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\"How does a peanut turn into peanut butter? Follow each step in the food production cycle-- from planting peanut seeds to spreading peanut butter on bread-- in this fascinating book!\"--P. [4] of cover.
Book
Extracts of Peanut Skins as a Source of Bioactive Compounds: Methodology and Applications
Peanut skins are a waste product of the peanut processing industry with little commercial value. They are also significant sources of the polyphenolic compounds that are noted for their bioactivity. The extraction procedures for these compounds range from simple single solvent extracts to sophisticated separation schemes to isolate and identify the large range of compounds present. To take advantage of the bioactivities attributed to the polyphenols present, a range of products both edible and nonedible containing peanut skin extracts have been developed. This review presents the range of studies to date that are dedicated to extracting these compounds from peanut skins and their various applications.
Journal Article
Extent of encounter with an embedded food influences how it is processed by an urbanizing macaque species
Abstract
Rapid urbanization exerts novel adaptive pressures on animals at the interface of natural and altered environments. Urban animals often rely on synthetic foods that require skilled extraction and flexible processing. We studied how synthetic treatment of an embedded food, peanut, determined its extraction and processing across groups of bonnet macaques (Macaca radiata) differing in encounter and familiarity with peanut. The possibility of the application of processing methods to similar foods was also tested. We found encounter- and form (native/shelled/skinned)-specific familiarity to peanuts, state (raw/boiled/roasted)-specific distinction in skinning, and encounter- and state-specific differences in methods of skinning. The group with the highest encounter with peanuts exhibited novel and manipulatively complex processing. Novel processing was also extended to peas and chickpeas. Our study establishes a strong relationship between familiarity with the condition of food and the processing methods used and further, demonstrates the probable role of categorization in extension of novel methods.
Journal Article
Bioconversion of a Peanut Oil Processing By-Product into a Novel α-Glucosidase Inhibitor: Hemi-Pyocyanin
Hemi-pyocyanin (HPC) is a heterocyclic nitrogenous compound with some reported potential medical effects. The current report aimed to investigate the potential use of organic industrial waste for the production of HPC via microbial fermentation. The novel antidiabetic activity of HPC was also accessed and reported in this work. A peanut oil processing by-product (groundnut cake) was screened as the best substrate for Pseudomonas aeruginosa TUN03 conversion to obtain high-yield HPC. This compound was further produced in a 14 L bioreactor system on a large scale (6 L per pilot) and reached higher productivity (35.1 μg/mL) in a shorter time course of cultivation (8 h) compared to fermentation on a small scale in flasks (19.5 μg/mL; 3 days of fermentation). On assessing its activity, HPC demonstrated potent inhibition against α-glucosidase, an antidiabetic enzyme, with a low IC50 value (0.572 mg/mL) and a maximum inhibition rate of 100%. In an in silico study, HPC was found to inhibit α-glucosidase with a good binding energy score (−9.0 kcal/mol) via interaction with amino acids Lys156, Leu313, and Arg315 at the active site, and three bonds (1 H-acceptor and 2 pi-H) were generated. The data from five Lipkin’s rules and ADMET-based pharmacokinetics and pharmacology revealed that HPC possesses drug-like properties and good ADMET properties within the required allotted limitations. The data obtained in the current work highlighted the potential application of groundnut cakes for the eco-friendly and scaled-up production of HPC, a new anti-α-glucosidase agent that should be further investigated for type 2 diabetes management.
Journal Article
Processing Foods without Peanuts and Tree Nuts
This chapter contains sections titled:
Introduction
Peanut and Tree Nut Allergy
Nutritional and Functional Properties of Peanuts and Tree Nuts
Nutritional and Functional Alternatives to Peanuts and Tree Nuts
Hidden and Unintentional Sources of Peanut and Tree Nut Allergens
Analytical Tools for Detection of Peanut and Tree Nut Allergens
Guidelines for Processing Foods “Free From” Peanuts and Tree Nuts
Conclusion
References
Book Chapter
Baseline Gastrointestinal Eosinophilia Is Common in Oral Immunotherapy Subjects With IgE-Mediated Peanut Allergy
Oral immunotherapy (OIT) is an emerging treatment for food allergy. While desensitization is achieved in most subjects, many experience gastrointestinal symptoms and few develop eosinophilic gastrointestinal disease. It is unclear whether these subjects have subclinical gastrointestinal eosinophilia (GE) at baseline. We aimed to evaluate the presence of GE in subjects with food allergy before peanut OIT.
