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In this paper, the glazes of a multicomponent system (SiO2-Al2O3-CaO-MgO-Na2O-K2O-BaO-ZnO-ZrO2) were examined. This work focuses on five glazes and the difference between them as the molar ratio of the alkali oxides Na2O/K2O. Analysis of fired glazes focused on changes in phase composition (qualitative and quantitative) microstructure performed during observations made by scanning electron microscopy (SEM). The changing molar ratios were also studied in the structure analysis based on the result of data obtained by middle infrared (MIR) and Raman spectroscopy. The characteristic temperatures of the analyzed glazes were also designated using high-stage microscopy. Surface properties such as the color and roughness of the fired glazes were measured by means of a spectrometer and confocal microscopy as well. The amount and type of crystalline phases with the molar ratio of alkali oxides in the analyzed glazes were changed. In the glazes, the crystalline phase of a solid solution of plagioclase was obtained. The results obtained indicate that glazes with a predominant potassium oxide are characterized by lower characteristic temperatures and greater surface smoothness. Structure analysis indicates a different role for the five-molar ratio of Na2O/K2O. [ABSTRACT FROM AUTHOR]

In this paper, the glazes of a multicomponent system (SiO2-Al2O3-CaO-MgO-Na2O-K2O-BaO-ZnO-ZrO2) were examined. This work focuses on five glazes and the difference between them as the molar ratio of the alkali oxides Na2O/K2O. Analysis of fired glazes focused on changes in phase composition (qualitative and quantitative) microstructure performed during observations made by scanning electron microscopy (SEM). The changing molar ratios were also studied in the structure analysis based on the result of data obtained by middle infrared (MIR) and Raman spectroscopy. The characteristic temperatures of the analyzed glazes were also designated using high-stage microscopy. Surface properties such as the color and roughness of the fired glazes were measured by means of a spectrometer and confocal microscopy as well. The amount and type of crystalline phases with the molar ratio of alkali oxides in the analyzed glazes were changed. In the glazes, the crystalline phase of a solid solution of plagioclase was obtained. The results obtained indicate that glazes with a predominant potassium oxide are characterized by lower characteristic temperatures and greater surface smoothness. Structure analysis indicates a different role for the five-molar ratio of Na2O/K2O.

Over the last decades, we have witnessed an increasing interest in food-related products containing vegetable oils. These oils can be obtained either by extraction or by mechanical pressing of different parts of plants (e.g., seeds, fruit, and drupels). Producers of nutraceuticals have ceaselessly searched for unique and effective natural ingredients. The enormous success of argan oil has been followed by discoveries of other interesting vegetable oils (e.g., pomegranate oil) containing several bioactives. This work describes the pomegranate fruit extract and seed oil as a rich source of conjugated linolenic acid as a metabolite of punicic acid (PA), deriving from the omega-5 family (ω-5). Through the chemical characterization of PA, its nutritional and therapeutic properties are highlighted together with the physiological properties that encourage its use in human nutrition. We analyzed the composition of all fatty acids with beneficial properties occurring in pomegranate seed oil using gas chromatography (GC) with flame-ionization detection (FID) analysis combined with Fourier transform infrared spectroscopy (FTIR). Pomegranate seed oil mainly consists of 9,11,13-octadic-trienoic acid (18:3), corresponding to 73 wt % of the total fatty acids. Nine components were identified by GC in PSO, varying between 0.58 and 73.19 wt %. Using midinfrared (MIR) spectroscopy, we compared the composition of pomegranate seed oil with that of meadowfoam seed oil (MSO), which is also becoming increasingly popular in the food industry due to its high content of long chain fatty acids (C20-22), providing increased oil stability. From the results of FTIR and MIR spectroscopy, we found that punicic acid is unique in PSO (73.19 wt %) but not in MSO. [ABSTRACT FROM AUTHOR]

Piezoelectric material is a kind of intelligent material which can generate voltage when the material is deformed. Polyvinylidene fluoride (PVDF) is widely studied for its excellent piezoelectric properties. Firstly, the crystal structure of polyvinylidene difluoride molecular was studied by middle infrared (MIR) spectroscopy. The crystal structure of polyvinylidene difluoride molecular includes α crystal structure, β crystal structure and γ crystal structure. Secondly, Temperature-dependent middle infrared (TD-MIR) spectroscopy and two dimensional middle infrared (2D-MIR) spectroscopy of polyvinylidene difluoride molecular were studied. With the increase of the temperature from 303 K to 523 K, the corresponding frequency and absorption intensity of polyvinylidene difluoride molecular crystal structure changed obviously, the crystal structure of polyvinylidene difluoride molecular was sensitive to the temperature. The crystal structure interchange mechanism was studied at length. The three-step MIR spectroscopy (MIR, TD-MIR and 2DMIR) can broaden the scope of the analysis of crystal structure and thermal denaturation of the important functional polymer materials with piezoelectric properties (polyvinylidene difluoride). [ABSTRACT FROM AUTHOR]

There is an urgent need to reuse and recycle textile fibers, since today, low recycling rates are achieved. Accurate classification methods for post-consumer textile waste are needed in the short term for a higher circularity in the textile and fashion industries. This paper compares different spectroscopic data from textile samples in order to correctly classify the textile samples. The accurate classification of textile waste results in higher recycling rates and a better quality of the recycled materials. The data fusion of near- and mid-infrared spectra is compared with single-spectrum information. The classification results show that data fusion is a better option, providing more accurate classification results, especially for difficult classification problems where the classes are wide and close to one another. The experimental results presented in this paper prove that the data fusion of near- and mid-infrared spectra is a good option for accurate textile-waste classification, since this approach allows the classification results to be significantly improved. [ABSTRACT FROM AUTHOR]

Pregnancy assessment is a very important tool for the reproductive management in efficient and profitable dairy farms. Nowadays, mid-infrared (MIR) spectroscopy is the method of choice in the routine milk recording system for quality control and to determine standard milk components. Since it is well known that there are changes in milk yield and composition during pregnancy, the aim of this study was to develop a discriminant model to predict the pregnancy state from routinely recorded MIR spectral data. The data for this study was from the Austrian milk recording system. Test day records of Fleckvieh, Brown Swiss and Holstein Friesian cows between 3 and 305 days of lactation were included in the study. As predictor variables, the first derivative of 212 selected MIR spectral wavenumbers were used. The data set contained roughly 400,000 records from around 40,000 cows and was randomly split into calibration and validation set by farm. Prediction was done with Partial Least Square Discriminant Analysis. Indicators of model fit were sensitivity, specificity, balanced accuracy and Area Under Receiver Operating Characteristic Curve (AUC). In a first approach, one discriminant model for all cows across the whole lactation and gestation lengths was applied. The sensitivity and specificity of this model in validation were 0.856 and 0.836, respectively. Splitting up the results for different lactation stages showed that the model was not able to predict pregnant cases before the third month of lactation and vice versa not able to predict non-pregnancy after the third month of lactation. Consequently, in the second approach a prediction model for each different (expected) pregnancy stage and lactation stage was developed. Balanced accuracies ranged from 0.523 to 0.918. Whether prediction accuracies from this study are sufficient to provide farmers with an additional tool for fertility management, it needs to be explored in discussions with farmers and breeding organizations.

