Development and validation of an analytical method by HPLC-DAD for determination of caffeine in products based on guarana extracts (Paullinia cupana)

Abstract Guarana (Paullinia cupana) is a native plant from the Amazon whose seeds contain a high concentration of caffeine. Aqueous extract of guarana is widely used in the world. In this study, the objective was to develop and validate a High-Performance Liquid Chromatography method for the determination of caffeine in extracts and commercial beverages based on guarana. A sensitive, simple, and viable high performance liquid chromatographic method without the need of an analyte extraction procedure was developed and validated according to Brazilian and international requirements. The method presented high performance, fulfilling Brazilian and international requirements, in addition to allowing product compliance tests. Results confirmed high selectivity and linearity (>0.999) between 5 to 135 ug/mL, with no significant matrix effect. Detection and quantification limits were 0.02 µg/mL and 2 µg/mL, respectively. Precision was less than 4 %, and accuracy varied from 99.9-120 %. Applicability of the method was demonstrated by conducting a limited evaluation in products containing caffeine. Commercial extracts showed quite different caffeine levels, while carbonated drinks follow Brazilian and American recommendations. Our results indicate that the developed method can be used to evaluate the quality of the guarana extract and of products containing caffeine

Fusarium head blight resistance exacerbates nutritional loss of wheat grain at elevated CO2.

The nutritional integrity of wheat is jeopardized by rapidly rising atmospheric carbon dioxide (CO2) and the associated emergence and enhanced virulence of plant pathogens. To evaluate how disease resistance traits may impact wheat climate resilience, 15 wheat cultivars with varying levels of resistance to Fusarium Head Blight (FHB) were grown at ambient and elevated CO2. Although all wheat cultivars had increased yield when grown at elevated CO2, the nutritional contents of FHB moderately resistant (MR) cultivars were impacted more than susceptible cultivars. At elevated CO2, the MR cultivars had more significant differences in plant growth, grain protein, starch, fructan, and macro and micro-nutrient content compared with susceptible wheat. Furthermore, changes in protein, starch, phosphorus, and magnesium content were correlated with the cultivar FHB resistance rating, with more FHB resistant cultivars having greater changes in nutrient content. This is the first report of a correlation between the degree of plant pathogen resistance and grain nutritional content loss in response to elevated CO2. Our results demonstrate the importance of identifying wheat cultivars that can maintain nutritional integrity and FHB resistance in future atmospheric CO2 conditions.

Determining the geographical origin of flaxseed based on stable isotopes, fatty acids and antioxidant capacity.

BACKGROUND: Flaxseed is an economically important oilseed crop whose geographic origin is of significant interest to producers and consumers because every region may exhibit particular quality characteristics. The lipid/fatty acid method of determining the geographic origin of flaxseed has not been found to be adequate. RESULTS: To improve the discrimination rate and the geographical traceability of this crop, the chemical profiles of the flaxseed samples were characterized via lipids/fatty acids, stable isotopes, and antioxidant capacity. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were also performed. A satisfactory discrimination rate of 98.6% was obtained after combining fatty acids, stable isotopes, and antioxidant capacity to trace the origin of flaxseed from five regions in northern China. CONCLUSION: This study provides an effective method for distinguishing the geographic origin of flaxseed. © 2021 Society of Chemical Industry.

Effects of Urtica dioica Seeds on Oxidative/ Nitrosative Stress Levels and Myeloperoxidase Activity in Muscle Ischemia/Reperfusion Injury

Abstract The purpose of this study is to evaluate the preventive effects of Urtica dioica (UD) on muscle ischemia/reperfusion (I/R) injury. A total of 27 male Wistar rats were divided into three groups as the control group (1), I/R + saline group (2), and I/R+UD group (3). Group 1 did not receive any treatment. Group 2 was administered a total of 2mL/kg saline (1mL/kg before ischemia and 1 mL/kg after reperfusion), and group 3 was given a total of 2mL of UD (1mL/kg before ischemia and 1mL/kg after reperfusion) as treatment. Saline and UD were administered via intraesophageal canula once a day for five days. At the end of five days, all the rats were exposed to muscle ischemia for 60 min followed by 60 min of reperfusion of the bilateral hindlimbs induced using a tourniquet. Muscle tissue histopathologies were evaluated by light microscopy. Furthermore, oxidative/nitrosative stress biomarkers such as catalase (CAT), superoxide dismutase (SOD), malondialdehyde (MDA), nitrotyrosine (3-NT), nitric oxide (NO), and myeloperoxidase (MPO) as an inflammatory marker in tissue samples were measured. UD treatment significantly decreased oxidative/nitrosative stress biomarker levels and MPO (p<0.05). We established that UD treatment could alleviate muscle injury induced by muscle I/R in rats by inhibiting the inflammation and oxidative/nitrosative stress

