Fruit and Vegetable Intake Assessed by Repeat 24 h Recalls, but Not by A Dietary Screener, Is Associated with Skin Carotenoid Measurements in Children.

Accurate measurement of fruit and vegetable (FV) intake is important for nutrition surveillance and evaluation of dietary interventions. We compared two tools for reporting FV intake to objective measurement of skin carotenoids among children. FV cups/day was assessed by repeated 24 h dietary recalls (24H FV) and the National Cancer Institute's All-Day Fruit and Vegetable Screener (NCI FV). Skin carotenoids were measured by repeated resonance Raman spectroscopy (RRS) of the palm. FV cups were regressed on RRS scores in unadjusted, field-based, and research-setting models with covariates feasible in each scenario. Data were baseline values from children aged 2-12 years in low-income households enrolled in a healthy eating randomized trial in four U.S. states (n = 177). Twenty-four-hour FV cups were associated with skin carotenoids in all models (p < 0.001) but NCI FV cups were not. Predicted RRS scores for discrete 24H FV cups provide a guide to interpretation of RRS in children (2 cups FV intake ~36,000 RRS units), with the research-setting scenario generally providing the narrowest prediction range (+/-1924). When self-reported data are required, 24 h recalls are more accurate than NCI FV screener data; and, when limited time, resources, or literacy must be considered, RRS scores can be quickly obtained and easily interpreted.

Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination.

A simple, fast, and efficient spark discharge-laser-induced breakdown spectroscopy (SD-LIBS) method was developed for determining rice botanic origin using predictive modeling based on support vector machine (SVM). Seventy-two samples from four rice varieties (Guri, Irga 424, Puitá, and Taim) were analyzed by SD-LIBS. Spectral lines of C, Ca, Fe, Mg, N and Na were selected as input variables for prediction model fitting. The SVM algorithm parameters were optimized using a central composite design (CCD) to find the better classification performance. The optimum model for discriminating rice samples according to their botanical variety was obtained using C = 5.25 and γ = 0.119. This model achieved 96.4% of correct predictions in test samples and showed sensitivities and specificities per class within the range of 92-100%. The developed method is robust and eco-friendly for rice botanic identification since its prediction results are consistent and reproducible and its application does not generate chemical waste.

Determinants and Suitability of Carotenoid Reflection Score as a Measure of Carotenoid Status.

Carotenoids, orange-coloured pigments found in vegetables, fruit, eggs and dairy foods, act as antioxidants and vitamin A precursors in the human body. Skin carotenoid concentration is a biomarker of vegetable and fruit intake. The aim was to identify determinants of skin carotenoid concentration by measuring "Veggie Meter™" carotenoid reflection spectroscopy scores (CRS) from the fingertip of adults with a range of ages, ethnicity and body size. Frequencies of daily intake of vegetables and fruit and weekly intake of pumpkin and carrot, dark green leafy vegetables (DGLV), eggs (yolk), and dairy were determined from a self-completed food-frequency-questionnaire. A total of 571 (324 Women, 247 Men) adults, aged 16 to 85 years, completed measurements. The CRS ranged from 83 to 769, with a median of 327. Women and men did not score differently. For all participants there were negative correlations of CRS with weight (r = -0.312) and BMI (r = -0.338) and positive correlations with weekly intakes of DGLV (r = 0.242) and carrots and pumpkin (r = 0.202). Based on a review of health outcomes associated with plasma carotenoids, 82% of the participants in the current study are at moderate risk, or more, of negative health outcomes. Determinants of carotenoid status were body size, intake of DGLV, carrots and pumpkin, and ethnicity.

Improving Diagnostic Yield of Capsule Endoscopy in Coeliac Disease: Can Flexible Spectral Imaging Colour Enhancement Play a Role?