Planetary surfaces can be complex mixtures of coarse and fine particles that exhibit linear and nonlinear mixing behaviors at mid‐infrared (MIR) wavelengths. Machine learning multivariate analysis can estimate modal mineralogy of mixtures and is favorable because it does not assume linear mixing across wavelengths. We used partial least squares (PLS) and least absolute shrinkage and selection operator (lasso), two types of machine learning, to build MIR spectral models to determine the surface mineralogy of the asteroid (101955) Bennu using OSIRIS‐REx Thermal Emission Spectrometer (OTES) data. We find that PLS models outperform lasso models. The cross‐validated root‐mean‐square error of our final PLS models (consisting of 317 unique spectra of samples derived from 13 analog mineral samples and eight meteorites) range from ∼4 to 13 vol% depending on the mineral group. PLS predictions in vol% of Bennu's average global composition are 78% phyllosilicate, 9% olivine, 11% carbonates, and 6% magnetite. Pyroxene is not predicted for the global average spectrum, though it has been detected in small amounts on Bennu. These mineral abundances confirm previous findings that the composition of Bennu is consistent with CI/CM chondrites with high degrees of aqueous alteration. The predicted mineralogy of two previously identified OTES spectral types vary minimally from the global average. In agreement with previous work, we interpret OTES spectral differences as primarily caused by relative abundances of fine particulates rather than major compositional variations. Plain Language Summary: The OTES instrument onboard the OSIRIS‐REx spacecraft collects infrared emission spectra that can, in principle, be used to determine the mineralogy of Bennu, the target asteroid of the OSIRIS‐REx mission. However, predicting mineral abundances on remote planetary bodies from infrared spectra is particularly complex when there are fine particles (<∼100 μm) on the surface. To circumvent this problem, we created a training set of mineral mixture spectra acquired under asteroid (vacuum) conditions and used machine learning to create models for mineral abundance predictions on asteroids like Bennu. Our results support previous findings that Bennu has a composition consistent with carbonaceous chondrites, the most primitive meteorites. Key Points: Machine learning models were constructed to predict phyllosilicate, olivine, carbonate, pyroxene, and magnetite abundances using mid‐infrared spectraMineral abundance predictions of Bennu indicate the composition is consistent with CI/CM chondrites with high degrees of aqueous alterationThe predicted mineralogy of two previously identified OSIRIS‐REx Thermal Emission Spectrometer spectral types vary minimally from the global average [ABSTRACT FROM AUTHOR]

MIR spectroscopy is becoming an increasingly important tool potentially useful for diagnosis purposes especially by studying body fluids. Indeed, diseases induce changes in the composition of fluids modifying the MIR spectra. However, such changes can be difficult to capture if the structure of the data is not considered. Our objective was to improve MIR spectra analysis by using approximation of the spectra by B-splines at different specific resolutions and to combine these spectra representations with a machine learning model to predict hepatic steatosis from serum study. The different resolutions make it possible to identify changes in shape over bands of various widths. The multiresolution model helps to improve the hepatic steatosis prediction compared to conventional approaches where the absorbances are considered as unstructured variables. In addition, B-splines provide both localized and compressed information that can be translated into biochemical terms more easily than with other classical approximation methods (wavelets, Fourier transforms).

In this paper, raw glazes from the SiO2–Al2O3–CaO–MgO–K2O–Na2O system were examined. Zirconium oxide, in five different amounts – 1.5, 3, 6, 12 and 24 wt%, was added to the chosen system and heat treated at 1230 °C, where they were kept for an hour. The addition of zirconium oxide, especially in greater amounts, caused the crystalline phase of zirconium silicate and intact ZrO2 grains to appear. The amounts of these crystalline phases indicate that zirconium cations are present in the amorphous phase. Analysis of structural measurements showed that zirconium ions (Zr4+) have a depolymerizing effect on the glassy matrix of the glazes; however, it does not change subnets of an aluminum-silicon subnetwork. This is a new perspective on the matter, because it confirms that the addition of zirconium oxide causes those ions to be present not only in crystalline phases, but also some Zr4+ cations are located in the aluminosilicate subnetwork (Si–O–Si(Al)), in interstitial spaces, and it interacts only with silicon-oxygen bonds. Measurements of the color in the CIE L*a*b* system of the obtained glazes confirmed that the addition of zirconium oxide results in glaze with significantly higher whiteness (L* parameter increases) and a more monochromatic color (a* and b* parameters tend toward zero). The results of the surface smoothness were also presented, which clearly confirm that the higher content of zirconium crystalline phases results in obtaining a surface with higher roughness.

Cinnamon authentication is an important subject in the field of spice adulteration studies due to its widespread applications in the food and pharmacy industries. Rapid, non-destructive, and smart approaches lead us to the best results. Cinnamomum verum (C. verum), as the high-priced species of cinnamon, is at risk of mixing with Cinnamomum cassia (C. cassia), black pepper, and clove. Thus, C. verum was selected as our analyte, and the rest constructed the adulterants group. In this work, diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) associated with chemometric procedures was utilized for the determination and classification of adulterated cinnamon samples. Two problems were defined: a) classification of multi-adulterated samples into three levels of adulteration (i.e. low, medium, and high) and b) detection of adulterated samples by predicting concentration values. Partial least squares-discrimination analysis (PLS-DA) and Support vector machine-discrimination analysis (SVM-DA) were used for classification problems to compare the performances of linear and non-linear models. Additionally, the robust principal component analysis-alternating conditional expectation (rPCA-ACE) model, as the representative of robust models, was utilized for regression to predict the concentration value of the analyte (C. verum) in samples. In fact, in the regression model, the adulteration of samples was determined using the concentration values of the analyte in FTIR spectra. In the classification models, statistical parameters such as accuracy, precision and error rate were calculated. The accuracy of PLS-DA and SVM-DA for the calibration set is 0.960 and 0.972, respectively. These results show better performance of SVM in the classification section. The rPCA-ACE model in the regression showed good efficiency performance with an R2 of calibration of 0.838. Therefore, the results demonstrate that DRIFTS coupled with chemometric data analysis would be a capable strategy for the determination and classification of adulterated cinnamon samples. [Display omitted] • DRIFTS with chemometrics were used for determination and classification of adulterated cinnamon samples. • PLS-DA and SVM-DA were utilized as the classification models. • The results showed better performance of SVM-DA in the classification section. • rPCA-ACE as a multivariate calibration model was used. [ABSTRACT FROM AUTHOR]

Abstract Background Ketosis in dairy cattle has been shown to cause a high morbidity in the farm and substantial financial losses to dairy farmers. Ketosis symptoms, however, are difficult to identify, therefore, the amount of ketone bodies (mainly β-hydroxybutyric acid, BHB) is used as an indicator of subclinical ketosis in cows. It has also been shown that milk BHB concentrations have a strong correlation with ketosis in dairy cattle. Mid-infrared spectroscopy (MIR) has recently became a fast, cheap and high-throughput method for analyzing milk components. The aim of this study was to perform a genome-wide association study (GWAS) on the MIR-predicted milk BHB to identify genomic regions, genes and pathways potentially affecting subclinical ketosis in North American Holstein dairy cattle. Results Several significant regions were identified associated with MIR-predicted milk BHB concentrations (indicator of subclinical ketosis) in the first lactation (SCK1) and second and later lactations (SCK2) in Holstein dairy cows. The strongest association was located on BTA6 for SCK1 and BTA14 on SCK2. Several SNPs on BTA6 were identified in regions and variants reported previously to be associated with susceptibility to ketosis and clinical mastitis in Jersey and Holstein dairy cattle, respectively. One highly significant SNP on BTA14 was found within the DGAT1 gene with known functions on fat metabolism and inflammatory response in dairy cattle. A region on BTA6 and three SNPs on BTA20 were found to overlap between SCK1 and SCK2. However, a novel region on BTA20 (55–63 Mb) for SCK2 was also identified, which was not reported in previous association studies. Enrichment analysis of the list of candidate genes within the identified regions for MIR-predicted milk BHB concentrations yielded molecular functions and biological processes that may be involved in the inflammatory response and lipid metabolism in dairy cattle. Conclusions The results of this study confirmed several SNPs and genes identified in previous studies as associated with ketosis susceptibility and immune response, and also found a novel region that can be used for further analysis to identify causal variations and key regulatory genes that affect clinical/ subclinical ketosis.

