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Non-destructive determination of taste-related substances using near-infrared spectroscopy (NIRS) is of great significance for effectively evaluating tea quality. In the present research work, NIRS (400–2400 nm) were correlated with tea polyphenol (TP), free amino acid (FAA), caffeine (CAFF), and total sugar (TS) content in 187 tea samples by processing of spectra using continuous wavelet transform (CWT). Some effective variable selection algorithms (variable combination population analysis and iterative retained information variable algorithm (VCPA-IRIV), competitive adaptive reweighted sampling (CARS), variable iterative space shrinkage approach (VISSA), bootstrapping soft shrinkage (BOSS), and genetic algorithm (GA) were used for the quantification of taste-related substances of tea samples. The developed models were established and evaluated for the content of taste substances in several varieties of tea, including PLS and the potential of some machine learning models such as Gaussian process regression, support vector regression, and random forest regression. The efficiency of the developed model was significantly enhanced with the use of CWT-BOSS-PLS for monitoring the state of each component. More than 99.34% of variables was reduced. The predicted R values for TP, FAA, CAFF, and TS were 0.6891, 0.8385, 0.6810, and 0.8638 with accuracy improved by 6%, 3%, 45%, and 8%. Overall, this study provides an important support method for the practical application of content analysis for tea taste components. [ABSTRACT FROM AUTHOR]

The grade of tea is closely related to tea quality, so the identification of tea grade is an important task. In order to improve the identification capability of the tea grade system, a fuzzy maximum uncertainty linear discriminant analysis (FMLDA) methodology was proposed based on maximum uncertainty linear discriminant analysis (MLDA). Based on FMLDA, a tea grade recognition system was established for the grade recognition of Chunmee tea. The process of this system is as follows: firstly, the near-infrared (NIR) spectra of Chunmee tea were collected using a Fourier transform NIR spectrometer. Next, the spectra were preprocessed using standard normal variables (SNV). Then, direct linear discriminant analysis (DLDA), maximum uncertainty linear discriminant analysis (MLDA), and FMLDA were used for feature extraction of the spectra, respectively. Finally, the k-nearest neighbor (KNN) classifier was applied to classify the spectra. The k in KNN and the fuzzy coefficient, m, were discussed in the experiment. The experimental results showed that when k = 1 and m = 2.7 or 2.8, the accuracy of the FMLDA could reach 98.15%, which was better than the other two feature extraction methods. Therefore, FMLDA combined with NIR technology is an effective method in the identification of tea grade. [ABSTRACT FROM AUTHOR]

The continuous evolution of deep learning has garnered significant attention in spectroscopy. This study focuses on identifying wheat flour, presenting a more efficient and accurate method by combining two-dimensional correlation spectroscopy (2D-COS) and deep learning techniques. A data set of 316 near-infrared (NIR) spectral samples of four types of wheat flour was collected. By applying three disparate 2D-COS techniques, i.e., synchronous, asynchronous, and integrated, we crafted 948 2D-COS images. These images, obtained by transforming the original one-dimensional spectra into 2D representations, offer richer information for deep learning analysis. The study introduced an 18-layer residual network incorporating a convolutional attention mechanism, specifically tailored for the 2D-COS analysis of wheat flour, aimed at enhancing the model's discriminative capabilities by refining the residual neural network's structure. Achieving an unprecedented recognition accuracy of 100% through methodical optimization and rigorous training on the synchronous 2D-COS data set of wheat flour, the proposed model is a testament to the efficacy of deep learning in spectroscopic analysis. To further exhibit the confluence of 2D-COS with deep learning, t-distributed stochastic neighbor embedding was employed to visualize the distinctive 2D-COS features within the deep learning architecture. Additionally, the model's performance was juxtaposed with prevailing NIR spectral recognition methods, including random forest, gradient boosting decision tree, and artificial neural network. This comparison cemented the proposed approach's superiority in wheat flour categorization. The findings of this study not only introduce a novel and efficient solution for wheat flour quality analysis but also underscore the significant potential of deep learning techniques in spectroscopy applications. [ABSTRACT FROM AUTHOR]

Biological activities of limonoid include antitumor, antioxidant, anti-inflammatory, etc., and its content in citrus seeds is particularly high with a low utilization rate, so it is an important potential source of limonoid. The miniature near-infrared spectra device provides a high-throughput, low-cost, and field analysis technique for the detection of limonoid from it. In this paper, the quantitative determination of limonin, nomilin, and obacunone in citrus seeds by a NIR instrument, and a precise benchtop NIR instrument were studied. UHPLC-MS/MS was applied as the reference analysis method. The root mean square error (RMSE), determination coefficient (R2), and the ratio of performance to deviation (RPD) were used to monitor the model. R2p, RPD, and RMSEp of the best model based on benchtop TANGO FT-NIR for limonin, nomilin, and obacunone were 0.8920/3.10/0.0141, 0.9250/3.72/0.0058, and 0.9081/3.36/0.0040, respectively, representing reliable results. The miniature device NIR-S-G1 was also good in the model for nomilin (0.9075/3.35/0.0097) and obacunone (0.8860/3.02/0.0050). This result indicated that the miniaturized NIR-S-G1 has a similar ability to TANGO FT-NIR in detecting nomilin and obacunone in citrus seeds. Their experimental NIR absorption was detected to better understand the characteristics of the model. This step, combined with the waveband selection algorithm and the two-dimensional correlation spectroscopy (2D-COS) analysis, explained the main factor that affects the difference in analytical performance between the two spectrometers: detection range and instrument resolution. [ABSTRACT FROM AUTHOR]

