NIR Data Research Articles

Feasibility study on discrimination of Polygonatum kingianum origins by NIR and MIR spectra data.

Most existing studies have focused on identifying the origin of species with protected geographical indications while neglecting to determine the proximate geographical origin of different species. In this study, we investigated the feasibility of using near- and mid-infrared spectroscopy to identify the origin of 156 Polygonatum kingianum samples from six regions in Yunnan, China. In this work, spectral images of different modes reveal more information about the P. kingianum. Comparing the performance of traditional machine learning models according to single spectrum and data fusion, the middle-level data fusion-principal component model has the best performance, and its sensitivity, specificity, and accuracy are all 1, and the model has the least number of variables. The residual convolutional neural network (ResNet) model constructed in the 1050-850cm-1 band confirms that fewer variables are beneficial in improving the accuracy of the model. In conclusion, this study verifies the feasibility of the proposed strategy and establishes a practical model to determine the source of P. kingianum.

Varying linear polarisation in the dust-free gamma-ray burst 210610B

Context. Long gamma-ray bursts (GRBs) are produced by the collapse of some very massive stars, that emit ultra-relativistic jets. When the jets collide with the interstellar medium they decelerate and generate the so-called afterglow emission, which has been observed to be polarised. Aims. We study the polarimetric evolution of the GRB 210610B afterglow, at z = 1.1341. This allows us to evaluate the role of geometric and/or magnetic mechanisms in the GRB afterglow polarisation. Methods. We observed GRB 210610B using imaging polarimetry with CAFOS on the 2.2 m Calar Alto Telescope and FORS2 on the 4 × 8.1 m Very Large Telescope. Complementary optical spectroscopy was obtained with OSIRIS on the 10.4 m Gran Telescopio Canarias. We studied the GRB light-curve from X-rays to the optical bands and the Spectral Energy Distribution (SED). This allowed us to strongly constrain the line-of-sight extinction. Finally, we studied the GRB host galaxy using optical to NIR data to fit the SED and derive its integrated properties. Results. GRB 210610B had a bright afterglow with a negligible line-of-sight extinction. Polarimetry was obtained at three epochs: during an early plateau phase, at the time when the light curve breaks, and after the light curve steepened. We observe an initial polarisation of ∼4% that goes to zero at the time of the break, and it then again increases to ∼2%, with a change in the position angle of 54 ± 9 deg. The spectrum shows features with very low equivalent widths. This indicate a small amount of material in the line of sight within the host. Conclusions. The lack of dust and the low amount of material in the line of sight to GRB 210610B allowed us to study the intrinsic polarisation of the GRB optical afterglow. The GRB polarisation signals are consistent with ordered magnetic fields in refreshed shock or/and hydrodynamics-scale turbulent fields in the forward shock.

Research on model transfer strategies based on the fusion of NIR-MIR spectral data

This study investigated the impact of different data fusion strategies on the performance of soluble solids content (SSC) prediction models based on near-infrared and mid-infrared spectroscopic techniques. In the data-level fusion approach, we applied standard normal variate and multiplicative scatter correction for pre-processing the NIR and MIR data. For the feature-level fusion, we utilized successive projections algorithm and competitive adaptive reweighted sampling to select informative wavelengths, and then applied direct orthogonal projection (DOP) for model transfer. The study employed a dataset of 150 honey samples to evaluate the impact of different data fusion strategies on model performance. To effectively evaluate model performance, we utilized the coefficient of R2 and RMSEP as evaluation metrics. By comparing the results of data-level fusion, feature-level fusion and single-spectrum model transfer, the results showed that spectral data fusion improved the model transfer performance compared to the single-spectrum approach, with feature-level fusion exhibiting the most significant advantages. The effective variable selection techniques in feature-level fusion successfully removed a substantial amount of interfering data and significantly reduced noise influence, thereby improving the model accuracy. Specifically, the use of feature-level fusion improved the predictive model’s R2 from 0.319 to 0.878 and reduced the RMSEP from 1.974 to 0.613°Brix, demonstrating the significant advantages of this approach in enhancing model transfer performance. The research findings provide important reference and theoretical support for future studies in the field of food quality assessment and other near-infrared spectroscopic data applications. This not only validates the effectiveness of the feature-level fusion approach, but also lays the foundation for establishing efficient and reliable predictive models.