We performed baseline esophagogastroduodenoscopies on 21 adults before undergoing peanut OIT. Subjects completed a detailed gastrointestinal symptom questionnaire. Endoscopic findings were assessed using the Eosinophilic Esophagitis (EoE) Endoscopic Reference Score (EREFS) and biopsies were obtained from the esophagus, gastric antrum, and duodenum. Esophageal biopsies were evaluated using the EoE Histologic Scoring System. Immunohistochemical staining for eosinophil peroxidase (EPX) was also performed. Hematoxylin and eosin and EPX stains of each biopsy were assessed for eosinophil density and EPX/mm
was quantified using automated image analysis.
All subjects were asymptomatic. Pre-existing esophageal eosinophilia (>5 eosinophils per high-power field [eos/hpf]) was present in five participants (24%), three (14%) of whom had >15 eos/hpf associated with mild endoscopic findings (edema, linear furrowing, or rings; median EREFS = 0, IQR 0-0.25). Some subjects also demonstrated basal cell hyperplasia, dilated intercellular spaces, and lamina propria fibrosis. Increased eosinophils were noted in the gastric antrum (>12 eos/hpf) or duodenum (>26 eos/hpf) in 9 subjects (43%). EPX/mm
correlated strongly with eosinophil counts (
= 0.71,
< 0.0001).
Pre-existing GE is common in adults with IgE-mediated peanut allergy. Eosinophilic inflammation (EI) in these subjects may be accompanied by mild endoscopic and histologic findings. Longitudinal data collection during OIT is ongoing.
Journal Article
Potential use of peanut by-products in food processing: a review
Peanut is one of the most important oil and protein producing crops in the world. Yet the amounts of peanut processing by-products containing proteins, fiber and polyphenolics are staggering. With the environmental awareness and scarcity of space for landfilling, wastes/by-product utilization has become an attractive alternative to disposal. Several peanut by-products are produced from crush peanut processes and harvested peanut, including peanut meal, peanut skin, peanut hull and peanut vine. Some of peanut by-products/waste materials could possibility be used in food processing industry, The by-products of peanut contain many functional compounds, such as protein, fiber and polyphenolics, which can be incorporated into processed foods to serve as functional ingredients. This paper briefly describes various peanut by-products produced, as well as current best recovering and recycling use options for these peanut byproducts. Materials, productions, properties, potential applications in food manufacture of emerging materials, as well as environmental impact are also briefly discussed.
Journal Article
Research Advances in the High-Value Utilization of Peanut Meal Resources and Its Hydrolysates: A Review
Peanut meal (PM) is a by-product of extracting oil from peanut kernels. Although peanut meal contains protein, carbohydrates, minerals, vitamins, and small amounts of polyphenols and fiber, it has long been used as a feed in the poultry and livestock industries due to its coarse texture and unpleasant taste. It is less commonly utilized in the food processing industry. In recent years, there has been an increasing amount of research conducted on the deep processing of by-products from oil crops, resulting in the high-value processing and utilization of by-products from various oil crops. These include peanut meal, which undergoes treatments such as enzymatic hydrolysis in industries like food, chemical, and aquaculture. The proteins, lipids, polyphenols, fibers, and other components present in these by-products and hydrolysates can be incorporated into products for further utilization. This review focuses on the research progress in various fields, such as the food processing, breeding, and industrial fields, regarding the high-value utilization of peanut meal and its hydrolysates. The aim is to provide valuable insights and strategies for maximizing the utilization of peanut meal resources.