The quality grading and sorting of 'Akizuki' pear, a high-quality fruit, is a vital part of the supply chain. Meanwhile, cork spot disorder, a common physiological issue that affects the healthy development of pear fruit, requires rapid and accurate identification. To further enhance the identification precision of cork spot disorder in 'Akizuki' pear, this study proposes a multi-source feature stable learning (MFSL) method based on neural architecture search and sample reweighting techniques, building upon the fusion of near-infrared spectrum and visual image. This method employs secondary reweighted optimization training on a multi-source fusion model, enabling it to fully learn label-related features and thereby enhance generalization. Experimental results show that the optimal modelling performance of the multi-source fusion feature has increased by 26.89 % in accuracy compared to the single spectrum and by 11.19 % compared to the single image. After optimization training, the testing accuracy of the model improved by 1.31 %, reaching 89.47 %, and the F1-score increased by 1.47 %, reaching 89.83 %. The results validate the effectiveness of the method in enhancing the model's generalization performance. The proposed MFSL method for the precise identification of cork spot disorder in 'Akizuki' pear in this study significantly improves the accuracy of the multi-source fusion model in symptom recognition. The research results have important reference value for the efficient grading and sorting of pear fruit quality. • Proposed a multisource feature stable learning (MFSL) method on NIR and CV. • MFSL enhances the diagnosis of the model for cork spot disorder in 'Akizuki' pear. • MFSL method exhibits the better stability and applicability of the model. • MFSL has the important significance for the fruit automated grading and sorting. [ABSTRACT FROM AUTHOR]

• Three strains of polygonati rhizoma and their ATR-FTIR/NIR spectra were collected. • Ethnic usage in China is collected. • PLS-DA, SVM, and CNN were established by feature variables fusion for discrimination. • The first two PCs of spectra were analyzed and the fingerprint region was screened. • The correlation between polysaccharide content and ATR-FTIR/NIR spectra was studied. Considering the high similarity among various rhizomes, the issue of adulteration and mixed use of polygonati rhizome (PR) in the market is widespread. Nowadays, the PR polysaccharides are the main pharmacological active ingredient. To ensure the uniformity of the product quality, it is essential to establish an effective and rapid method for PR identification. Herein, the polysaccharides content was determined via ATR-FTIR and NIR spectra of PR. The correlation between polysaccharides content and spectra were analyzed. Additionally, the partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and convolutional neural networks (CNN) were established using the data of ATR-FTIR spectra, NIR spectra, characteristic bands and feature variables. The obtained difference of the polysaccharides content was particularly large. By extracting latent variables and combining data fusion to establish PLS-DA, SVM and CNN, PR can be accurately identified. The content of PR polysaccharides can be predicted based on the absorbance according to ATR-FTIR and NIR spectra. [ABSTRACT FROM AUTHOR]

The classification of iron ore rock plays a crucial role in assisting the mine to develop scientifically sound mining strategies. Given the limitations of traditional manual identification and chemical techniques in terms of cost and detection time, it is crucial to develop an economical, high-precision, and efficient classification method for iron ore rocks. This paper takes an iron mine in Anshan as the research center and aims to establish an iron ore rock classification model by combining the improved extreme learning machine (ELM) with near-infrared spectra (NIRS). The NIRS of iron ore rock is denoised and feature extracted using the Savitzky–Golay filter and competitive adaptive reweighted sampling (CARS) methods, respectively. In this paper, we propose the IAdaBoost algorithm by introducing the sample similarity relationship into the weight-updating strategy of the AdaBoost integration algorithm. The weighted extreme learning machine (WELM) is adopted as the base learner for the IAdaBoost algorithm. Furthermore, we employ a multi-strategy improved gray wolf optimization (IGWO) algorithm to optimize the learning rate and the number of integrated base learners in the IAdaBoost algorithm. Consequently, we propose the IGWO-IAdaBoost-WELM model. Compared to other machine learning models, our proposed model performs better and can classify iron ore rocks consistently and efficiently. Meanwhile, we also used the model to invert the remote sensing images of the iron ore mining area, and the results show that the model can achieve fast, efficient, and large-scale iron ore rock classification, which provides an important reference for mining. [Display omitted] • Combining spectroscopy with machine learning methods to classify iron ore rocks. • The machine learning structure was improved using an integrated algorithm and its parameters were optimized using a multi-strategy improved optimization algorithm. • Inversion of remote sensing images of mining areas using improved machine learning methods enables fast, efficient and large-scale iron ore rock classification, which provides an important reference for mining. [ABSTRACT FROM AUTHOR]

The grade of tea is closely related to tea quality, so the identification of tea grade is an important task. In order to improve the identification capability of the tea grade system, a fuzzy maximum uncertainty linear discriminant analysis (FMLDA) methodology was proposed based on maximum uncertainty linear discriminant analysis (MLDA). Based on FMLDA, a tea grade recognition system was established for the grade recognition of Chunmee tea. The process of this system is as follows: firstly, the near-infrared (NIR) spectra of Chunmee tea were collected using a Fourier transform NIR spectrometer. Next, the spectra were preprocessed using standard normal variables (SNV). Then, direct linear discriminant analysis (DLDA), maximum uncertainty linear discriminant analysis (MLDA), and FMLDA were used for feature extraction of the spectra, respectively. Finally, the k-nearest neighbor (KNN) classifier was applied to classify the spectra. The k in KNN and the fuzzy coefficient, m, were discussed in the experiment. The experimental results showed that when k = 1 and m = 2.7 or 2.8, the accuracy of the FMLDA could reach 98.15%, which was better than the other two feature extraction methods. Therefore, FMLDA combined with NIR technology is an effective method in the identification of tea grade.