A three-stage wavelength selection algorithm for near-infrared spectroscopy calibration

The near-infrared spectral data is highly high dimensional and contains redundant information, it is necessary to identify the most representative characteristic wavelengths before modeling to improve model accuracy and reliability. At present, there are many methods for selecting the characteristic wavelengths of NIR spectroscopy, but the collinearity among wavelengths is still a main issue that leads to poor model effects. Therefore, this study proposes a three-stage wavelength selection algorithm (Stage III) to reduce redundancy in NIR spectral data and collinearity between wavelength variables, resulting in a simpler and more accurate predictive model. The research uses a public NIR data set of corn samples as its subject. Initially, the wavelengths with the higher correlation coefficients are chosen after calculating the relationship coefficients between every wavelength vector and the concentration vector. On this basis, the correlation coefficients between the vectors of each wavelength point are calculated, and those wavelength points with smaller correlation coefficients with other wavelength points are selected. Ultimately, the stepwise regression analysis selects the wavelengths that provide substantial value to the model as the variables for modeling, leading to the development of a multiple linear regression model. The results show that the model using the three-stage wavelength selection algorithm outperforms those using the full spectrum, Stages I and Stage II, and the coefficient of determination of the test set of the Stage III-MLR model achieved an accuracy of 0.9360. Instead of the successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS), Stage III is better in the model prediction accuracy. Therefore, the three-stage wavelength selection algorithm is an effective wavelength selection algorithm that can effectively model NIR spectroscopy, reduce the collinearity between the wavelength variables, simplify the complexity of the model, and improve the prediction precision of the model.

Calibration transfer between NIR instruments using optimally predictive calibration subsets.

In this study, a new approach for the selection of informative standardization samples from the original calibration set for the transfer of a calibration model between NIR instruments is proposed and evaluated. First, a calibration model is developed, after variable selection by the Final Complexity Adapted Models (FCAM) method, using the significance of the PLS regression coefficients (FCAM-SIG) as selection criterion. Then, the resulting model is used for the selection of the best fitting subset of calibration samples with optimally predictive ability, called the optimally predictive calibration subset (OPCS). Next, the standardization samples are selected from the OPCS. The spectra on the slave instruments are transferred to corresponding spectra on the master instrument by the widely used Piecewise Direct Standardization (PDS) method. Thereafter, for the test set on the slave instrument, a 3D response surface plot is drawn for the root mean squared error of prediction (RMSEP) as a function of the number of OPCS samples and window sizes used for the PDS method. Finally, the smallest set of calibration samples, in combination with the optimal window size, providing the optimal RMSEP, is selected as standardization set. The proposed OPCS approach for the selection of standardization samples is tested on two real-life NIR data sets providing 13 X-y combinations to model. The results show that the obtained numbers of OPCS-based standardization samples are statistically significantly lower than those obtained with the widely used representative sample selection method of Kennard and Stone, while the predictive performances are similar.

Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine

This study constructs a model for the rapid identification of the origins of edible sunflower (Helianthus) using Kernel Extreme Learning Machine (KELM) with multi-source information fusion technology. Near-infrared spectroscopy (NIRS) and nuclear magnetic resonance spectroscopy (NMRS) were utilized to analyze 180 sunflower samples from the Xinjiang, Heilongjiang, and Inner Mongolia regions. Initially, the identification models for the origin of sunflowers using NIR and NMR data were compared between two algorithms: the Extreme Learning Machine (ELM) and KELM, combined with various spectral preprocessing methods. The experiment found that the NIR spectral model preprocessed with standard normal variate (SNV) using the KELM algorithm was the most accurate, achieving accuracies of 98.7% in the training set and 97.2% in the test set. The spin-echo NMR spectral model preprocessed with non-local means (NLMs) using the KELM algorithm was the second best, with accuracies of 98.4% in the training set and 96.4% in the test set. To further improve the accuracy of the identification models, innovative sunflower origin identification models were developed based on data layer fusion and feature layer fusion using NIRS and NMRS. In the data layer fusion model, the KELM algorithm model was optimal, achieving a test set accuracy and F1 score of 98.2% and 98.18%, respectively, an improvement of 1.0% over the best single data source model. In the feature layer fusion model, four types of feature-layer information-fusion identification models were established using two feature extraction algorithms, Competitive Adaptive Reweighted Sampling (CARS) and Variable Importance Projection (VIP), combined with joint feature and simple merging feature strategies. The CARS-KELM algorithm combined with the joint feature method was found to be the best, achieving 100% accuracy in both the training and test sets, an improvement of 2.8% over the best single data source model. Identifying the origin of edible sunflower using NIRS and NMRS is demonstrated as feasible by the results. The best single-spectrum sunflower origin identification model was achieved using the KELM algorithm with SNV preprocessing. The feature layer fusion method combining NIRS and NMRS data is suitable for handling the task of sunflower origin identification. This method significantly improves the recognition accuracy of the model compared to a single model, achieving fast and accurate origin identification of edible sunflowers. The research results provide a new method for rapid identification of sunflower origin.

Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms

The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers), it is vital to identify geographical origin. Near infrared spectroscopy can be used to detect the specific content of organic moieties in agricultural and food products. The present study implemented the combinatorial method of FT-NIR spectroscopy with chemometrics to identify geographical origin of Khao Dawk Mali 105 rice. Rice samples were collected from 2 different region including the north and northeast of Thailand. NIR spectra data were collected in range of 12,500 – 4,000 cm−1 (800–2,500 nm). Five machine learning algorithms including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), C-support vector classification (C-SVC), backpropagation neural networks (BPNN), hybrid principal component analysis-neural network (PC-NN) and K-nearest neighbors (KNN) were employed to classify NIR data of rice samples with full wavelength and selected wavelength by Extremely Randomized Trees (Extra trees) algorithm. Based on the findings, geographical origin of rice could be specified quickly, cheaply, and reliably using combination of NIRS and machine learning. All models creating by full wavelength and selected wavelength exhibited accuracy between 65 and 100 % for identifying geographical region of rice. It was proven that NIR spectroscopy may be used for the quick and non-destructive identification of geographical origin of Khao Dawk Mali 105 rice.

Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques.

Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible-near-infrared (Vis-NIR) and NIR) were utilized to acquire separate HSI data, which were then fused and analyzed toforecast the starch and alcohol contents during the fermentation of lees. Five preprocessing methods were first used to preprocess the Vis-NIR, NIR, and the fused Vis-NIR and NIR data, after which partial least squares regression models were established to determine the best preprocessing method. Following, competitive adaptive reweighted sampling, successive projection algorithm, and principal component analysis algorithms were used to extract the characteristic wavelengths to accurately predict the starch and alcohol levels. Finally, support vector machine (SVM)-AdaBoost and XGBoost models were built based on the low-level fusion (LLF) and intermediate-level fusion (ILF) of single Vis-NIR and NIR as well as the fused data. The results showed that the SVM-AdaBoost model built using the LLF data afterpreprocessing by standard normalized variable was most accurate for predicting the starch content, with an of 0.9976 and a root mean square error of prediction (RMSEP) of 0.0992. The XGBoost model built using ILF data was most accurate for predicting the alcohol content, with an of 0.9969 and an RMSEP of 0.0605. In conclusion, the analysis of fused data from distinct HSI technologies facilitates rapid and precise determination of the starch and alcohol contents in fermented grains.

Near-Infrared Spectroscopy and Chemometrics for Effective Online Quality Monitoring and Process Control during Pelagic Fishmeal and Oil Processing.