Abstract Background Development and ripening of tomato (Solanum lycopersicum) fruit are important processes for the study of crop biology related to industrial horticulture. Versatile uses of tomato fruit lead to its harvest at various points of development from early maturity through to red ripe, traditionally indicated by parameters such as size, weight, colour, and internal composition, according to defined visual ‘grading’ schemes. Visual grading schemes however are subjective and thus objective classification of tomato fruit development and ripening are needed for ‘high-tech’ horticulture. To characterize the development and ripening processes in whole tomato fruit (cv. Moneymaker), a biospectroscopy approach is employed using compact portable ATR-FTIR spectroscopy coupled with chemometrics. Results The developmental and ripening processes showed unique spectral profiles, which were acquired from the cuticle-cell wall complex of tomato fruit epidermis in vivo. Various components of the cuticle including Cutin, waxes, and phenolic compounds, among others, as well as from the underlying cell wall such as celluloses, pectin and lignin like compounds among others. Epidermal surface structures including cuticle and cell wall were significantly altered during the developmental process from immature green to mature green, as well as during the ripening process. Changes in the spectral fingerprint region (1800–900 cm− 1) were sufficient to identify nine developmental and six ripening stages with high accuracy using support vector machine (SVM) chemometrics. Conclusions The non-destructive spectroscopic approach may therefore be especially useful for investigating in vivo biochemical changes occurring in fruit epidermis related to grades of tomato during development and ripening, for autonomous food production/supply chain applications.

BACKGROUND: Olive oil provides a wide range of health‐promoting compounds. The quality of olive oil is an even more complex concept as it is affected by several factors, such as variety, season, stage of maturation, extraction processing, and so on. The main objective of this study was to determine the potential of chemical and mid‐infrared spectroscopy techniques to determine the quality and authenticity of virgin olive oil (VOO). For this, we studied 41 VOOs originating from five regions of Morocco (Fez/Meknes, Eastern, Northern, Beni‐Mellal/Khenifra, and Marrakech/Safi) and produced using different agricultural and technological conditions during two successive crop seasons (2015–2016 and 2016–2017). RESULTS: By applying principal component analysis and factorial discriminant analysis with leave‐one‐out validation to the mid‐infrared spectroscopy, clear discrimination between VOO samples according to their geographic origin and variety was observed, with correct classification rates of 91.87% and 91.87% being observed respectively. The application of partial least‐squares regression to mid‐infrared and chemical data sets allowed excellent prediction of free acidity, peroxide value, k270, and chlorophyll level with R2 of 0.99, 0.97, 0.98, and 0.93 respectively, and good prediction of k232 (R2 = 0.84). CONCLUSION: The results demonstrate that mid‐infrared spectroscopy coupled with chemometric tools could be used as a rapid screening tool for evaluating the overall quality and authenticity of VOO. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Mid-infrared (MIR) spectroscopy is the method of choice for the standard milk recording system, to determine milk components including fat, protein, lactose and urea. Since milk composition is related to health and metabolic status of a cow, MIR spectra could be potentially used for disease detection. In dairy production, mastitis is one of the most prevalent diseases. The aim of this study was to develop a calibration equation to predict mastitis events from routinely recorded MIR spectra data. A further aim was to evaluate the use of test day somatic cell score (SCS) as covariate on the accuracy of the prediction model. The data for this study is from the Austrian milk recording system and its health monitoring system (GMON). Test day data including MIR spectra data was merged with diagnosis data of Fleckvieh, Brown Swiss and Holstein Friesian cows. As prediction variables, MIR absorbance data after first derivatives and selection of wavenumbers, corrected for days in milk, were used. The data set contained roughly 600,000 records and was split into calibration and validation sets by farm. Calibration sets were made to be balanced (as many healthy as mastitis cases), while the validation set was kept large and realistic. Prediction was done with Partial Least Squares Discriminant Analysis, key indicators of model fit were sensitivity and specificity. Results were extracted for association between spectra and diagnosis with different time windows (days between diagnosis and test days) in validation. The comparison of different sets of predictor variables (MIR, SCS, MIR + SCS) showed an advantage in prediction for MIR + SCS. For this prediction model, specificity was 0.79 and sensitivity was 0.68 in time window -7 to +7 days (calibration and validation). Corresponding values for MIR were 0.71 and 0.61, for SCS they were 0.81 and 0.62. In general, prediction of mastitis performed better with a shorter distance between test day and mastitis event, yet even for time windows of -21 to +21 days, prediction accuracies were still reasonable, with sensitivities ranging from 0.50 to 0.57 and specificities remaining unchanged (0.71 to 0.85). Additional research to further improve prediction equation, and studies on genetic correlations among clinical mastitis, SCS and MIR predicted mastitis are planned.

Abstract Fruit juice does not have the same nutritional value as fresh fruit with regards to the vitamin, mineral and dietary fibre contents and the antioxidant properties. However, it can be part of a healthy diet if it is produced with the minimal addition of sugar. This study aimed to evaluate the performance of the FTIR-ATR technique to discriminate the authenticity (concerning the addition of other pulps) and amount of sugar in peach nectars. This technique is usually used in food analysis because it does not require sample preparation, is quick and allows for the determination of several parameters with a single sample aliquot. The nutritional information provided on the labels of 69 samples of 23 different brands of commercial peach juice, was analysed. The differences in the nutritional composition and in the ingredients were determined according to an analysis of the labels. The largest differences observed between the samples were the sugar contents, the percentages of pulp and the addition of other pulps. All samples were analysed by FTIR-ATR equipped with a controlled temperature flow-through cell. The spectral multivariate analysis suggested it was possible to identify differences in the amount of sugar present and identify the presence of fruit pulps other than peach.

The problem of classifying substances using MIR laser and sensors with low signal-to-noise ratio remains challenging. The existing methods rely largely on using lasers at multiple wavelengths and expensive high quality sensors. We propose and demonstrate a statistical method that classifies spectral data generated from MIR imaging spectroscopy experiments using few wavelengths and inexpensive detector arrays while still achieving high accuracy. Results with quantifiable analytic performance are obtained by attributing probability distribution functions to the images obtained and implementing a binary decision process. Our method can provide a solution with as few as a single measurement and allows the use of low SNR sensors. This can increase throughput and lower costs on security checkpoints, pharmaceutical production monitoring, industrial quality control, and similar applications. [ABSTRACT FROM AUTHOR]

Soil fertility in arid areas is poor due to the climatic conditions, weak natural biomass and inappropriate agricultural practices. Soil fertility can be determined by asses the chemical composition of soil organic carbon (SOC) and soil organic matter (SOM) quantity in different systems of soil management. To identify efficient field management practices for enhanced soil fertility in arid regions, the impact of combination tillage with olive mill wastewater as compared to native vegetation were studied for more than 20 years of field experiment. Soil samples were collected from three field management treatments and three replicates for each treatment localized in Chaâl area in southern Tunisia. Field experiment includes uncultivated soil (NC) for more than 80 years (since 1936), with native vegetation, cultivated and tilled soil (CT1) and tilled soil with addition of 5 l.m−2 of olive mill wastewater (CT2). Soil properties were assessed and principal component analysis (PCA) was executed. In addition, structural functional carbon groups were investigated using MIR spectroscopy. The results showed that NC had the highest significant values of moisture content, soil organic matter, and exchangeable cations. This is likely to be due to the role of humified organic matter with colloidal properties. Significant reduction in pH and cation exchange capacity values were found in CT2 conversely to CT1. Olive mill wastewater mineralization provided soluble ions increasing electrical conductivity. MIR analyzes determined higher absorbance of aromatic/aliphatic C in NC and CT2 more than in CT1. Long-term using olive mill wastewater application improved soil carbon groups, which lead to the SOC stabilization for long-term sequestration in arid climates. [ABSTRACT FROM AUTHOR]