Global patterns of potential future plant diversity hidden in soil seed banks.

Soil seed banks represent a critical but hidden stock for potential future plant diversity on Earth. Here we compiled and analyzed a global dataset consisting of 15,698 records of species diversity and density for soil seed banks in natural plant communities worldwide to quantify their environmental determinants and global patterns. Random forest models showed that absolute latitude was an important predictor for diversity of soil seed banks. Further, climate and soil were the major determinants of seed bank diversity, while net primary productivity and soil characteristics were the main predictors of seed bank density. Moreover, global mapping revealed clear spatial patterns for soil seed banks worldwide; for instance, low densities may render currently species-rich low latitude biomes (such as tropical rain-forests) less resilient to major disturbances. Our assessment provides quantitative evidence of how environmental conditions shape the distribution of soil seed banks, which enables a more accurate prediction of the resilience and vulnerabilities of plant communities and biomes under global changes.

Image processing techniques to estimate weight and morphological parameters for selected wheat refractions.

The geometric and color features of agricultural material along with related physical properties are critical to characterize and express its physical quality. The experiments were conducted to classify the physical characteristics like size, shape, color and texture and then workout the relationship between manual observations and using image processing techniques for weight and volume of the four wheat refractions i.e. sound, damaged, shriveled and broken grains of wheat variety PBW 725. A flatbed scanner was used to acquire the images and digital image processing method was used to process the images and output of image analysis was compared with the actual measurements data using digital vernier caliper. A linear relationship was observed between the axial dimensions of refractions between manual measurement and image processing method with R2 in the range of 0.798-0.947. The individual kernel weight and thousand grain weight of the refractions were observed to be in the range of 0.021-0.045 and 12.56-46.32 g respectively. Another linear relationship was found between individual kernel weight and projected area estimated using image processing methodology with R2 in the range of 0.841-0.920. The sphericity of the refractions varied in the range of 0.52-0.71. Analyses of the captured images suggest ellipsoid shape with convex geometry while the same observation was recorded by physical measurements also. A linear relationship was observed between the volume of refractions derived from measured dimensions and calculated from image with R2 in the range of 0.845-0.945. Various color and grey level co-variance matrix texture features were extracted from acquired images using the open-source Python programming language and OpenCV library which can exploit different machine and deep learning algorithms to properly classify these refractions.

Extended seed rain period of Adenostoma fasciculatum impacts diverse seed predators.

AIMS: The principal chaparral species in California, Adenostoma fasciculatum, an evergreen, sclerophyllous shrub, is broadly distributed and provides habitat and food resources for a large and diverse animal community. The effects of climate change, including elevated temperatures, fire frequency and severity, along with increased urban encroachment, have placed pressure on chaparral habitats in California. Our goal is to investigate aspects of reproductive ecology as a measure of the potential resiliency of A. fasciculatum. We focus on seed rain (all seed falling into the seed traps regardless of origin) and seed banks in the context of plant-animal interactions and regeneration. METHODS: Stand recovery following disturbance is achieved through both resprouting and germination from established persistent soil seed banks. In this study we focus on seed ecology using a series of experiments to document the length and quantity of seed rain, seed predation, parsing the importance of the community of granivores, and evaluating the connection between stand age and germination rate from soil seed banks. IMPORTANT FINDINGS: Our research documented an 8-month seed rain duration with over 1 million seeds per m2, multiple seed predators including passerines (songbirds) and rodents, and points to the possibility of native ants playing a role in the seed dispersal process. This is important given the recent advancement of the invasive Argentine ant (Linepthema humile) into Californian chaparral. This research demonstrates a clear relationship between A. fasciculatum and both resident and migratory granivores in the chaparral. We documented that a 39-year-old stand had higher germination rates than those which were 16, 20, 41 and 71 years old and how seed banks play a major role in assuring resiliency following fire. These findings are important for wildland managers to assure the continued resiliency of A. fasciculatum.

