Macro-micro exploration on dynamic interaction between aflatoxigenic Aspergillus flavus and maize kernels using Vis/NIR hyperspectral imaging and SEM technology.

Aspergillus flavus and its toxic metabolites-aflatoxins infect and contaminate maize kernels, posing a threat to grain safety and human health. Due to the complexity of microbial growth and metabolic processes, dynamic mechanisms among fungal growth, nutrient depletion of maize kernels and aflatoxin production is still unclear. In this study, visible/near infrared (Vis/NIR) hyperspectral imaging (HSI) combined with the scanning electron microscope (SEM) was used to elucidate the critical organismal interaction at kernel (macro-) and microscopic levels. As kernel damage is the main entrance for fungal invasion, maize kernels with gradually aggravated damages from intact to pierced to halved kernels with A. flavus were cultured for 0-120 h. The spectral fingerprints of the A. flavus-maize kernel complex over time were analyzed with principal components analysis (PCA) of hyperspectral images, where the pseudo-color score maps and the loading plots of the first three PCs were used to investigate the dynamic process of fungal infection and to capture the subtle changes in the complex with different hardness of the maize matrix. The dynamic growth process of A. flavus and the interactions of fungus-maize complexes were explained on a microscopic level using SEM. Specifically, fungus morphology, e.g., hyphae, conidia, and conidiophore (stipe) was accurately captured on the microscopic level, and the interaction process between A. flavus and nutrient loss from the maize kernel tissues (i.e., embryo, and endosperm) was described. Furthermore, the growth stage discrimination models based on PLSDA with the results of CCRC = 100 %, CCRV = 97 %, CCRIV = 93 %, and the prediction models of AFB1 based on PLSR with satisfactory performance (R2C = 0.96, R2V = 0.95, R2IV = 0.93 and RPD = 3.58) were both achieved. In conclusion, the results from both macro-level (Vis/NIR-HSI) and micro-level (SEM) assessments revealed the dynamic organismal interactions in A. flavus-maize kernel complex, and the detailed data could be used for modeling, and quantitative prediction of aflatoxin, which would establish a theoretical foundation for the early detection of fungal or toxin contaminated grains to ensure food security.

Survey of Meat Collected from Commercial Broiler Processing Plants Suggests Low Levels of Semicarbazide Can Be Created during Immersion Chilling.

ABSTRACT: Semicarbazide (SEM) is routinely employed as an indicator for the use of nitrofurazone, a banned antimicrobial. The validity of SEM as a nitrofurazone marker has been scrutinized because of other possible sources of the compound. Nonetheless, a U.S. trade partner rejected skin-on chicken thighs because of SEM detection and suspected nitrofurazone use. Because nitrofurazone has been banned in U.S. broiler production since 2003, we hypothesized that incidental de novo SEM formation occurs during broiler processing. To assess this possibility, raw leg quarters were collected from 23 commercial broiler processing plants across the United States and shipped frozen to our laboratory, where liquid chromatography-mass spectrometry was used to quantitatively assess for SEM. Leg quarter samples were collected at four points along the processing line: hot rehang (transfer from the kill line to the evisceration line), prechill (before the chilling process), postchill (immediately following chilling), and at the point of pack. Thigh meat with skin attached was removed from 535 leg quarters and analyzed in triplicate for SEM concentrations. The concentrations ranged from 0 to 2.67 ppb, with 462 (86.4%) of 535 samples below the regulatory decision level of 0.5 ppb of SEM. The 73 samples over the 0.5-ppb limit came from 21 plants; 53 (72.6%) of positive samples were in meat collected after chilling (postchill or point of pack). The difference in both prevalence and concentration of SEM detected before and after chilling was highly significant (P < 0.0001). These data support our hypothesis that SEM detection in raw broiler meat is related to de novo creation of the chemical during processing.

Spatio-temporal patterns of Aspergillus flavus infection and aflatoxin B1 biosynthesis on maize kernels probed by SWIR hyperspectral imaging and synchrotron FTIR microspectroscopy.

