Use of High-Resolution X-Ray Micro-Computed Tomography for the Analysis of Internal Structural Changes in Maize Infected with Fusarium verticillioides

Assessment of Mechanical Damage and Germinability in Flaxseeds Using Hyperspectral Imaging

The high demand for flax as a nutritious edible oil source combined with increasingly restrictive import regulations for oilseeds mandates the exploration of novel quantity and quality assessment methods. One pervasive issue that compromises the viability of flaxseeds is the mechanical damage to the seeds during harvest and post-harvest handling. Currently, mechanical damage in flax is assessed via visual inspection, a time-consuming, subjective, and insufficiently precise process. This study explores the potential of hyperspectral imaging (HSI) combined with chemometrics as a novel, rapid, and non-destructive method to characterize mechanical damage in flaxseeds and assess how mechanical stresses impact the germination of seeds. Flaxseed samples at three different moisture contents (MCs) (6%, 8%, and 11.5%) were subjected to four levels of mechanical stresses (0 mJ (i.e., control), 2 mJ, 4 mJ, and 6 mJ), followed by germination tests. Herein, we acquired hyperspectral images across visible to near-infrared (Vis-NIR) (450-1100 nm) and short-wave infrared (SWIR) (1000-2500 nm) ranges and used principal component analysis (PCA) for data exploration. Subsequently, mean spectra from the samples were used to develop partial least squares-discriminant analysis (PLS-DA) models utilizing key wavelengths to classify flaxseeds based on the extent of mechanical damage. The models developed using Vis-NIR and SWIR wavelengths demonstrated promising performance, achieving precision and recall rates >85% and overall accuracies of 90.70% and 93.18%, respectively. Partial least squares regression (PLSR) models were developed to predict germinability, resulting in R2-values of 0.78 and 0.82 for Vis-NIR and SWIR ranges, respectively. The study showed that HSI could be a potential alternative to conventional methods for fast, non-destructive, and reliable assessment of mechanical damage in flaxseeds.

Toward in-process technology-aided automation for enhanced microbial food safety and quality assurance in milk and beverages processing

Ensuring the safety of food products is critical to food production and processing. In food processing and production, several standard guidelines are implemented to achieve acceptable food quality and safety. This notwithstanding, due to human limitations, processed foods are often contaminated either with microorganisms, microbial byproducts, or chemical agents, resulting in the compromise of product quality with far-reaching consequences including foodborne diseases, food intoxication, and food recall. Transitioning from manual food processing to automation-aided food processing (smart food processing) which is guided by artificial intelligence will guarantee the safety and quality of food. However, this will require huge investments in terms of resources, technologies, and expertise. This study reviews the potential of artificial intelligence in food processing. In addition, it presents the technologies and methods with potential applications in implementing automated technology-aided processing. A conceptual design for an automated food processing line comprised of various operational layers and processes targeted at enhancing the microbial safety and quality assurance of liquid foods such as milk and beverages is elaborated.

Exploring the Developmental Progression of Endosperm Cavity Formation in Maize Grain and the Underlying Molecular Basis Using X-Ray Tomography and Genome Wide Association Study

Endosperm cavity (EC) in maize grain reduces yield and causes grain breakage during mechanical harvesting, hence representing a major problem in the maize industry. Despite this, little is known regarding the biological processes governing EC formation. Here, we attempted to address this issue by (i) determining the spatial and temporal progression of EC in a non-invasive manner and (ii) identifying candidate genes that may be linked to the formation of EC by using a genome wide association study (GWAS). Visualization and measurement using X-ray micro-computed tomography established that EC first appeared at the central starch endosperm at about 12 days after pollination (DAP) and became enlarged thereafter. GWAS-based screening of a panel of 299 inbred lines with a wide range of EC size identified nine candidate genes that showed significant association with EC formation. Most of the candidate genes exhibited a decrease at 12 DAP, coinciding with the timing of EC appearance. Among them, ZmMrp11 was annotated as a member encoding a multidrug resistance-associated protein that has been shown in other studies to sequestrate toxic metabolites from the cytosol to the vacuole, thereby detoxifying the cellular environment. This, together with the reduced expression of ZmMrp11 in maize grains from 12 DAP, prompted us to propose that the low expression of ZmMrp11 may block cellular detoxification in the maize endosperm cells, leading to cell death and ultimately the formation of EC.

Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts

Cereal grains and nuts are represented as the economic backbone of many developed and developing countries. Kernels of cereal grains and nuts are prone to mold infection under high relative humidity and suitable temperature conditions in the field as well as storage conditions. Health risks caused by molds and their molecular metabolite mycotoxins are, therefore, important topics to investigate. Strict regulations have been developed by international trade regulatory bodies for the detection of mold growth and mycotoxin contamination across the food chain starting from the harvest to storage and consumption. Molds and aflatoxins are not evenly distributed over the bulk of grains, thus appropriate sampling for detection and quantification is crucial. Existing reference methods for mold and mycotoxin detection are destructive in nature as well as involve skilled labor and hazardous chemicals. Also, these methods cannot be used for inline sorting of the infected kernels. Thus, analytical methods have been extensively researched to develop the one that is more practical to be used in commercial detection and sorting processes. Among various analytical techniques, optical imaging and spectroscopic techniques are attracting growers' attention for their potential of nondestructive and rapid inline identification and quantification of molds and mycotoxins in various food products. This review summarizes the recent application of rapid and nondestructive optical imaging and spectroscopic techniques, including digital color imaging, X-ray imaging, near-infrared spectroscopy, fluorescent, multispectral, and hyperspectral imaging. Advance chemometric techniques to identify very low-level mold growth and mycotoxin contamination are also discussed. Benefits, limitations, and challenges of deploying these techniques in practice are also presented in this paper.

Application of an expert system of X- ray micro computed tomography imaging for identification of Sitophilus oryzae infestation in stored rice grains

BACKGROUND

The plausibility of image texture analysis to assess X-ray images of S. oryzae-infested rice after variable storage days (fresh, 45, 90, 135, 180 and 225 days) was investigated using an X-ray micro computed tomography instrument. Subsequently, image acquisition, pre-processing, and the extraction of the image textural features was done using volume graphics VGL 2.2 software. Morphological features (radius, diameter, volume, compactness, sphericity, defect volume, and voids) were extracted from the x, y, and z views of the rice grain and used as inputs for principal component analysis (PCA).

RESULTS

Clear grouping was observed between the fresh, 45 and 225-day-old S. oryzae-infested rice grains with a classification accuracy of 88.34%. The voids (884 248.53 μm³ ) and defect volume distribution (137 428.04 μm³ ) were found to be the maximum in 225-day-old samples. The similarity or the distance indices values between fresh and 255-day-old S. oryzae-infested rice samples were found to be 35 038.08, which resulted in clear discrimination between different storage days in S. oryzae-infested rice grains.

CONCLUSION

This work contributes to the potential use of image texture analysis to aid in distinguishing S. oryzae-infested rice grains from fresh rice grains. © 2019 Society of Chemical Industry.