A rapid and non-destructive detection of Escherichia coli on the surface of fresh-cut potato slices and application using hyperspectral imaging
Introduction
With the acceleration of people’s pace of life and pursuit of convenience and health, the demand for fresh-cut fruit and vegetable is increasing dramatically (Liu et al., 2019), and a large number of processed potatoes have appeared in markets for more than a decade (Zhou et al., 2019). However, some foodborne pathogens are faster to grow on the exposed surface of fresh-cut products caused by mechanical injury after minimal processing, including Escherichia coli (Yuan et al., 2019), Salmonella (Cuggino et al., 2020), Listeria monocytogenes (Scollard et al., 2016), and Penicillium expansum (Rios De Souza et al., 2020), which would pose a threat to human health or exacerbate the decay of fresh-cut products to reduce their edible value and shorten the shelf life, so early detection for pathogens is necessary (Condurso et al., 2020). Traditional biological culture-dependent methods often need long-time and laborious work, involving complex processes of sample preparation, serial dilution, plating on a suitable medium and a certain incubation time to obtain visible colonies (Hameed et al., 2018). Recent researchers have focused on developing rapid and accurate techniques to detect pathogens in food, such as polymerase chain reaction (PCR), biosensor and enzyme-linked immunosorbent assay (ELISA), but these methods are often technically complicated, requiring well-trained specialists and inevitably damaging samples (Kang, 2019; Ye et al., 2019; Nagaraj et al., 2016).
Hyperspectral imaging (HSI) can collect data of three-dimensional patterns, two spatial and one wavelength dimensions to obtain related qualitative and quantitative information as a rapid and non-destructive detection method (Bonah et al., 2019; He and Sun, 2015). Compared with complex and destructive traditional detection approaches, the advantage of HSI becomes outstanding because this method requires little or even no sample preparation and can measure several chemical compositions and quality attributes simultaneously (Zhao et al., 2020). Recent studies have investigated HSI as a non-destructive and effective method for prediction of fruit quality attributes such as ripeness (Munera et al., 2017), pH (Li et al., 2018a,2018b), soluble solids content (SSC) (Zhang et al., 2019), firmness (Li et al., 2018a,2018b), chilling injury (Babellahi et al., 2020), and fungal infections (Siedliska et al., 2018).
HSI has also been applied to evaluate pathogens on food, based on the assumption that the metabolic activity of pathogens would lead to biochemical changes and metabolic byproducts, which could provide characteristic fingerprints that potentially indicated the contamination in food (Wang et al., 2018). For example, Cheng and Sun (2015) developed a multiple linear regression (MLR) model with residual predictive deviation (RPD) and the coefficient of determination of prediction (R2P) was 5.22 and 0.870 by the HSI method in the 400-1000 nm spectral range to measure E. coli in grass carp. Besides, Huang et al. (2013) exploited a non-destructive detection of total viable count (TVC) in pork with a back-propagation artificial neural network (BP-ANN) algorithm which built a prediction model (R2P = 0.8308, the root mean square error of prediction (RMSEP) = 0.243) by HSI (400-1000 nm). Although the above researchers have confirmed the feasibility of the HSI system in several meat products, only few studies have reported the experimental result in fresh-cut fruit and vegetables. For example, Siripatrawan et al. (2011) assessed E. coli contamination in packaged fresh spinach by HSI ranging from 400-1000 nm combined with PCA and ANN to predict the number of E. coli with R2 of 0.97.
Our previous research has shown that photodynamic treatment (PDT) has a satisfactory performance to inactivate E. coli on fresh-cut apples and is considered as a very promising approach for antimicrobial approach (Tao et al., 2019). In this study, using HSI to establish a non-destructive method and explore the optimal illumination time for PDT was the first step to explore the feasibility of practical application based on the HSI system. The specific research objectives were: (1) to use 400-1000 nm hyperspectral imaging method to image the fresh-cut potato slices with E. coli on the surface; (2) to build the best model based on full-spectrum and characteristic wavelength by genetic algorithm (GA); (3) to obtain optimum illumination time of PDT on fresh-cut potato slices with best inactivation effect using the established optimal model.
Section snippets
Sample preparation
Fresh potatoes with similar shape and stage of ripening, without apparent defects, physical injuries or disease infections were purchased from a local supermarket (Qingdao, China). After washed with distilled water, the potatoes were cut into 2 cm × 2 cm × 2 mm slices using a vegetable slicer under sterile environments.
