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For those chemical compounds absorbing in the UV-Vis region and not readily applicable to routine mass spectrometry ionisation methods, liquid chromatography coupled to diode array detection is a convenient platform to perform metabolite profiling. Data processing by hand is labour-intensive and error prone. In the present study a strategy based on multivariate curve resolution, and its implementation in an R package called alsace are described. The final result of an analysis is a table containing peak heights or peak areas for all features of the individual injections. The capabilities of the software, providing elements such as splitting the data into separate, possibly overlapping time windows, merging the results of the individual time windows, and parametric time warping to align features, are illustrated using a cassava-derived data set. [ABSTRACT FROM AUTHOR]

A comparison between long (rich) and short (poor) pruning methods known as "Standard" and "Click" respectively, was performed on the cultivar Golden Delicious grafted on M9 rootstock. The cultivar was trained as Slender Spindle and Bi-axis systems. The experiment was conducted by using an integrated pest management system in the first two years followed by an organic method. Biometrical data in terms of trunk cross sectional area, height, width and depth of the trees and canopy volume were annually detected. Plants pruned with the Click method resulted more compact particularly in the Bi-axis, which had a significantly lower depth compared to the Slender Spindle training system. The increase of pruning severity in the Click method didn't affect fruit number and yield but led to increase mildly the fruit size. No differences were found between the training systems in terms of fruit size. Trees pruned with Click method resulted with a higher percentage of scab infection shoots after the primary infection even though this is a preliminary result because is related to one year only of observation. [ABSTRACT FROM AUTHOR]

Untargeted metabolomics are rapidly becoming an important tool for studying complex biological samples. Gas chromatography-mass spectrometry (GC-MS) is the most widely used analytical technology for metabolomic analysis of compounds that are volatile or can be chemically derivatised into volatile compounds. Unfortunately, data processing and analysis are not straightforward and the field is dominated by vendor-supplied software that does not always allow easy integration for large laboratories with different instruments. This paper presents an open-source pipeline for high-throughput GC-MS data processing, written in the R language and available as package metaMS. It features rapid annotation using in-house databases, and also provides support for building and validating such databases. The results are presented in simple-to-use tables, summarising the relative concentrations of identified compounds and unknowns in all samples. The use of the pipeline is illustrated using three experimental data sets. [ABSTRACT FROM AUTHOR]

We present automated data analysis of high-throughput high-performance liquid chromatography with diode array detection (HPLC-DAD) data using multivariate curve resolution. This technique provides spectra and elution profiles of all UV-Vis active compounds present in the mixture. The specifics of using this method in noninteractive fashion are discussed. A case study on the stability of isoprenoids in grape extracts under two different experimental regimes serves to illustrate the potential of the method: quantitative results clearly show that the addition of triethylamine is beneficial in that carotenoid, chlorophyll, and tocopherol compounds are much more stable and in this way can be kept up to at least 30 days without any sign of degradation. [ABSTRACT FROM AUTHOR]

Optimal solvent conditions for grape sample preparation were investigated for the purpose of metabolite profiling studies, with the aim of obtaining as many features as possible with the best analytical repeatability. Mixtures of water, methanol and chloroform in different combinations were studied as solvents for the extraction of ground grapes. The experimental design used a two stage study to find the optimum extraction medium. The extracts obtained were further purified using solid phase extraction and analysed using a UPLC full scan TOF MS with both reversed phase and hydrophilic interaction chromatography. The data obtained were processed using data extraction algorithms and advanced statistical software for data mining. The results obtained indicated that a fairly broad optimal area for solvent composition could be identified, containing approximately equal amounts of methanol and chloroform and up to 20% water. Since the water content of the samples was variable, the robustness of the optimal conditions suggests these are appropriate for large scale profiling studies for characterisation of the grape metabolome. [ABSTRACT FROM AUTHOR]