INTRODUCTION: Flexible spectral imaging colour enhancement (FICE) is a form of virtual chromoendoscopy that is incorporated in the capsule reading software and that can be used by reviewers to enhance the delineation of lesions in the small bowel. This has been shown to be useful in the detection of pigmented (ulcers, angioectasias) lesions. However, its application to coeliac disease (CD) images from small bowel capsule endoscopies (SBCEs) has rarely been studied. METHODS: This was a European, multicentre study that included 5 expert capsule reviewers who were asked to evaluate a number of normal and abnormal de-identified images from SBCEs of patients with CD to determine whether the use of FICE and blue light can improve the detection of CD-related changes. RESULTS: Sensitivity and specificity of conventional white light in the delineation of CD-related changes were 100%. The next best image modification was FICE 1 with a sensitivity of 80% and a specificity of 100%. There was no difference between conventional white light, FICE and blue light for the identification of CD-related changes. There was a low agreement (Fleiss kappa 0.107; p = 0.147) between expert reviewers in selecting the best image modification that detected CD-related changes. CONCLUSIONS: FICE and blue light were not found to be superior to conventional white light in the delineation of macroscopic changes related to CD on SBCEs.

Validation of speed-resolved laser Doppler perfusion in a multimodal optical system using a blood-flow phantom.

The PeriFlux 6000 EPOS system combines diffuse reflectance spectroscopy (DRS) and laser Doppler flowmetry (LDF) for the assessment of oxygen saturation (expressed in percentage), red blood cell (RBC) tissue fraction (expressed as volume fraction, %RBC), and perfusion (%RBC × mm / s) in the microcirculation. It also allows the possibility of separating the perfusion into three speed regions (0 to 1, 1 to 10, and >10 mm / s). We evaluate the speed-resolved perfusion components, i.e., the relative amount of perfusion within each speed region, using a blood-flow phantom. Human blood was pumped through microtubes with an inner diameter of 0.15 mm. Measured DRS and LDF spectra were compared to Monte Carlo-simulated spectra in an optimization routine, giving the best-fit parameters describing the measured spectra. The root-mean-square error for each of the three speed components (0 to 1, 1 to 10, and >10 mm / s, respectively) when describing the blood-flow speed in the microtubes was 2.9%, 8.1%, and 7.7%. The presented results show that the system can accurately discriminate blood perfusion originating from different blood-flow speeds, which may enable improved measurement of healthy and dysfunctional microcirculatory flow.

Functional spectroscopic imaging reveals specificity of glutamate response in mouse brain to peripheral sensory stimulation.

Non-invasive investigation of physiological changes and metabolic events associated with brain activity in mice constitutes a major challenge. Conventionally, fMRI assesses neuronal activity by evaluating activity-evoked local changes in blood oxygenation levels (BOLD). In isoflurane-anaethetized mice, however, we found that BOLD signal changes during paw stimulation appear to be dominated by arousal responses even when using innocuous stimuli. Widespread responses involving both hemispheres have been observed in response to unilateral stimulation. MRS allows probing metabolic changes associated with neuronal activation and provides a complementary readout to BOLD fMRI for investigating brain activity. In this study we evaluated the sensitivity of a free induction decay (FID) based spectroscopic imaging (MRSI) protocol for the measurement of alterations in glutamate levels elicited by unilateral electrical paw stimulation at different current amplitudes. Coronal MRSI maps of glutamate distribution with 17 × 17 voxels of 1 µl volume have been recorded with a temporal resolution of 12 min. Significant region-specific increases in glutamate levels have been observed in the contralateral but not in the ispiateral S1 somatosensory cortex upon stimulation. The amplitude of glutamate changes increased in a dose-dependent manner with the stimulus amplitude. The study demonstrates feasibility of functional MRSI in mice for studying activity-evoked glutamate changes in a temporo-spatially resolved manner.

Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy.

Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laser-induced breakdown spectroscopy (LIBS) is an interesting analytical technique for PDO certification, since it provides fast multielemental analysis requiring minimal sample treatment. In this work LIBS spectral data from rice analysis were evaluated for PDO certification of Argentine brown rice. Samples from two PDOs were analyzed by LIBS coupled to spark discharge. The selection of spectral data was accomplished by extreme gradient boosting (XGBoost), an algorithm currently used in machine learning, but rarely applied in chemical issues. Emission lines of C, Ca, Fe, Mg and Na were selected, and the best performance of classification were obtained using k-nearest neighbor (k-NN) algorithm. The developed method provided 84% of accuracy, 100% of sensitivity and 78% of specificity in classification of test samples. Furthermore, it is simple, clean and can be easily applied for rice certification.