Hand sorting for different types of waste textiles is time-consuming, laborious and inaccurate. The non-destructive and efficient identification of fibers in waste fabrics is of great significance to the reuse of textile materials. In this paper, 593 samples were selected as the research objects, including polyester, cotton, wool, viscose, nylon, silk, acrylic, polyester/nylon, polyester/cotton, polyester/wool and silk/cotton waste textiles. The near-infrared spectrum of each sample was obtained by a portable near-infrared spectrometer, and the influence of environmental humidity and fabric thickness on the near-infrared spectrum of the sample was discussed to obtain the best test conditions. On this basis, the back propagation artificial neural network (BP-ANN) was applied to the qualitative classification of waste textiles to complete the automatic identification of fabric components in the sorting process. Firstly, a standard sample set was established by waveform clipping and normalization, and a BP-ANN deep web suitable for near-infrared spectroscopy was established. Then the BP network was trained according to the input near-infrared spectrum data of known sample categories and the classification results of the preset 11 types of labels, and the weights and thresholds of each layer were adjusted in the repeated training process. Finally, a 1500 × 100 × 11 network structure was established when the network error was the smallest, and the number of corresponding hidden layer nodes was 100. When the number of training steps was 500, the sum of squared errors reached 0.001, and the model recognition effect was the best. Meanwhile, the validity of the model was verified by inspecting additional 299 samples outside the model, and the recognition accuracy rate of the established model also exceeded 99%, which verified the effectiveness of the model. These results show that this near-infrared qualitative analysis model can more accurately classify and identify waste textiles, especially polyester waste textiles. In addition, it provides a new idea for the recycling and reuse of waste textiles for enterprises. [ABSTRACT FROM AUTHOR]

Although Fourier transform infrared spectroscopy has been widely used in research on the aging of asphalt binders, it is a cumbersome, time-consuming, and environmentally polluting process using toxic solvents to extract and recover asphalt binders from asphalt mixtures for spectral measurement and analysis. However, little effort has been devoted towards its non-destructive spectral measurement, which is important for intelligent monitoring of pavement deterioration conditions. This paper focused on the non-destructive investigation of comparative near-infrared spectra of asphalt mixture specimens designed using the Marshall method after laboratory short- and long-term aging with a portable near-infrared spectrometer. The near-infrared spectra showed that asphalt mixtures from different aging states appeared significantly different fingerprints. The near-infrared fingerprints and aging states were also statistically significantly different according to the analysis of one-way analysis of variance. These distinctive near-infrared fingerprints could be attributed to the varied amounts of chemical functional groups, mainly hydrogen-containing groups during oxidative aging. This study provided a non-destructive and in-situ detection of the aging of asphalt mixtures. • The portable NIR spectrometer is used for the measurement of asphalt mixtures. • Asphalt mixtures from four aging states have unique and distinct NIR fingerprints. • ANOVA indicates a statistical difference in NIR spectra from four aging states. • NIR spectroscopy is suitable for in-situ detection of aging of asphalt mixtures. [ABSTRACT FROM AUTHOR]

Cork spot disorder is a common physiological disease that severely affects the quality and yield of the 'Akizuki' pear. In this study, we propose a novel method, Near-infrared spectra and Visual images with Neural Architecture Search (NV-NAS), to achieve a highly accurate diagnosis of cork spot disorder. This method automatically generates fusion features by combining near-infrared spectral and visual image features and then searches for the best-performing model structure. The best result was obtained with near-infrared spectral features preprocessed by first derivatives (FD) and the visual image features extracted by DenseNet121 in the NV-NAS method. On the validation set, the accuracy was 95.5%, and on the test set, the accuracy was 88.2%. Compared to the modeling results of a single feature, the accuracy improved by 26.3% over spectra and by 11.2% over images on the test set. Additionally, in experiments with models of different depths, deep image features were more conducive to feature fusion, with accuracy improvements of 21.7% and 30.1% compared to features from the middle or bottom layers, respectively. The results indicate that the model built on the fusion feature shows high generalization performance and holds significant reference value for the more accurate diagnosis of the 'Akizuki' pear cork spot disorder. • NV-NAS method effectively diagnoses 'Akizuki' pear cork spot disorder. • The model built by the NV-NAS method demonstrates excellent generalization. • The model with fusion data is better than others by the single spectrum or image. [ABSTRACT FROM AUTHOR]

Summary: To realise accurate and nondestructive detection on moisture content of maize seed based on visible/near‐infrared (Vis/NIR) and near‐infrared (NIR) hyperspectral imaging technology, the hyperspectral images on two sides (embryo and endosperm sides) of each maize seed of four varieties were collected. The effects of average spectra extraction regions, that is centroid region and whole seed region, and different spectral preprocessing methods, were investigated. Uninformative variable elimination (UVE) was used to extract the feature wavelengths, and the partial least squares regression (PLSR) prediction models were established. The results showed that extracting the average spectra from the centroid region did better than from the whole seed region, and S‐G smoothing was prior to other preprocessing methods. The PLSR models established with NIR spectra had better performance than that with Vis/NIR spectra. The model developed for a single variety was superior to that for all varieties together. [ABSTRACT FROM AUTHOR]

The identification of maize haploid seeds is a significant process in genetic research and modern maize breeding. Adopting near-infrared spectroscopy technology to distinguish haploid seeds from hybrid seeds has the advantages of being non-destructive, rapid and low cost. However, due to the influence of light, temperature, humidity, near-infrared intensity, instrument and dynamic change of seed activity, the near-infrared spectra of maize seeds showed high dimensional nonlinear characteristics. In this study, to make full use of the class label information, a nonlinear feature analytical method for haploid maize seeds identification based on Supervised Virtual Sample Kernel Locality Preserving Projection (SVSKLPP) has been proposed. The experimental results showed that the nonlinear identification model SVSKLPP achieved strong classification performance to identify the maize haploid seeds. Moreover, compared with the linear feature extraction methods Principal Component Analysis, Orthogonal Linear Discriminant Analysis, Locality Preserving Projection, Supervised Virtual Sample Locality Preserving Projection and nonlinear feature extraction methods Kernel Locality Preserving Projection, Isomap, Locally Linear Embedding, Laplacian Eigenmaps and Local Tangent Space Alignment, the SVSKLPP model achieved a better performance. The average accuracy, sensitivity and specificity using method SVSKLPP were 97.1%, 98.8% and 95.4% respectively, and it also had high robustness. The overall results show that the SVSKLPP-NIR methodology was efficient in accurately identifying haploid maize seeds, thus demonstrating its capabilities for application in haploid breeding for crop variety improvement. [ABSTRACT FROM AUTHOR]

