Food allergy remains a public health, business, and regulatory challenge. Risk analysis (RA) and risk management (RM) of food allergens are of great importance and analysis for food allergens is necessary for both. The current workhorse techniques for allergen analysis (enzyme linked immunosorbent assay [ELISA] and real-time polymerase chain reaction) exhibit recognized challenges including variable and antibody specific responses and detection of species DNA rather than allergen protein, respectively. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) enables protein identification, with potential for multiplex analysis and traceability to the System of International units (SI), aiding global measurement standardization. In this review, recent literature has been systematically reviewed to assess progress in LC-MS/MS and define the potential and benefits of matrix-assisted laser desorption/ionization-time-of-flight MS (MALDI-ToF-MS) technology for allergen analysis. MALDI-ToF-MS of initially intact protein is already applied to verify in silico-derived peptide sequences for LC-MS/MS analysis. We describe the origins of MALDI and its future perspectives, including affinity bead-assisted assays coupled to MALDI. Based on the proliferation of reliable and reproducible MALDI-based clinical applications, the technique should emulate the detection capability (sensitivity) of established allergen detection techniques, whilst reducing technical support and having equivalent multiplexing potential to competing techniques, for example, LC-MS/MS and ELISA. Although unlikely to offer inherent SI traceability, MALDI-based allergen analysis will complement existing MS approaches for allergens. Affinity bead-MALDI appears capable of higher throughput at lower cost per sample than almost any existing technique, enabling repeated sub-sampling as a way to reduce representative sampling issues.

As the fourth most diagnosed cancer, cervical cancer (CC) is one of the major causes of cancer-related mortality affecting females globally, particularly when diagnosed at advanced stage. Discoveries of CC biomarkers pave the road to precision medicine for better patient outcomes. High throughput omics technologies, characterized by big data production further accelerate the process. To date, various CC biomarkers have been discovered through the advancement in technologies. Despite, very few have successfully translated into clinical practice due to the paucity of validation through large scale clinical studies. While vast amounts of data are generated by the omics technologies, challenges arise in identifying the clinically relevant data for translational research as analyses of single-level omics approaches rarely provide causal relations. Integrative multi-omics approaches across different levels of cellular function enable better comprehension of the fundamental biology of CC by highlighting the interrelationships of the involved biomolecules and their function, aiding in identification of novel integrated biomarker profile for precision medicine. Establishment of a worldwide Early Detection Research Network (EDRN) system helps accelerating the pace of biomarker translation. To fill the research gap, we review the recent research progress on CC biomarker development from the application of high throughput omics technologies with sections covering genomics, transcriptomics, proteomics, and metabolomics.

Escherichia coli O157:H7, a Shiga-producing E. coli is a major pathogenic E. coli strain which since the early 1980s has become a crucial food and water-borne pathogen. Several management strategies can be applied to control the spread of infection; however early diagnosis represents the optimum preventive strategy to minimize the infection. Therefore, it is crucial to detect this pathogen in a fast and efficient manner in order to reduce the morbidity and mortality. Currently used gold standard tests rely on culture and pre-enrichment of E. coli O157:H7 from the contaminated source; they are time consuming and laborious. Molecular methods such as polymerase chain reaction are sensitive; however, they require expensive instrumentation. Therefore, there is a requirement for Accurate, Sensitive, Specific, User friendly, Rapid, Equipment free and Deliverable (ASSURED) detection methods for use in the laboratory and in the field. Emerging technologies such as isothermal amplification methods, biosensors, surface enhanced Raman Spectroscopy, paper-based diagnostics and smartphone-based digital methods are recognized as new approaches in the field of E. coli O157:H7 diagnostics and are discussed in this review. Mobile PCR and CRISPR-Cas diagnostic platforms have been identified as new tools in E. coli O157:H7 POC diagnostics with the potential for implementation by industry. This review describes advances and progress in the field of E. coli O157:H7 diagnosis in the context of food and water industry. The focus is on emerging high throughput point-of-care (POC) E. coli O157:H7 diagnostics and the requirement for the transformation to service routine diagnostics in the food and water industry.

The review aimed to identify the different high-throughput phenotyping (HTP) techniques that used for quality evaluation in cassava and yam breeding programmes, and this has provided insights towards the development of metrics and their application in cassava and yam improvements. A systematic review of the published research articles involved the use of NIRS in analysing the quality traits of cassava and yam was carried out, and Scopus, Science Direct, Web of Sciences and Google Scholar were searched. The results of the review established that NIRS could be used in understanding the chemical constituents (carbohydrate, protein, vitamins, minerals, carotenoids, moisture, starch, etc.) for high-throughput phenotyping. This study provides preliminary evidence of the application of NIRS as an efficient and affordable procedure for HTP. However, the feasibility of using mid-infrared spectroscopy (MIRS) and hyperspectral imaging (HSI) in combination with the NIRS could be further studied for quality traits phenotyping.