Culture conditions of Escherichia coli strains and preparation for fresh-cut potato slices
E. coli provided by Professor Xiangzhao Mao was used in the research, and the colony was cultured overnight in 10 mL Luria-Bertani (LB) medium in a shaker 37 °C. Then, the
Reference measurements of E. coli on the surface of fresh-cut potato slices
The values of E. coli on the surface of fresh-cut potato slices in five sample groups (E1, E2, E3, E4, and E5), varied from 4.52 to 6.37 log CFU g-1 (Fig. S1), in a reasonable range, and such wide variability of reference measurement data could enable calibration models to be more robust and stable.
A total of 91 samples were used as the calibration set, and 37 samples were used as the prediction set (Table 1). It was worth noting that the range of E. coli in the calibration set covered the
Conclusion
This research showed the potential of using the HSI system in the Vis-NIR (400-1000 nm) region to predict E. coli on the surface of fresh-cut potato slices and developed the optimum BP-NN model (R2 = 0.976). Besides, by using the BP-NN model, the study evaluated the inactivation performance of the innovation antimicrobial method on E. coli on the surface of potato slices. These results indicated the feasibility of the HSI method for the detection of E. coli on the surface of fresh-cut potato
Funding
This work was supported by the National Natural Science Foundation of China (31801594) and Natural Science Foundation of Shandong Province (ZR2019BC049).
CRediT authorship contribution statement
Danrui Li: Writing - original draft, Software. Fang Zhang: Funding acquisition, Writing - review & editing, Conceptualization. Jinshen Yu: Methodology, Formal analysis. Xuefeng Chen: Writing - review & editing. Bingjie Liu: Conceptualization. Xianghong Meng: Conceptualization.
Declaration of Competing Interest
The authors report no declarations of interest.
Acknowledgements
Authors thank Mr. Junkang Zhang who gave help during the model building.
References (38)
- et al.
Early detection of chilling injury in green bell peppers by hyperspectral imaging and chemometrics
Postharv. Biol. Technol.
(2020) - et al.
Study of modeling optimization for hyperspectral imaging quantitative determination of naringin content in pomelo peel
Comput. Electron. Agr.
(2019) - et al.
A new approach for the shelf-life definition of minimally processed carrots
Postharv. Biol. Technol.
(2020) - et al.
Effects of ultraviolet light and curcumin-mediated photodynamic inactivation on microbiological food safety: a study in meat and fruit
Photodiagn. Photodyn.
(2020) - et al.
Modelling the combined effect of chlorine, benzyl isothiocyanate, exposure time and cut size on the reduction of salmonella in fresh-cut lettuce during washing process
Food Microbiol.
(2020) - et al.
Detecting chilling injury in red delicious apple using hyperspectral imaging and neural networks
Postharv. Biol. Technol.
(2009) - et al.
A review of neural networks in plant disease detection using hyperspectral data
Inf. Process. Agr.
(2018) - et al.
Conventional and emerging detection techniques for pathogenic bacteria in food science: a review
Trends. Food Sci. Tech.
(2018) - et al.
Hyperspectral imaging technology for rapid detection of various microbial contaminants in agricultural and food products
Trends. Food Sci. Tech.
(2015) - et al.
Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging
Food Res. Int.
(2013)
Essential processing methods of hyperspectral images of agricultural and food products
Chemometr. Intell. Lab.
Basic principles for developing real-time PCR methods used in food analysis: a review
Trends. Food Sci. Tech.
Application of hyperspectral imaging for nondestructive measurement of plum quality attributes
Postharv. Biol. Technol.
SSC and pH for sweet assessment and maturity classification of harvested cherry fruit based on NIR hyperspectral imaging technology
Postharv. Biol. Technol.
Effect of purslane (Portulaca oleracea L.) extract on anti-browning of fresh-cut potato slices during storage
Food Chem.
Protein content evaluation of processed pork meats based on a novel single shot (Snapshot) hyperspectral imaging sensor
J. Food. Eng.
Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging
J. Food. Eng.
Development of IgY based sandwich ELISA for the detection of staphylococcal enterotoxin g (SEG), an Egc toxin
Int. J. Food. Microbiol.
Hyperspectral imaging as a powerful tool for identification of papaya seeds in black pepper
Food Control
Cited by (10)
Food quality 4.0: From traditional approaches to digitalized automated analysis
2023, Journal of Food EngineeringPectin/sodium alginate/xanthan gum edible composite films as the fresh-cut package
2021, International Journal of Biological MacromoleculesCitation Excerpt :With the accelerated pace in modern life, more and more people are preferring fresh-cut fruits and vegetables both at home and in the catering industry, since it can bring much convenience, saving lots of time for other cultural events, sports, and entertainment [1].
Hyperspectral Imaging for Fresh-Cut Fruit and Vegetable Quality Assessment: Basic Concepts and Applications
2023, Applied Sciences (Switzerland)Intelligent detection for fresh-cut fruit and vegetable processing: Imaging technology
2022, Comprehensive Reviews in Food Science and Food Safety