Even-odd alternation of the melting points of α,ω-disubstituted linear alkanes such as alkane-α,ω-diols, alkane-α,ω-dinitriles and α,ω-diaminoalkanes is well known. Melting points for compounds with an even number of carbons in their alkyl chains are systematically higher than those for compounds with an odd number of carbons. In order to clarify the origin of this alternation, near-infrared absorption spectra of linear alkane-α,ω-diols with 3 to 9 carbon atoms in their alkyl chains were measured in the liquid and solid states. The band due to the first overtone of the O H stretching mode was investigated. The temperature-dependent spectra of all alkane-α,ω-diols in their liquid states were found to be similar; no even-odd alternation was observed. In the solid state, however, spectra of alkane-α,ω-diols with even and odd numbers of carbon atoms differed greatly. Spectra of alkane-α,ω-diols with an odd number of carbon atoms in their solid states were similar to those in the liquid states, although the variation of spectra observed upon lowering the temperature of liquid seemed to continue when the liquids were frozen. In contrast, spectra of alkane-α,ω-diols with an even number of carbon atoms in their liquid and solid states were found to be quite different. New bands appeared upon freezing. The observed even-odd alternation of the spectra observed for alkane-α,ω-diols in their solid states is presumably caused by their even-odd alternation of crystal structures. [ABSTRACT FROM AUTHOR]

• An online NIR system was developed to collect single seed spectrum. • A dynamic white reference acquisition method was developed. • The system was evaluated for protein and insect infestation detection. Maize is one of the most important crops in the world. The protein content and insect infestation significantly influence seed vigor and growth. Therefore, it is vital to detect the protein content and insect infestation seeds rapidly and non-destructively. In this study, a near-infrared (NIR) spectra acquisition device (900–1700 nm) was designed and employed for on-line seed quality detection. Support vector machine (SVM), logistic regression (LR), and partial least square regression discrimination analysis (PLS-DA) were used for insect infestation seeds classification. PLS and least squares-support vector machine (LS-SVM) were adopted for protein detection. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were applied to select the feature wavelengths to reduce the redundant data and identify important information. As for insect infestation seeds, CARS-SPA-LR model achieved the classification using only 7 features with an accuracy of 0.83. In terms of protein prediction, the LS-SVM models obtained the best results for grain protein content (GPC, %) and absolute GPC (Ab_GPC, mg/kernel), respectively. The optimal models only used 22 and 21 feature wavelengths selected by CARS-SPA, with the RMSEP of 3.38 % and 2.38 mg/kernel, and RPD was 2.08 and 2.11, respectively. The results indicated that the NIR on-line acquisition system could be applied for qualitative and quantitative analysis of maize seeds. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the effects of different high-intensity ultrasonication (HIU) pretreatment on the structure and properties of soybean protein isolate (SPI) as well as enzymatic hydrolysis of SPI by bromelain and antioxidant activity of hydrolysates. The HIU-treated SPI fractions showed a decrease in the proportion of α-helices and β-turns and an increase in the content of β-sheets and random coils based on Fourier-transform infrared spectroscopy. Near-infrared spectra and fluorescence spectra analyses provided support for the changes in secondary and tertiary structures of SPI after ultrasound treatment. The particle size of SPI decreased from 217.20 nm to 141.23 nm and the absolute zeta potential increased. Scanning electron microscopy showed that HIU treatment changed apparent morphology. Dynamic and static light scattering of ultrasonicated samples showed that SPI structure had changed from hard-sphere to hollow-sphere or polydisperse and monodisperse gaussian coils. HIU pretreatment significantly increased the hydroxyl-radical scavenging and the degree of hydrolysis of the SPI hydrolysates.

Spinach is prone to spoilage in the course of preservation. Spinach leaves stored at different temperatures for different durations will have varying degrees of freshness. In order to monitor the freshness of spinach leaves during storage, a rapid and non-destructive method—hyperspectral imaging technology—was applied in this study. Visible near-infrared reflectance (Vis-NIR) (380−1030 nm) and near-infrared reflectance (NIR) (874−1734 nm) hyperspectral imaging systems were used. Spinach leaves preserved at different temperatures with different durations (0, 3, 6, 9 days at 4 °C and 0, 1, 2 days at 20 °C) were studied. Principal component analysis (PCA) was adopted as a qualitative analysis method. The second-order derivative spectra were utilized to select effective wavelengths. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and extreme learning machine (ELM) were used to build models based on full spectra and effective wavelengths. All three models achieved good results, with accuracies above 92% for both Vis-NIR spectra and NIR spectra. ELM obtained the best results, with all accuracies reaching 100%. The overall results indicate the possibility of the freshness identification of spinach preserved at different temperatures for different durations using two kinds of hyperspectral imaging systems.