High-accuracy and fast determination of chromium content in rice leaves based on collinear dual-pulse laser-induced breakdown spectroscopy and chemometric methods.

Dual-pulse laser-induced breakdown spectroscopy (DPLIBS) and chemometric methods were used to predict chromium content in rice leaves, along with the purpose for increasing the detection sensitivity and accuracy. The influence of important parameters in DPLIBS were investigated and optimized. Then, partial least square (PLS) was used to establish chromium content prediction models, and the value of regression coefficient based on PLS was applied to determine feature variables. In addition, multivariate and univariate analysis were used to verify the modeling performance of selected feature variables. The results indicated that support vector machine model based on feature variables achieved the best performance, with correlation coefficient of 0.9946, root mean square error of 4.85 mg/kg and residual predictive deviation of 9.70 in prediction set. The proposed method provides a high-accuracy and fast approach for chromium content prediction in rice leaves, which could potentially be used for toxic and nutrient elements detection in food.

Spectral Spatial Variation.

Automatic carcinoma detection from hyper/multi spectral images is of essential importance due to the fact that these images cannot be presented directly to the clinician. However, standard approaches for carcinoma detection use hundreds or even thousands of features. This would cost a high amount of RAM (random access memory) for a pixel wise analysis and would slow down the classification or make it even impossible on standard PCs. To overcome this, strong features are required. We propose that the spectral-spatial-variation (SSV) is one of these strong features. SSV is the residuum of the three dimensional hyper spectral data cube minus its approximation with a fitting in a small volume of the 3D image. By using it, the classification results of carcinoma detection in the stomach with multi spectral imaging will be increase significantly compared to not using the SSV. In some cases, the AUC can be even as high as by the usage of 72 spatial features.

Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury.

Muscle fat infiltration (MFI) of the deep cervical spine extensors has been observed in cervical spine conditions using time-consuming and rater-dependent manual techniques. Deep learning convolutional neural network (CNN) models have demonstrated state-of-the-art performance in segmentation tasks. Here, we train and test a CNN for muscle segmentation and automatic MFI calculation using high-resolution fat-water images from 39 participants (26 female, average = 31.7 ± 9.3 years) 3 months post whiplash injury. First, we demonstrate high test reliability and accuracy of the CNN compared to manual segmentation. Then we explore the relationships between CNN muscle volume, CNN MFI, and clinical measures of pain and neck-related disability. Across all participants, we demonstrate that CNN muscle volume was negatively correlated to pain (R = -0.415, p = 0.006) and disability (R = -0.286, p = 0.045), while CNN MFI tended to be positively correlated to disability (R = 0.214, p = 0.105). Additionally, CNN MFI was higher in participants with persisting pain and disability (p = 0.049). Overall, CNN's may improve the efficiency and objectivity of muscle measures allowing for the quantitative monitoring of muscle properties in disorders of and beyond the cervical spine.

A Scheme for Ultrasensitive Detection of Molecules with Vibrational Spectroscopy in Combination with Signal Processing.

We show that combining vibrational spectroscopy with signal processing can result in a scheme for ultrasensitive detection of molecules. We consider the vibrational spectrum as a signal on the energy axis and apply a matched filter on that axis. On the example of a nerve agent molecule, we show that this allows detection of a molecule by its vibrational spectrum, even when the recorded spectrum is completely buried in noise when conventional spectroscopic detection is impossible. Detection is predicted to be possible with signal-to-noise ratios in the recorded spectra as low as 0.1. We have studied the importance of the spectral range used for detection as well as of the quality of the computed spectrum used to program the filter, specifically, the role of anharmonicity, of the exchange correlation functional, and of the basis set. The use of the full spectral range rather than of a narrow spectral window with key vibrations is shown to be advantageous, as well as accounting for anharmonicity.

