Comparison of variable selection algorithms on vis-NIR hyperspectral imaging spectra for quantitative monitoring and visualization of bacterial foodborne pathogens in fresh pork muscles

Bonah, Ernest; Huang, Xingyi; Aheto, Joshua Harrington; Ren, Yi; Yu, Shanshan; Hongyang, Tu

doi:10.1016/j.infrared.2020.103327

Cited by 37 publications

(17 citation statements)

References 32 publications

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“…Investigation of the most informative features, data fusion strategies and high-level technologies, in terms of image capture and spectral acquisition, could be key to the generalization of outcomes in the most effective way. FTIR and MSI/HSI have been investigated in a wider range of applications, including food safety [ 57 , 58 ].…”

Section: Resultsmentioning

confidence: 99%

Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers

Fengou

Liu

Roumani

et al. 2022

Foods

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The rapid assessment of the microbiological quality of highly perishable food commodities is of great importance. Spectroscopic data coupled with machine learning methods have been investigated intensively in recent years, because of their rapid, non-destructive, eco-friendly qualities and their potential to be used on-, in- or at-line. In the present study, the microbiological quality of chicken burgers was evaluated using Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) in tandem with machine learning algorithms. Six independent batches were purchased from a food industry and stored at 0, 4, and 8 °C. At regular time intervals (specifically every 24 h), duplicate samples were subjected to microbiological analysis, FTIR measurements, and MSI sampling. The samples (n = 274) acquired during the data collection were classified into three microbiological quality groups: “satisfactory”: 4–7 log CFU/g, “acceptable”: 7–8 log CFU/g, and “unacceptable”: >8 logCFU/g. Subsequently, classification models were trained and tested (external validation) with several machine learning approaches, namely partial least squares discriminant analysis (PLSDA), support vector machine (SVM), random forest (RF), logistic regression (LR), and ordinal logistic regression (OLR). Accuracy scores were attained for the external validation, exhibiting FTIR data values in the range of 79.41–89.71%, and, for the MSI data, in the range of 74.63–85.07%. The performance of the models showed merit in terms of the microbiological quality assessment of chicken burgers.

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Section: Resultsmentioning

confidence: 99%

Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers

Fengou

Liu

Roumani

et al. 2022

Foods

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“…Variable selection algorithm was applied for wavelength reduction [29] . For this study a modified variable combination population analysis (VCPA) designed by Yun, Bin, Liu, Xu, Yan, Cao and Xu [20] which retains 100 variables to be further processed by genetic algorithm (GA) was applied.…”

Section: Resultsmentioning

confidence: 99%

Nondestructive monitoring, kinetics and antimicrobial properties of ultrasound technology applied for surface decontamination of bacterial foodborne pathogen in pork

Bonah

Huang

Yang

et al. 2021

Ultrasonics Sonochemistry

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“…The algorithms’ predictions were evaluated through various metrics including root mean square errors of: (i) calibration, (ii) cross validation and (iii) prediction on the prediction set. The results indicated that the combination of Variable Combination Population Analysis (VCPA), informative variables (IRIV) and Genetic Algorithm (GA), may be a suitable set of algorithms for quantitative monitoring of foodborne pathogens in food samples through HSI [ 63 ].…”

Section: Spectral Imaging Techniquesmentioning

confidence: 99%

Recent Advances and Applications of Rapid Microbial Assessment from a Food Safety Perspective

Pampoukis

Lytou

Argyri³

et al. 2022

Sensors

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Unsafe food is estimated to cause 600 million cases of foodborne disease, annually. Thus, the development of methods that could assist in the prevention of foodborne diseases is of high interest. This review summarizes the recent progress toward rapid microbial assessment through (i) spectroscopic techniques, (ii) spectral imaging techniques, (iii) biosensors and (iv) sensors designed to mimic human senses. These methods often produce complex and high-dimensional data that cannot be analyzed with conventional statistical methods. Multivariate statistics and machine learning approaches seemed to be valuable for these methods so as to “translate” measurements to microbial estimations. However, a great proportion of the models reported in the literature misuse these approaches, which may lead to models with low predictive power under generic conditions. Overall, all the methods showed great potential for rapid microbial assessment. Biosensors are closer to wide-scale implementation followed by spectroscopic techniques and then by spectral imaging techniques and sensors designed to mimic human senses.

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Comparison of variable selection algorithms on vis-NIR hyperspectral imaging spectra for quantitative monitoring and visualization of bacterial foodborne pathogens in fresh pork muscles

Cited by 37 publications

References 32 publications

Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers

Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers

Nondestructive monitoring, kinetics and antimicrobial properties of ultrasound technology applied for surface decontamination of bacterial foodborne pathogen in pork

Recent Advances and Applications of Rapid Microbial Assessment from a Food Safety Perspective

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