Abstract: In compliance with the European law (EC No. 510/2006), geographical indications and designations of origin for agricultural products and foodstuffs must be protected against mislabelling. This is particularly important for PDO hard cheeses, as Parmigiano Reggiano, that can cost up to the double of the no-PDO competitors. This paper presents two statistical models, based on isotopic and elemental composition, able to trace the origin of cheese also in grated and shredded forms, for which it is not possible to check the logo fire-marked on the rind. One model is able to predict the origin of seven types of European hard cheeses (in a validation step, 236 samples out of 240 are correctly recognised) and the other specifically to discriminate the PDO Parmigiano Reggiano cheese from 9 European and 2 extra-European imitators (260 out of 264 correct classifications). Both models are based on Random Forests. The most significant variables for cheese traceability common in both models are δ 13C, δ 2H, δ 15N, δ 34S and Sr, Cu, Mo, Re, Na, U, Bi, Ni, Fe, Mn, Ga, Se, and Li. These variables are linked not only to geography, but also to cow diet and cheese making processes. [Copyright &y& Elsevier]

Abstract: Biomarker identification, i.e., finding those variables that indicate true differences between two or more populations, is an ever more important topic in the omics sciences. In most cases, the number of variables far exceeds the number of samples, making biomarker identification extremely difficult. We present a strategy based on the stability of putative biomarkers under perturbation of the data, and show that in several cases important gains can be achieved. The strategy is very general and can be applied with all common biomarker identification methods; it also has the advantage that it does not rely on error estimates from crossvalidation, that in this setting tend to be highly variable. [Copyright &y& Elsevier]

Our current knowledge of the general factor requirement in transcription by the three mammalian RNA polymerases is based on a small number of model promoters. Here, we present a comprehensive chromatin immunoprecipitation (ChIP)-on-chip analysis for 28 transcription factors on a large set of known and novel TATA-binding protein (TBP)-binding sites experimentally identified via ChIP cloning. A large fraction of identified TBP-binding sites is located in introns or lacks a gene/mRNA annotation and is found to direct transcription. Integrated analysis of the ChIP-on-chip data and functional studies revealed that TAF12 hitherto regarded as RNA polymerase II (RNAP II)-specific was found to be also involved in RNAP I transcription. Distinct profiles for general transcription factors and TAF-containing complexes were uncovered for RNAP II promoters located in CpG and non-CpG islands suggesting distinct transcription initiation pathways. Our study broadens the spectrum of general transcription factor function and uncovers a plethora of novel, functional TBP-binding sites in the human genome. [ABSTRACT FROM AUTHOR]

Clustering is often useful for analyzing and summarizing information within large datasets. Model-based clustering methods have been found to be effective for determining the number of clusters, dealing with outliers, and selecting the best clustering method in datasets that are small to moderate in size. For large datasets, current model-based clustering methods tend to be limited by memory and time requirements and the increasing difficulty of maximum likelihood estimation. They may fit too many clusters in some portions of the data and/or miss clusters containing relatively few observations. We propose an incremental approach for data that can be processed as a whole in memory, which is relatively efficient computationally and has the ability to find small clusters in large datasets. The method starts by drawing a random sample of the data, selecting and fitting a clustering model to the sample, and extending the model to the full dataset by additional EM iterations. New clusters are then added incrementally, initialized with the observations that are poorly fit by the current model. We demonstrate the effectiveness of this method by applying it to simulated data, and to image data where its performance can be assessed visually. [ABSTRACT FROM AUTHOR]

Markov random field (MRF) clustering, utilizing both spectral and spatial interpixel dependency information, often improves classification accuracy for remote sensing images, such as multichannel polarimetric synthetic aperture radar (SAR) images. However, it is heavily sensitive to initial conditions such as the choice of the number of clusters and their parameters. In this paper, an initialization scheme for MRF clustering approaches is suggested for remote sensing images. The proposed method derives suitable initial cluster parameters from a set of homogeneous regions, and estimates the number of clusters using the pseudo- likelihood information criterion (PLIC). The method works best for an image consisting of many large homogeneous regions, such as agricultural crops areas. It is illustrated using a well-known polarimetric SAR image of Flevoland in the Netherlands. The experiment shows a superior performance compared to several other methods, such as fuzzy C-means and iterated conditional modes (ICM) clustering. [ABSTRACT FROM AUTHOR]