Near-infrared spectroscopy has become a common quality assessment tool for fishmeal products during the last two decades. However, to date it has not been used for active online quality monitoring during fishmeal processing. Our aim was to investigate whether NIR spectroscopy, in combination with multivariate chemometrics, could actively predict the changes in the main chemical quality parameters of pelagic fishmeal and oil during processing, with an emphasis on lipid quality changes. Results indicated that partial least square regression (PLSR) models from the NIR data effectively predicted proximate composition changes during processing (with coefficients of determination of an independent test set at RCV2 = 0.9938, RMSECV = 2.41 for water; RCV2 = 0.9773, RMSECV = 3.94 for lipids; and RCV2 = 0.9356, RMSECV = 5.58 for FFDM) and were successful in distinguishing between fatty acids according to their level of saturation (SFA (RCV2=0.9928, RMSECV=0.24), MUFA (RCV2=0.8291, RMSECV=1.49), PUFA (RCV2=0.8588, RMSECV=2.11)). This technique also allowed the prediction of phospholipids (PL RCV2=0.8617, RMSECV=0.11, and DHA (RCV2=0.8785, RMSECV=0.89) and EPA content RCV2=0.8689, RMSECV=0.62) throughout processing. NIR spectroscopy in combination with chemometrics is, thus, a powerful quality assessment tool that can be applied for active online quality monitoring and processing control during fishmeal and oil processing.

NIR Spectroscopy application to determine the carbohydrate and lipid content in green bean coffee

Coffee flavor is influenced by the chemical concentration including the carbohydrate and lipid content. Determination of the chemical content is still using the chemical method that spend time, expensive and destructive, then a suitable method which is fast and non destructive is required especially to fulfill the needs of coffee industries. This study aimed to apply NIRS method to determine the carbohydrate and lipid content from Indonesia green bean coffee from several origin to increase the data distribution, NIR wave of 1000-2500 nm, followed by determination of the carbohydrate and lipid content using chemical method. After that, NIR data pretreatment using multiple scatter correction (MSC), first and second derivative of savitzky golay, normalization and the combinations then calibrate both the spectra and chemical data by Partial Least Square (PLS) method. Result showed that NIR spectroscopy can be used to determine the carbohydrate and lipid content in green bean coffee could be seen by the high coefficient correlation, RPD and consistency value, while the CV is lower than 2.

Wavelength selection method for near-infrared spectroscopy based on Max-Relevance Min-Redundancy

Near-infrared spectroscopy (NIRS) is a rapid, nondestructive analytical technique utilized in various fields. However, the NIR data, which consists of hundreds of dimensions, may exhibit considerable duplication in the spectrum information. This redundancy might impair modeling effectiveness. As a result, feature selection on the spectral data becomes critical. The Max-Relevance Min-Redundancy (mRMR) method stands out among the different feature selection techniques for dimensional reduction. The approach depends on mutual information (MI) between random variables as the basis for feature selection and is unaffected by modeling methods. However, it is necessary to clarify the benefits of the maximum correlation minimal redundancy algorithm in the context of near-infrared spectral feature selection, as well as its adaptability to various modeling methods. This research focuses on the NIR spectral dataset of maize germination rate, and the mRMR method is utilized to select spectral features. Based on the preceding foundation, we create models for Support Vector Regression, Gaussian Process Regression, Random Forest, and Neural Networks. The experimental findings demonstrate that, among the feature selection methods employed in this paper, the Max-Relevance Min-Redundancy algorithm outperforms others regarding the corn germination rate dataset.

An improved sample container cup and its effect on NIRS of tobacco smoke for quality stability estimation

This paper describes a design of an improved self-made Bruker NIR cup and analyzes the effect of the equipment modification to fit the Cambridge filter pad, which enhances experimental efficiency and reduces operational complexity. A self-made NIR cup based on the classical NIR cup is designed to speed up the operation process and reduce the experiment's time cost. To estimate the effect of this equipment modification, the NIR spectra from the classical sample cup and the new self-made cup are compared and analyzed. Furthermore, the quality evaluation results from NIR data of the two cups are also compared according to a distance metric chemometrics method, which shows quality analytical values between these two cups are approaching each other while the experiment efficiency is improved.•This paper introduces a newdesign of a self-made container cup improved from the Bruker's traditional sample container cup to better fit the filter pad and improve the experiment efficiency and convenience.•This paper also analyzes the effect of this container cup change by comparing the NIR spectra before and after modification.