Geographical discrimination of red garlic (Allium sativum L.) produced in Italy by means of multivariate statistical analysis of ICP-OES data.

Sixty-five samples of red garlic (Allium sativum L.) coming from four different production territories of Italy were analysed by means of inductively coupled plasma optical emission spectrometry. The garlic samples were discriminated according to the geographical origin using the content of seven elements (Ba, Ca, Fe, Mg, Mn, Na and Sr). Both classification and class modelling methods by using linear discriminant analysis (LDA) and soft independent model class analogy (SIMCA), respectively, were applied. Classification ability and modelling efficiency were evaluated on an external prediction set (21 garlic samples) designed by application of duplex Kennard-Stone algorithm. All the calibration and prediction samples were correctly classified by means of LDA. The class models developed using SIMCA exhibited high sensitivity (almost all the calibration and external samples were accepted by the respective classes) and good specificity (the majority of extraneous samples were refused by each class model).

Determination of nutrients in sugarcane juice using slurry sampling and detection by ICP OES.

The fractional factorial and Doehlert designs for optimization of a slurry sampling procedure to determine of nutrients in sugarcane juice by inductively coupled plasma optical emission spectrometry (ICP OES) were applied. External calibration curves were used for direct analysis of the slurry. This procedure allowed determination of Ca, Cu, Fe, K and Mg with limits of detection (LoD) obtained of 2.0, 0.04, 0.2, 1.0 and 1.5 mg L-1, respectively. The precision was expressed as relative standard deviation (%RSD), being better than 1.4% (n = 3). Accuracy was confirmed by comparison with sample digestion method. The results for analysis of fourteen sugarcane juices samples demonstrated that the nutrients Ca, Cu, Fe, K and Mg have average contents of 108, 0.506, 6.40, 470 and 114 mg L-1, respectively. The proposed analytical method is a good alternative for simultaneous determination of nutrients in sugarcane juice using introduction of slurries and detection by ICP OES.

Evaluation of macro and micronutrient elements content from soft drinks using principal component analysis and Kohonen self-organizing maps.

This study approaches the determination of nine elements from Brazilian carbonated soft drinks of several flavors and manufactures using inductively coupled plasma optical emission spectrometry (ICP OES). The concentrations of the elements varied as follows: (in µg L-1: Cu: 4.00-78.0; Fe: 74.0-506; Mn: 20.0-66.0; Zn: 104-584) and (in mg L-1: Ca: 4.81-16.2; K: 6.73-260; Na: 26.0-175; S: 1.43-5.41; P: 0.186-219). Principal component analysis has shown some tendencies to form two groups according to the drink flavor (orange and cola), but only cola presented a clear and complete separation. Using Kohonen maps, it was observed a tendency to form three flavor groups: (i) cola, (ii) orange and lemon, and (iii) guarana. However, this last tool proved to be more accurate in the groups' formation.

A Catalog of Spectra, Albedos, and Colors of Solar System Bodies for Exoplanet Comparison.

We present a catalog of spectra and geometric albedos, representative of the different types of solar system bodies, from 0.45 to 2.5 µm. We analyzed published calibrated, uncalibrated spectra, and albedos for solar system objects and derived a set of reference spectra and reference albedos for 19 objects that are representative of the diversity of bodies in our solar system. We also identified previously published data that appear contaminated. Our catalog provides a baseline for comparison of exoplanet observations to 19 bodies in our own solar system, which can assist in the prioritization of exoplanets for time intensive follow-up with next-generation extremely large telescopes and space-based direct observation missions. Using high- and low-resolution spectra of these solar system objects, we also derive colors for these bodies and explore how a color-color diagram could be used to initially distinguish between rocky, icy, and gaseous exoplanets. We explore how the colors of solar system analog bodies would change when orbiting different host stars. This catalog of solar system reference spectra and albedos is available for download through the Carl Sagan Institute.

