Classical and emerging non-destructive technologies for safety and quality evaluation of cereals: A review of recent applications

A Miniaturized and Highly Sensitive "Windmill" Three-Channel Fluorescence Detector for Simultaneous Detection of Various Mycotoxins

A novel "windmill" three-channel light-emitting diode induced fluorescence detector (LED-IF) was proposed to maximize the excitation efficiency and fluorescence collection efficiency. Compared with the typical collinear arrangement, the fluorescence intensity of the three channels was increased by 7.85, 3.88, and 2.94 times, respectively. The compact shaping optical path was designed to obtain higher excitation efficiency and a lower background stray light effect caused by high divergence angle high-power ultraviolet (UV)-LEDs simultaneously, which increased the sensitivity of three channels by 4.6 to 5.7 times. It was found that using a photodiode (PD) with a flat window and a larger photosensitive surface can collect the Lambertian emission fluorescence in the flow cell more efficiently, increasing the signal-to-noise ratio of each channel 1.3 to 1.8 times. The limits of detection (LODs, 3 times peak-peak noise) of aflatoxin B2 (AFB2), ochratoxin (OTA), and zearalenone (ZEN) were 0.33, 1.80, and 28.2 ng/L, respectively. Finally, six mycotoxins were analyzed simultaneously by the detector coupling with HPLC. The results showed that the sensitivity of the detector was at the best level to date, which was better than that of the top commercial fluorescence detectors (FLDs). The developed detector has the advantages of having small volume, low cost, and long lifetime and being robust, which has wide application and market prospects.

Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology

(1) Background: To achieve the rapid, non-destructive detection of corn freshness and staleness for better use in the storage, processing and utilization of corn. (2) Methods: In this study, three varieties of corn were subjected to accelerated aging treatment to study the trend in fatty acid values of corn. The study focused on the use of hyperspectral imaging technology to collect information from corn samples with different aging levels. Spectral data were preprocessed by a convolutional smoothing derivative method (SG, SG1, SG2), derivative method (D1, D2), multiple scattering correction (MSC), and standard normal transform (SNV); the characteristic wavelengths were extracted by the competitive adaptive reweighting method (CARS) and successive projection algorithm (SPA); a neural network (BP) and random forest (RF) were utilized to establish a prediction model for the quantification of fatty acid values of corn. And, the distribution of fatty acid values was visualized based on fatty acid values under the corresponding optimal prediction model. (3) Results: With the prolongation of the aging time, all three varieties of corn showed an overall increasing trend. The fatty acid value of corn can be used as the most important index for characterizing the degree of aging of corn. SG2-SPA-RF was the quantitative prediction model for optimal fatty acid values of corn. The model extracted 31 wavelengths, only 12.11% of the total number of wavelengths, where the coefficient of determination RP2 of the test set was 0.9655 and the root mean square error (RMSE) was 3.6255. (4) Conclusions: This study can provide a reliable and effective new method for the rapid non-destructive testing of corn freshness.

Monitoring of carbon dioxide and equilibrium moisture content for early detection of physicochemical and morphological changes in soybeans stored in vertical silos

In the context of grain storage, impurities and soybeans defects in soybeans can significantly impact the equilibrium moisture content. This, cause moisture migration and heating of the stored product, leading to increased respiratory activity. Furthermore, temperature measurements within stored grain mass do not provide sufficient information for effective grain quality monitoring, primarily due to the grains excellent thermal insulating properties. To address this issue, we propose a different approach: monitoring the equilibrium moisture content and CO2 concentration as indicators of soybean respiration within the intergranular spaces of the stored grain mass. This study propose monitoring the CO2 concentration in the intergranular air along with environmental variables for early detection of physicochemical and morphological changes in soybeans stored in vertical silos using near infrared spectroscopy, X-ray diffraction and scanning electron microscopy. Thermogravimetry and spectrometry analyses revealed that the interrelationships among variables had a direct impact on soybean quality attributes. Specifically, the presence of soybeans with 5.2 % impurities led to an increased in respiration rates, resulting in a CO2 concentration of up to 5000 ppm and the consumption of up to 3.6 % of dry matter. Consequently, there were changes in the percentage of ash, proteins, fibers, and oils compositions. These findings highlight the potential for indirect assessments, enabling the prediction of physicochemical quality and contamination of soybeans stored in vertical silos through continuous monitoring of CO2 concentration and equilibrium moisture content.