The noise of near-infrared spectra and spectral information redundancy can affect the accuracy of calibration and prediction models in near-infrared analytical technology. To address this problem, the improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and permutation entropy (PE) were used to propose a new method for pretreatment and wavelength selection of near-infrared spectra signal. The near-infrared spectra of glucose solution was used as the research object, the improved CEEMDAN energy entropy was then used to reconstruct spectral data for removing noise, and the useful wavelengths are selected based on PE after spectra segmentation. Firstly, the intrinsic mode functions of original spectra are obtained by improved CEEMDAN algorithm. The useful signal modes and noisy signal modes were then identified by the energy entropy, and the reconstructed spectral signal is the sum of useful signal modes. Finally, the reconstructed spectra were segmented and the wavelengths with abundant glucose information were selected based on PE. To evaluate the performance of the proposed method, support vector regression and partial least square regression were used to build the calibration model using the wavelengths selected by the new method, mutual information, successive projection algorithm, principal component analysis, and full spectra data. The results of the model were evaluated by the correlation coefficient and root mean square error of prediction. The experimental results showed that the improved CEEMDAN energy entropy can effectively reconstruct near-infrared spectra signal and that the PE can effectively solve the wavelength selection. Therefore, the proposed method can improve the precision of spectral analysis and the stability of the model for near-infrared spectra analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • This work provides a new field analysis method for the accurate identification of Moutan Cortex products. • Handheld near infrared spectroscopy for accurate quantification of Gallic acid, 5-HMF, paeoniflorin and paeonol. • Global models were established to detect various processing states of Moutan Cortex. Now, almost all solutions are moving toward miniaturization of devices. Stir-frying, as a food processing process, presents field analysis challenges to the NIR foundation. Therefore, in this paper, Moutan Cortex, a functional food product that requires strict control of stir-frying degree, with a variety of biological activities such as antioxidant, anti-inflammatory and protection of cardiovascular system, was investigated to realize the qualitative and quantitative analysis of the product on site through miniaturized spectrometer NIR-S-G1. TANGO FT-NIR, a precision benchtop instrument, was applied as a reference to observe the field analysis performance of the instrument. The results show that the handheld device can accurately judge the products of different stir-frying degrees by SVM model with the accuracy rate of 100%. At the same time, it can accurately and quantitatively monitor the four main components in the process. Also combinations of optimization algorithms (Competitive Adaptive Weighted sampling, variable combination population analysis combined with iteratively retaining informative variables and genetic algorithm) and regression models (Partial least squares regression and Support Vector Machine) were investigated to perform a better result. Therefore, this work provided a reference for the field analysis of NIR technology from qualitative and quantitative analysis in this specific analytical application. [ABSTRACT FROM AUTHOR]

Blood species identification is of great significance for blood supervision and wildlife investigations. The current methods used to identify the blood species are destructive. Near-Infrared spectroscopy method is known for its non-invasive properties. In this research, we combined Near-Infrared diffuse transmitted spectra and Partial Least Square Discrimination Analysis (PLS-DA) to identify three blood species, including macaque, human and mouse. Blind test and external test were used to assess the PLS-DA model. The model performed 100% accuracy in its identification between three blood species. This approach does not require a specific knowledge of biochemical features for each individual class but relies on a spectroscopic statistical differentiation of the whole components. This is the first time to demonstrate Near-Infrared diffuse transmitted spectra have the ability to identify the species of origin of blood samples. The results also support a good potential of absorption and scattering spectroscopy for species identification in practical applications for real-time detection. [ABSTRACT FROM AUTHOR]

[Display omitted] • A review of the existing hybrid methods for analyzing near-infrared spectra is presented. • A systematic classification of developed variable selection algorithms based on hybrid strategy is presented. • Suggestions for connecting two or three different algorithms to create new hybrid methods are provided. • The proposals on the development of new hybrid methods are given. Wavelength (variable) selection is considered as a means of solving the curse of dimensionality, which is an important step in multivariate calibration of near-infrared spectra (NIR) and a topic of interest in NIR spectroscopy. A large number of variable selection methods have been developed. They possess diverse advantages and disadvantages because of their different principles and application scope. To overcome their drawbacks and fully utilize their merits, a hybrid strategy, which combines several methods was proposed, and has been proven effective in the selection of informative variables from high-dimensional NIR spectra. Based on the hybrid strategy, many variable selection methods have been developed. To aid people in learning about the various hybrid methods, this paper systematically classifies these methods and elucidates their principles and features. Further, two forms of developing new hybrid methods are suggested based on the existing single variable selection method. Every single method is classified into the stages of the suggested hybrid strategy based on its theory to enable more convenient and efficient utilization. Finally, several proposals are presented to better apply and promote the development of hybrid methods for analyzing NIR spectra in the future. [ABSTRACT FROM AUTHOR]

Near-infrared spectra of methanol-acetone and ethanol-acetone mixtures in the entire mole fraction range in increments of 0.1 were recorded in the region of 7500–6000 cm−1. The first overtone bands of the hydroxyl (OH) groups were assigned to the polymeric and oligomeric OH associations. In both solutions, the frequency of the polymeric OH band decreased with the increase in the mole fraction of alcohol, which indicated the increase in the hydrogen bonding strength. The integrated area of the polymeric OH band followed the opposite trend to the frequency with the mole fraction. The nonlinearity of the plot of the integrated band area of the polymeric OH band of methanol versus the mole fraction of acetone revealed the nonideal character of the methanol-acetone mixtures, whereas the opposite was observed for the ethanol-acetone mixtures. These observations have been explained in terms of mechanical and electrical anharmonicities of the OH oscillator. [ABSTRACT FROM PUBLISHER]

Quantitative spectra-temperature relationship (QSTR) between near-infrared (NIR) spectra and temperature has been used for quantitative determination of the compositions in mixtures. In this work, QSTR is studied using multilevel simultaneous component analysis (MSCA) and the spectral data of the samples with different concentrations measured at different temperatures. MSCA model contains a between-individual model describing the differences between the individuals and a within-individual model capturing the differences within the data of all the individuals. NIR spectra of five different compositions (water–ethanol–isopropanol) measured at seven temperatures were analyzed. A between-temperature model describing the effect of temperature and a within-temperature model describing the variation of concentration were obtained, from which QSTR model is established and quantitative analysis is achieved. Furthermore, the difference between the between-temperature or within-temperature models of different mixtures is used to study the composition of the solvent. [ABSTRACT FROM AUTHOR]

