Abstract
The present study was aimed at developing a low-cost but rapid technique for qualitative and quantitative detection of beef adulterated with pork. An electronic nose based on colorimetric sensors was proposed. The fresh beef rib steaks and streaky pork were purchased and used from the local agricultural market in Suzhou, China. The minced beef was mixed with pork ranging at levels from 0%~100% by weight at increments of 20%. Protein, fat, and ash content were measured for validation of the differences between the pure beef and pork used in basic chemical compositions. Fisher linear discriminant analysis (Fisher LDA) and extreme learning machine (ELM) were utilized comparatively for identification of the ground pure beef, beef–pork mixtures, and pure pork. Back propagation-artificial neural network (BP-ANN) models were built for prediction of the adulteration levels. Results revealed that the ELM model built was superior to the Fisher LDA model with higher identification rates of 91.27% and 87.5% in the training and prediction sets respectively. Regarding the adulteration level prediction, the correlation coefficient and the root mean square error were 0.85 and 0.147 respectively in the prediction set of the BP-ANN model built. This suggests, from all the results, that the low-cost electronic nose based on colorimetric sensors coupled with chemometrics has a great potential in rapid detection of beef adulterated with pork.
Highlights
Meat embodies an excellent source of numerous essential nutrients for human beings.The improvement in the average incomes of many has caused an upsurge in worldwide demand for meat [1]
The number of principal components (PCs) is less than the original variables, in the case of not losing much significant original information, so that we can decrease the influence of redundant information and simplify the later process of analyzing the problem [38].The results obtained in this study shows the cumulative contribution rate of the top nine PCs was 91.55%, which means that the top nine PCs of the colorimetric sensors characteristic variables could be used as the inputs for modeling, since they could account most of the information for the original variables
The result of the extreme learning machine (ELM) model built was superior to that of Fisher linear discriminant analysis (LDA), with higher identification rates—i.e., 91.27% and 87.5% in the training and prediction sets, respectively. This outcome may be rates—i.e., 91.27% and 87.5% in the training and prediction sets, respectively. This outcome may be said to be attributed to the relationships between the data matrices, colorimetric sensor outcomes, and said to be attributed to the relationships between the data matrices, colorimetric sensor outcomes, the category labels, being more complex than linear results from the basic principles of the colorimetric and the category labels, being more complex than linear results from the basic principles of the sensor technique; this technique is partially cross-sensitive to odorants, in conjunction with the good colorimetric sensor technique; this technique is partially cross-sensitive to odorants, in conjunction capacity for self-learning, as well as the self-adjusting property of ELM algorithm [22]
Summary
Meat embodies an excellent source of numerous essential nutrients for human beings.The improvement in the average incomes of many has caused an upsurge in worldwide demand for meat [1]. The high commercial value coupled with the increased demand for meat has attracted the attention of adulterators for several years [2].As a result, several concerns have been raised especially due to the continuous reports of adulterants in meat products that compromise their safety or quality [3,4]. Despite the risk of revenue lost through product recalls, arrest, and prosecution, somehow meat adulteration continues to attract many people. A typical case of intentional meat adulteration is the inter-species meat confounding aimed at deceiving consumers by replacing expensive meats with cheaper alternatives as is the case of adulterated pork in beef [5]. It is vital to develop an effective technique for meat adulteration examination, in order to guarantee high-quality meat and meat products for the consumers
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