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Enhancing traceability of wheat quality through the supply chain

With the growing global population, the need for food is expected to grow tremendously in the next few decades. One of the key tools to address such growing food demand is minimizing grain losses and optimizing food processing operations. Hence, several research studies are underway to reduce grain losses/degradation at the farm (upon harvest) and later during the milling and baking processes. However, less attention has been paid to changes in grain quality between harvest and milling. This paper aims to address this knowledge gap and discusses possible strategies for preserving grain quality (for Canadian wheat in particular) during unit operations at primary, process, or terminal elevators. To this end, the importance of wheat flour quality metrics is briefly described, followed by a discussion on the effect of grain properties on such quality parameters. This work also explores how drying, storage, blending, and cleaning, as some of the common post-harvest unit operations, could affect grain's end-product quality. Finally, an overview of the available techniques for grain quality monitoring is provided, followed by a discussion on existing gaps and potential solutions for quality traceability throughout the wheat supply chain.

Non-Destructive Hyperspectral Imaging for Rapid Determination of Catalase Activity and Ageing Visualization of Wheat Stored for Different Durations

(1) In order to accurately judge the new maturity of wheat and better serve the collection, storage, processing and utilization of wheat, it is urgent to explore a fast, convenient and non-destructively technology. (2) Methods: Catalase activity (CAT) is an important index to evaluate the ageing of wheat. In this study, hyperspectral imaging technology (850-1700 nm) combined with a BP neural network (BPNN) and a support vector machine (SVM) were used to establish a quantitative prediction model for the CAT of wheat with the classification of the ageing of wheat based on different storage durations. (3) Results: The results showed that the model of 1ST-SVM based on the full-band spectral data had the best prediction performance (R² = 0.9689). The SPA extracted eleven characteristic bands as the optimal wavelengths, and the established model of MSC-SPA-SVM showed the best prediction result with R² = 0.9664. (4) Conclusions: The model of MSC-SPA-SVM was used to visualize the CAT distribution of wheat ageing. In conclusion, hyperspectral imaging technology can be used to determine the CAT content and evaluate wheat ageing, rapidly and non-destructively.

A Scientometric Review of Grain Storage Technology in the Past 15 Years (2007-2022) Based on Knowledge Graph and Visualization

Food storage helps to ensure the food consumption needs of non-agricultural populations and to respond to major natural disasters or other emergencies, and the application of food storage technology can reduce post-harvest food losses. However, there are still obvious shortcomings in coping with large grain losses. Therefore, quantitative analysis of the research hotspots and evolutionary trends of grain storage technology is important to help the development of grain storage technology. This article uses the Web of Science database from 2007 to 2022 as a data sample with the help of CiteSpace software to analyze the basic situation, research hotspots, and evolutionary trends to draw a series of relevant knowledge maps. Visual analysis revealed that the number of publications had grown rapidly since 2015. First, the Journal of Stored Products Research, Journal of Economic Entomology, and Journal of Agricultural and Food Chemistry, with citation frequencies of 929, 536, and 453, should be focused on in order to keep up with the latest research developments in this field. The United States, China, and Brazil occupy dominant positions in relation to grain storage technology studies in general. Purdue University, Kansas State University, and Agricultural Research Institute ranked the top three in terms of the number and centrality of publications. In terms of research hotspots, the centrality of temperature, insects, carbon dioxide, and quality were 0.16, 0.09, 0.08, and 0.08. It shows that the field of grain storage technology in recent years has focused on grain storage temperature, pest control, and grain storage quality research. From the perspective of the evolution trend, the life cycle of emergent words lasts for several years, after which the strength of emergent words slowly decreases and is replaced by new emergent words. Mortality was the first keyword to appear and remained from 2007 to 2011, indicating that research on fumigants and their toxicity, as well as pest mortality under air fumigation and chemical fumigation conditions, became more popular during this period. In recent years, new terms have emerged that had never been used before, such as "grain quality" (2019-2022) and "stability" (2020-2022). We can find that people pursue food quality more with the improvement of people's living standards. In this context, future research should seek more efficient, safe, economical, and environmentally friendly methods of grain storage and continuously improve the level of scientific grain storage.

Neural network in food analytics

Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.

Applications of machine learning techniques for enhancing nondestructive food quality and safety detection

In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.

Detecting Aflatoxin B1 in Peanuts by Fourier Transform Near-Infrared Transmission and Diffuse Reflection Spectroscopy