Highlights: [•] Dual stacking steps are used for multivariate calibration of near-infrared spectra. [•] A selective weighting strategy is introduced that only a subset of all available sub-models is used for model fusion. [•] Using two public near-infrared datasets, the proposed method achieved competitive results. [•] The method can be widely applied in many fields, such as Mid-infrared spectra data and Raman spectra data. [ABSTRACT FROM AUTHOR]

Abstract: In this work, we propose a signal selection procedure for determination of hemoglobin (Hb) concentration in whole blood using near infrared (NIR) transmission spectral signals. A dataset of 190 whole blood NIR transmission spectra with reference Hb concentrations was used to evaluate the method. Spectral signals were selected based on the squared correlation coefficient (R 2) between the signal and the Hb concentration. An improved uninformative variable elimination (UVE) procedure was performed to remove redundant signals from the primary selected signal set. A partial least squares (PLS) regression model was built with the final selected signals and the corresponding Hb concentrations. The results indicate that the proposed method is effective at increasing the predictive power of the NIR–PLS spectral model for determining Hb concentration in whole blood samples. [Copyright &y& Elsevier]

The population ratio between the excited states of rubidium in the electrodeless discharge rubidium vapour lamp is calculated according to the near-infrared spectra in the region of 780-1550 nm. By using a 1529 nm laser, we measure the density of natural rubidium atoms at the 5P3/2 level. The populations of different excited states are then clarified. [ABSTRACT FROM AUTHOR]

Abstract: We study a new optical effect, a spectral manifestation of coherent backscattering, which reveals itself as systematic variations in the depth of absorption bands with changing phase angle. We used Cassini VIMS near-infrared spectra of Saturn''s icy satellite Rhea in order to identify and characterize the spectral change with phase angle, focusing on the change in the depth of water–ice absorption bands. To model realistic characteristics of the surfaces of icy satellites, which are most likely covered by micron-sized densely packed particles, we perform simulations using a theoretical approach based on direct computer solutions of the macroscopic Maxwell equations. Our results show that this approach can reproduce the observed phase-angle variations in the depth of the absorption bands. The modeled changes in the absorption bands are strongly affected by physical properties of the regolith, especially by the size and packing density of the ice particles. Thus, the phase-angle spectral variations demonstrate a promising remote-sensing capability for studying properties of the surfaces of icy bodies and other objects that exhibit a strong coherent backscattering effect. [Copyright &y& Elsevier]

We present new spectral synthesis models for solar metallicity stellar populations, based on a library of stellar spectra that extends across near-IR wavelengths out to 2.4 µm at a resolution approaching 1000. We show that the spectra of massive star clusters in the starburst galaxy M 82 can be reproduced very well with these models. We compare near-IR spectroscopic ages with optical ages, and discuss the main sources of (systematic) errors that still affect those ages. [ABSTRACT FROM AUTHOR]

Abstract: Using a high-resolution tunable diode laser photoacoustic spectrometer, self-, N2 and O2 pressure broadening coefficients for the first 11 transitions of 12C16O2 in the R branch of the (30012)←(00001) overtone band at the 6348cm−1 have been revisited at room temperature (∼298K). Air-broadening parameters have also been calculated from the N2 and O2 measurements. The dependence of the broadening on rotational quantum number m is discussed. The recorded lineshapes are fitted with standard Voigt line profiles in order to determine the collisional broadening coefficients of carbon dioxide transitions. The results are compared to our previous measurements and to the values reported in the HITRAN04 database and by other research group with a different spectroscopic technique. [Copyright &y& Elsevier]

Abstract: Pressure-induced foreign-broadening lineshape parameters of the carbon dioxide rovibrational transitions belonging to the (30012)←(00001) overtone band near the 1.573μm wavelength region are measured by using a tunable diode laser photoacoustic spectrometer. The spectroscopic analysis has concerned the first 11 lines of the R branch. For these lines, the air- and Ar-broadening coefficients are measured at room temperature (∼298K). The measured broadening coefficients of all the transitions of 12C16O2 are compared with those given in the HITRAN04 database and former measurements with a different spectroscopic method. Agreements and discrepancies are underlined and briefly discussed. The recorded lineshapes are fitted with standard Voigt line profiles in order to determine the collisional broadening coefficient of carbon dioxide transitions. [Copyright &y& Elsevier]

Abstract: To record the infrared emission of hot molecular gases an optimized emission apparatus for the Bruker IFS 120 HR high-resolution FT-IR spectrometer has been constructed. Using this apparatus the hot gas emission spectrum of HCN at 1420K has been recorded in the wavenumber region of 6000–6800cm−1 with a resolution of 0.044cm−1. This work reports the analysis of 33 bands with 58 subbands (9200 line positions). Thirty-seven rovibronic states of HCN including at 12603cm−1 have been characterized for the first time and for 25 other states it was possible to improve the existing spectroscopic constants substantially. The very dense emission spectrum with many overlapping features was analyzed with new spectrum analysis software implemented using the MathematicaTM computer algebra system. Spectroscopic constants have now been determined for 220 HCN rovibronic states. For 102 states the rovibrational spectroscopic constants have been determined for the first time or improved substantially using emission spectra measured in Giessen. [Copyright &y& Elsevier]

Abstract: Temperature-constrained cascade correlation networks (TCCCNs) were used to identify powdered rhubarbs based on their near-infrared spectra. Different network configurations that used multiple network models with single output (Uni-TCCCN) and single networks with multiple outputs (Multi-TCCCN) were compared. Comparative studies were made by using Latin-partitions and leave-one-out cross-validation methods. Results showed that multiple networks with single output predicted generally better than single network with multiple outputs. Better results with TCCCN models were obtained compared with conventional back propagation neural networks (BPNNs). The effects of parameters on correct identification and parameter optimizations were discussed in detail. With optimized neural network training parameters, NIR spectra from powdered rhubarb samples were classified by a TCCCN model with 100% accuracy. [Copyright &y& Elsevier]