Alien plant species on roadsides of the northwestern Patagonian steppe (Argentina).

The introduction of alien species represents one of the greatest threats to biodiversity worldwide. Highway construction increases the dispersal and invasion of exotic plant species. This study examined the assembly process of the plant communities to determine whether the roadsides of the Patagonian steppe represent a reservoir and dispersal source of invasive exotic species. We analyzed the composition of exotic and native species and functional groups present in the established vegetation and seed banks of roadsides and reference areas nearby. The type of dispersal of exotic and native species at the roadsides was also evaluated. Total cover and that of exotic and native species was lower at the roadsides than in the reference areas; however, at the roadsides the cover and seed abundance of exotic species was higher than that of native species. In the roadsides vegetation, native shrubs such as Acaena splendens predominated, along with exotic perennial herbs and grasses which were mainly represented by Rumex acetosella. In the seed bank the predominant species were exotic perennial herbs, also represented by R. acetosella, annual exotic species such as Epilobium brachycarpum and Verbascum thapsus, and annual native species such as Heliotropium paronychioides. No exotic shrubs were found either at the roadsides or in the reference areas. The species at the roadsides did not present a dominant type of dispersal. The abundance of exotic species at the roadsides, both in the aboveground vegetation and the seed bank, may be due to the stressful environment and the characteristics of the species themselves, such as the ability to form seed banks. This work revealed that the roadsides of the Patagonian steppe constitute reservoirs of invasive exotic species, highlighting the importance of identifying them and controlling their spread, with a view to generating ecosystem management programs.

Influences of grain and protein characteristics on in vitro protein digestibility of modern and ancient wheat species.

BACKGROUND: The resistance of proteins to gastrointestinal digestion contributes to their ability to act as allergens. Near-complete digestion of protein in wheat products is important with respect to avoiding the potential immunogenic effects of undigested peptides. Five modern US wheat classes (soft red winter, hard winter, hard red spring, club and durum) including 17 wheat varieties, as well as three ancient wheat classes (spelt, emmer and einkorn) including nine wheat varieties, were analyzed for kernel hardness (KH) and flour protein characteristics, in addition to in vitro protein digestibility (IVPD) of cooked flour, flour without albumins and globulins (FWOAG), gluten, albumins, and globulins, aiming to identify the factors influencing the protein digestibility of flour. RESULTS: IVPDs of flour, FWOAG, gluten and albumins of wheat varieties ranged from 86.5% to 92.3%, 85.8% to 90.3%, 90.6% to 94.6% and 74.8% to 85.1%, respectively. The IVPD of gluten was significantly higher than the IVPDs of flour and FWOAG, indicating that non-protein components substantially affect protein digestibility. Significant differences were observed in IVPDs of flour and albumins among eight wheat classes, but not in the IVPDs of FWOAG, gluten and globulins. There were apparent differences in undigested protein bands and intensities of wheat classes with low and high flour IVPDs. KH and albumin proportion exhibited negative and positive relationships, respectively, with flour IVPD. CONCLUSION: The results of the present study demonstrate that KH, non-protein components and albumin proportion have a major influence on protein digestion and need to be considered when developing wheat cultivars with higher protein digestibility.

A cost-effective maize ear phenotyping platform enables rapid categorization and quantification of kernels.

High-throughput phenotyping systems are powerful, dramatically changing our ability to document, measure, and detect biological phenomena. Here, we describe a cost-effective combination of a custom-built imaging platform and deep-learning-based computer vision pipeline. A minimal version of the maize (Zea mays) ear scanner was built with low-cost and readily available parts. The scanner rotates a maize ear while a digital camera captures a video of the surface of the ear, which is then digitally flattened into a two-dimensional projection. Segregating GFP and anthocyanin kernel phenotypes are clearly distinguishable in ear projections and can be manually annotated and analyzed using image analysis software. Increased throughput was attained by designing and implementing an automated kernel counting system using transfer learning and a deep learning object detection model. The computer vision model was able to rapidly assess over 390 000 kernels, identifying male-specific transmission defects across a wide range of GFP-marked mutant alleles. This includes a previously undescribed defect putatively associated with mutation of Zm00001d002824, a gene predicted to encode a vacuolar processing enzyme. Thus, by using this system, the quantification of transmission data and other ear and kernel phenotypes can be accelerated and scaled to generate large datasets for robust analyses.

