Non-destructive sensing of the freshness of packed cod fish using conductivity and pH electrodes

Heising, Jenneke; Bartels, P.V.; Boekel, M.A.J.S. van; Dekker, Matthijs

doi:10.1016/j.jfoodeng.2013.10.008

Cited by 28 publications

(19 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Remark that new research and technologies are in course to make these measurements faster and nondestructive, what would allow having at hand very reliable statistics of the initial conditions. In this respect, impedance methods (see for example Koutsoumanis & Nychas (2000)) can be used to determine microbial counts, whereas TVB-N content can be measured using non-destructive colorimetry sensor arrays (Huang et al, 2015) or using different types of ph-electrodes (Heising et al, 2014a;Bhadra et al, 2015).…”

Section: Demonstration On the Use Of The Smart Sensor In Practicementioning

confidence: 99%

Smart sensor to predict retail fresh fish quality under ice storage

García¹,

Cabo

Herrera

et al. 2017

Journal of Food Engineering

View full text Add to dashboard Cite

Fish wastage and market prices highly depend on accurate and reliable predictions of product shelf life and quality. The Quality Index Method (QIM) and EU grading criteria for whitefish (Council Regulation(EC) No 2406/96, 1996) are established sensory methods used in the market to monitor fish quality. Each assessment requires the consultation of a panel of trained experts. The indexes refer exclusively to the current state of the fish without any predictions about its evolution in the following days. This work proposes the development of a smart quality sensor which enables to measure quality and to predict its progress through time. The sensor combines information of biochemical and microbial spoilage indexes with dynamic models to predict quality in terms of the QIM and EU grading criteria. Besides, the sensor can account for the variability inside the batch if spoilage indexes are measured in more than one fish sample. The sensor is designed and tested to measure quality in fresh cod (Gadus morhua) under commercial ice storage conditions. Only two spoilage indexes, psychrotrophic counts and total volatile base-nitrogen content, were required to get accurate estimations of the two usual established sensory methods. The sensor is able to account for biological variability as shown with the validation and demonstration data sets. Moreover, new research and technologies are in course to make these measurements faster and non-destructive, what would allow having at hand a smart non-intrusive fish quality sensor.

show abstract

Section: Demonstration On the Use Of The Smart Sensor In Practicementioning

confidence: 99%

Smart sensor to predict retail fresh fish quality under ice storage

García¹,

Cabo

Herrera

et al. 2017

Journal of Food Engineering

View full text Add to dashboard Cite

show abstract

“…Ammonia and volatile amines, such as dimethylamine (C2) or trimethylamine (C3), are generated in food through chemical or microbial protein and amino acid degradation, and they are considered to be key indicators of freshness in meat and fish products. [33,34] The determination of volatile amines in food is usually performed by means of high performance liquid chromatography (HPLC), following a pre-derivatization step. [35]…”

Section: Ptr-tof-ms Analysismentioning

confidence: 99%

Volatile compound changes during shelf life of driedBoletus edulis: comparison between SPME-GC-MS and PTR-ToF-MS analysis

et al. 2015

View full text Add to dashboard Cite

Drying process is commonly used to allow long time storage of valuable porcini mushrooms (Boletus edulis). Although considered a stable product dried porcini flavour changes during storage. Monitoring of volatile compounds during shelf life may help to understand the nature of the observed changes. In the present work two mass spectrometric techniques were used to monitor the evolution of volatile compounds during commercial shelf life of dried porcini. Solid phase microextraction (SPME) coupled to gas chromatography - mass spectrometry (GC-MS) allowed the identification of 66 volatile compounds, 36 of which reported for the first time, monitored during the commercial shelf life of dried porcini. Proton transfer reaction - time of flight - mass spectrometry (PTR-ToF-MS) , a direct injection mass spectrometric technique, was shown to be a fast and sensitive instrument for the general monitoring of volatile compound evolution during storage of dried porcini. Furthermore, PTR-ToF-MS grants access to compounds whose determination would otherwise require lengthy pre-concentration and/or derivatization steps such as ammonia and small volatile amines. The two techniques, both used for the first time to study dried porcini, provided detailed description of time evolution of volatile compounds during shelf life. Alcohols, aldehydes, ketones and monoterpenes diminish during the storage while carboxylic acids, pyrazines, lactones and amines increase. The storage temperature modifies the rate of the observed changes influencing the final quality of the dried porcini. We showed the advantages of both techniques, suggesting a strategy to be adopted to follow time evolution of volatile compounds in food products during shelf life, based on the identification of compounds by GC-MS and the rapid time monitoring by PTR-ToF-MS measurements in order to maximize the advantages of both techniques.

show abstract

“…Changes in pH values on the surface of the beef and fish samples are useful in tracking the freshness of food samples [17][18][19]. The accumulation of lactic acid after post mortem glycolysis causes a decrease in pH.…”

Section: Ph Values To Determine Food Freshnessmentioning

confidence: 99%

Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer

Moon

Kim²,

et al. 2020

Sensors

View full text Add to dashboard Cite

There has been strong demand for the development of an accurate but simple method to assess the freshness of food. In this study, we demonstrated a system to determine food freshness by analyzing the spectral response from a portable visible/near-infrared (VIS/NIR) spectrometer using the Convolutional Neural Network (CNN)-based machine learning algorithm. Spectral response data from salmon, tuna, and beef incubated at 25 °C were obtained every minute for 30 h and then categorized into three states of “fresh”, “likely spoiled”, and “spoiled” based on time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of experimental objects. In addition, a CNN-based machine learning algorithm with a shift-invariant feature can minimize the effect of the variation caused using multiple devices in a real environment. The accuracy of the obtained machine learning model based on the spectral data in predicting the freshness was approximately 85% for salmon, 88% for tuna, and 92% for beef. Therefore, our study demonstrates the practicality of a portable spectrometer in food freshness assessment.

show abstract

Non-destructive sensing of the freshness of packed cod fish using conductivity and pH electrodes

Cited by 28 publications

References 9 publications

Smart sensor to predict retail fresh fish quality under ice storage

Smart sensor to predict retail fresh fish quality under ice storage

Volatile compound changes during shelf life of driedBoletus edulis: comparison between SPME-GC-MS and PTR-ToF-MS analysis

Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer

Contact Info

Product

Resources

About