Abstract: Monte Carlo cross validation (MCCV) is used in two data sets including 125 and 1643 near-infrared (NIR) spectra of biological samples, respectively, to ascertain the number of samples left out for validation in MCCV and the dimension of PLS models consequently. With the selected number of samples in validation set, the suitable number of latent variables (LV) may be chosen correctly. The results obtained show that root mean squared error of calibration (RMSEC), root mean squared error of cross validation (RMSECV) and LV number are sensitive to the number of samples left out for validation when too many samples are left out. Based on this, RMSEC and RMSECV are suggested as criteria to assist the ascertainment of the number of samples left out for validation in MCCV. This method is easy and convenient to use. For a larger data set, more samples may be left out, but the suitable number of samples left out will decrease if the measurement error level is high. [Copyright &y& Elsevier]

We present a new method to detect and reconstruct fluid pathways in fossil hydrothermal systems that is based on systematic study of white micas in hydrothermally altered rock, using near-infrared spectroscopy. This method, developed in an Early Archean volcanic sequence in the Panorama district in Western Australia, uses new near-infrared spectroscopic data and published geological and geochemical data of fossil submarine hydrothermal systems in the area. Analysis of new near-infrared spectroscopic data revealed that the abundance of white mica and its Al content in altered volcanic rock vary systematically along fossil fluid pathways, from zones of low-temperature recharge to zones of high-temperature discharge, as a function of hydrothermal fluid chemistry, temperature of alteration, coexisting minerals, and composition of volcanic host rock. The abundance of white mica relative to that of chlorite and the Al content of white micas can be used to discriminate among the hydrothermal alteration facies along these fluid pathways, which permits detection and reconstruction of fossil fluid pathways. [ABSTRACT FROM AUTHOR]

Orthogonal signal correction (OSC) is a preprocessing technique used for correction of instrumental drift, bias and scatter in near-infrared spectra. OSC separates the variation into orthogonal factors, where the factors contain the variation within the X matrix data that is not correlated with the y matrix vector data. The aim of this study is to investigate different orthogonal factor selection methods, which will enhance the performance of the OSC routine for quantitative analysis of near-infrared spectra. In order for factor selection methods to be applied to OSC, an implementation of an existing OSC algorithm is used; this method computes OSC factors in a principal component manner. A binarized weighting matrix is then applied to the OSC factors for the purpose of OSC factor subset selection/nonselection. The optimization strategies used for subset selection of OSC factors were a genetic algorithm and stepwise selection. Three data sets were formed: (1) no preprocessing, (2) preprocessing by removal of sequential OSC factors and (3) preprocessing by removal of an OSC factor subset. Combinations of spectral predictors were selected from these data sets by hill climbing, feature selection, genetic algorithm and full-spectrum modeling. Partial least squares regression was undertaken to form calibration models. It was found that selection of OSC factor subsets produced better standard errors of prediction relative to data preprocessed by sequential selection of OSC factors. [Copyright &y& Elsevier]

Near-infrared (NIR) spectrometers are widely used as online analyzers. We propose a transfer algorithm of a calibration model for the near-infrared spectrometer. The method, which is based on direct standardization in the compressed wavelet domain, is called WTDS. Calibration transfer in the compressed wavelet domain permits a reduction in processing time for the analysis of large spectral data sets. A calibration transfer based on a compression of wavelet coefficients offers increased robustness against testing errors, with no significant loss of performance. We select the proper set of wavelet coefficients based on the hard thresholding approach. The WTDS shows an improvement in calibration transfer performance, although wavelet coefficients are compressed. [Copyright &y& Elsevier]

A series of new augmented classical least squares (ACLS) methods have been developed that show considerable promise for improving multivariate spectral calibrations. The normal limitations of classical least squares (CLS) methods are largely overcome by using information derived from the spectral residuals during the CLS calibration in the development of the new ACLS models. The resulting models are able to provide prediction ability comparable to that of partial least squares (PLS) even when only concentration information about a single analyte is known in the calibration data set. By combining ACLS calibration with our recently described prediction augmented CLS (PACLS) method, we are able to rapidly update spectral calibration models during prediction for the presence of unmodeled chemical components in the unknown samples, system drift, or changes in spectrometers. With PACLS updating, prediction ability is often better than possible with PLS. One of the new calibration augmented CLS methods is combined with PACLS to demonstrate the ability of the method to transfer multivariate calibrations between near-infrared (NIR) spectrometers on a system of multi-component organic solvents. [Copyright &y& Elsevier]

Ormosil dipped thin films and cast films were prepared using tetramethoxysilane and trimethoxysilylpropylmethacrylate (TMSPM). Structural changes during thermally induced polymerisation of the organic groups were investigated using near- and mid-infrared (IR) spectroscopy. IR spectra of the ormosil dipped thin films, dried at 60°C, have shown that further heat-treatment of the films at 160°C leads to the free radical polymerisation of C=C bonds in TMSPM. In the mid-infrared spectra, the intensity of the band at around 3500 cm−1, due to O–H bonds, increased with the progress of polymerisation of vinyl groups. Near-infrared spectra of the cast films showed that this increase in intensity of the O–H band is due to the increase of monomeric water molecules hydrogen bonded to silanols. We suggest that optical loss measurements must be made in a dry atmosphere or, with a cover coating to protect the ormosil from adsorption of water, to reduce this source of optical loss for waveguides based on TMSPM. [ABSTRACT FROM AUTHOR]