High-throughput phenotyping is playing an increasingly important role in many areas of agriculture. Breeders will use it to obtain values for the traits of interest so that they can estimate genetic value and select promising varieties; growers may be interested in having predictions of yield well in advance of the actual harvest. In most phenotyping applications, image analysis plays an important role, drastically reducing the dependence on manual labor while being non-destructive. An automatic phenotyping system combines a reliable acquisition system, a high-performance segmentation algorithm for detecting fruits in individual images, and a registration algorithm that brings the images (and the corresponding detected plants or plant components) into a coherent spatial reference frame. Recently, significant advances have been made in the fields of robotics, image registration, and especially image segmentation, which each individually have improved the prospect of developing a fully integrated automatic phenotyping system. However, so far no complete phenotyping systems have been reported for routine use in a production environment. This work catalogs the outstanding issues that remain to be resolved by describing a prototype phenotyping system for a production tomato greenhouse, for many reasons a challenging environment. [ABSTRACT FROM AUTHOR]

The increased usage of alternative Ayurvedic treatments as potential health-beneficial therapies emphasizes the importance of studying its efficacy in sound placebo-controlled intervention trials. An example of such a traditional Ayurvedic herbal preparation is Mohana Choorna, a mixture composed of 20 different herbs and used to prevent and treat type 2-diabetes (T2D). We studied the efficacy of "Mohana Choorna" on T2D-related parameters in subjects with impaired glucose tolerance. In a double blind, placebo-controlled cross-over trial, 19 overweight (BMI > 27 kg/m2) subjects aged 50–70 years with an impaired glucose tolerance received two four-week interventions, i.e., herbal or placebo with a four-week wash-out between interventions. HbA1c, glucose, insulin, triglycerides, cholesterol, blood pressure and augmentation index were measured before and after both interventions at fasting and during a glucose tolerance test. After both interventions, urine was collected to measure treatment exposure using LCMS-based metabolomics and whole genome gene-expression in adipose tissue of 13 subjects. The herbal intervention did not affect plasma glucose triglycerides, cholesterol, blood pressure or the augmentation index but showed a trend towards an increased insulin, HOMA-IR and postprandial insulin levels (p = 0.054, p = 0.056 and p = 0.095 respectively). An increase in expression of inflammation-related gene sets in adipose tissue was observed after the herbal intervention compared to placebo. Urine metabolomic analysis did not reveal a correlation of the presence of specific plant metabolites with "health markers". Our findings suggest that there is no substantiating evidence to claim that four weeks' use of the Ayurvedic herbal supplement Mohana Choorna beneficially affects glucose homeostasis. [ABSTRACT FROM AUTHOR]

To identify the potential anabolic properties of a dairy-plant protein blend as compared to single plant-based and single dairy protein, the postprandial amino acid (AA) response of pea protein, milk protein, micellar casein, and a casein–pea protein blend was investigated in healthy older adults (age 72.3 ± 3.4 years, BMI 25.3 ± 2.9 kg/m2). Plasma AA levels were measured, before and up to 5 h after ingestion of each 20 g protein. Blending casein–pea in a 60/40 mixture resulted in improved plasma AA availability, i.e. area under the curve (AUC) and peak height, of total (essential) AA and of key AAs methionine and leucine compared to pea only, while preserving the higher availability of arginine. The casein/pea blend clearly showed an AA response that was in between that of its single constituents, indicating that blending could be a solution to improve a lower quality (plant) protein, which could be of relevance for older adults. [ABSTRACT FROM AUTHOR]