Bioimpedance Spectroscopy Imprecisely Assesses Lean Body Mass in Pediatric Dialysis Patients.

OBJECTIVES: Alterations in body compositions are strongly associated with poor outcomes in end-stage renal disease patients. Hence, assessment of lean body mass is crucial for clinically monitoring these patients. The use of multifrequency bioimpedance spectroscopy measurements has also been advocated, but their usefulness in children is questioned. We investigated whether their application is appropriate for lean body mass measurement in pediatric patients receiving chronic dialysis. METHODS: Lean body mass estimates as assessed by multifrequency bioimpedance spectroscopy and by deuterium dilution were obtained for 15 patients (mean age 10.9 ±â€Š3.6 years). RESULTS: Lean body mass (mean ±â€Šstandard deviation) determined by bioimpedance was 24.2 ±â€Š10.7 and 24.4 ±â€Š10.3 kg by deuterium technique. Bland-Altman analysis showed a mean (±standard deviation) difference between the 2 methods of -0.25 ±â€Š2.30 kg with 95% limits of agreement of -4.80 to 4.25 kg. In a multiple linear regression model, the hydration status was associated with measurement bias after adjusting for age, sex, weight, and body surface area. CONCLUSIONS: Our results show a high level of agreement between measurements by bioimpedance and deuterium technique, but the limits of agreement were wide. These findings do not support the use of bioimpedance to individually assess lean body mass in pediatric dialysis patients with and without overhydration.

Evaluation of the Chemical Composition of Synthetic Leather Using Spectroscopy Techniques.

Synthetic leather samples from Brazil and Paraguay were evaluated in this study using three spectroscopy techniques: inductively coupled plasma optical emission spectrometry (ICP-OES), laser-induced breakdown spectroscopy (LIBS), and wavelength dispersive X-ray fluorescence (WDXRF). The obtained information from each technique was separately inspected with principal component analysis (PCA). The concentrations of the elements determined in the synthetic leathers using ICP-OES decreased in the following order: Ca > Cr > Mg > Ba > Pb > Al > Fe > Zn > Sb > Ni with a concentration range below the limit of quantification (

Rapid identification of bovine mastitis pathogens by MALDI-TOF Mass Spectrometry / Rápida identificação de agentes causadores de mastite por espectrometria de massas MALDI-TOF

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been shown to be an alternative method for identification of bacteria via their protein profile spectra, being able to identify bacteria at the genus, species and even at subspecies level. With the aim of large-scale identification of pathogens causing mastitis by this platform, a total of 305 isolates of bacteria identified from cows with subclinical mastitis were analyzed by conventional microbiological culture (MC) as well as by MALDI-TOF MS coupled with Biotyper data processing. Approximately 89% of the identifications performed by MALDI-TOF MS were consistent with results obtained by MC. From the remaining isolates (11%), 6.3% of isolates were classified as misidentified (discordance for both genus and species level), and 4.7% showed identification agreement at the genus level but not at the species level, being classified as unidentified at species level. The disagreement results were mostly associated with identification of Streptococcus and Enterococcus species probably due to the narrow phenotypic similarity between these two genera. These disagreement results suggest that biochemical assays might be prone to identification errors and, MALDI-TOF MS therefore may be an alternative to overcome incorrect species-specific identification. Standard microbiological methods for bovine mastitis diagnosis are time consuming, laborious and prone to errors for some bacteria genera. In our study, we showed that MALDI-TOF MS coupled with Biotyper may be an alternative method for large-scale identification of bacteria isolated from milk samples compared to classical microbiological routine protocols.(AU)