Influence of Te replacement by Bi in In10Se70Te20-xBix films and its structural, optical, morphological, surface wettability and thermal behaviors for optoelectronic applications

The current investigation is based on Bi doping into In–Se–Te alloy and its impact on the surface morphology, structure, and optical behaviors. The transition from In10Se70Te20 to In10Se70Bi20 composition modified its structural phases as observed from X-ray diffraction study. The crystallite size increased from 9.55 nm to 14.04 nm. The lattice strain decreased from 0.0234 to 0.0106 with Bi content. Raman study infers the microstructural and phase formation in the films. The structure change is associated with change in surface morphology as noticed from field emission scanning electron microscopy images. The composition of In10Se70Te20-xBix (x = 0, 5, 10, 15, 20 at %) films were observed from energy dispersive X-ray analysis. The UV–Vis–NIR data showed the decrease in transmittance from 80 % to 10 % at 2100 nm wavelength with Bi%. The decrease in direct bandgap from 1.64 eV to 1.37 eV was discussed by Davis-Mott model for density of defect states. The increase in Urbach energy from 101 meV to 161 meV is based on the transition from In10Se70Te20 to In10Se70Bi20 composition. The optical parameters like optical density, extinction coefficient and absorption coefficient were increased with Bi addition. The calculated static linear refractive index by different model at different Bi% satisfies Moss's relation. The enhanced non-linear susceptibility from 2.96 × 10−10 esu to 3.33 × 10−10 esu and non-linear refractive index from 1.58 × 10−7 esu to 1.86 × 10−7 esu with Te replacement by Bi may be suitable for non-linear optical devices. The change in different theoretically calculated physical parameters were found to be in accordance with the experimental data. The surface wettability study showed the transition from hydrophilic to hydrophobic nature with increase in Bi %. The obtained experimental results for the films may be used for various photonic and optoelectronic device applications.

Individual Tree Species Identification and Crown Parameters Extraction Based on Mask R-CNN: Assessing the Applicability of Unmanned Aerial Vehicle Optical Images

Automatic, efficient, and accurate individual tree species identification and crown parameters extraction is of great significance for biodiversity conservation and ecosystem function assessment. UAV multispectral data have the advantage of low cost and easy access, and hyperspectral data can finely characterize spatial and spectral features. As such, they have attracted extensive attention in the field of forest resource investigation, but their applicability for end-to-end individual tree species identification is unclear. Based on the Mask R-CNN instance segmentation model, this study utilized UAV hyperspectral images to generate spectral thinning data, spectral dimensionality reduction data, and simulated multispectral data, thereby evaluating the importance of high-resolution spectral information, the effectiveness of PCA dimensionality reduction processing of hyperspectral data, and the feasibility of multispectral data for individual tree identification. The results showed that the individual tree species identification accuracy of spectral thinning data was positively correlated with the number of bands, and full-band hyperspectral data were better than other hyperspectral thinning data and PCA dimensionality reduction data, with Precision, Recall, and F1-score of 0.785, 0.825, and 0.802, respectively. The simulated multispectral data are also effective in identifying individual tree species, among which the best result is realized through the combination of Green, Red, and NIR bands, with Precision, Recall, and F1-score of 0.797, 0.836, and 0.814, respectively. Furthermore, by using Green–Red–NIR data as input, the tree crown area and width are predicted with an RMSE of 3.16m2 and 0.51m, respectively, along with an rRMSE of 0.26 and 0.12. This study indicates that the Mask R-CNN model with UAV optical images is a novel solution for identifying individual tree species and extracting crown parameters, which can provide practical technical support for sustainable forest management and ecological diversity monitoring.