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

The potential benefit of data fusion based on different complementary analytical techniques was investigated for two different classification tasks in the field of foodstuff authentication. Sixty-four honey samples from three different botanical origins and 53 extra virgin olive oil samples from three different geographical areas were analyzed by attenuated total reflection IR spectroscopy (ATR/FT-IR) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS). The obtained datasets were combined in a low-level data fusion approach with a subsequent multivariate classification by principal component analysis-linear discriminant analysis (PCA-LDA) or partial least squares-discriminant analysis (PLS-DA). Performing a back projection of PCA loadings, the influence of variables in the FT-IR spectra (one-dimensional) and the GC-IMS profiles (two-dimensional) on the discrimination was visualized within the original axis of the two data sources. Validation results of the classification models were compared to the results that could be obtained by using the individual data blocks separately. For both the honey and olive oil samples, a decreased cross-validation error rate and more robust model was obtained due to the low-level data fusion. The results show that data fusion is an effective strategy for improving the classification performance, particularly for challenging classification tasks such as the discrimination of olive oils with different geographical origin. [ABSTRACT FROM AUTHOR]

Mid-infrared (MIR) spectroscopy is the method of choice for the standard milk recording system, to determine milk components including fat, protein, lactose and urea. Since milk composition is related to health and metabolic status of a cow, MIR spectra could be potentially used for disease detection. In dairy production, mastitis is one of the most prevalent diseases. The aim of this study was to develop a calibration equation to predict mastitis events from routinely recorded MIR spectra data. A further aim was to evaluate the use of test day somatic cell score (SCS) as covariate on the accuracy of the prediction model. The data for this study is from the Austrian milk recording system and its health monitoring system (GMON). Test day data including MIR spectra data was merged with diagnosis data of Fleckvieh, Brown Swiss and Holstein Friesian cows. As prediction variables, MIR absorbance data after first derivatives and selection of wavenumbers, corrected for days in milk, were used. The data set contained roughly 600,000 records and was split into calibration and validation sets by farm. Calibration sets were made to be balanced (as many healthy as mastitis cases), while the validation set was kept large and realistic. Prediction was done with Partial Least Squares Discriminant Analysis, key indicators of model fit were sensitivity and specificity. Results were extracted for association between spectra and diagnosis with different time windows (days between diagnosis and test days) in validation. The comparison of different sets of predictor variables (MIR, SCS, MIR + SCS) showed an advantage in prediction for MIR + SCS. For this prediction model, specificity was 0.79 and sensitivity was 0.68 in time window -7 to +7 days (calibration and validation). Corresponding values for MIR were 0.71 and 0.61, for SCS they were 0.81 and 0.62. In general, prediction of mastitis performed better with a shorter distance between test day and mastitis event, yet even for time windows of -21 to +21 days, prediction accuracies were still reasonable, with sensitivities ranging from 0.50 to 0.57 and specificities remaining unchanged (0.71 to 0.85). Additional research to further improve prediction equation, and studies on genetic correlations among clinical mastitis, SCS and MIR predicted mastitis are planned. [ABSTRACT FROM AUTHOR]

Mid‐infrared spectroscopic analysis of the Moon and other airless bodies requires a full accounting of spectral variation due to the unique thermal environment in airless body regoliths and the substantial differences between spectra acquired under airless body conditions and those measured in an ambient environment on Earth. Because there exists a thermal gradient within the upper hundreds of microns of lunar regolith, the data acquired by the Diviner Lunar Radiometer Experiment are not isothermal with wavelength. While this complication has been previously identified, its effect on other known variables that contribute to spectral variation, such as particle size and porosity, have yet to be well characterized in the laboratory. Here we examine the effect of particle size on mid‐infrared spectra of silicates common to the Moon measured within a simulated lunar environment chamber. Under simulated lunar conditions, decreasing particle size is shown to enhance the spectral contrast of the Reststrahlen bands and transparency features, as well as shift the location of the Christiansen feature to longer wavelengths. This study shows that these variations are detectable at Diviner spectral resolution and emphasizes the need for simulated environment laboratory data sets, as well as hyperspectral mid‐infrared instruments on future missions to airless bodies. Plain Language Summary: When trying to determine the composition of a planetary surface, it is important to have a basis for comparison. Currently, infrared data acquired from missions to airless bodies, like the Moon and asteroids, are mostly compared to data measured under ambient terrestrial conditions, and the difference in measurement environment complicates analysis. In this work, we measure minerals of varying particle size in the laboratory under a simulated lunar environment to understand how this variable affects the data, and whether we can detect the variations with the Lunar Reconnaissance Orbiter Diviner Lunar Radiometer Experiment. Acquiring infrared data under simulated lunar environment conditions will improve our interpretation of data not only from the Moon but also from other airless planetary bodies such as Mercury and asteroids. Key Points: Particle size is an important variable affecting the position of the Christiansen feature under a simulated lunar environmentParticle size variation is within the detectable limits of the Diviner Lunar Radiometer ExperimentFuture mid‐infrared missions will benefit from a simulated lunar environment spectral library [ABSTRACT FROM AUTHOR]

Abstract This study presents the results of the synthesis and characterization of chemically bonded phosphate ceramics (CBPC) material based on silica residues from furnaces used in power-generation plant enriched with iron (III) oxide and silicon carbide. The sparsely soluble oxide, as required for the successful chemical bonding, was magnesium oxide and the phosphate derivative was ammonium dihydrogen phosphate. Various amounts of water were added. Morphology analysis of the SEM microphotographs revealed the presence of magnesium phosphates and magnesium silicates, confirmed by the MIR spectra. TG/DSC analysis provided an information on the thermal stability of the prepared phosphate ceramics, whereas mechanical tests gave an information on its mechanical strength. The results firmly suggest that silica residues contaminated with iron (III) oxide and silicon carbide (which normally interfere with setting of traditional cements) could find potential applications in CBPC ceramics which can be used inter alia in construction industry. Graphical abstract Image 109 Highlights • Silica dust enriched with Fe 2 O 3 and SiC was used as silicate mineral in CBPC. • The optimum addition of water is the mass ratio of 5:2 of powder to water. • Thermal effects were related to the removal of water about 115 °C and 185–205 °C. • Samples with the mass ratio 5:2 are characterized by the presence of MgO·xSiO 2. • Obtained CBPC cements can be applied in construction and refractory materials. [ABSTRACT FROM AUTHOR]

Abstract Sampling density is a parameter that allows to identify the spatial variability of properties of interest in soils and is essential for precision agriculture practices, such as fertilizer application in variable rate. Soil sensors and spectroscopic techniques have been investigated and reported as promising tools, although showing some limitation in characterizing the chemical fertility of soils. One of the causes of the limitations is the way samples are prepared. Therefore, the aim of this work was to evaluate the effect of sieving and of drying temperature, on the determination of total nitrogen and phosphorus and potassium contents, by using Vis-NIR and Mid-Infrared spectroscopies. We found that Diffuse Reflectance Spectroscopy in the Visible and Near-Infrared region of the spectra shows better performance of the prediction models than of the Attenuated Total Reflectance Spectroscopy in the Mid-Infrared region. We did not identify significant influence of drying temperature neither of soil particle size on the predictive quality of the models generated via Vis-NIR spectroscopy; thus, the extra expenditure of time and cost for a more intense preparation of the soil samples would not be justifiable. In addition, we infer that the high prediction error may limit the applicability of spectroscopy for guiding the variable-rate application of fertilizers within the scope of precision agriculture. Highlights • Vis-NIR spectroscopy shows better performance than MIR spectroscopy. • Drying temperature does not influence the predictive quality of spectroscopic models. • Soil particles size shows minimal influence in the Vis-NIR model performance. • Prediction errors limit the applicability of spectroscopy on precision agriculture. [ABSTRACT FROM AUTHOR]