Aflatioxin B₁ (AFB₁) has been recognized by the International Agency of Research on Cancer as a group 1 carcinogen in animals and humans. A fast, batch, and real-time control and no chemical pollution method was developed for the discrimination and quantification prediction of AFB₁-infected peanuts by applying Fourier transform near-infrared (FT-NIR) coupled with chemometrics. Initially, the near-infrared transmission (NIRT) and diffuse reflection (NIRR) modules were applied to collect spectra of the samples. The principal component analysis (PCA) method was employed to extract the characteristic wavelength, followed by different preprocessing methods (seven methods) to build an effective linear discriminant analysis (LDA) classification and partial least squares (PLS) quantification models. The results showed that, for both the NIRT or NIRR modules, the LDA classification models satisfactorily distinguished peanuts infected with AFB₁ or from those not infected, with external validation showing a 100% correct identification rate and a 0% misjudgment rate. In addition, combined with the concentration of AFB₁ in peanuts determined by enzyme-linked immunoassay assay, the best partial least squares (PLS) models were established, with a combination of the first derivative and the Norris derivative filter smoothing pretreatment (R_c² = 0.937 and 0.984, RMSECV = 3.92% and 2.22%, RPD = 3.98 and 7.91 for NIRR and NIRT, respectively). The correlation coefficient between the predicted value and the reference value in the external verification was 0.998 and 0.917, respectively. This study highlights that both spectral acquisition modules meet the requirements of online, rapid, and accurate identification of peanut AFB₁ infection in the early stages.

Design of a Phenotypic Sensor About Protein and Moisture in Wheat Grain

A near-infrared (NIR) spectrometer can perceive the change in characteristics of the grain reflectance spectrum quickly and nondestructively, which can be used to determine grain quality information. The full-band spectral information of samples of multiple physical states can be measured using existing instruments, yet it is difficult for the full-band instrument to be widely used in grain quality detection due to its high price, large size, non-portability, and inability to directly output the grain quality information. Because of the above problems, a phenotypic sensor about grain quality was developed for wheat, and four wavelengths were chosen. The interference of noise signals such as ambient light was eliminated by the phenotypic sensor using the modulated light signal and closed sample pool, the shape and size of the incident light spot of the light source were determined according to the requirement for collecting the reflectance spectrum of the grain, and the luminous units of the light source with stable light intensity and balanced luminescence were developed. Moreover, the sensor extracted the reflectance spectrum information using a weak optical signal conditioning circuit, which improved the resolution of the reflectance signal. A grain quality prediction model was created based on the actual moisture and protein content of grain obtained through Physico-chemical analyses. The calibration test showed that the R² of the relative diffuse reflectance (RDR) of all four wavelengths of the phenotypic sensor and the reflectance of the diffusion fabrics were higher than 0.99. In the noise level and repeatability tests, the standard deviations of the RDR of two types of wheat measured by the sensor were much lower than 1.0%, indicating that the sensor could accurately collect the RDR of wheat. In the calibration test, the root mean square errors (RMSE) of protein and moisture content of wheat in the Test set were 0.4866 and 0.2161%, the mean absolute errors (MAEs) were 0.6515 and 0.3078%, respectively. The results showed that the NIR phenotypic sensor about grain quality developed in this study could be used to collect the diffuse reflectance of grains and the moisture and protein content in real-time.

Advanced process analytical tools for identification of adulterants in edible oils - A review

This review summarizes the use of spectroscopic processes-based analytical tools coupled with chemometric techniques for the identification of adulterants in edible oil. Investigational approaches of process analytical tools such asspectroscopy techniques, nuclear magnetic resonance (NMR), hyperspectral imaging (HSI), e-tongue and e-nose combined with chemometrics were used to monitor quality of edible oils. Owing to the variety and intricacy of edible oil properties along with the alterations in attributes of the PAT tools, the reliability of the tool used and the operating factors are the crucial components which require attention to enhance the efficiency in identification of adulterants. The combination of process analytical tools with chemometrics offers a robust technique with immense chemotaxonomic potential. These involves identification of adulterants, quality control, geographical origin evaluation, process evaluation, and product categorization.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

Reflectance based non-destructive determination of colour and ripeness of tomato fruits

The preference and quality of tomato fruit are primarily determined by its apparent colour and appearance. Non-destructive and rapid methods for assessment of tomato colour and ripeness are therefore of immense significance. This study was conducted to identify reflectance-based indices and to develop models for the non-destructive determination of colour and ripeness (maturity) of tomato fruits. Tomato fruits of two varieties and two hybrids, representing different ripening stages were investigated. Fruits were either harvested directly from the plants or they were picked up from the lots stored at 25 °C. Reflectance from individual fruit was recorded in a spectrum ranging from 350 to 2500 nm. These fruits at different ripening stages were ranked on a relative ripening score (0.0-8.5). Obtained data (reflectance and ripening score) were subjected to chemometric analysis. In total, six models were developed. The first-best model was based on the index R₅₂₁ (reflectance at wavelength 521 nm) i.e., y (colour/ripeness) =  - 2.456 ln (x) - 1.093 where x is R₅₂₁. This model had a root mean standard error of prediction (RMSEP) ≥ 0.86 and biasness =  - 0.09. The second-best model y = 2.582 ln (x) - 0.805 was based on the index R₅₄₆ (x) and had RMSEP ≥ 0.89 and biasness = 0.10. Models could bifurcate tomatoes into basic ripening stages and also red and beyond red tomato fruits from other stages across the varieties/hybrids and ripening conditions [for plant harvested (fresh) and stored (aged) fruits]. Findings will prove useful in developing simple and thereby cost-effective tools for rapid screening/sorting of tomato fruits based on their colour or ripeness not only for basic research (phenotyping) but also for the purpose of processing, value-addition, and pharmaceutical usages.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12298-022-01126-2.

