Multispectral imaging for rapid and non-destructive determination of aerobic plate count (APC) in cooked pork sausages

Ma, Fanyuan; Yao, Jing; Xie, Tingting; Liu, Changhong; Chen, Wei; Chen, Conggui; Zheng, Lei

doi:10.1016/j.foodres.2014.05.010

Cited by 42 publications

(30 citation statements)

References 34 publications

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“…A detailed description of the instrument (shown in Fig. 1) has been reported in previous study (Ma et al, 2014b). The system should be preheated at least 30 min before image acquisition and subsequently calibrated using one uniform bright, one uniform dark and one geometric disc which is white a grid of black dots.…”

Section: Image Acquisition and Analysismentioning

confidence: 99%

Feasibility of combining spectra with texture data of multispectral imaging to predict heme and non-heme iron contents in pork sausages

Qin

Shi

et al. 2016

Food Chemistry

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Section: Image Acquisition and Analysismentioning

confidence: 99%

Feasibility of combining spectra with texture data of multispectral imaging to predict heme and non-heme iron contents in pork sausages

Qin

Shi

et al. 2016

Food Chemistry

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“…Ma et al (2014) used a multispectral imaging system in the Vis/NIR region to determine the aerobic plate count (APC) in cooked pork sausages. A novel algorithm was reported for feature selection to develop linear and non-linear regression of L*, a*, and b* color from multispectral images of pork samples (Sharifzadeh et al, 2014).…”

Section: Porkmentioning

confidence: 99%

Optical Methods and Techniques for Meat Quality Inspection

Peng¹,

Dhakal²

2015

Trans.ASABE

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“…A non-invasive method based on multispectral imaging in the visible and near infrared (NIR) regions to predict the aerobic plate count in cooked pork sausages has been considered recently [6].…”

Section: Introductionmentioning

confidence: 99%

An intelligent decision support system for the detection of meat spoilage using multispectral images

Alshejari

Kodogiannis

2016

Neural Comput & Applic

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In food industry, quality and safety are considered important issues worldwide that are directly related to health and social progress. The use of vision technology for quality testing of food production has the obvious advantage of being able to continuously monitor a production using non-destructive methods, thus increasing the quality and minimizing cost. The performance of an intelligent decision support system has been evaluated in monitoring the spoilage of minced beef stored either aerobically or under modified atmosphere packaging, at different storage temperatures (0, 5, 10, and 15 °C) utilising multispectral imaging information. This paper utilises a neuro-fuzzy model which incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. Initially, meat samples are classified according to their storage conditions, while identification models are then utilised for the prediction of the Total Viable Counts of bacteria.The innovation of the proposed approach is further extended to the identification of the temperature used for storage, utilizing only imaging spectral information. Results indicated that spectral information in combination with the proposed modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage.

show abstract

Multispectral imaging for rapid and non-destructive determination of aerobic plate count (APC) in cooked pork sausages

Cited by 42 publications

References 34 publications

Feasibility of combining spectra with texture data of multispectral imaging to predict heme and non-heme iron contents in pork sausages

Feasibility of combining spectra with texture data of multispectral imaging to predict heme and non-heme iron contents in pork sausages

Optical Methods and Techniques for Meat Quality Inspection

An intelligent decision support system for the detection of meat spoilage using multispectral images

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