Abstract Tidal marshes are coastal ecosystems that store large amounts of sedimentary organic carbon (OC). Reducing the current uncertainty on the amount of OC stored these sediments requires the analysis of a large number of sediment samples. Soil sensing techniques, using mid infrared (MIR) spectroscopy combined with partial least squares regression (PLSR), have been proven to be a valid alternative for standard OC measurements in a wide range of terrestrial ecosystems. However, the application of these techniques to tidal marsh sediments has been very limited and the error associated with calculated sedimentary OC stocks using MIR spectroscopy/PLSR data has up till now not been evaluated. Therefore, we assessed the potential of MIR spectroscopy/PLSR to predict the OC concentration and OC stock of tidal marsh sediments in a temperate estuary (Scheldt estuary, Belgium and The Netherlands). Based on a cross-validation procedure, the results show that MIR spectroscopy/PLSR predicts the OC concentration of tidal marsh sediments along the entire estuary with a high accuracy (R2 = 0.94, RMSE = 0.56% OC). Similar accuracies were obtained for the prediction of sedimentary OC concentrations from independent tidal marshes. Organic carbon concentrations from tidal marshes in the salt portion of the estuary were, however, predicted more accurately using a PLSR model trained exclusively with sediment samples from this salinity zone. Combining depth profiles of predicted OC concentrations with measured bulk densities resulted in predictions of total OC stocks with a relative error < 4% for freshwater and brackish marshes, while errors for salt marshes were up to 30%. As MIR spectroscopy/PLSR is not able to accurately predict bulk density or OC density, standard field measurements of bulk density remain necessary to reliably predict sedimentary OC stocks in tidal marshes. Highlights • mid-IR spectroscopy predictions of the OC % of tidal marsh sediments are evaluated. • The average absolute error for independent tidal marsh sediments is 0.48% OC. • The spatial scale in the estuary at which predictions are made influences the results. • Bulk density measurements remain necessary to calculate accurate SOC stocks. [ABSTRACT FROM AUTHOR]

Main conclusion: ATR-FTIR spectroscopy with subsequent multivariate analysis non-destructively identifies plant-pathogen interactions during disease progression, both directly and indirectly, through alterations in the spectral fingerprint.Plant-environment interactions are essential to understanding crop biology, optimizing crop use, and minimizing loss to ensure food security. Damage-induced pathogen infection of delicate fruit crops such as tomato (Solanum lycopersicum) are therefore important processes related to crop biology and modern horticulture. Fruit epidermis as a first barrier at the plant-environment interface, is specifically involved in environmental interactions and often shows substantial structural and functional changes in response to unfavourable conditions. Methods available to investigate such systems in their native form, however, are limited by often required and destructive sample preparation, or scarce amounts of molecular level information. To explore biochemical changes and evaluate diagnostic potential for damage-induced pathogen infection of cherry tomato (cv. Piccolo) both directly and indirectly, mid-infrared (MIR) spectroscopy was applied in combination with exploratory multivariate analysis. ATR-FTIR fingerprint spectra (1800-900 cm−1) of healthy, damaged or sour rot-infected tomato fruit were acquired and distinguished using principal component analysis and linear discriminant analysis (PCA-LDA). Main biochemical constituents of healthy tomato fruit epidermis are characterized while multivariate analysis discriminated subtle biochemical changes distinguishing healthy tomato from damaged, early or late sour rot-infected tomato indirectly based solely on changes in the fruit epidermis. Sour rot causing agent Geotrichum candidum was detected directly in vivo and characterized based on spectral features distinct from tomato fruit. Diagnostic potential for indirect pathogen detection based on tomato fruit skin was evaluated using the linear discriminant classifier (PCA-LDC). Exploratory and diagnostic analysis of ATR-FTIR spectra offers biological insights and detection potential for intact plant-pathogen systems as they are found in horticultural industries. [ABSTRACT FROM AUTHOR]

In recent times, the popularity of adding value to under-utilized legumes have increased to enhance their use for human consumption. Acacia seed (AS) is an underutilized legume with over 40 edible species found in Australia. The study aimed to qualitatively characterize the chemical composition of 14 common edible AS species from 27 regions in Australia using mid-infrared (MIR) spectroscopy as a rapid tool. Raw and roasted (180 °C, 5, 7, and 9 min) AS flour were analysed using MIR spectroscopy. The wavenumbers (1045 cm−1, 1641 cm−1, and 2852–2926 cm−1) in the MIR spectra show the main components in the AS samples. Principal component analysis (PCA) of the MIR data displayed the clustering of samples according to species and roasting treatment. However, regional differences within the same AS species have less of an effect on the components, as shown in the PCA plot. Statistical analysis of absorbance at specific wavenumbers showed that roasting significantly (p < 0.05) reduced the compositions of some of the AS species. The results provided a foundation for hypothesizing the compositional similarity and/or differences among AS species before and after roasting.

The aim of this work was to study the potential of mid-infrared spectroscopy to evaluate the chemical composition and quality parameters of foal meat according to differences based on slaughter ages and finishing diets. In addition, the wavelength ranges which contribute to this meat quality differentiation were also determined. Important characteristics as moisture and total lipid content were well predicted using Mid-Infrared Spectroscopy (MIR)with Rv2 values of 82% and 66%, respectively. Regarding fatty acids, the best models were obtained for arachidonic, vaccenic, docosapentaenoic acid (DPA), and docosahexaenoic acid (DHA) with Rv2 values over 65%. Quality parameters, as instrumental colour and texture and sensory attributes did not reach high prediction coefficients (R2). With the spectra data of the region 2198–1118 cm−1, samples were accurately classified according to slaughter age (78%) and finishing diet (72%). This preliminary research shows the potential of MIR spectroscopy as an alternative tool to traditional meat chemical composition methods. Finally, the wavelength range of the spectrum from 2198 to 1118 cm−1 showed good results for classification purposes.