Quality differentiation of cocoa beans: implications for geographical indications.

Geographical indications may stimulate collective actions of governance for quality control, trade and marketing as well as innovation based on the use of local resources and regional biodiversity. Cocoa production, however, dominated by small family agriculture in tropical regions, has rarely made use of such strategies. This review is aimed at understanding major research interests and emerging technologies helpful for the origin differentiation of cocoa quality. Results from literature search and cited references of publications on cocoa research were imported into VOSviewer for data analysis, which aided in visualizing major research hotpots. Co-occurrence analysis yielded major research clusters which guided the discussion of this review. Observed was a consensus recognizing cocoa quality resulting from the interaction of genotype, fermentation variables and geographical origin. A classic view of cocoa genetics based on the dichotomy of 'fine versus bulk' has been reexamined by a broader perspective of human selection and cocoa genotype evolution. This new approach to cocoa genetic diversity, together with the understanding of complex microbiome interactions through fermentation, as well as quality reproducibility challenged by geographical conditions, have demonstrated the importance of terroir in the production of special attributes. Cocoa growing communities around the tropics have been clearly enabled by new omics and chemometrics to systematize producing conditions and practices in the designation of specifications for the differentiation of origin quality. © 2021 Society of Chemical Industry.

Identification of the variety of maize seeds based on hyperspectral images coupled with convolutional neural networks and subregional voting.

BACKGROUND: Maize is one of the most important food crops in the world. Many different varieties of maize seeds are similar in size and appearance, so distinguishing the varieties of maize seed is a significant research topic. This study used hyperspectral image processing coupled with convolutional neural network (CNN) and a subregional voting method to recognize different varieties of maize seed. RESULTS: First, visible and near-infrared (NIR-visible) hyperspectral images were obtained. Savitzky-Golay (SG) smoothing and first derivative (FD) were used to pretreat the raw spectra and highlight the spectral differences of samples of different varieties. Second, the region of interest (ROI) of each sample was divided into several subregions according to the shape and the number of pixels. Then, a method was proposed for reshaping the images of pixel spectra for the CNN and the training model was established. Finally, using subregional voting, one prediction result was generated from the prediction results of several original subregions in one sample. The results showed that, for six varieties of normal maize seeds, the tests identified embryoid and non-embryoid forms with 93.33% and 95.56% accuracy, respectively. For six varieties of sweet maize seeds, the test accuracy in embryoid and non-embryoid forms was 97.78% and 98.15%, respectively. CONCLUSION: The maize seed was identified accurately. The present study demonstrated that the CNN model for spectral image coupled with subregional voting represents a new approach for the identification of varieties of maize seed. © 2021 Society of Chemical Industry.

Physiological traits, yield, and yield components relationship in winter and spring canola.

BACKGROUND: Understanding the relationship between physiological traits with yield and yield components is an essential step towards developing high-yielding and high-quality canola (Brassica napus L.) cultivars. This study aimed to explore further the relationship between some physiological features, including radiation use efficiency (RUE), and seed yield in canola. RESULTS: Significant differences were found among cultivars regarding maximum leaf area index (LAImax ) and required days to achieve maximum LAI (DLAImax ). All cultivars obtained the minimum LAI required to intercept 90% of the incident radiation, but at different times. Some cultivars like SW102 and Shirali had the same fraction of intercepted photosynthetically active radiation (IPAR) when LAI was maximal, but SW102 had higher IPAR. This indicated that SW102 was more efficient in irradiation capacity and may have a higher photosynthesis rate when exposed to the high irradiation conditions. The average canola RUE in the current study was 3.80 and 3.63 g MJ-1 m-2 in 2014 and 2015, respectively. In general, the crop growth rate was higher in the first year than in the second year due to the fewer cloudy days and more incident radiation. CONCLUSION: Results indicated that duration of growth, crop growth rate, and harvest index were crucial for enhancing biomass and seed yield. Also, a relatively high correlation was found between the RUE and DLAImax . The cultivars that reached their maximum LAI later demonstrated higher RUE, and consequently had higher biological and seed yield. The results obtained could be used to develop an improved canola crop growth model and breeding programs. © 2021 Society of Chemical Industry.

