Electronic noses for food quality: A review

Loutfi, Amy; Coradeschi, Silvia; Mani, Ganesh Kumar; Shankar, P.; Rayappan, John Bosco Balaguru

doi:10.1016/j.jfoodeng.2014.07.019

Cited by 630 publications

(355 citation statements)

References 79 publications

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“…Regarding to detecting the rich information of Cucumber image, most of the existing researches are based on the laboratory and other simple environment [3] . Under the complex background of the fruit stem and leaf-light-stent, shade, uneven illumination and overlapping become the main factors of seriously affect the recognition accuracy and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Cucumber appearance quality detection under complex background based on image processing

Liu

Niu

2018

International Journal of Agricultural and Biological Engineering

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Abstract:Cucumber fruit appearance quality is an important basis of growth status. In order to improve the quality detection accuracy and processing efficiency of cucumber color image under complicated background, an improved GrabCut algorithm was proposed to extract the cucumber boundary. Firstly, including pixel size normalization, rectangular box set and scale image resolution, pretreatments of cucumber image were adopted to reduce the iteration times and operation time of GrabCut algorithm. Then, the Gaussian mixture model was chosen to find out the possible prospect of target region and background region in the preprocessed rectangular frame on the preliminary modeling. Meanwhile, by the optimization of K-means cluster to the initial GMM model, the effective target area was extracted. Finally, the whole image noise and serrated boundary was removed by morphological operations to segment the outline of the complete target prospects with appropriate structure size. And then the cucumber appearance quality detection instrument was designed to extract the texture and shape features exactly, so that it could obtain cucumber appearance quality and evaluate its growth effectively. With the segmentation experiments by almost 300 cucumber original images from greenhouse in Shandong Province, the results showed that the improved GrabCut algorithm could effectively extract the complete and smooth boundary of cucumber. With relatively high segmentation evaluation index, the precision was 93.88%, the recall rate was 99.35%, the F-Measure reached 96.53%, and the misclassification error was controlled at minimum 5.84%. The average running time was shortened to 1.4023 s. The comparison results showed that the improved GrabCut algorithm was the best, followed by the SLIC and traditional GrabCut method. Cucumber appearance quality detection instrument could also extract more accurate feature parameters. And it could meet the basic growth condition assessment by automatic image processing. Keywords: cucumber, complicated background, quality detection, image processing, GrabCut DOI: 10.25165/j.ijabe.20181104.3090Citation: Ye H J, Liu C Q, Niu P Y. Cucumber appearance quality detection under complex background based on image processing. Int J Agric & Biol Eng, 2018; 11(4): 193-199.

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Section: Introductionmentioning

confidence: 99%

Cucumber appearance quality detection under complex background based on image processing

Liu

Niu

2018

International Journal of Agricultural and Biological Engineering

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“…The technique was developed at the end of the last century (Olsson et al, 2000;Oshita et al, 2006). At the current stage of evolution of sensor technology, a device like the e-nose offers new possibilities not only for assessment of grain quality but generally in food (Loutfi et al, 2015), cosmetic, or pharmaceutical industries, as well as in environment control and clinical disease diagnosis (Casalinuovo et al, 2006;Guz et al, 2015;Pathange et al, 2006;Peris and Escuder, 2009).…”

Section: Introductionmentioning

confidence: 99%

Electronic nose with polymer-composite sensors for monitoring fungal deterioration of stored rapeseed

Gancarz

Wawrzyniak

Gawrysiak‐Witulska

et al. 2017

International Agrophysics

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A b s t r a c t. Investigations were performed to examine the possibility of using an electronic nose to monitor development of fungal microflora during the first eighteen days of rapeseed storage. The Cyranose 320 device manufactured by Sensigent was used to analyse volatile organic compounds. Each sample of infected material was divided into three parts and the degree of spoilage was measured in three ways: analysis of colony forming units, determination of ergosterol content, and measurement of volatile organic compounds with the e-nose. Principal component analysis was performed on the generated patterns of signals and six groups of different spoilage levels were isolated. An analysis of sensorgrams for a few sensors with a strong signal for each group of rapeseed spoilage was performed. The ratio of the association time to the steady state was calculated. This ratio was different for the low level and the highest level of ergosterol and colony forming units. The results have shown that the e-nose can be a useful tool for quick estimation of the degree of rapeseed spoilage.

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“…Multiple novel processing and monitoring technologies including ultrasound and electrochemical (conductive) biosensors [30] have been exploited to enhance the productivity and process efficiency of food fermentation.…”

Section: Introductionmentioning

confidence: 99%

The Application of State-of-the-Art Analytic Tools (Biosensors and Spectroscopy) in Beverage and Food Fermentation Process Monitoring

et al. 2017

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Abstract:The production of several agricultural products and foods are linked with fermentation. Traditional methods used to control and monitor the quality of the products and processes are based on the use of simple chemical analysis. However, these methods are time-consuming and do not provide sufficient relevant information to guarantee the chemical changes during the process. Commonly used methods applied in the agriculture and food industries to monitor fermentation are those based on simple or single-point sensors, where only one parameter is measured (e.g., temperature or density). These sensors are used several times per day and are often the only source of data available from which the conditions and rate of fermentation are monitored. In the modern food industry, an ideal method to control and monitor the fermentation process should enable a direct, rapid, precise, and accurate determination of several target compounds, with minimal to no sample preparation or reagent consumption. Here, state-of-the-art advancements in both the application of sensors and analytical tools to monitor beverage and food fermentation processes will be discussed.

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Electronic noses for food quality: A review

Cited by 630 publications

References 79 publications

Cucumber appearance quality detection under complex background based on image processing

Cucumber appearance quality detection under complex background based on image processing

Electronic nose with polymer-composite sensors for monitoring fungal deterioration of stored rapeseed

The Application of State-of-the-Art Analytic Tools (Biosensors and Spectroscopy) in Beverage and Food Fermentation Process Monitoring

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