Abstract Vast amounts of soil organic matter (SOM) have been preserved in arctic soils over millennia time scales due to the limiting effects of cold and wet environments on decomposer activity. With the increase in high latitude warming due to climate change, the potential decomposability of this SOM needs to be assessed. In this study, we investigated the capability of mid infrared (MIR) spectroscopy to quickly predict soil carbon and nitrogen concentrations and carbon (C) mineralized during short-term incubations of tundra soils. Active layer and upper permafrost soils collected from four tundra sites on the North Slope of Alaska were incubated at 1, 4, 8 and 16 °C for 60 days. All incubated soils were scanned to obtain the MIR spectra and analyzed for total organic carbon (TOC) and total nitrogen (TN) concentrations, and salt-extractable organic matter carbon (SEOM). Partial least square regression (PLSR) models, constructed using the MIR spectral data for all soils, were excellent predictors of soil TOC and TN concentrations and good predictors of mineralized C for these tundra soils. We explored whether we could improve the prediction of mineralized C by splitting the soils into the groups defined by the influential factors and thresholds identified in a principal components analysis: (1) TOC >10%, (2) TOC < 10%, (3) TN < 0.6%, (4) TN > 0.6%, (5) acidic tundra, and (6) non-acidic tundra. The best PLSR mineralization models were found for soils with TOC < 10% and TN < 0.6%. Analysis of the PLSR loadings and beta coefficients from these models indicated a small number of influential spectral bands. These bands were associated with clay content, phenolics, aliphatics, silicates, carboxylic acids, and amides. Our results suggest that MIR could serve as a useful tool for quickly and reasonably estimating the initial decomposability of tundra soils, particularly for mineral soils and the mixed organic-mineral horizons of cryoturbated soils. Highlights • Mid infrared spectroscopy can predict carbon mineralization potential of tundra soil. • MIR was most effective at predicting active-layer mineral and upper permafrost soils. • MIR showed SOM associations with clay, amount of phenolics and polysaccharides. • MIR calibrations will reduce the need for incubation studies in tundra soils. • Development of a MIR database would improve upscaling studies and benchmark models. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Biodiesel content of cotton blended with diesel. • Quality control. • Chemometric analysis. Abstract Two methodologies were developed to quantify and classify the content of cotton biodiesel in blends with diesel using medium infrared spectroscopy associated with partial least squares (PLS) and partial least squares discriminant analysis (PLS-DA). The PLS model, developed to determine the biodiesel content, was validated on the basis of some merit figures: selectivity, sensitivity, analytical sensitivity, limit of detection, limit of quantification and test for systematic error. The fit of this model was also evaluated using the correlation of current and predicted values of the calibration and prediction sets – a high correlation was observed, with correlation coefficient exceeding 0.99, and relatively low errors for the parameters. Qualitative monitoring was done using the PLS-DA model, whose efficiency was analyzed based on parameters of sensitivity and specificity. These parameters showed 100% correct classification in the samples used for calibration and prediction of biodiesel content in the Brazilian B10 fuel for diesel engines. The good results for application of the two models suggest that these analytical methodologies are feasible and efficient and can be used by inspection bodies for quantitative and qualitative control of this fuel. In addition, these methodologies are quick, of low cost and allow in-situ analysis with portable equipment. [ABSTRACT FROM AUTHOR]

Raman and infrared spectroscopy are fast, simple and useful methods of identifying and distinguishing between different rock samples, which often originate from different sources. We analyzed nephrite samples from Polish deposits (Złoty Stok in the Sudetes and Jordanów Śląski in the Fore-Sudetic Block). Studied nephrites amphiboles, with the general formula (Ca 2 (Mg,Fe) 5 Si 8 O 22 (OH) 2 ), magnesium and iron contents, in terms of Fe/(Fe+Mg) per formula unit, are as follows: Jordanów Śląski (0.06–0.10), Złoty Stok type 1 (0.10–0.20) and Złoty Stok type 2 (0.04–0.18). Our spectroscopic study is consistent with results of previous petrographic microscopy, scanning electron microscopy and chemical composition of constituting minerals, measured by the electron microprobe; which methods were applied to the same nephrite deposits. Results of Jordanów and Złoty Stok nephrites studies were compared with data available in literature, which confirmed petrographic composition of studied samples. In addition, in case of actinolite nephrite samples (Fe/(Fe+Mg) > 0.10), qualification of the studied minerals to actinolite with content of Fe ion below 15% and 30% in sample from Jordanów Śląski and Złoty Stok, respectively, is possible based solely on applied spectroscopic methods. Spectroscopic studies also allowed to note the relationship between the obtained results and the genetic origin (serpentinite-related or dolomite-related) of the studied nephrites. Findings confirmed that spectroscopic methods are not only applicable and useful, but, which is very important in gemological and archaeometric practice, also non-destructive. [ABSTRACT FROM AUTHOR]

In this study, we tested the potential of visible-near infrared (VNIR, 350–2500 nm) and mid-infrared spectroscopy (MIR, 2500–15,000 nm) for quantification and prediction of soil parameters to support the Saxon Permanent Soil Monitoring Program. As Saxony is characterised by a large variety of soil metal concentrations which can negatively affect essential soil functions, the presented study is strongly focused on the forecast of soil metal contents. As data basis, a total of 203 soil samples of the Saxon Permanent Soil Monitoring Program, collected between 1998 and 2013 at 48 representative locations with respect to soil type, parent material, land use and climate conditions, were used. The chemical analysis provided information regarding total soil organic carbon content (TOC) and pH-value as well as element concentrations (Al, As, Ca, Cu, Fe, K, Mn, Na, Ni, Pb, Zn). VNIR-spectra were collected utilising an ASD FieldSpec Pro FR while an Agilent 4300 Handheld FTIR spectrometer was applied to cover the MIR wavelength region. Spectra pre-processing comprised the application of multi scatter correction (MSC), standard normal variate (SNV), continuum removal (CR) and the first and second derivatives. To model the relationship between soil spectral and chemical properties, and to predict element concentration, the spectra and chemical data were used as input for Partial Least Square Regression (PLSR) models. The definition of training data was realised on Kennard-Stone sampling algorithm and we selected 103 samples for model calibration and 100 samples for model validation. In general, the MIR-spectra and the MSC- and SNV-pre-processing improved the model performance. We obtained promising model results for TOC, Al, Fe, K and Ni with R 2 -values between 0.70 and 0.88. Moderate results were obtained for Ca (R 2 = 0.61) and Mn (R 2 = 0.43). We conclude that VNIR and MIR spectroscopy has the potential to quickly provide reliable information regarding major soil parameters and metal contents and is thus a promising alternative approach to support soil analysis within the Saxon Permanent Soil Monitoring Program. [ABSTRACT FROM AUTHOR]

Retrogradation behavior is an important physicochemical property of starch during storage. A fast and sensitive method was developed for determining the retrogradation degree (RD) in corn starch by mid-infrared (MIR), Raman spectroscopy, and combination of MIR and Raman. MIR and Raman spectra were collected from different retrogradation starch and then processed by partial least squares (PLS), interval PLS (iPLS), synergy interval PLS (siPLS), and backward interval PLS (biPLS). Two different levels of fusion data extracted from MIR and Raman spectra were analyzed by PLS. The developed models demonstrated that both MIR and Raman techniques combined with chemometrics can be used to determine the RD in starch. The PLS model built by medium-level fusion approach achieved the most satisfied performance with a correlation coefficient of 0.9658. Integrating MIR and Raman technique combined with chemometrics improved the prediction performance of RD in comparison with a single technique. [ABSTRACT FROM AUTHOR]

The identification of black polymers which contain about 0.5 to 3 mass percent soot or black master batch is still an essential problem in recycling sorting processes. Near infrared spectroscopy (NIRS) of non-black polymers offers a reliable and fast identification, and is therefore suitable for industrial application. NIRS is consequently widely used in polymer sorting plants. However, this method cannot be used for black polymers because small amounts of carbon black or soot absorb all light in the NIR spectral region. Spectroscopy in the mid infrared spectral region (MIR) offers a possibility to identify black polymers. MIR spectral measurements carried out with Fourier-transform infrared spectrometers (FTIR) are not fast enough to meet economic requirements in sorting plants. By contrast, spectrometer systems based on the photon up-conversion technique are fast and sensitive enough and can be applied to sort black polymer parts. Such a system is able to measure several thousand spectra per second hence is suitable for industrial applications. The results of spectral measurements of black polymers are presented. [ABSTRACT FROM AUTHOR]

