Evaluation of beef flavor attribute based on sensor array in tandem with support vector machines

Beef is an important red meat with abundant nutrition, and flavor is one of the most significant factors influencing beef quality. In this study, sensor array in tandem with support vector machine (SVM) was used to predict beef flavor attribute. Sensor array consisting of 12 ion-selective electrodes and 1 reference electrode was used to collect ion signal from beef samples. Besides, data acquisition card (DAQ card) and electrochemical workstation were applied to convert the ion signal to voltage signal, respectively. For the data analysis, SVM technique was used to build forecasting models for evaluating M. longissimus dorsi (LDs) flavor by combined voltage signals from DAQ card. Besides, data from electrochemical workstation were analyzed with SVM as well, which was applied to verify accuracy of data from DAQ card. The SVM with radial basis kernel function showed a better result with accuracy of 90% using data from DAQ card, and the accuracy of electrochemical workstation reached 90%. Therefore, it was possible to confirm that the integration of sensor array and SVM analysis provides an effective way for evaluating the flavor attribute of LDs. What’s more, the results of this research also indicate that DAQ card can replace the electrochemical workstation when converting the ion signal to voltage signal with a better performance.

[1]  Vincenzo Chiofalo,et al.  Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment – A review , 2017 .

[2]  Lingtao Zhu,et al.  Sensor-array-based evaluation and grading of beef taste quality. , 2017, Meat science.

[3]  F. Smulders,et al.  Effect of pelvic suspension and cooking temperature on the tenderness of electrically stimulated and aged beef, assessed with shear and compression tests. , 1998, Meat science.

[4]  A. J. McAfee,et al.  Red meat consumption: an overview of the risks and benefits. , 2010, Meat science.

[5]  M. Omid,et al.  Detection of poultry egg freshness by dielectric spectroscopy and machine learning techniques , 2015 .

[6]  George-John E. Nychas,et al.  Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis , 2013 .

[7]  A. Vilela,et al.  Structural Equation Modelling (SEM) applied to sensory profile of Vinho Verde monovarietal wines. , 2018, Food research international.

[8]  M. Valle.  Electronic Tongues Employing Electrochemical Sensors , 2010 .

[10]  J. Wood,et al.  Effects of breed, diet and muscle on fat deposition and eating quality in pigs. , 2004, Meat science.

[11]  Lingtao Zhu,et al.  A predictive model for the evaluation of flavor attributes of raw and cooked beef based on sensor array analyses. , 2019, Food research international.

[12]  Salvador Alegret,et al.  Integrated Analytical Systems , 2003 .

[13]  D. Cornforth,et al.  Effect of muscle type and cooking temperature on liver-like off-flavour of five beef chuck muscles , 2010 .

[14]  Bipan Tudu,et al.  A review on combined odor and taste sensor systems , 2016 .

[15]  Necla Demir,et al.  Classification of impacted blueberries during storage using an electronic nose. , 2011, Journal of the science of food and agriculture.

[16]  Tomasz Markiewicz,et al.  Classification of milk by means of an electronic nose and SVM neural network , 2004 .

[17]  Da-Wen Sun,et al.  Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: A review , 2014 .

[18]  Jun Wang,et al.  An optimization of the MOS electronic nose sensor array for the detection of Chinese pecan quality , 2017 .

[19]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[20]  L. Vosshall,et al.  Molecular architecture of smell and taste in Drosophila. , 2007, Annual review of neuroscience.

[21]  Yang Wang,et al.  Modeling structural and compositional changes of beef during human chewing process , 2015 .

[22]  Ronei J. Poppi,et al.  Support vector machines in tandem with infrared spectroscopy for geographical classification of green arabica coffee , 2017 .

[23]  Yankun Peng,et al.  Prediction of beef quality attributes using VIS/NIR hyperspectral scattering imaging technique , 2012 .

[24]  A. Zamojska,et al.  The monitoring of methane fermentation in sequencing batch bioreactor with flow-through array of miniaturized solid state electrodes. , 2010, Talanta.

[25]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[26]  George-John E. Nychas,et al.  A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage , 2013 .

[27]  Simon X. Yang,et al.  A comparative study for least angle regression on NIR spectra analysis to determine internal qualities of navel oranges , 2015, Expert Syst. Appl..

[28]  Xiuqin Rao,et al.  Quality and safety assessment of food and agricultural products by hyperspectral fluorescence imaging. , 2012, Journal of the science of food and agriculture.