The dynamics mechanisms regulating the growth and AFB1 production of Aspergillus flavus during its interactions with maize kernels remain unclear. In this study, shortwave infrared hyperspectral imaging (SWIR-HSI) and synchrotron radiation Fourier transform infrared (SR-FTIR) microspectroscopy were combined to investigate chemical and spatial-temporal changes in incremental damaged maize kernels induced by A. flavus infection at macroscopic and microscopic levels. SWIR-HSI was employed to extract spectral information of A. flavus growth and quantitatively detect AFB1 levels. Satisfactory full-spectrum models and simplified multispectral models were obtained respectively by partial least squares regression (PLSR) for three types of samples. Furthermore, SR-FTIR microspectroscopy coupled with two-dimensional correlation spectroscopy (2DCOS) was utilized to reveal the possible sequence of dynamic changes of nutrient loss and trace AFB1 in maize kernels. It exhibited new insights on how to quantify the spatio-temporal patterns of fungal infection and AFB1 accumulation on maize and provided theoretical basis for online sorting.

Development of Imaging System for Online Detection of Chicken Meat with Wooden Breast Condition.

In recent years, the wooden breast condition has emerged as a major meat quality defect in the poultry industry worldwide. Broiler pectoralis major muscle with the wooden breast condition is characterized by hardness upon human palpation, which can lead to decrease in meat value or even reduced consumer acceptance. The current method of wooden breast detection involves a visual and/or tactile evaluation. In this paper, we present a sideview imaging system for online detection of chicken breast fillets affected by the wooden breast condition. The system can measure a physical deformation (bending) of an individual chicken-breast fillet through high-speed imaging at about 200 frames per second and custom image processing techniques. The developed image processing algorithm shows the over 95% classification performance in detecting wooden breast fillets.

Tenderness classification of fresh broiler breast fillets using visible and near-infrared hyperspectral imaging.

The aim of this study was to classify and visualize tenderness of intact fresh broiler breast fillets using hyperspectral imaging (HSI) technique. A total of 75 chicken fillets were scanned by HSI system of 400-1000nm in reflectance mode. Warner-Bratzler shear force (WBSF) value was used as reference tenderness indicator and fillets were grouped into least, moderately and very tender categories accordingly. To extract additional image textural features, principal component analysis (PCA) transform of images were conducted and gray level co-occurrence matrix (GLCM) analysis was implemented in region of interests (ROIs) on first three PC score images. Partial least square discriminant analysis (PLS-DA) or radial basis function-support vector machine (RBF-SVM) was developed for predicting tenderness based on full wavelengths (CCR=0.92), selected wavelengths (CCR=0.84), textural or combined data (CCR=0.88). Classification maps were created by pixels prediction in images and breast fillet tenderness was readily discernible. Overall, HSI technique is a promising methodology for predicting tenderness of intact fresh broiler breast meat.

In-Package Inactivation of Pathogenic and Spoilage Bacteria Associated with Poultry Using Dielectric Barrier Discharge-Cold Plasma Treatments.

The goal of this study was to test the efficacy of in-package dielectric barrier discharge-cold plasma (DBD-CP) treatment to inactivate poultry-associated spoilage (Pseudomonas fluorescens) and pathogenic (Salmonella enterica Typhimurium, Campylobacter jejuni) bacteria. Liquid cultures of the bacterial isolates were sealed within packages containing ambient air (Trial 1) or modified air (65% O2:30% CO2:5% N2; Trial 2). The packages were subjected to treatment times ranging from 30 to 180 s, and after 24 h incubation at 4 °C, bacterial titers were determined. The DBD-CP system completely inactivated the four isolates tested, although the in-package gas composition and treatment times were isolate-specific. Both C. jejuni isolates were completely inactivated between 30 s (modified air) and 120 s (ambient air), while modified air was required for the complete inactivation of S. typhimurium (90 s) and P. fluorescens (180 s). This DBD-CP system is effective for inactivating major poultry-associated spoilage and pathogenic bacteria in liquid culture, and through this study, system parameters to optimize inactivation were determined. This study demonstrates the potential for DBD-CP treatment to inactivate major bacteria of economic interest to the poultry industry, thus potentially allowing for reduced spoilage (e.g., longer shelf life) and increased safety of poultry products.

Effect of Sample Preparation on the Discrimination of Bacterial Isolates Cultured in Liquid Nutrient Media Using Laser-Induced Breakdown Spectroscopy (LIBS).