The effects of radiation on polymeric materials are a topic of concern in a wide range of industries including the sterilization, and the nuclear power industry. While much work has concentrated on systems like polyolefins that are radiation sterilized, some work has been done on epoxy systems. The epoxy system studied is an epoxy/amine paint which is representative of the paint that covers the inner surfaces of the French nuclear reactor containment buildings. In case of a severe accident on a Nuclear Power Plant, fission products can be released from the nuclear fuel to the reactor containment building. Among them, volatile iodine (I 2 ) can be produced and can interact with the epoxy-paint. This paint is also subjected to gamma radiation damages (due to the high dose in the containment coming from radionuclides released from the fuel). So the epoxy-paint studied was exposed to gamma radiation under air atmosphere after being loaded with I 2 or not. The aim of this study is to characterize by FTIR spectroscopy the iodine-paint interactions, then to identify the radiation damages on the epoxy-paint, and to check their effects on these iodine-paint interactions. This work shows the potential of multi-block analysis method (ANOVA-PCA and COMDIM = AComDim) for such a study as it allows to identify the nature of iodine/epoxy-paint interactions and to characterize the gamma radiation damages on the epoxy-paint. AComDim method conduces to the extraction of Common Components to different tables and highlights factors of influence and their interactions. [ABSTRACT FROM AUTHOR]

Oxyhydroxides, soil texture and soil organic carbon (SOC) fractions are key parameters determining organic carbon cycling in soils. Standard laboratory methods to determine these soil properties are, however, time–consuming and expensive. Visible near infrared (Vis–NIR) and mid infrared (MIR) spectroscopy have been recognized as a promising alternative, but previous studies have not explicitly considered the above–mentioned soil properties as compositional data. The fractional components in compositional data are interrelated but their sum should be unity – these features should be represented in the spectral modeling process to minimize the prediction bias. In this study, two unique datasets were used to test these premises. The first one consisted of 655 samples collected from agricultural terraces and lynchets across Europe, which were scanned to acquire MIR spectra, while in the second one 4516 samples from private gardens across Flanders, Belgium were used to acquire Vis–NIR spectra. Memory–based learning models were optimized using both raw data (conventional method) and transformed data of soil properties by additive log–ratio (alr), centered log–ratio (clr), and isometric log–ratio (ilr) transformation methods. Results showed that the log–ratio transformation methods produced predictions as accurate as the conventional method, whilst also added two significant benefits: (1) they ensured the predicted fractions added up to 100% and (2) they reduced the number of samples with extreme prediction errors. We found that for 11 out of 18 investigated soil properties, the three log–ratio transformation methods provided similar model performance, whilst ilr outperformed clr for the prediction of silt and sand content of garden soils, for coarse particulate SOC (>250 µm) and microaggregate–associated SOC (250–53 µm) of terrace soils. For the remaining three properties (Al oxyhydroxides) alr outperformed ilr. Fair to excellent predictive models (RPD from 1.4 to 4.3; R2 from 0.50 to 0.95) were achieved for soil oxyhydroxides (Fe, Al, Mn) and soil texture from MIR spectra. Our approach also enabled accurate predictions of silt and sand content of garden soils using Vis–NIR spectra (RPD = 1.9; R2 = 0.72), although accuracy for clay was lower (RPD = 1.3; R2 = 0.49). This study demonstrates that combining soil infrared spectroscopy with a compositional data analysis is a promising technique that enables cost-effective and reliable quantification of soil properties relevant to SOC stability, thus offering a practical opportunity to assess the role of SOC in global C cycling. • Combining IR spectra with compositional data analysis provides obvious benefits. • Log-ratio transformation ensures the predicted fractions added up to 100%. • Log-ratio method reduces the number of samples with extreme prediction errors. • The best log–ratio transformation method depends on the targeted soil properties. • IR spectra plus compositional data analysis yields reliable data for SOC studies. [ABSTRACT FROM AUTHOR]

B and T-lymphocytes are major players of the specific immune system, responsible by an efficient response to target antigens. Despite the high relevance of these cells' activation in diverse human pathophysiological processes, its analysis in clinical context presents diverse constraints. In the present work, MIR spectroscopy was used to acquire the cells molecular profile in a label-free, simple, rapid, economic, and high-throughput mode. Recurring to machine learning algorithms MIR data was subsequently evaluated. Models were developed based on specific spectral bands as selected by Gini index and the Fast Correlation Based Filter. To determine if it was, possible to predict from the spectra, if B and T lymphocyte were activated, and what was the molecular fingerprint of T- or B- lymphocyte activation. The molecular composition of activated lymphocytes was so different from naïve cells, that very good prediction models were developed with whole spectra (with AUC=0.98). Activated B lymphocytes also present a very distinct molecular profile in relation to activated T lymphocytes, leading to excellent prediction models, especially if based on target bands (AUC=0.99). The identification of critical target bands, according to the metabolic differences between B and T lymphocytes and in association with the molecular mechanism of the activation process highlighted bands associated to lipids and glycogen levels. The method developed presents therefore, appealing characteristics to promote a new diagnostic tool to analyze and discriminate B from T-lymphocytes. [Display omitted] [ABSTRACT FROM AUTHOR]

The aim of this work was to investigate the potential of Fourier transform infrared spectroscopy (FTIR) to detect and predict the bacterial load of salmon fillets ( Salmo salar ) stored at 3, 8 and 30 °C under three packaging conditions: air packaging (AP) and two modified atmospheres constituted by a mixture of 50%N 2 /40%CO 2 /10%O 2 with lemon juice (MAPL) and without lemon juice (MAP). Fresh salmon samples were periodically examined for total viable counts (TVC), specific spoilage organisms (SSO) counts, pH, FTIR and sensory assessment of freshness. Principal components analysis (PCA) allowed identification of the wavenumbers potentially correlated with the spoilage process. Linear discriminant analysis (LDA) of infrared spectral data was performed to support sensory data and to accurately identify samples freshness. The effect of the packaging atmospheres was assessed by microbial enumeration and LDA was used to determine sample packaging from the measured infrared spectra. It was verified that modified atmospheres can decrease significantly the bacterial load of fresh salmon. Lemon juice combined with MAP showed a more pronounced delay in the growth of Brochothrix thermosphacta , Photobacterium phosphoreum , psychrotrophs and H 2 S producers. Partial least squares regression (PLS-R) allowed estimates of TVC and psychrotrophs, lactic acid bacteria, molds and yeasts, Brochothrix thermosphacta , Enterobacteriaceae , Pseudomonas spp. and H 2 S producer counts from the infrared spectral data. For TVC, the root mean square error of prediction (RMSEP) value was 0.78 log cfu g − 1 for an external set of samples. According to the results, FTIR can be used as a reliable, accurate and fast method for real time freshness evaluation of salmon fillets stored under different temperatures and packaging atmospheres. [ABSTRACT FROM AUTHOR]

The problem of oxidation stability of petroleum products often arises under the protracted transportation or tankage of products in the maritime terminal storages or in the national repositories for a prolonged time. So, this paper presents the results of study of auto-oxidation kinetics of hydrocarbons in the gasoline, carried out by applying the method of middle range IR spectroscopy (MIR) monitoring aimed at the prediction of fuel oxidation stability. The results of study enabled to propose the virtual chemometrical model based on the MIR spectrum analysis which was applicable for testers which register the changes in concentration of olefins. As the object of research there was taken the gasoline which was carrying Ni > 50–90 of different structures of C2–C10 olefins in unordered set of hydrocarbons which degrade the petroleum product.