We discuss photonic crystal properties of semiconductor-opal composites and explore the possibility of using these structures as photonic bandgap (PEG) materials in the visible and near-infrared region of the spectrum. Integrated reflectance in the optical region is used to show that the photonic crystal behavior of InP opal is enhanced with respect to the bare matrix as a result of the new refractive index contrast. Order-N band structure and transfer matrix calculations have proved to be in good agreement with the experiments. In addition, a strong coupling effect has been observed between CdS photoluminescence and the photonic structure, which leads to a partial inhibition of the spontaneous emission [ABSTRACT FROM PUBLISHER]

In the near-infrared (NIR) spectra of oil, information about fatty acid composition is concentrated in the range of 1600-2200 nm. Principal-component analysis (PCA) was applied on the standardized full NIR spectral data of this region for vegetable oils to totally capture the NIR spectral pattern. Nine varieties of vegetable oils (soybean, corn, cottonseed, olive, rice bran, peanut, rapeseed, sesame and coconut oil) could be successfully classified from their PCA scores. Examining the contribution of wavelengths to PCA scores showed that wavelengths with a high loading weight were assigned to characteristic absorption regions that correspond to specific fatty acid moieties. This classification is related to the fatty acid composition of an oil, and it can be carried out rapidly and easily after eigenvectors were obtained. [ABSTRACT FROM AUTHOR]

The main objectives of the study are to understand and explore critical feature wavelengths of the obtained near-infrared (NIR) data relating to dianhong black tea quality categories, we propose a multi-variable selection strategy based on the variable space optimization from big to small which is the kernel idea of a variable combination of the improved genetic algorithm (IGA) and particle swarm optimization (PSO) in this study. A rapid description based on the NIR technology is implemented to assess black tea tenderness and rankings. First, 700 standard samples from dianhong black tea of seven quality classes are scanned using a NIR system. The raw spectra acquired are preprocessed by Savitzky-Golay (SG) filtering coupled with standard normal variate transformation (SNV). Then, the multi-variable selection algorithm (IGA-PSO) is applied to compare with the single method (the IGA and PSO) and search the optimal characteristic wavelengths. Finally, the identification models are developed using a decision tree (DT), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM) based on different kernel functions combined with the effective features from the above variables screening paths for the discrimination of black tea quality. The results show that the IGA-PSO-SVM model with a radial basis function achieves the best predictive results with the correct discriminant rate (CDR) of 95.28% based on selected four characteristic variables in the prediction process. The overall results demonstrate that NIR combined with a multi-variable selection method can constitute a potential tool to understand the most important features involved in the evaluation of dianhong black tea quality helping the instrument manufacturers to achieve the development of low-cost and handheld NIR sensors. Unlabelled Image • Rapid description of black tea quality based on multi-variable selection strategy • Feature information was selected by the GA, IGA and PSO. • Classification models for black tea categories developed using DT, PLS-DA and SVM • The SVM discriminator is implemented with four different kernel functions. • The IGA-PSO-SVM model obtained better predictive results based on four feature wavelengths. [ABSTRACT FROM AUTHOR]

Variable (feature or wavelength) selection is a critical step in multivariate calibration of near-infrared (NIR) spectra. The high-resolution NIR or its imaging instruments usually generate hundreds or thousands of wavelengths, which make the variable selection methods tend to appear a high risk of overfitting, low efficiency, or requiring large computational abilities. Thus, it is a great challenge to efficiently select informative variables and obtain an optimal variable combination in a huge variable space. We propose a hybrid strategy for efficiently selecting variables based on three steps including rough selection, fine selection and optimal selection. The strong interpretability method like wavelength interval selection method (interval partial least squares, iPLS) was first used to roughly select informative intervals and shrink the variable space. Wavelength point selection methods such as variable importance in projection (VIP) and modified variable combination population analysis (mVCPA) were used to continuingly shrink the variable space from large to small in order to remain the very important variables. In the third step, applying some optimization methods such as iteratively retaining informative variables (IRIV) and genetic algorithm (GA) is to find an optimal variable combination from the remaining variables. It makes full use of the advantages of various involved methods and makes up for their disadvantages when facing high dimensional data. Two NIR datasets were employed to investigate the performance of the three-step hybrid strategy. It can significantly improve the prediction performance of the models built when compared with other single or hybrid methods (iPLS, VIP, iPLS-VIP, iPLS-VCPA, iPLS-mVCPA, VIP-GA, VIP-IRIV, mVCPA-GA, mVCPA-IRIV), indicating that the three-step hybrid strategy, including iPLS-VIP-IRIV, iPLS-VIP-GA, iPLS-mVCPA-GA and iPLS-mVCPA-IRIV, could efficiently select informative variables. Therefore, the three-step hybrid strategy is a good alternative for variable selection methods in the face of high dimensional NIR spectral data. Unlabelled Image • A hybrid variable selection strategy based on three steps including rough selection, fine selection and optimal selection, was proposed. • The hybrid strategy takes full use of the advantage of each selected single method. • Provide a better prediction performance when compared with the single methods and the combinations of two kinds of methods. [ABSTRACT FROM AUTHOR]

The octane number is often used in the evaluation of the gasoline quality due to its antiknock property. The traditional ASTM-CFR internal combustion engine method costs an enormous amount of expenditure and time to determine its level of each product upgrade. The extreme learning machine (ELM) and its improved models are a new type of single-hidden layer feed-forward neural network (SLFN). This structure has advantages over the traditional neural network technology in regression tasks. Based on the response relationship between the near-infrared spectra and the octane number, 60 groups of collected gasoline data are divided into training and test sets. The ELM, online sequential extreme learning machine (OS-ELM), and self-adaptive evolutionary extreme learning machine (SaDE-ELM) models are applied to the prediction task of the octane number. The statistical parameters RMSE, CORR, and R2 and the execution time are used as the performance comparison criteria for the three models. The simulation results show that the OS-ELM and the SaDE-ELM improve the prediction accuracy, the generalization ability, the stability of the ELM in different levels. [ABSTRACT FROM AUTHOR]