As a malignant tumor type, nasopharyngeal carcinoma (NPC) is characterized by distinct geographical, ethnic and genetic differences; presenting a major threat to human health in many countries, especially in Southern China. At present, no accurate and effective methods are available for the early diagnosis, efficacious evaluation or prognosis prediction for NPC. As such, a large number of patients have locoregionally advanced NPC at the time of initial diagnosis. Many patients show toxic reactions to overtreatment and have risks of cancer recurrence and distant metastasis owing to insufficient treatment. To solve these clinical problems, high‑throughput \'‑omics\' technologies are being used to screen and identify specific molecular biomarkers for NPC. Because of the lack of comprehensive descriptions regarding NPC biomarkers, the present study summarized the research progress that has been made in recent years to discover NPC biomarkers, highlighting the existing problems that require exploration. In view of the lack of authoritative reports at present, study design factors that affect the screening of biomarkers are also discussed here and prospects for future research are proposed to provide references for follow‑up studies of NPC biomarkers.

Microplastic research is a rapidly developing field, with urgent needs for high throughput and automated analysis techniques. We conducted a review covering image analysis from optical microscopy, scanning electron microscopy, fluorescence microscopy, and spectral analysis from Fourier transform infrared (FT-IR) spectroscopy, Raman spectroscopy, pyrolysis gas-chromatography mass-spectrometry, and energy dispersive X-ray spectroscopy. These techniques were commonly used to collect, process, and interpret data from microplastic samples. This review outlined and critiques current approaches for analysis steps in image processing (color, thresholding, particle quantification), spectral processing (background and baseline subtraction, smoothing and noise reduction, data transformation), image classification (reference libraries, morphology, color, and fluorescence intensity), and spectral classification (reference libraries, matching procedures, and best practices for developing in-house reference tools). We highlighted opportunities to advance microplastic data analysis and interpretation by (i) quantifying colors, shapes, sizes, and surface topologies with image analysis software, (ii) identifying threshold values of particle characteristics in images that distinguish plastic particles from other particles, (iii) advancing spectral processing and classification routines, (iv) creating and sharing robust spectral libraries, (v) conducting double blind and negative controls, (vi) sharing raw data and analysis code, and (vii) leveraging readily available data to develop machine learning classification models. We identified analytical needs that we could fill and developed supplementary information for a reference library of plastic images and spectra, a tutorial for basic image analysis, and a code to download images from peer reviewed literature. Our major findings were that research on microplastics was progressing toward the use of multiple analytical methods and increasingly incorporating chemical classification. We suggest that new and repurposed methods need to be developed for high throughput screening using a diversity of approaches and highlight machine learning as one potential avenue toward this capability.

The significance of thermodynamic solubility in biopharmaceutical compound or drug characterization as well as the importance of having methods that accurately establish it have been extensively addressed. Nonetheless, its precise determination continues to remain a challenging task to accomplish. Even more so when the number of compounds to evaluate is high and the available amount of each compound is low, both of which are inevitable for the compound characterization during the drug development process. Except for the shake-flask method which is still considered as the \'gold standard\' in obtaining thermodynamic data, it is currently difficult to say that another satisfactory model which is routinely used to determine thermodynamic solubility is being applied. Therefore, this review summarizes the various experimental approaches which are based on the classical shake flask method but have yet attempted to speed up the experimental process of obtaining such data more conveniently. The most important experimental features of these approaches are provided to the reader. Some advantages and disadvantages associated with each approach are also highlighted, consequently offering a resource to those looking for the most appropriate of the approaches that have already fared well at determining the biopharmaceutically relevant drug solubility.

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR) and pyrolysis-molecular beam mass spectrometry (Py-mbms) are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis) and for building regression models (partial least square regression) between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated. This review aims to serve as a guide for choosing the most effective data analysis methods for NIR and Py-mbms characterization of biomass.

Gas chromatography is widely applied to separate, identify, and quantify components of samples in a timely manner. Increasing demand for analytical throughput, instrument portability, environmental sustainability, and more economical analysis necessitates the development of new gas chromatography instrumentation. The applications of resistive column heating technologies have been espoused for nearly thirty years and resistively heated gas chromatography has been commercially available for the last ten years. Despite this lengthy period of existence, resistively heated gas chromatography has not been universally adopted. This low rate of adoption may be partially ascribed to the saturation of the market with older convection oven technology, coupled with other analytical challenges such as sampling, injection, detection and data processing occupying research. This article assesses the advantages and applications of resistive heating in gas chromatography and discusses practical considerations associated with adoption of this technology.