Commercial Organic Versus Conventional Whole Rye and Wheat Flours for Making Sourdough Bread: Safety, Nutritional, and Sensory Implications

Organic farming is gaining a broad recognition as sustainable system, and consumer demand for organic products has increased dramatically in the recent past. Whether organic agriculture delivers overall advantages over conventional agriculture is, however, contentious. Here, the safety, nutritional, and sensory implications of using commercial organic rye, soft, and durum wheat flours rather than conventional-made sourdough bread have been investigated. Culture-dependent and culture-independent approaches were used to explore the microbial architecture of flours and to study their dynamics during sourdough propagation. Besides biochemical features, the main nutritional (amino acid content, asparagine level, and antioxidant activity) characteristics of sourdoughs were investigated, and their effect on the structural, nutritional, and sensory profiles of breads assessed. Overall, the organic farming system led to flours characterized by lower content of asparagine and cell density of Enterobacteriaceae while showing higher concentration of total free amino acids. Differences of the flours mirrored those of sourdoughs and breads. The use of sourdough fermentation guaranteed a further improvement of the flour characteristics; however, a microbial and sensory profile simplification as well as a slight decrease of the biochemical parameters was observed between breads with sourdough after one-cycle fermentation and 10 days of propagation.

Prevalence and concentration of fumonisins in cereal-based foods: a global systematic review and meta-analysis study

Cereal-based foods are utilized as an essential food segment worldwide. Nevertheless, their contamination by mycotoxins, also fumonisins, could pose a critical health risk. The present research provides the first systematic review regarding the prevalence and concentration of fumonisins in cereal-based food with the aid of a meta-analysis. In this regard, some international databases PubMed, Web of Science, Google Scholar, and Scopus were explored during the last 30 years. Among 9729 screened articles, 73 articles (which meet the proposed inclusion criteria), including 11,132 data, were incorporated in the performed meta-analysis. The overall rank order regarding the concentration of fumonisins in cereal-based foods was corn-based foods > wheat-based foods > other cereal foods > barley-based foods > rice-based foods > oat-based foods. Based on the prevalence of fumonisins, the overall rank order was other cereal foods > corn-based foods > rice-based foods > wheat-based foods > oat-based foods > barley-based food. The present meta-analysis results can be a beneficial database for risk assessment model progress, which can help industries and organizations decrease the presence of fumonisins in cereal-based food.

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed.

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Artificial neural network has been intensively used for Hyperspectral image (HSI) analysis.

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Support vector machines and random forest techniques are gaining momentum for HSI analysis.

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Deep learning applications has potential for implementation in real time HSI analysis.

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Lifelong machine learning needs further research to incorporate the seasonal variations in food quality.

Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.

Recent advances in heat-moisture modified cereal starch: Structure, functionality and its applications in starchy food systems

Cereals, one of the starch sources, have a tremendous and steady production worldwide. Starchy foods constitute the major part of daily calorie intake for humans. As a simple and green modification approach, heat-moisture treatment (HMT) could change the granular surface characteristics and size, crystalline and helical structure, as well as molecular organization of cereal starch. The changing degree is contingent on HMT parameters and botanical origin. Based on the hierarchical structure, this paper reviews functionalities of heat-moisture modified cereal starch (HMCS) reported in latest years. The functionality of HMCS could be affected by co-existing non-starch ingredients through non-covalent/covalent interactions, depolymerization or simply attachment/encapsulation. Besides, it summarizes the modulation of HMCS in dough rheology and final food products' quality. Selecting proper HMT conditions is crucial for achieving nutritious products with desirable sensory and storage quality. This review gives a systematic understanding about HMCS for the better utilization in food industry.

Virulence Factors in the Phytopathogen-Host Interactions: An Overview

Phytopathogens are responsible for great losses in agriculture, once they are able to subvert or elude the host defense mechanisms through virulence factors secretion for their dissemination. Herein, it is reviewed phytotoxins that act as virulence factors and are produced by bacterial phytopathogens (Candidatus Liberibacter spp., Erwinia amylovora, Pseudomonas syringae pvs and Xanthomonas spp.) and fungi (Alternaria alternata, Botrytis cinerea, Cochliobolus spp., Fusarium spp., Magnaporthe spp., and Penicillium spp.), which were selected in accordance to their worldwide importance due to the biochemical and economical aspects. In the current review, it is sought to understand the role of virulence factors in the pathogen-host interactions that result in plant diseases.