Laser-induced breakdown spectroscopy (LIBS) is used as the basis for discrimination between two genera of gram-negative bacteria and two genera of gram-positive bacteria representing pathogenic threats commonly found in poultry processing rinse waters. Because LIBS-based discrimination relies primarily upon the relative proportions of inorganic cell components including Na, K, Mg, and Ca, this study aims to determine the effects of trace mineral content and pH found in the water source used to isolate the bacteria upon the reliability of the resulting discriminant analysis. All four genera were cultured using tryptic soy agar (TSA) as the nutrient medium, and were grown under identical environmental conditions. The only variable introduced is the source water used to isolate the cultured bacteria. Cultures of each bacterium were produced using deionized (DI) water under two atmosphere conditions, reverse osmosis (RO) water, tap water, phosphate buffered saline (PBS) water, and TRIS buffered water. After 3 days of culture growth, the bacteria were centrifuged and washed three times in the same water source. Bacteria were then freeze dried, mixed with microcrystalline cellulose, and a pellet was made for LIBS analysis. Principal component analysis (PCA) was used to extract related variations in LIBS spectral data among the four bacteria genera and six water types used to isolate the bacteria, and Mahalanobis discriminant analysis (MDA) was used for classification. Results indicate not only that the four genera can be discriminated from each other in each water type, but that each genus can be discriminated by water type used for isolation. It is concluded that in order for LIBS to be a reliable and repeatable method for discrimination of bacteria grown in liquid nutrient media, care must be taken to insure that the water source used in purification of the culture be precisely controlled regarding pH, ionic strength, and proportionate amounts of mineral cations present.

Detection by hyperspectral imaging of shiga toxin-producing Escherichia coli serogroups O26, O45, O103, O111, O121, and O145 on rainbow agar.

The U.S. Department of Agriculture, Food Safety Inspection Service has determined that six non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) are adulterants in raw beef. Isolate and phenotypic discrimination of non-O157 STEC is problematic due to the lack of suitable agar media. The lack of distinct phenotypic color variation among non-O157serogroups cultured on chromogenic agar poses a challenge in selecting colonies for confirmation. In this study, visible and near-infrared hyperspectral imaging and chemometrics were used to detect and classify non-O157 STEC serogroups grown on Rainbow agar O157. The method was first developed by building spectral libraries for each serogroup obtained from ground-truth regions of interest representing the true identity of each pixel and thus each pure culture colony in the hyperspectral agar-plate image. The spectral library for the pure-culture non-O157 STEC consisted of 2,171 colonies, with spectra derived from 124,347 of pixels. The classification models for each serogroup were developed with a k nearest-neighbor classifier. The overall classification training accuracy at the colony level was 99%. The classifier was validated with ground beef enrichments artificially inoculated with 10, 50, and 100 CFU/ml STEC. The validation ground-truth regions of interest of the STEC target colonies consisted of 606 colonies, with 3,030 pixels of spectra. The overall classification accuracy was 98%. The average specificity of the method was 98% due to the low false-positive rate of 1.2%. The sensitivity ranged from 78 to 100% due to the false-negative rates of 22, 7, and 8% for O145, O45, and O26, respectively. This study showed the potential of visible and near-infrared hyperspectral imaging for detecting and classifying colonies of the six non-O157 STEC serogroups. The technique needs to be validated with bacterial cultures directly extracted from meat products and positive identification of colonies by using confirmatory tests such as latex agglutination tests or PCR.

Surface enhanced Raman scattering (SERS) with biopolymer encapsulated silver nanosubstrates for rapid detection of foodborne pathogens.

A biopolymer encapsulated with silver nanoparticles was prepared using silver nitrate, polyvinyl alcohol (PVA) solution, and trisodium citrate. It was deposited on a mica sheet to use as SERS substrate. Fresh cultures of Salmonella Typhimurium, Escherichia coli, Staphylococcus aureus and Listeria innocua were washed from chicken rinse and suspended in 10 ml of sterile deionized water. Approximately 5 µl of the bacterial suspensions was placed on the substrate individually and exposed to 785 nm HeNe laser excitation. SERS spectral data were recorded over the Raman shift between 400 and 1800 cm(-1) from 15 different spots on the substrate for each sample; and three replicates were done on each bacteria type. Principal component analysis (PCA) model was developed to classify foodborne bacteria types. PC1 identified 96% of the variation among the given bacteria specimen, and PC2 identified 3%, resulted in a total of 99% classification accuracy. Soft Independent Modeling of Class Analogies (SIMCA) of validation set gave an overall correct classification of 97%. Comparison of the SERS spectra of different types of gram-negative and gram-positive bacteria indicated that all of them have similar cell walls and cell membrane structures. Conversely, major differences were noted around the nucleic acid and amino acid structure information between 1200 cm(-1) and 1700 cm(-1) and at the finger print region between 400 cm(-1) and 700 cm(-1). Silver biopolymer nanoparticle substrate could be a promising SERS tool for pathogen detection. Also this study indicates that SERS technology could be used for reliable and rapid detection and classification of food borne pathogens.

