Nanna Viereck

Determination of Dry Matter Content in Potato Tubers by Low-Field Nuclear Magnetic Resonance (LF-NMR)

The objective of this study was to develop a calibration model between time-domain low-field nuclear magnetic resonance (LF-NMR) measurements and dry matter (DM) content in single potatoes. An extensive sampling procedure was used to collect 210 potatoes from eight cultivars with a wide range in DM content, ranging from 16 to 28%. The exponential NMR relaxation curves were resolved into four mono-exponential components using a number of solution diagnostics. Partial least-squares (PLS) regression between NMR parameters (relaxation time constants T(2,1-4) and magnitudes M(0,1-4)) and DM content resulted in a model with low error (RMSECV, 0.71; RMSEP, 0.60) and high correlation (r(CV), 0.97; r(test), 0.98) between predicted and actual DM content. Correlation between DM content and each of the proton populations revealed that M(0,1) (T(2,1), 3.6 ms; SD, 0.3 ms; r, 0.95) and M(0,4) (T(2,4), 508 ms; SD, 53 ms; r, -0.90) were the major contributors to the PLS regression model.

Quantification of blockiness in pectins - a comparative study using vibrational spectroscopy and chemometrics.

The gelling properties of pectins are related not only to the degree of esterification (DE), but also to the distribution of the ester groups. In this study, we have examined an experimentally designed series of 31 pectins originating from the same mother pectin and de-esterified using combinations of two different enzymatic mechanisms. The potential of using infrared (IR), Raman, and near infrared (NIR) spectroscopies combined with chemometrics for reliable and rapid determination of the DE and distribution patterns of methyl ester groups in a designed set of pectin powders was investigated. Quantitative calibration models using partial least squares (PLS) regression were developed and compared. The calibration models for prediction of DE obtained on extended inverse signal correction (EISC)-treated spectra of all three spectroscopic methods yielded models with cross-validated prediction errors (RMSECV) between 1.1%p and 1.6%p DE and correlation coefficients of 0.99. A calibration model predicting degree of random de-esterification (R) and block de-esterification (B) was developed for each spectroscopic method, yielding RMSECV values between 4.4 and 6.7 and correlation coefficients (r) between 0.79 and 0.92. Variable selection using interval PLS (iPLS) significantly improved the prediction of R for IR spectroscopy, yielding RMSECV of 3.5 and correlation coefficients of 0.95. All three spectroscopic methods were able to distinguish the spectral patterns of pectins with different enzyme treatments in simple classification models by principal component analysis (PCA). Extended canonical variate analysis revealed one specific signal in the Raman (1045cm(-1)) spectrum and one significant area (1250-1400cm(-1)) in the IR spectrum which are able to classify the pectin samples according to the four different enzyme treatments. In both Raman and IR spectra, the signal intensity decreased in the sequence R-B>B>B-R>R>re-methylated pectin.

Exploring abiotic stress on asynchronous protein metabolism in single kernels of wheat studied by NMR spectroscopy and chemometrics

Extreme climate events are being recognized as important factors in the effects on crop growth and yield. Increased climatic variability leads to more frequent extreme conditions which may result in crops being exposed to more than one extreme event within a growing season. The aim of this study was to examine the implications of different drought treatments on the protein fractions in grains of winter wheat using 1H nuclear magnetic resonance spectroscopy followed by chemometric analysis. Triticum aestivum L. cv. Vinjett was studied in a semi-field experiment and subjected to drought episodes either at terminal spikelet, during grain-filling or at both stages. Principal component trajectories of the total protein content and the protein fractions of flour as well as the 1H NMR spectra of single wheat kernels, wheat flour, and wheat methanol extracts were analysed to elucidate the metabolic development during grain-filling. The results from both the 1H NMR spectra of methanol extracts and the 1H HR-MAS NMR of single kernels showed that a single drought event during the generative stage had as strong an influence on protein metabolism as two consecutive events of drought. By contrast, a drought event at the vegetative growth stage had little effect on the parameters investigated. For the first time, 1H HR-MAS NMR spectra of grains taken during grain-filling were analysed by an advanced multiway model. In addition to the results from the chemical protein analysis and the 1H HR-MAS NMR spectra of single kernels indicating that protein metabolism is influenced by multiple drought events, the 1H NMR spectra of the methanol extracts of flour from mature grains revealed that the amount of fumaric acid is particularly sensitive to water deficits.