FT-NIR and linear discriminant analysis to classify chickpea seeds produced with harvest aid chemicals.

Spectroscopy and machine learning (ML) algorithms have provided significant advances to the modern food industry. Instruments focusing on near-infrared spectroscopy allow obtaining information about seed and grain chemical composition, which can be related to changes caused by field pesticides. We investigated the potential of FT-NIR spectroscopy combined with Linear Discriminant Analysis (LDA) to discriminate chickpea seeds produced using different desiccant herbicides at harvest anticipation. Five herbicides applied at three moments of the plant reproductive stage were utilized. The NIR spectra obtained from individual seeds were used to build ML models based on LDA algorithm. The models developed to identify the herbicide and the plant phenological stage at which it was applied reached 94% in the independent validation set. Thus, the LDA models developed using near-infrared spectral data provided to be efficient, quick, non-destructive, and accurate to identify differences between seeds due to pre-harvest herbicides application.

Influence of starch fine structure and storage proteins on the eating quality of rice varieties with similar amylose contents.

BACKGROUND: Rice eating quality largely dictates consumer preference, and the demand for new rice varieties with excellent eating quality from farmers is increasing. Identification of factors contributing to eating quality is helpful for developing high-quality rice varieties. RESULTS: Two groups of rice with different apparent amylose content (AACs) were used in this study. One group contained four varieties with low AACs (8.8-9.4%), whereas the other contained four traditional varieties with medium AACs (17.2-17.5%). The physicochemical properties, starch fine structure and crystallinity and storage protein composition of the two groups were analyzed. We found that, in both groups, the rice varieties with high eating quality had more short-chain amylopectin, lower glutelin and prolamin content, and a higher albumin content. In addition, the low-AAC varieties produced opaque endosperms, which may result from an increased number of pores in the center of starch granules. CONCLUSIONS: Both the fine structure of starch and the storage protein composition were closely related to rice eating quality. In both groups, short branch-chain amylopectin, short-chain amylopectin [degree of polymerization (DP) 6-12], and albumin had positive effects on eating quality. By contrast, long branch-chain amylopectin, long-chain amylopectin (DP 35-60), glutelin and prolamin had adverse effects on eating quality of rice. © 2020 Society of Chemical Industry.

Interspecific variation of seed morphological and micro-morphological traits in the genus Vicia (Fabaceae).

Seed macro- and micro-morphology were analyzed to evaluate their capacity to discriminate species in the genus Vicia (Fabaceae). To assess the interspecific variation of the taxa in the genus Vicia, 41 accessions were obtained from the USDA-ARS germplasm collection in the USA and 19 accessions were collected from Korea. Seed morphological characteristics such as shape, color, mottling, finish, length, width, diameter, hilum shape, hilum color, hilum length, and lens distance from the hilum were examined under a stereomicroscope. Testa texture characteristics such as testa pattern, papillae type, density, height, ribbing, surface deposits, and peaks topped with wax were examined under scanning electron microscopy. Various gross-morphological traits of seeds of Vicia species have been analyzed and compared. The present study revealed significant variation in testa traits. Testa were papillose and papillose with mounds, the latter being observed only in Vicia lathyroides. The present study revealed 20 key traits that could be used to diagnose Vicia species and classify them.

Identification of the volatile profiles of 22 traditional and newly bred maize varieties and their porridges by PTR-QiTOF-MS and HS-SPME GC-MS.