In this study, we investigated the relationship between (D/H) 1, (D/H) 2 and δ 13 C of ethanol and δ 18 O of water in wine, and variables describing the climate and the geography of the production area, using exploratory visualisation tools, regression analysis and linear modelling. For the first time, a large amount of data (around 4000 wine samples collected over 11 years in Italy) and all the official isotopic parameters, as well as a large number of significant climatic and geographical descriptors (date of harvest, latitude, longitude, elevation, distance from the sea, amount of precipitation, maximum daily temperature, minimum daily temperature, mean daily temperature, δ 18 O and δ 2 H of precipitation) were considered. δ 18 O, followed by (D/H) 1 , was shown to have the strongest relationship with climate and location. The dominant variables were latitude, with a negative relationship, δ 18 O and δ 2 H of precipitation and temperature, both with positive relationships. The identified correlations and models could be used to predict the isotopic composition of authentic wines, offering increased possibilities for detecting fraud and mislabelling. [ABSTRACT FROM AUTHOR]

Key message: We evaluate self-organizing maps (SOM) to identify adaptation zones and visualize multi-environment genotypic responses. We apply SOM to multiple traits and crop growth model output of large-scale European sunflower data. Genotype-by-environment interactions (G × E) complicate the selection of well-adapted varieties. A possible solution is to group trial locations into adaptation zones with G × E occurring mainly between zones. By selecting for good performance inside those zones, response to selection is increased. In this paper, we present a two-step procedure to identify adaptation zones that starts from a self-organizing map (SOM). In the SOM, trials across locations and years are assigned to groups, called units, that are organized on a two-dimensional grid. Units that are further apart contain more distinct trials. In an iterative process of reweighting trial contributions to units, the grid configuration is learnt simultaneously with the trial assignment to units. An aggregation of the units in the SOM by hierarchical clustering then produces environment types, i.e. trials with similar growing conditions. Adaptation zones can subsequently be identified by grouping trial locations with similar distributions of environment types across years. For the construction of SOMs, multiple data types can be combined. We compared environment types and adaptation zones obtained for European sunflower from quantitative traits like yield, oil content, phenology and disease scores with those obtained from environmental indices calculated with the crop growth model Sunflo. We also show how results are affected by input data organization and user-defined weights for genotypes and traits. Adaptation zones for European sunflower as identified by our SOM-based strategy captured substantial genotype-by-location interaction and pointed to trials in Spain, Turkey and South Bulgaria as inducing different genotypic responses. [ABSTRACT FROM AUTHOR]

Highlights: [•] We present a review on the statistical tools used to deal with multiple comparisons. [•] We discuss the fundamental theoretical concepts and ideas. [•] We present application to metabolomics and proteomics. [ABSTRACT FROM AUTHOR]

A human intervention trial was conducted to study amino acid uptake of the novel Lemna protein concentrate (LPC) in comparison to whey (WPC). The study was a cross-over, double-blind, controlled trial in which 12 healthy participants received 20 grams of LPC and WPC in randomised order. The LPC consumption resulted in a significant lower postprandial increase in almost all individual amino acids, total amino acid (TAA) and total essential amino acids (TEAA) compared to WPC based on area under the curve (AUC) calculations. When the AUC after WPC consumption was set at 100%, LPC showed a relative AUC of 60.4% for TAA and 66.3% for the TEAA. Interindividual variation for LPC was high with an uptake of TEAA of LPC compared to WPC ranging from 18.2 to 94.2%. Human intervention trials can partly replace animal trials as they fully reflect the human situation and provide estimates on individual variations. [ABSTRACT FROM AUTHOR]

