Hyunjeong Cho

Quantitative analysis of Sudan dye adulteration in paprika powder using FTIR spectroscopy

ABSTRACT As adulteration of foodstuffs with Sudan dye, especially paprika- and chilli-containing products, has been reported with some frequency, this issue has become one focal point for addressing food safety. FTIR spectroscopy has been used extensively as an analytical method for quality control and safety determination for food products. Thus, the use of FTIR spectroscopy for rapid determination of Sudan dye in paprika powder was investigated in this study. A net analyte signal (NAS)-based methodology, named HLA/GO (hybrid linear analysis in the literature), was applied to FTIR spectral data to predict Sudan dye concentration. The calibration and validation sets were designed to evaluate the performance of the multivariate method. The obtained results had a high determination coefficient (R2) of 0.98 and low root mean square error (RMSE) of 0.026% for the calibration set, and an R2 of 0.97 and RMSE of 0.05% for the validation set. The model was further validated using a second validation set and through the figures of merit, such as sensitivity, selectivity, and limits of detection and quantification. The proposed technique of FTIR combined with HLA/GO is rapid, simple and low cost, making this approach advantageous when compared with the main alternative methods based on liquid chromatography (LC) techniques. GRAPHICAL ABSTRACT

Prevalence and Evaluation Strategies for Viral Contamination in Food Products: Risk to Human Health - A Review.

ABSTRACT Nowadays, viruses of foodborne origin such as norovirus and hepatitis A are considered major causes of foodborne gastrointestinal illness with widespread distribution worldwide. A number of foodborne outbreaks associated with food products of animal and non-animal origins, which often involve multiple cases of variety of food streams, have been reported. Although several viruses, including rotavirus, adenovirus, astrovirus, parvovirus, and other enteroviruses, significantly contribute to incidence of gastrointestinal diseases, systematic information on the role of food in transmitting such viruses is limited. Most of the outbreak cases caused by infected food handlers were the source of 53% of total outbreaks. Therefore, prevention and hygiene measures to reduce the frequency of foodborne virus outbreaks should focus on food workers and production site of food products. Pivotal strategies, such as proper investigation, surveillance, and reports on foodborne viral illnesses, are needed in order to develop more accurate measures to detect the presence and pathogenesis of viral infection with detailed descriptions. Moreover, molecular epidemiology and surveillance of food samples may help analysis of public health hazards associated with exposure to foodborne viruses. In this present review, we discuss different aspects of foodborne viral contamination and its impact on human health. This review also aims to improve understanding of foodborne viral infections as major causes of human illness as well as provide descriptions of their control and prevention strategies and rapid detection by advanced molecular techniques. Further, a brief description of methods available for the detection of viruses in food and related matrices is provided.

Fluorescence hyperspectral imaging technique for foreign substance detection on fresh-cut lettuce

BACKGROUND Non-destructive methods based on fluorescence hyperspectral imaging (HSI) techniques were developed to detect worms on fresh-cut lettuce. The optimal wavebands for detecting the worms were investigated using the one-way ANOVA and correlation analyses. RESULTS The worm detection imaging algorithms, RSI-I(492-626)/492 , provided a prediction accuracy of 99.0%. The fluorescence HSI techniques indicated that the spectral images with a pixel size of 1 × 1 mm had the best classification accuracy for worms. CONCLUSION The overall results demonstrate that fluorescence HSI techniques have the potential to detect worms on fresh-cut lettuce. In the future, we will focus on developing a multi-spectral imaging system to detect foreign substances such as worms, slugs and earthworms on fresh-cut lettuce.

Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging.

Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to discrimination between sound and discolored lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectral reflectance images obtained in the 400–1000 nm wavelength range. The optimal wavebands for discriminating between discolored and sound lettuce surfaces were determined using one-way analysis of variance. Multi-spectral imaging algorithms developed using ratio and subtraction functions resulted in enhanced classification accuracy of above 99.9% for discolored and sound areas on both adaxial and abaxial lettuce surfaces. Ratio imaging (RI) and subtraction imaging (SI) algorithms at wavelengths of 552/701 nm and 557–701 nm, respectively, exhibited better classification performances compared to results obtained for all possible two-waveband combinations. These results suggest that hyperspectral reflectance imaging techniques can potentially be used to discriminate between discolored and sound fresh-cut lettuce.