A espectrometria de massas (MALDI-TOF MS) tem mostrado ser um método alternativo para a identificação de bactérias, sendo capaz de identificar as bactérias causadoras de mastite em gênero, espécie ou até mesmo subespécie. Com o objetivo de identificar os patógenos causadores de mastite em grande-escala por esta plataforma, um total de 305 isolados bacterianos oriundos de vacas com mastite subclínica foram analisados pela cultura microbiológica convencional (CM) e pela MALDI-TOF MS acoplada ao software Biotyper. Aproximadamente 89% das identificações realizadas pela MALDI-TOF MS foram consistentes com os resultados obtidos pela CM. Do restante de isolados bacterianos (11%), 6,3% foram classificados como identificação errônea (discordância de gênero e espécie), e 4,7% apresentaram concordância de gênero, mas discordância da espécie. Os resultados que apresentaram divergência estavam mais associados com a identificação das espécies de Streptococcus spp. e Enterococcus spp. devido à similaridade fenotípica entre os dois gêneros. Estes resultados divergentes sugerem que os ensaios bioquímicos podem ser propensos a erros de identificação, por isso a MALDI-TOF MS pode ser considerada um método alternativo para superar os erros de identificação da CM. A cultura microbiológica padrão e os ensaios bioquímicos utilizados na identificação de agentes causadores de mastite são demorados, trabalhosos e propensos a erros quando utilizados na identificação em nível de espécie. No presente estudo, demonstramos que a MALDI-TOF MS acoplada ao software Biotyper pode ser considerada um método alternativo de identificação de bactérias causadoras de mastite em grande-escala quando comparado com a cultura microbiológica convencional.(AU)

Determinação do caseinomacropeptídeo em leite UAT por espectroscopia no infravermelho próximo e regressão por mínimos quadrados parciais / Determination of caseinomacropeptide in uht milk using near infrared spectroscopy and pls regression

O presente trabalho foi realizado com o objetivo de desenvolver um método para a determinação de CMP em leite UAT por meio da aplicação da espectroscopia de infravermelho próximo. Leites UAT de oito marcas diferentes foram utilizados para a construção dos modelos de calibração. Os resultados demonstram que, para o desenvolvimento de um modelo de calibração adequado para a determinação de CMP em leite UAT, deve-se utilizar, juntamente com o método de regressão PLS, o método de seleção de espectros máxima distância e os pré-tratamentos 2ª derivada e variável normal padronizada. Além disso, pôde-se determinar que as regiões do infravermelho próximo mais correlacionadas com os movimentos vibracionais dos aminoácidos presentes no CMP foram: 1100-1310; 1400-1430; 1490-1550; 1640-1680; 1780-1970; 2020-2100 e 2310-2350nm. Conclui-se que a espectroscopia de infravermelho próximo pode ser uma alternativa para a determinação de CMP em leite UAT, desde que haja um conjunto de calibração com amostras representativas da população a ser predita no futuro.(AU)

his work´s objective was to develop an UHT milk caseinomacropeptide determination method trough NIR spectroscopy application. Eight UHT milk trademarks are used to produce a mathematical calibration model. The results of NIR analysis suggested that to produce a suitable calibration model, partial least-square regression (PLSR) must be used, with maximum distance in wavelenght space to select spectra, pre - treatment with 2nd derivative and standard normal variant (SNV). Also, suitable near-infrared regions more correlated with CPM aminoacids vibrational movements: 1100-1310; 1400-1430; 1490-1550; 1640-1680; 1780-1970; 2020-2100; and 2310-2350nm. Therefore, NIR spectroscopy can be an alternative to caseinomacropeptide determination of UHT milk, since there was a representative calibration set with a large enough and representative sample of entire population to be predicted in the future.(AU)

An Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data.

Using the spectrum data for quality prediction always suffers from noise and colinearity, so variable selection method plays an important role to deal with spectrum data. An efficient elastic net with regression coefficients method (Enet-BETA) is proposed to select the significant variables of the spectrum data in this paper. The proposed Enet-BETA method can not only select important variables to make the quality easy to interpret, but also can improve the stability and feasibility of the built model. Enet-BETA method is not prone to overfitting because of the reduction of redundant variables realized by elastic net method. Hypothesis testing is used to further simplify the model and provide a better insight into the nature of process. The experimental results prove that the proposed Enet-BETA method outperforms the other methods in terms of prediction performance and model interpretation.