Pea protein is an interesting alternative for animal-based proteins due to its good availability, low cost, and relatively balanced amino acid (AA) profile. Its digestibility may be affected by heat treatment and food texture. Our aim was to study in vivo AA absorption kinetics and gastric behavior of pea protein products differing in heat treatment and texture and compare this with in vitro digestion. We included fourteen males in this randomized crossover trial with three iso-caloric and iso-volumetric treatments: a 420-mL heated drink, 420-mL unheated drink and 105-g heated gel (semi-solid) consumed with 315 mL water, all containing 20 g pea protein. Gastric MRI scans were made until 90 min post-prandial. Blood samples were collected at baseline and up to 5 h. All treatments were tested with an in vitro digestion model (INFOGEST). Heat treatment did not alter AA absorption kinetics and gastric emptying. Time to maximum peak was delayed for the gel treatment (total AAs: 66.9 versus 48.0 min for both drinks, essential AAs: 75.4 versus 50.0 and 46.6 min for the drinks). For the gel treatment initial emptying was faster due to the rapid passage of water. In vitro , the degree of hydrolysis was highest for the unheated drink in the gastric phase and for the gel treatment in the intestinal phase. In conclusion, heat treating pea protein products does not affect digestion. In contrast, texture of pea protein products can be altered to influence the rate of gastric emptying and AA absorption without affecting total AA absorption. [Display omitted] • We compared gastric digestion and absorption kinetics of pea protein test products. • Products differed in texture and heating and were tested in vivo and in vitro. • Heating had no effect on gastric emptying and amino acid absorption. • Semi-solid texture delayed these measures, but total absorption was similar. • In vitro results were similar for texture differences, but not for heat treatment. [ABSTRACT FROM AUTHOR]

Isotope Ratio Mass Spectrometry (IRMS), 1 H Nuclear Magnetic Resonance ( 1 H NMR), conventional chemical analysis and chemometric elaboration were used to assess quality and to define and confirm the geographical origin of 177 Italian PDO (Protected Denomination of Origin) olive oils and 86 samples imported from Tunisia. Italian olive oils were richer in squalene and unsaturated fatty acids, whereas Tunisian olive oils showed higher δ 18 O, δ 2 H, linoleic acid, saturated fatty acids β-sitosterol, sn -1 and 3 diglyceride values. Furthermore, all the Tunisian samples imported were of poor quality, with a K 232 and/or acidity values above the limits established for extra virgin olive oils. By combining isotopic composition with 1 H NMR data using a multivariate statistical approach, a statistical model able to discriminate olive oil from Italy and those imported from Tunisia was obtained, with an optimal differentiation ability arriving at around 98%. [ABSTRACT FROM AUTHOR]

Abstract: Phenolic compounds account for the most important class of secondary metabolites in raspberries and fulfill a broad range of biological functions in plants. Due to their presence in fruits they are also considered as important bioactive compounds in human nutrition and are closely related to fruit quality. In the present study a targeted UPLC–MS/MS method was used to screen various phenolic compounds in fruits of red and yellow raspberry cultivars. In total 50 phenolic compounds were detected above the quantification limit. Beside the obvious lack of anthocyanins, all yellow fruits analysed here lack procyanidin B1. The presence of this dimer, along with B3 dimers is described for the first time in raspberry fruits. Also for the first time, dihydrochalcone and stilbene derivatives and the quercetin metabolite, isorhamnetin with its glycosides, were identified in considerable concentrations in raspberries. Based on a PCA plot the red cultivar “Heritage” and the yellow “Alpen Gold” could clearly be separated from the other tested cultivars due to their distinct metabolite profiles/concentrations. This study allowed to obtain a comprehensive profile of the phenolic composition of the different raspberry varieties. The obtained data will lead to a better understanding of the overall biosynthetic network of polyphenols in raspberry and will help to explain responsible factors for the different metabolite profiles in ongoing studies. [Copyright &y& Elsevier]

Highlights: [•] The concepts of warping and alignment are introduced. [•] The most important warping methods are critically reviewed and explained. [•] Reference selection, evaluation and place of warping in preprocessing are discussed. [•] Some pitfalls, especially for LC–MS and similar data, are addressed. [•] Examples are provided, together with programming scripts to rework and extend them. [Copyright &y& Elsevier]

