Detection of aflatoxin B level and revelation of its dynamic accumulation process using visible/near-infrared hyperspectral and microscopic imaging.

Understanding and controlling the dynamic process of aflatoxin B (AFB) accumulation by Aspergillus flavus (A. flavus) remains challenging. In this study, the A.

Combined Relaxation Spectra for the Prediction of Meat Quality: A Case Study on Broiler Breast Fillets with the Wooden Breast Condition.

This study evaluated the potential of using combined relaxation (CRelax) spectra within time-domain nuclear magnetic resonance (TD-NMR) measurements to predict meat quality. Broiler fillets affected by different severities of the wooden breast (WB) conditions were used as case-study samples because of the broader ranges of meat-quality variations. Partial least squares regression (PLSR) models were established to predict water-holding capacity (WHC) and meat texture, demonstrating superior CRelax capabilities for predicting meat quality.

Development of Multimodal Fusion Technology for Tomato Maturity Assessment.

The maturity of fruits and vegetables such as tomatoes significantly impacts indicators of their quality, such as taste, nutritional value, and shelf life, making maturity determination vital in agricultural production and the food processing industry. Tomatoes mature from the inside out, leading to an uneven ripening process inside and outside, and these situations make it very challenging to judge their maturity with the help of a single modality. In this paper, we propose a deep learning-assisted multimodal data fusion technique combining color imaging, spectroscopy, and haptic sensing for the maturity assessment of tomatoes.

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.

Semisupervised Deep Learning for the Detection of Foreign Materials on Poultry Meat with Near-Infrared Hyperspectral Imaging.

A novel semisupervised hyperspectral imaging technique was developed to detect foreign materials (FMs) on raw poultry meat. Combining hyperspectral imaging and deep learning has shown promise in identifying food safety and quality attributes. However, the challenge lies in acquiring a large amount of accurately annotated/labeled data for model training.

Detection of Aflatoxin B in Single Peanut Kernels by Combining Hyperspectral and Microscopic Imaging Technologies.

To study the dynamic changes of nutrient consumption and aflatoxin B (AFB) accumulation in peanut kernels with fungal colonization, macro hyperspectral imaging technology combined with microscopic imaging was investigated. First, regression models to predict AFB contents from hyperspectral data ranging from 1000 to 2500 nm were developed and the results were compared before and after data normalization with Box-Cox transformation. The results indicated that the second-order derivative with a support vector regression (SVR) model using competitive adaptive reweighted sampling (CARS) achieved the best performance, with R = 0.

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.

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

The dynamics mechanisms regulating the growth and AFB 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.

Integration of spectral and textural features of visible and near-infrared hyperspectral imaging for differentiating between normal and white striping broiler breast meat.

White striping (WS), an emerging muscle myopathy in poultry industry, is gaining increasing attention globally. In this study, visible and near-infrared hyperspectral imaging (HSI, 400-1000 nm) was investigated for developing an optical sensing technique to differentiate WS broiler breast fillets (pectoralis major) from normal fillets. The minimum noise fraction (MNF), followed by an inverse MNF (IMNF), was conducted to improve the signal-to-noise ratio of hyperspectral images during the pre-processing process.

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.

Rapid classification of intact chicken breast fillets by predicting principal component score of quality traits with visible/near-Infrared spectroscopy.

In this study visible/near-infrared spectroscopy (Vis/NIRS) was evaluated to rapidly classify intact chicken breast fillets. Five principal components (PC) were extracted from reference quality traits (L, pH, drip loss, expressible fluid, and salt-induced water gain). A quality grades classification method by PC score was proposed.

Evaluation of factors in development of Vis/NIR spectroscopy models for discriminating PSE, DFD and normal broiler breast meat.

1. To evaluate the performance of visible and near-infrared (Vis/NIR) spectroscopic models for discriminating true pale, soft and exudative (PSE), normal and dark, firm and dry (DFD) broiler breast meat in different conditions of preprocessing methods, spectral ranges, characteristic wavelength selection and water-holding capacity (WHC) indexes were assessed. 2.

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.

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.

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).