Meat Quality Assessment by Electronic Nose (Machine Olfaction Technology)

Abstract

Over the last twenty years, newly developed chemical sensor systems (so called “electronic noses”) have made odor analyses possible. These systems involve various types of electronic chemical gas sensors with partial specificity, as well as suitable statistical methods enabling the recognition of complex odors. As commercial instruments have become available, a substantial increase in research into the application of electronic noses in the evaluation of volatile compounds in food, cosmetic and other items of everyday life is observed. At present, the commercial gas sensor technologies comprise metal oxide semiconductors, metal oxide semiconductor field effect transistors, organic conducting polymers, and piezoelectric crystal sensors. Further sensors based on fibreoptic, electrochemical and bi-metal principles are still in the developmental stage. Statistical analysis techniques range from simple graphical evaluation to multivariate analysis such as artificial neural network and radial basis function. The introduction of electronic noses into the area of food is envisaged for quality control, process monitoring, freshness evaluation, shelf-life investigation and authenticity assessment. Considerable work has already been carried out on meat, grains, coffee, mushrooms, cheese, sugar, fish, beer and other beverages, as well as on the odor quality evaluation of food packaging material. This paper describes the applications of these systems for meat quality assessment, where fast detection methods are essential for appropriate product management. The results suggest the possibility of using this new technology in meat handling.

Electronic Noses and Tongues: Applications for the Food and Pharmaceutical Industries

“…For meats, sensory quality, shelf life spoilage, off-flavor, taints and authenticity are areas where volatile changes make e-nose screening of samples useful [19]. A KAMINA e-nose with a MOS microarray and LDA was used to evaluate pork meat freshness when stored at 4 and 25 °C.…”

A statistical model-based diagnostic scheme for cost-effective determination of freshness in fish industry

“…If (ζ⋅ω n ) i is the product of ζ and ω n values of the i-th dominant pole at test run r, then the selected function admits the following form: g r (ζ,ω n ) = (ζ⋅ω n ) 1 + (ζ⋅ω n ) 2 + (ζ⋅ω n ) 3 (5) In other words, for each fish sample a vector X u (see subsection II.C) including the values g r (ζ,ω n ), with r=1,…,20 is formed. If the fish 0_1 is considered as freshness benchmark, then its corresponding vector X f , is compared with each one of all other vectors X u (corresponding each time to the fish samples 0_2, 3_1, 3_2, 6_1, 6_2 and 6_3) by means of the KS test (3). As shown in results of Table I, the correct hypothesis is selected at α=0.001 risk level for all fish samples tested.…”

“…Their major disadvantage is, however, their low sensitivity in detecting early post mortem changes [8]. For instance, the tool in [3] distinguishes between "spoiled" and "unspoiled" meat products. For fish products, this would translate into high effectiveness in detecting the degree of spoilage (that is, how close a fish is for being discarded as unacceptable), but low ability in distinguishing between fish of various early freshness stages.…”

Quantifying of total volatile basic nitrogen (TVB-N) content in chicken using a colorimetric sensor array and nonlinear regression tool

“…[6][7][8] These techniques are alternatives to traditional methods and are quick, easy to handle and do not require sample preparation or the use of chemical reagents. The sensors array in an E-nose system usually consists of numerous non-specic sensors and an odor stimulus, which generates a characteristic ngerprint from the sensors array.…”