Abstract: Recently the first applications in food science and technology of the newly available volatile organic compound (VOC) detection technique proton transfer reaction‐mass spectrometry, coupled with a time of flight mass analyzer (PTR-TOF-MS), have been published. In comparison with standard techniques such as GC-MS, PTR-TOF-MS has the remarkable advantage of being extremely fast but has the drawback that compound identification is more challenging and often not possible without further information. In order to better exploit and understand the analytical information entangled in the PTR-TOF-MS fingerprint and to link it with SPME/GC-MS analyses we employed two multivariate calibration methods, PLS and the more recent LASSO. We show that, while in some cases it is sufficient to consider a single PTR-TOF-MS peak in order to predict the intensity of a SPME/GC-MS peak, in general a multivariate approach is needed. We compare the performances of PLS and LASSO in terms of prediction capabilities and interpretability of the model coefficients and conclude that LASSO is more suitable for this problem. As case study, we compared GC and PTR-MS data for different matrices, namely olive oil and grana cheese. [Copyright &y& Elsevier]

Abstract: We present an improved version of parametric time warping, which enables the method to be used in LC–MS measurements in proteomics. The new features include a new similarity measure for comparing warped chromatograms, an insurance against peaks at the extremes of the chromatograms disappearing because of the warping, and the possibility to select and use multiple traces in searching the optimal alignment. Moreover, we present an alignment strategy combining global and individual alignments for aligning LC–MS data that exhibit different shifts within the same sample, at the same retention time. Using an LC–MS data set consisting of E. coli homogenates that were measured in several batches over a period of six months, we show the benefits of the improved algorithm and the merits of the new strategy. The algorithm is publicly available as the R package ptw. [ABSTRACT FROM AUTHOR]

Nowadays, a healthier and more sustainable lifestyle is the subject of much research. One example is the use of crossover trials to investigate the uptake of proteins, usually from alternatives to animal-based sources, by healthy volunteers. The data analysis is complex and requires many decisions on the part of the scientists involved. Such a process can be streamlined and made more objective and reproducible through bespoke software. This paper describes such a software package, aaresponse , for the R environment, available as open source. It features ample visualization functions, supports consistent curation strategies, and compares amino acid uptake of different protein meals (interventions) through the use of mixed models analysing parameters of interest like the area under the curve (AUC). The defining feature is the use of parametric curves to fit the amino acid levels over time, increasing the robustness of the approach and allowing for more strict quality control strategies., (© 2024. The Author(s).)

Important considerations in the choice of future sustainable protein sources for human application are tolerance, nutritional quality, and potential health benefits. We evaluated, in a double-blind cross-over intervention trial, tolerance, nutritional quality, and potential health effects of two sustainable protein sources. Thirty-six apparently healthy older adults (age 62.3 ± 7.2yrs, BMI 25 ± 3 kg/m2) received 40 g/day bovine-plasma protein (BP), corn protein (CP) or, as a benchmark, whey protein (WP) for one week with a washout period of one week in-between. In 12 participants, we also determined postprandial amino acid (PAA) uptake kinetics upon consumption of 20 g BP, CP, or WP. Changes in self-reported gastrointestinal complaints and intestinal permeability assessed using a multi-sugar acetylsalicylic acid test did not differ between the interventions. Clear differences in PAA responses were observed after consumption of the different proteins, but clear essential amino acid responses were observed for all proteins. BP consumption resulted in a small but significant increase in blood pressure outcomes, and CP consumption resulted in a small but significant decrease in insulin levels when compared to the other interventions. In conclusion, alternative protein concentrates and isolates studied here can be consumed in relative high quantities without experiencing unwanted GI complaints or gut barrier dysfunction and they can be a good source of essential amino acids. The rise in blood pressure observed during the BP intervention, potentially linked to the elevated salt content of the BP, constitutes a potential health issue. Future studies with longer intervention periods might however be recommended., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (© 2023 The Authors.)