Process understanding and monitoring: A glimpse into data strategies for miniaturized NIR spectrometers

BackgroundThe implementation of process analytical technologies (PAT) has gained attention since 2004 when its formal introduction through the U.S. Food and Drug Administration was introduced. Manufacturers that need to evaluate the employment of new monitoring systems could face different challenges: identification of suitable sensors, verification of data meaning, evaluation of several statistical strategies to obtain insights about data and achieve process understanding and finally, the actual possibilities for monitoring. Kefir fermentations were chosen as an example because of the chemical and physical transformations that occurred during the process, which could be common to several other fermentation processes. In order to pave the way for monitoring establish the information contained in the data and find the right tools for extracting them is of extreme importance. Strategies to identify different experimental conditions in the spectra acquired with a miniaturized NIR (1350-2550 nm) during process occurrence were addressed. ResultsThe study aims to offer insights into good practices and steps to pave the way for process monitoring with handheld NIR data. The main aspects of interest for batch processes in preliminary evaluations were investigated and discussed. On the one hand, process understanding and, on the other, the possibilities for process monitoring and endpoint determination were examined. The combination of different statistical tools allowed the extraction of information from the data and the identification of the link between them and the chemical and physical changes during the process. In addition, insights into the spectra characteristics in the studied spectroscopic range for kefir fermentation were reported. SignificanceThe capabilities for miniaturized NIR spectra to represent and statistical strategies to characterize different experimental conditions in a real case fermentation occurrence were proved. The strengths and limitations of some of the common approaches to catch changes in fermentation condition were highlighted. For the various statistical approaches, the chances offered in the research and development stages and to set the scene for monitoring and end-point detection were explored.

Improvement on the discrimination of Amomum tsao-ko origins using NIR combined with sequential pre-processing through orthogonalization (SPORT) and PLS-DA

Amomum tsao-ko Crevost et Lemaire (A. tsao-ko) is mainly used as spices in Chinese hotpot and meat cooking for improving taste and removing peculiar smell. To guarantee the quality of A. tsao-ko, the demand of quick and efficient analysis method is in urge. In this study, A. tsao-ko from 5 regions (including 248 individuals) were analyzed by NIR. Furthermore, sequential preprocessing through orthogonalization (SPORT) was performed to fuse the complementary information of several scatter correction techniques for obtaining the high-quality model. The result showed that in the best results obtained on the scatter correction modeling, the accuracy values of training set and test set were respectively identical to 0.8 and 0.84. However, SPORT method can correctly discriminate all the samples. Hence, SPORT model outperformed the single scatter correction modeling. Furthermore, compared with the single scatter correction modeling, SPORT could not only take the user out of the loop of the selection of the best pre-processing but also could give insight into the incremental contributions of each processed NIR data. Therefore, SPORT could be used as a reliable method for quality control of A. tsao-ko.

Environmentally friendly method in determining the water content of coffee by NIR Spectroscopy

Coffee quality could be determined from several aspects, one of those is the water content inside. Along the storage, it must be low and for coffee export it must be below 12.5%. Nowadays, the determination of water content is conducted by the chemical method like the gravimetry method which spend time, destructive also can not represent all of the products. Therefore, it is not in line with the industries which need an actual measurement that could measure all of the products nondestructively and also keep considering the environment. This study aimed to develop a PLS model to determine the water content of green bean coffee, nondestructively. This study used green bean coffee from several region in Indonesia with a high range data concentration (7.18% - 12.16%), a NIR wavelength of 1000-2500 nm, followed by determination of water content using gravimetry method. After that, the NIR data was processed using data pretreatment such as normalization, first derivative, second derivative, multiple scatter correction and standard normal variate and followed by the calibration using PLS and validation. Result showed that NIR spectroscopy could be used to calculate the percentage of water inside coffee beans, indicated by high R value above 0.8% and high consistency above 90%.