Classification and structural analysis of live and dead Salmonella cells using Fourier transform infrared spectroscopy and principal component analysis.

Fourier transform infrared spectroscopy (FT-IR) was used to detect Salmonella Typhimurium and Salmonella Enteritidis food-borne bacteria and to distinguish between live and dead cells of both serotypes. Bacteria cells were prepared in 10(8) cfu/mL concentration, and 1 mL of each bacterium was loaded individually on the ZnSe attenuated total reflection (ATR) crystal surface (45° ZnSe, 10 bounces, and 48 mm × 5 mm effective area of analysis on the crystal) and scanned for spectral data collection from 4000 to 650 cm(-1) wavenumber. Analysis of spectral signatures of Salmonella isolates was conducted using principal component analysis (PCA). Spectral data were divided into three regions such as 900-1300, 1300-1800, and 3000-2200 cm(-1) based on their spectral signatures. PCA models were developed to differentiate the serotypes and live and dead cells of each serotype. Maximum classification accuracy of 100% was obtained for serotype differentiation as well as for live and dead cells differentiation. Soft independent modeling of class analogy (SIMCA) analysis was carried out on the PCA model and applied to validation sample sets. It gave a predicted classification accuracy of 100% for both the serotypes and its live and dead cells differentiation. The Mahalanobis distance calculated in three different spectral regions showed maximum distance for the 1800-1300 cm(-1) region, followed by the 3000-2200 cm(-1) region, and then by the 1300-900 cm(-1) region. It showed that both of the serotypes have maximum differences in their nucleic acids, DNA/RNA backbone structures, protein, and amide I and amide II bands.

Comparison of FTIR spectra between Huanglongbing (citrus greening) and other citrus maladies.

Fourier transform infrared (FTIR) spectroscopy has the ability to quickly identify the presence of specific carbohydrates in plant materials. The presence of the disease huanglongbing (HLB) in the leaves of infected citrus plants has a distinctive spectrum that can be used to distinguish an infected plant from a healthy plant. However, many citrus diseases display similar visible symptoms and are of concern to citrus growers. In this study several citrus diseases (citrus leaf rugose virus, citrus tristeza virus, citrus psorosis virus, and Xanthomonas axonopodis ) and nutrient deficiencies (iron, copper, zinc, manganese, and magnesium) were compared with HLB using FTIR spectroscopy to determine if the spectra alone can be used to identify plants that are infected with HLB instead of another disease. The results indicate that the spectra of some diseases and deficiencies more closely resemble those of apparently healthy plants and some share the carbohydrate transformation that has been seen in the spectra of HLB-infected plants.

Detection of citrus Huanglongbing by Fourier transform infrared-attenuated total reflection spectroscopy.

Citrus Huanglongbing (HLB, also known as citrus greening disease) was discovered in Florida in 2005 and is spreading rapidly amongst the citrus growing regions of the state. Detection via visual symptoms of the disease is not a long-term viable option. New techniques are being developed to test for the disease in its earlier presymptomatic stages. Fourier transform infrared-attenuated total reflection (FT-IR-ATR) spectroscopy is a candidate for rapid, inexpensive, early detection of the disease. The mid-infrared region of the spectrum reveals dramatic changes that take place in the infected leaves when compared to healthy non-infected leaves. The carbohydrates that give rise to peaks in the 900-1180 cm(-1) range are reliable in distinguishing leaves from infected plants versus non-infected plants. A model based on chemometrics was developed using the spectra from 179 plants of known disease status. This model then correctly predicted the status of >95% of the plants tested.