BACKGROUND: Low adoption of maize varieties bred to address the nutritional needs of the growing African population limits their impact. Aroma is essential in consumer preference, but has hitherto hardly been studied. We analysed the volatile organic compounds of flours and porridges of 22 maize varieties belonging to four nutritionally distinct groups, namely provitamin A maize, quality protein maize, yellow and white maize. RESULTS: Proton-transfer-reaction quadrupole ion time-of-flight mass spectrometry (PTR-QiTOF-MS) analysis generated 524 mass peaks ranging from 16.007 to 448.089 m/z. Principal component analysis separated the varieties belonging to the four groups. With headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME GC-MS), 48 volatile compounds were identified in maize flour and 21 in maize porridge, including hexane, nonane, pentanoic acid, 1-octen-3-ol, 1-hexanol, hexanal, nonanal, 2-pentylfuran and 2-heptanone. Volatile compounds such as 1,2,4-trimethyl benzene, associated with thermal degradation of carotenoids, increased in the porridge of yellow and provitamin A maize. CONCLUSION: The results indicate that PTR-QiTOF-MS and HS-SPME GC-MS combined with multivariate analysis are instrumental to study the volatile aroma compounds of different maize varieties.

Comparison of seed fatty and amino acids in edamame dried using two oven-drying methods and mature soybeans.

BACKGROUND: Edamame is a vegetable soybean (Glycine max) rich in protein with well-balanced amino acid profiles, including all the essential amino acids. It is also relatively high in oil consisting of five fatty acids. In this study, fatty and amino acid contents in oven-dried edamame and mature seeds in 54 genotypes were analyzed to help edamame breeding and relevant research. RESULTS: Significant differences (P < 0.01) between dried edamame and mature soybeans were found for all traits, suggesting that the contents of seed fatty and amino acids varied with drying method and/or growth stage. The genotypic effects were significant for amino acids in all cases and mostly for fatty acids. Fatty acid content was more variable than amino acids in both edamame and mature soybeans. The broad-sense heritability estimates of traits in mature soybeans (51.89-90.37%) were larger than those in edamame seeds (13.10-81.83%), and the estimates for amino acids were larger than those for fatty acids. Correlations between the two edamame drying methods or between the edamame and mature soybeans were significant for all fatty and amino acids with few exceptions, and the correlation coefficients for amino acids (0.36-0.89) were larger than those for fatty acids (0.29-0.81). CONCLUSIONS: Selection for both fatty and amino acids in edamame seems less effective than that in mature soybeans, and the improvement of amino acids would be more achievable than fatty acid improvement. Indirect selection through mature soybeans may benefit the edamame improvement for most of the amino acids. © 2020 Society of Chemical Industry.

Cultivar Discrimination of Single Alfalfa (Medicago sativa L.) Seed via Multispectral Imaging Combined with Multivariate Analysis.

Rapid and accurate discrimination of alfalfa cultivars is crucial for producers, consumers, and market regulators. However, the conventional routine of alfalfa cultivars discrimination is time-consuming and labor-intensive. In this study, the potential of a new method was evaluated that used multispectral imaging combined with object-wise multivariate image analysis to distinguish alfalfa cultivars with a single seed. Three multivariate analysis methods including principal component analysis (PCA), linear discrimination analysis (LDA), and support vector machines (SVM) were applied to distinguish seeds of 12 alfalfa cultivars based on their morphological and spectral traits. The results showed that the combination of morphological features and spectral data could provide an exceedingly concise process to classify alfalfa seeds of different cultivars with multivariate analysis, while it failed to make the classification with only seed morphological features. Seed classification accuracy of the testing sets was 91.53% for LDA, and 93.47% for SVM. Thus, multispectral imaging combined with multivariate analysis could provide a simple, robust and nondestructive method to distinguish alfalfa seed cultivars.

Quantitative NMR Methodology for the Authentication of Roasted Coffee and Prediction of Blends.

In response to the need from the food industry for new analytical solutions, a fit-for-purpose quantitative 1H NMR methodology was developed to authenticate pure coffee (100% arabica or robusta) as well as predict the percentage of robusta in blends through the study of 292 roasted coffee samples in triplicate. Methanol was chosen as the extraction solvent, which led to the quantitation of 12 coffee constituents: caffeine, trigonelline, 3- and 5-caffeoylquinic acid, lipids, cafestol, nicotinic acid, N-methylpyridinium, formic acid, acetic acid, kahweol, and 16-O-methylcafestol. To overcome the chemical complexity of the methanolic extract, quantitative analysis was performed using a combination of traditional integration and spectral deconvolution methods. As a result, the proposed methodology provides a systematic methodology and a linear regression model to support the classification of known and unknown roasted coffees and their blends.