Journal Article
Assessing pediatric clinician adherence to the guidelines for prevention of peanut allergy: a natural language processing study
Background
Early introduction of peanut products to infants around 4- to 6- months of age may reduce peanut allergy incidence. However, clinician adherence to the National Institute of Allergy and Infectious Diseases’ 2017 Addendum Guidelines for the Prevention of Peanut Allergy (PPA), which recommend early peanut introduction based on risk levels, has been reportedly low. Documentation of clinician peanut introduction recommendations and peanut allergy risk in electronic health records (EHR) is variable and often in the form of unstructured data. Therefore, this study aims to develop and validate a Natural Language Processing (NLP) approach to identify and measure pediatric clinicians’ adherence to the PPA guidelines (peanut introduction recommendations and peanut allergy risk assessments, including eczema severity), as documented in EHR systems.
Methods
An NLP pipeline was developed to process clinical notes and patient instructions from EHRs in the Intervention to Reduce Early Peanut Allergy in Children (iREACH) trial. iREACH is a two-arm, cluster-randomized, controlled clinical trial that evaluates an intervention to enhance clinician adherence to the PPA guidelines. The database includes 4- and 6-month well-child care visits from 30 practices across three clinical networks in Illinois. The development of the NLP was organized into three main phases: exploratory (reviewed EHR notes to identify concepts for developing NLP algorithms), training (resulting in the first version of the NLP algorithm), and validation (based on gold standard datasets). Chart reviews were conducted to review the accuracy and reliability of the NLP model. The NLP pipeline assessed peanut introduction recommendations and severe eczema.
Results
NLP achieved high precision (0.98), recall (0.94) and overall performance F-measure (0.96) for identifying peanut introduction recommendations across the three networks. However, identifying severe eczema proved more challenging, with precision of 0.52, recall of 0.92, and overall performance F-measure of 0.67 across the three networks. Therefore, manual review was used to confirm the severe eczema results for the trial.
Conclusions
Considering the pragmatic study design, clinical notes were the only feasible source of data, yet documentation of severe eczema varied considerably across networks. Future refinement and validation of the severe eczema NLP pipeline are required. NLP is a valuable tool in assessing large EHR data sets in pragmatic, multi-site clinical trials.
Trial registration
This trial is registered on ClinicalTrials.gov (NCT04604431). Registered on October 14, 2020.
Journal Article
A novel method for peanut variety identification and classification by Improved VGG16
Crop variety identification is an essential link in seed detection, phenotype collection and scientific breeding. This paper takes peanut as an example to explore a new method for crop variety identification. Peanut is a crucial oil crop and cash crop. The yield and quality of different peanut varieties are different, so it is necessary to identify and classify different peanut varieties. The traditional image processing method of peanut variety identification needs to extract many features, which has defects such as intense subjectivity and insufficient generalization ability. Based on the deep learning technology, this paper improved the deep convolutional neural network VGG16 and applied the improved VGG16 to the identification and classification task of 12 varieties of peanuts. Firstly, the peanut pod images of 12 varieties obtained by the scanner were preprocessed with gray-scale, binarization, and ROI extraction to form a peanut pod data set with a total of 3365 images of 12 varieties. A series of improvements have been made to VGG16. Remove the F6 and F7 fully connected layers of VGG16. Add Conv6 and Global Average Pooling Layer. The three convolutional layers of conv5 have changed into Depth Concatenation and add the Batch Normalization(BN) layers to the model. Besides, fine-tuning is carried out based on the improved VGG16. We adjusted the location of the BN layers. Adjust the number of filters for Conv6. Finally, the improved VGG16 model's training test results were compared with the other classic models, AlexNet, VGG16, GoogLeNet, ResNet18, ResNet50, SqueezeNet, DenseNet201 and MobileNetv2 verify its superiority. The average accuracy of the improved VGG16 model on the peanut pods test set was 96.7%, which was 8.9% higher than that of VGG16, and 1.6–12.3% higher than that of other classical models. Besides, supplementary experiments were carried out to prove the robustness and generality of the improved VGG16. The improved VGG16 was applied to the identification and classification of seven corn grain varieties with the same method and an average accuracy of 90.1% was achieved. The experimental results show that the improved VGG16 proposed in this paper can identify and classify peanut pods of different varieties, proving the feasibility of a convolutional neural network in variety identification and classification. The model proposed in this experiment has a positive significance for exploring other Crop variety identification and classification.
Journal Article