Valorization and utilization of brewers' spent grain (BSG) are of great interest in terms of reducing food waste and promoting more sustainable food systems. In this study, we aimed to evaluate the nutritional value of upcycled barley/rice proteins (BRP) extracted from BSG and compare this with pea proteins (PP). A randomized, cross-over, double-blind controlled trial was conducted with twelve participants (age: 24 ± 2.8 years, BMI: 23.3 ± 3.0 kg/m 2 ). During three separate visits with a one-week washout period between visits, participants received 20 g BRP, PP, or the benchmark protein whey (WP). Blood-free amino acids (AA) were measured to determine postprandial AA uptake kinetics. The estimated total AA (TAA) uptake of BRP was 69% when compared to WP and 87% when compared to PP. The time to reach the maximum values was similar between the three protein sources. When comparing individual essential AA responses between BRP and PP, we observed higher responses in methionine and tryptophane and lower responses in lysine, histidine, and isoleucine for BRP compared to PP. This study demonstrates that BRP exhibits comparable postprandial TAA uptake profiles to PP. The findings highlight the complementarity of BRP and PP, which may offer the potential for blending approaches to optimize protein quality for overall health.

In plants, secondary metabolite profiles provide a unique opportunity to explore seasonal variation and responses to the environment. These include both abiotic and biotic factors. In field experiments, such stress factors occur in combination. This variation alters the plant metabolic profiles in yet uninvestigated ways. This data set contains trait and mass spectrometry data of thirteen grassland species collected at four time points in the growing season in 2017. We collected above-ground vegetative material of seven grass and six herb species that were grown in plant communities with different levels of diversity in the Jena Experiment. For each sample, we recorded visible traits and acquired shoot metabolic profiles on a UPLC-ESI-Qq-TOF-MS. We performed the raw data pre-processing in Galaxy-W4M and prepared the data for statistical analysis in R by applying missing data imputation, batch correction, and validity checks on the features. This comprehensive data set provides the opportunity to investigate environmental dynamics across diverse neighbourhoods that are reflected in the metabolomic profile.

Introduction: Batch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account., Objectives: This paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects., Methods: Batch correction methods usually consist of regression models, possibly also accounting for trends within batches. To fit these models quality control samples (QCs), injected at regular intervals, can be used. Also study samples can be used, provided that the injection order is properly randomized. Normalization methods, not using information on batch labels or injection order, can correct for batch effects as well. Introducing two easy-to-use quality criteria, we assess the merits of these batch correction strategies using three large LC-MS and GC-MS data sets of samples from Arabidopsis thaliana ., Results: The three data sets have very different characteristics, leading to clearly distinct behaviour of the batch correction strategies studied. Explicit inclusion of information on batch and injection order in general leads to very good corrections; when enough QCs are available, also general normalization approaches perform well. Several approaches are shown to be able to handle non-detects-replacing them with very small numbers such as zero seems the worst of the approaches considered., Conclusion: The use of quality control samples for batch correction leads to good results when enough QCs are available. If an experiment is properly set up, batch correction using the study samples usually leads to a similar high-quality correction, but has the advantage that more metabolites are corrected. The strategy for handling non-detects is important: choosing small values like zero can lead to suboptimal batch corrections.

The generally accepted benefits of small lateral dimensions of microreactors (1 microm to 1 mm) enable a different way of performing synthetic chemistry: Extremely short contact times in the millisecond range can circumvent the need for performing highly exothermic and fast reactions at very low temperatures. In order to fully exploit this technology, such fast processes need to be redesigned and investigated for optimal reaction conditions, which can differ drastically from the ones traditionally applied. In a comprehensive study, we optimized the selective Swern-Moffatt oxidation of benzyl alcohol to benzaldehyde by varying five experimental parameters, including reaction time and temperature. Employing an ultrashort mixing and reaction time of only 32 ms, the optimal temperature was determined to be 70 degrees C, approximately 150 degrees C higher than in the conventional batch conditions. This remarkable difference shows both the potency of continuous-flow chemistry as well as the urgency of a paradigm shift in reaction design for continuous-flow conditions.