Bulk Carbohydrate Grain Filling of Barley β-Glucan Mutants Studied by 1H HR MAS NMR

ABSTRACT Temporal and genotypic differences in bulk carbohydrate accumulation in three barley genotypes differing in the content of mixed linkage β-(1→3),(1→4)-D-glucan (β-glucan) and starch were investigated using proton high-resolution, magic angle spinning, nuclear magnetic resonance (1H HR MAS NMR) during grain filling. For the first time, 1H HR MAS NMR spectra of flour from immature barley seeds are analyzed. Spectral assignments are made using two-dimensional (2D) NMR methods. Both α- and β-glucan biosynthesis were characterized by inspection of the spectra as well as by calibration to the reference methods for starch and β-glucan content. Starch was quantified with very good calibrations to the α-(1→4) peak (5.29–5.40 ppm) and the region 3.67–3.83 ppm covering starch glycopyranosidic protons from H5 and H6. In contrast, the spectral inspection of the β-anomeric region 4.45–4.85 ppm showed unexpected lack of intensity in the high β-glucan mutant lys5f at seed maturity, resulting in poor calibration ...

Monitoring Thermal Processes by NMR Technology

Publisher Summary This chapter illustrates that nuclear magnetic resonance (NMR) measurements can serve as a window to monitoring thermal processes, which occur inside complex and optically opaque food matrices, despite the low sensitivity of NMR, as changes can be observed in a noninvasive manner. Especially, excellent is the technique to detect changes in the distribution and mobility of water. The multiparametric and multifaceted NMR in combination with multivariate data analysis seems to have the potential to be a direct source of information for the molecular understanding of the complex phenomena occurring in food during thermal processing, including denaturation, mass transport phenomena, phase transitions, and even the formation and release of flavor and toxic compounds. NMR is capable of providing complex multivariate information concerning food samples and systems, and has a great potential for monitoring thermal transformation processes in liquids, gels, and suspensions. Predictive mathematical models in combination with spectroscopic sensors have an enormous potential in food research and industry to control and monitor the quality of raw materials, food processing, and final products. A growing interest is therefore foreseen in advanced fingerprinting methods, including high-resolution NMR sensors.

Antibiotic Treatment Preventing Necrotising Enterocolitis Alters Urinary and Plasma Metabolomes in Preterm Pigs

Necrotising enterocolitis (NEC) is a serious gut inflammatory condition in premature neonates, onset and development of which depend on the gut microbiome. Attenuation of the gut microbiome by antibiotics can reduce NEC incidence and severity. However, how the antibiotics-suppressed gut microbiome affects the whole-body metabolism in NEC-sensitive premature neonates is unknown. In formula-fed preterm pigs, used as a model for preterm infants, plasma and urinary metabolomes were investigated by LC-MS and 1H NMR, with and without antibiotic treatment immediately after birth. While it reduced the gut microbiome density and NEC lesions as previously reported, the antibiotic treatment employed in the current study affected the abundance of 44 metabolites in different metabolic pathways. In antibiotics-treated pigs, tryptophan metabolism favored the kynurenine pathway, relative to the serotonin pathway, as shown by specific metabolites. Metabolites associated with the gut microbiome, including 3-phenyllactic acid, 4-hydroxyphenylacetic acid, and phenylacetylglycine, all from phenylalanine, and three bile acids showed lower levels in the antibiotics-treated pigs where the gut microbiome was extensively attenuated. Findings in the current study warrant further investigation of metabolic and developmental consequences of antibiotic treatment in preterm neonates.

CHAPTER 8: NMR Foodomics

In modern science and technology, it is often underappreciated that foods from a chemical, physical and biological perspective are complex multifactorial systems that are extremely difficult to measure and evaluate. From a chemical perspective, foods are complex chemical mixtures of heterogeneous classes of molecules dominated by the four basic food constituents: water, fats, carbohydrates and proteins. To add further complexity, food manufacturing processes often consists of a series of unit operations that are designed to induce certain functional traits to the food materials being processed. This chapter will seek to give an overview of the possibilities and limitations of using the 1H NMR metabolomics platform to study food and food systems (foodomics). The merger of food science with advanced analytical tools, such as high-resolution NMR and multivariate data analysis chemometrics, has proven to be tremendously successful. The “weak whispers” from the protons in our food have been proven to contain a plethora of information about our aliments and to be useful in multiple applications within advanced quality control, which is not possible with existing optical spectroscopies, in particular for addressing the multiple and systemic issues related to safety and quality of food.

Investigating Depth Profiles from Porcine Adipose Tissue by HR MAS NMR Spectroscopy

One of the important quality parameters in porcine carcass grading for determining farmer payment and carcass sorting before splitting and cutting is the quality of the fat in the porcine carcass. Therefore, it is of supreme importance to obtain detailed knowledge about the chemical composition of the fat layers. In this study, we have investigated the potential of using high resolution magic angle spinning (HR MAS) NMR to investigate the depth profiles of porcine adipose fat tissue, with the purpose of estimating the variation in fatty acid composition as a function of feeding scheme and of fat layer. The measured spectra resulted in well separated profiles of the fat layers, and the signals associated with the double bonds in the fatty acids present, was used to study the total degree of unsaturation. The standard method for determining the fat quality is obtaining of the iodine value (IV), which measures the degree of unsaturation through the total number of double bonds in the fatty acids. For the possible prediction of the IV in porcine fat by HR MAS NMR spectroscopy using partial least squares regression, the iodine values measured in related studies was used.