Fires exert many changes on the physical, chemical, morphological, mineralogical, and biological properties of soil that, in turn, affect the soil's hydrology and nutrient flux, modifying its ability to support vegetation and resist erosion. The ash produced by forest fires is a complex mixture composed of organic and inorganic particles with varied properties. This research was conducted to study and characterized ash properties produced at different temperatures and with different soil organic matter combinations. The samples, which included two treatments of soils with underlying mixed leaves and branches composed mainly by Pinus halepensis , Pistacia lentiscus , Cistus salviifolius and typical herbaceous vegetation, versus samples of mixed leaves and branches alone. Both were exposed to 400 °C and 600 °C heat in a muffle furnace for 2 h. The residue ash was generally grayish, consisting of mixed-sized particles that preserved almost none of the original characteristics of the fuel, and was deposited in ash layers with diverse physicochemical and textural properties. The results of this study highlight the differences between all examined samples and strongly support the assumption that ash produced from a complex vegetation-soil system is a new substance with unique structural, textural, and mineralogical properties. Moreover, the ash produced at different temperatures appeared in distinct layering patterns. [ABSTRACT FROM AUTHOR]

Fourier transform near-infrared (NIR) and mid-infrared (MIR) imaging techniques are essential tools to characterize the chemical morphology of plant. The transmission imaging mode is mostly used to obtain easy-to-interpret spectra with high signal-to-noise ratio. However, the native chemical compositions and physical structures of plant samples may be altered when they are microtomed for the transmission tests. For the direct characterization of thick plant samples, the combination of the reflection NIR imaging and the attenuated total reflection (ATR) MIR imaging is proposed in this research. First, the reflection NIR imaging method can explore the whole sample quickly to find out typical regions in small sizes. Next, each small typical region can be measured by the ATR-MIR imaging method to reveal the molecular structures and spatial distributions of compounds of interest. As an example, the chemical morphology of Areca nut section is characterized directly by the above approach. [ABSTRACT FROM AUTHOR]

Headspace-Mass Spectrometry (HS-MS), Fourier Transform Mid-Infrared spectroscopy (FT-MIR) and UV-Visible spectrophotometry (UV–vis) instrumental responses have been combined to predict virgin olive oil sensory descriptors. 343 olive oil samples analyzed during four consecutive harvests (2010–2014) were used to build multivariate calibration models using partial least squares (PLS) regression. The reference values of the sensory attributes were provided by expert assessors from an official taste panel. The instrumental data were modeled individually and also using data fusion approaches. The use of fused data with both low- and mid-level of abstraction improved PLS predictions for all the olive oil descriptors. The best PLS models were obtained for two positive attributes (fruity and bitter) and two defective descriptors (fusty and musty), all of them using data fusion of MS and MIR spectral fingerprints. Although good predictions were not obtained for some sensory descriptors, the results are encouraging, specially considering that the legal categorization of virgin olive oils only requires the determination of fruity and defective descriptors. [ABSTRACT FROM AUTHOR]

Context:Xysmalobium undulatum(L.) Aitonfvar. (Asclepiadaceae), commonly known as uzara, is an ethnomedicinally important plant from southern Africa used to treat a variety of ailments. In addition to local use in African Traditional Medicine (ATM), formulations containing uzara have been successfully marketed by a number of pharmaceutical companies. Despite its commercialization, published adequate quality control (QC) protocols are lacking. Objective: The study was conducted to develop QC protocols for uzara based on chromatographic and spectroscopic techniques. Materials and methods: High performance thin layer chromatography (HPTLC) and liquid chromatography coupled to mass spectrometry (LC-MS) were used to develop phytochemical fingerprints of ethanolic root extracts of 47 uzara samples collected from eight distinct localities in South Africa. Mid-infrared (MIR) spectroscopy was also explored as a suitable alternative technique for rapid and economic quantification of uzarin. Results: Adequate chromatographic profiles were obtained using both HPTLC and LC-MS analyses. The chromatographic patterns showed qualitative similarities among plants collected from different locations. The levels of uzarin, the major constituent of uzara, were highly variable between locations, ranging from 17.8 to 139.9 mg/g (dry weight). A good coefficient of determination (R2 = 0.939) and low root mean square error of prediction (RMSEP = 7.9 mg/g) confirmed the accuracy of using MIR-PLS calibration models for the quantification of uzarin. Discussion and conclusion: Both HPTLC and LC-MS can be used as tools in developing quality control procedures for uzara. MIR in combination with chemometrics provides a fast alternative method for the quantification of uzarin. [ABSTRACT FROM AUTHOR]

Human mesenchymal stem/stromal cells (MSCs) have received considerable attention in the field of cell-based therapies due to their high differentiation potential and ability to modulate immune responses. However, since these cells can only be isolated in very low quantities, successful realization of these therapies requires MSCs ex-vivo expansion to achieve relevant cell doses. The metabolic activity is one of the parameters often monitored during MSCs cultivation by using expensive multi-analytical methods, some of them time-consuming. The present work evaluates the use of mid-infrared (MIR) spectroscopy, through rapid and economic high-throughput analyses associated to multivariate data analysis, to monitor three different MSCs cultivation runs conducted in spinner flasks, under xeno-free culture conditions, which differ in the type of microcarriers used and the culture feeding strategy applied. After evaluating diverse spectral preprocessing techniques, the optimized partial least square (PLS) regression models based on the MIR spectra to estimate the glucose, lactate and ammonia concentrations yielded high coefficients of determination ( R2 ≥ 0.98, ≥0.98, and ≥0.94, respectively) and low prediction errors (RMSECV ≤ 4.7%, ≤4.4% and ≤5.7%, respectively). Besides PLS models valid for specific expansion protocols, a robust model simultaneously valid for the three processes was also built for predicting glucose, lactate and ammonia, yielding a R2 of 0.95, 0.97 and 0.86, and a RMSECV of 0.33, 0.57, and 0.09 mM, respectively. Therefore, MIR spectroscopy combined with multivariate data analysis represents a promising tool for both optimization and control of MSCs expansion processes. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:447-455, 2016 [ABSTRACT FROM AUTHOR]

• Application of MIR spectroscopy for Predection of soil organic carbon. • Predicting the OC contents in soils of the middle Indo-Gangetic plains of India. • We tested a combo of 5 spectral preprocessing and four multivariate prediction models. • Organic carbon was predicted using 280 soil samples from surface to 15 cm depth. • PLSR, RF, SVR and MARS were compared as calibration methods using soil MIR spectra. Soil organic carbon (SOC) sequestration provides an opportunity to mitigate climate change impacts, since soils are the largest store of terrestrial carbon. Accurate estimates of SOC content across landscapes are therefore important to monitor and manage efficiently these SOC stocks. Mid-infrared (MIR) spectroscopy has been increasingly applied as a rapid, cost-effective, and accurate method for predictive soil analysis. This study assessed the performance of MIR spectroscopy for SOC prediction at a regional scale in the Indo-Gangetic plains, 280 soil samples were collected covering Inceptisols, Entisols and Alfisols and their spectra recorded. Five preprocessing techniques ((absorbance, multiplicative scatter correction (MSC), standard normal variate (SNV), Savitzky–Golay smoothing first derivative (SG-FD) and Savitzky–Golay smoothing second derivative (SG-SD)) and four multivariate methods (partial least-squares regression (PLSR), random forest (RF), support vector regression (SVR) and multivariate adaptive regression splines (MARS)) were evaluated to predict SOC from MIR spectra. The considerable prediction accuracy and robustness were achieved using the PLSR model (R V 2 = 0.78, RMSE V = 0.04, and RPD V = 2.07), RF model (R V 2 = 0.65, RMSE V = 0.09, and RPD V = 1.01), SVR model (R V 2 = 0.65, RMSE V = 0.09, and RPD V = 1.12), and MARS model (R V 2 = 0.67, RMSE V = 0.09, and RPD V = 1.20). Findings from this study identified the reliability of SOC determinations by examining how preprocessing techniques and multivariate methods affect spectral analyses. [ABSTRACT FROM AUTHOR]