Differentiation of Beef and Fish Meals in Animal Feeds Using Chemometric Analytic Models

Purpose: The research presented in this paper applied the chemometric analysis to the near-infrared spectral data fromline-scanned hyperspectral images of beef and fish meals in animal feeds. The chemometric statistical models weredeveloped to distinguish beef meals from fish ones. Methods: The meal samples of 40 fish meals and 15 beef meals wereline-scanned to obtain hyperspectral images. The spectral data were retrieved from each of 3600 pixels in the Region ofInterest (ROI) of every sample image. The wavebands spanning 969 nm to 1551 nm (across 176 spectral bands) wereselected for chemometric analysis. The partial least squares regression (PLSR) and the principal component analysis (PCA)methods of the chemometric analysis were applied to the model development. The purpose of the models was to correctlyclassify as many beef pixels as possible while misclassified fish pixels in an acceptable amount. Results: The results showedthat the success classification rates were 97.9% for beef samples and 99.4% for fish samples by the PLSR model, and 85.1%for beef samples and 88.2% for fish samples by the PCA model. Conclusion: The chemometric analysis-based PLSR and PCAmodels for the hyperspectral image analysis could differentiate beef meals from fish ones in animal feeds.

Characterization of E coli biofim formations on baby spinach leaf surfaces using hyperspectral fluorescence imaging

Bacterial biofilm formed by pathogens on fresh produce surfaces is a food safety concern because the complex extracellular matrix in the biofilm structure reduces the reduction and removal efficacies of washing and sanitizing processes such as chemical or irradiation treatments. Therefore, a rapid and nondestructive method to identify pathogenic biofilm on produce surfaces is needed to ensure safe consumption of fresh, raw produce. This research aimed to evaluate the feasibility of hyperspectral fluorescence imaging for detecting Escherichia.coli (ATCC 25922) biofilms on baby spinach leaf surfaces. Samples of baby spinach leaves were immersed and inoculated with five different levels (from 2.6x104 to 2.6x108 CFU/mL) of E.coli and stored at 4°C for 24 h and 48 h to induce biofilm formation. Following the two treatment days, individual leaves were gently washed to remove excess liquid inoculums from the leaf surfaces and imaged with a hyperspectral fluorescence imaging system equipped with UV-A (365 nm) and violet (405 nm) excitation sources to evaluate a spectral-image-based method for biofilm detection. The imaging results with the UV-A excitation showed that leaves even at early stages of biofilm formations could be differentiated from the control leaf surfaces. This preliminary investigation demonstrated the potential of fluorescence imaging techniques for detection of biofilms on leafy green surfaces.

Detection of fecal contamination on beef meat surfaces using handheld fluorescence imaging device (HFID)

Current meat inspection in slaughter plants, for food safety and quality attributes including potential fecal contamination, is conducted through by visual examination human inspectors. A handheld fluorescence-based imaging device (HFID) was developed to be an assistive tool for human inspectors by highlighting contaminated food and food contact surfaces on a display monitor. It can be used under ambient lighting conditions in food processing plants. Critical components of the imaging device includes four 405-nm 10-W LEDs for fluorescence excitation, a charge-coupled device (CCD) camera, optical filter (670 nm used for this study), and Wi-Fi transmitter for broadcasting real-time video/images to monitoring devices such as smartphone and tablet. This study aimed to investigate the effectiveness of HFID in enhancing visual detection of fecal contamination on red meat, fat, and bone surfaces of beef under varying ambient luminous intensities (0, 10, 30, 50 and 70 foot-candles). Overall, diluted feces on fat, red meat and bone areas of beef surfaces were detectable in the 670-nm single-band fluorescence images when using the HFID under 0 to 50 foot-candle ambient lighting.