibrational analysis of liquid n-butylmethylether

The complex refractive index spectrum in the NIR and MIR range of liquid n-butylmethylether (NBME) was determined. Thin film recording technique was required to obtain quantitative transmission spectra in the MIR range. The experimental part was supported by a conformer population analysis and anharmonic vibrational analysis based on the B2PLYP/N07D method. The resulting population of 11 NBME conformers was analyzed and their calculated anharmonic vibrational spectra and calculated abundances (at 298 K) were used to obtain the resultant spectrum of n-butylmethylether. The band assignment procedure was based on the calculated potential energy distributions.     

基于近红外光谱分析技术的栽培香菇产地快速鉴别

建立了一种基于近红外光谱分析技术的香菇产地鉴别方法。利用近红外光谱仪扫描不同主产地的香菇干样,获得样品的近红外漫反射光谱。利用偏最小二乘判别分析(PLSDA)分别建立了吉林、湖北、福建3个省份栽培香菇的产地判别模型,同时使用光谱预处理和波长筛选技术对判别模型进行优化,最后使用预测样品对模型进行验证。结果表明,使用原始光谱建立的模型能够初步实现对产地的判别,使用光谱预处理技术扣除光谱中的背景信息,同时利用波长筛选技术选择特定波长对模型进行优化后,可进一步提高预测正确率。该方法为香菇产地真实性溯源提供了一种新方法,对香菇产业发展具有重要的实际意义。     

Quality control of the powder pharmaceutical samples of sulfaguanidine by using NIR reflectance spectrometry and temperature-constrained cascade correlation networks

Temperature-constrained cascade correlation networks (TCCCNs) were applied to the identification of the powder pharmaceutical samples of sulfaguanidine based on near infrared (NIR) diffuse reflectance spectra and their first derivative spectra. This work focused on the comparison of performances of the uni-output TCCCN (Uni-TCCCN) and multi-output (Multi-TCCCN) by near infrared diffuse reflectance spectra and their first derivative spectra of sulfaguanidine. The TCCCN models were verified with independent prediction samples by using the “cross-validation” method. The networks were used to discriminate qualified, un-qualified and counterfeit sulfaguanidines pharmaceutical powders. The results showed that single outputs network generally worked better than the multiple outputs networks, and the first derivative spectra were more suitable for the identification comparing with original diffuse reflectance spectra. With proper network parameters the pharmaceutical powders can be classified at rate of 100% in this work. Also, the effects of parameters and related problems were discussed.     

Classification of cultivation area of ginseng by near infrared spectroscopy and ICP-AES

A rapid and nondestructive near infrared (NIR) method using soft independent modeling of class analogy (SIMCA) for the classification of cultivation area (Korea and China) was evaluated and confirmed. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of ginseng samples were also investigated to find out the differences between Korean samples and Chinese samples. These differences make NIR spectroscopic method viable. The average value of each Korean and Chinese ginseng sample for crude fiber, crude protein, starch, and 10 inorganic constituents were measured and compared with F-test and t-test. The inorganic constituents were also measured by induced coupled plasma-atomic emission spectroscopy (ICP-AES). It could be found that the amount of starch and ten inorganic elements for example Na, Mg, P, K, Ca, Mn, Fe, Ni, Cu and Zn in ginseng samples are considerably different based on cultivation area. SIMCA has been applied to the inorganic data to investigate the possibility of ICP-AES as classification tool. However, it was observed that the result was not equal to than NIR spectra data. The overall results showed the availability of NIR method using SIMCA would be adequate for classification of cultivation of ginseng, since NIR spectra includes useful and various information on chemical properties in spite of broad and overlapped bands.     

Discrimination of Rhizoma Corydalis from two sources by near-infrared spectroscopy supported by the wavelet transform and least-squares support vector machine methods

Near-infrared spectroscopy (NIRS) was applied for direct and rapid collection of characteristic spectra from Rhizoma Corydalis, a common traditional Chinese medicine (TCM), with the aim of developing a method for the classification of such substances according to their geographical origin. The powdered form of the TCM was collected from two such different sources, and their NIR spectra were pretreated by the wavelet transform (WT) method. A training set of such Rhizoma Corydalis spectral objects was modeled with the use of the least-squares support vector machines (LS-SVM), radial basis function artificial neural networks (RBF-ANN), partial least-squares discriminant analysis (PLS-DA) and K-nearest neighbors (KNN) methods. All the four chemometrics models performed reasonably on the basis of spectral recognition and prediction criteria, and the LS-SVM method performed best with over 95% success on both criteria. Generally, there are no statistically significant differences in all these four methods. Thus, the NIR spectroscopic method supported by all the four chemometrics models, especially the LS-SVM, are recommended for application to classify TCM, Rhizoma Corydalis, samples according to their geographical origin.     

Infrared spectroscopy and outer product analysis for quantification of fat, nitrogen, and moisture of cocoa powder

The combination of the near infrared (NIR) and Fourier-transform infrared (FTIR) absorbance spectra (1100-2500 nm and 4000-600 cm⁻¹) of 100 cocoa powder samples was used to build calibration models for the determination of the content of fat, nitrogen, and moisture. The samples that comprised the dataset had an average composition of 13.51% of fat, 3.77% nitrogen, and 3.98% moisture. The fat content ranged from 2.42 to 22.00%, the nitrogen from 0.88 to 4.48%, and moisture from 1.60 to 7.80%. For NIR, the relative root mean square error of cross-validation (RMSECV) was 7.0% (R² = 0.96) for fat, 1.7% (R² = 0.98) for nitrogen, and 5.2% (R² = 0.94) for moisture. For FTIR, the relative RMSECV was 10.4% (R² = 0.94) for fat and 3.9% (R² = 0.95) for nitrogen. However, for moisture, it was not possible to build a calibration model with suitable predictability. The combination of the NIR and FTIR domains (data fusion) by outer product analysis PLS1 allowed to predict these parameters and to characterise frequencies in one domain based on the information of the other domain. This work allows to conclude that the second derivative of NIR is the recommended procedure to quantify fat, nitrogen, and moisture content in cocoa powders by infrared spectroscopy.     

Fourier transform near- and mid-infrared spectroscopy can distinguish between the commercially important Pelargonium sidoides and its close taxonomic ally P. reniforme

Pelargonium sidoides is indigenous to South Africa and abundant in the Eastern Cape Province. Several herbal products have been formulated using P. sidoides of which Umckaloabo^® is probably the most popular and successfully marketed in Germany. The objective of this study was to discriminate between P. sidoides and Pelargonium reniforme by FT-IR spectroscopy. Absorbance spectra were collected for P. sidoides (n = 96) and its close taxonomic ally P. reniforme (n = 57) in the near infrared (NIR) and mid infrared (MIR) regions. The spectroscopic data were analysed using chemometric computations including principal component analysis and orthogonal projections to latent structures discriminant analysis. Phytochemical variation of 5.79% in the NIR dataset (R²X(cum) = 0.962; Q²(cum) = 0.918) and 9.22% variation in the MIR dataset (R²X(cum) = 0.497; Q²(cum) = 0.658) was responsible for the separation of the two species. Seven absorption areas were identified as putative biomarkers responsible for the differences between the two species. These results indicate that FT-NIR and FT-MIR spectroscopy can be used to discriminate between these two closely related species which occupy a sympatric distribution in South Africa.