Gasoline discrimination using infrared spectroscopy and virtual samples based on measurement uncertainty

In a previous work, we proposed a methodology for pair-wise discrimination of gasoline samples by creating virtual samples based on physicochemical assays or distillation curves. Satisfactory results were achieved, although specialist and specific apparatus (not commonly available at police laboratories) were required. The present study goes a step further and for the first time investigates the possibility of infrared (IR) spectroscopy to enable a virtual samples-based methodology for comparison of gasoline samples in pairs. IR spectroscopy feasibility for in situ applications is attractive for forensic investigations. The performances of one handheld NIR device and one dual-range (FT-NIR and FT-IR) benchtop spectrometer were evaluated. The estimation of uncertainty in infrared spectral measurement (needed to generate virtual samples) is barely discussed in literature. So far, there are no literature reports describing quantification and comparison of measurement uncertainties for the spectral acquisitions evaluated here, especially regarding their use for generating virtual samples. A stepwise procedure to quantify uncertainties associated with IR spectral acquisition, at each wavenumber, is described. This method can be useful for understanding both the sources of variability in IR measurements and the system under investigation. Uncertainty estimation was based on experimental data and considered intermediate precision, repeatability and variations in sample temperature as sources of variability. Virtual samples were employed in a discrimination approach using SIMCA models. Results for portable NIR, FT-NIR and FT-IR data sets showed complete discrimination for 96.3%, 93.4% and 93.7% of the 1431 pairs of gasoline samples evaluated, respectively. These results were comparable and similar to those obtained for the physicochemical properties data set (95.7%), although slightly inferior to the result obtained for distillation curves (99.2%). Using IR non-destructive methods in this case could enable faster investigations and simpler analysis, especially for the low-cost handheld spectrometer. In a screening approach, atmospheric distillation assays can be employed only if infrared techniques are not capable of distinguishing the samples subject to comparison. In this work, a pair of samples was considered to be completely discriminated only when a null false positive error (FPR) was achieved, although a more flexible criterium may be acceptable in practice. Finally, the methodology could be extended to other applications where sample comparison is important.

The Infrared Extinction Law in the Ophiuchus Molecular Cloud Based on the UKIDSS and Spitzer c2d Surveys

Investigating the extinction properties in dense molecular clouds is of significant importance for understanding the behavior of interstellar dust and its impact on observations. In this study, we comprehensively examined the extinction law in the Ophiuchus cloud across a wavelength range from 0.8 μm to 8 μm. To achieve this, we analyzed NIR and MIR data obtained from the UKIDSS GCS and the Spitzer c2d survey, respectively. By fitting a series of color–color diagrams, we determined color-excess ratios EJ−λ/EJ−K for seven passbands. These ratios were then directly converted to derive the relative extinction law Aλ/AK. Our findings demonstrate that the Ophiuchus cloud exhibits a characteristic of flat MIR extinction, consistent with previous studies. Additionally, our results reveal variations in the extinction law with extinction depth, indicating a flatter trend from the NIR to MIR bands as extinction increases. Notably, our analysis reveals no significant difference in the MIR extinction law among the four dark clouds: L1712, L1689, L1709, and L1688. However, distinct variations were observed in the extinction law for regions outside the dark clouds, specifically L1688N and L1688W. These regions displayed lower color-excess ratios EJ−λ/EJ−K in the Spitzer/IRAC bands. This observation lends support to the dust growth occurring in the dense regions of the Ophiuchus cloud.

Assessment of extra virgin olive oil quality by miniaturized near infrared instruments in a rapid and non-destructive procedure

Food fraud in olive oil is a major concern for consumers and authorities due to the health risks and economic impacts. Common frauds include blending with other cheaper non-olive oils, or misleading labelling. The main issue is that legislation and methods presently used in routine laboratories are not always up to date with current fraudulent practices, making detection difficult, so new analytical methods development is required.This study focuses on developing an affordable and non-destructive analysis method based on NIR spectroscopy and chemometrics for EVOO quality assessment, specifically by monitoring 7 parameters of interest in EVOO measured by official methods and used to develop calibrations through NIR data. For this, two NIR low-cost portable instruments were employed, studied in-depth and compared with a NIR benchtop instrument. Calibration results enabled detection of atypical olive oils and excellent accuracy, especially for palmitic and oleic acid predictions, demonstrating the potential of the instruments.