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ARS Home » Southeast Area » Athens, Georgia » U.S. National Poultry Research Center » Quality and Safety Assessment Research Unit » Research » Publications at this Location » Publication #376807
Research Project: Assessment and Improvement of Poultry Meat, Egg, and Feed Quality

Location: Quality and Safety Assessment Research Unit

Title: Rapid classification of intact chicken breast fillets by predicting principal component score of quality traits with visible/near-Infrared spectroscopy

Author
item YANG, YI - China Agricultural University
item Zhuang, Hong
item Yoon, Seung-Chul
item WANG, WEI - China Agricultural University
item JIANG, HONGZHE - China Agricultural University
item JIA, BEIBEI - China Agricultural University

Submitted to: Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/28/2017
Publication Date: 9/30/2017
Citation: Yang, Y., Zhuang, H., Yoon, S.C., Wang, W., Jiang, H., Jia, B. 2017. Rapid classification of intact chicken breast fillets by predicting principal component score of quality traits with visible/near-Infrared spectroscopy. Food Chemistry. https://doi.org/10.1016/j.foodchem.2017.09.148.
DOI: https://doi.org/10.1016/j.foodchem.2017.09.148

Interpretive Summary: As the consumer demand for chicken meat has increased in recent years, the assessment and classification of meat quality attributes during processing are becoming important for optimizing the utilization of raw meat materials. DFD (dark, firm, and dry) and PSE (pale, soft, and exudative) are two major meat-quality defects in the poultry industry. It is known that the exhaustion and stress before slaughter results in PSE or DFD meat. DFD meat is prone to microbial contamination and PSE meat is regarded as defective because of its pale appearance, and soft texture. Therefore, because of the low economic value of PSE or DFD meat, rapid sensing and sorting of PSE and DFD meat will be valuable to the poultry industry. The standard methods to characterize PSE, normal, and DFD include pH and lightness (L*) measurements although their accuracies are limited. The current study proposed a multivariate classification model with visible/near-infrared spectroscopy. The model used five variables of L*, pH, drip loss, expressible fluid, and salt-induced water gain measurements that are important to measure PSE and DFD. The model transformed the measurement data of five variables with principal component analysis to a different linear space, where the data dimensionality necessary for the model was reduced to 1 from 5. The spectroscopic model with the single response variable of the most significant principal component showed about 80% accuracy in classifying the PSE, normal, and DFD meat categories.

Technical Abstract: In this study visible/near-infrared spectroscopy (Vis/NIRS) was evaluated to rapidly classify intact chicken breast fillets. Five principal components (PC) were extracted from reference quality traits (L*, pH, drip loss, expressible fluid, and salt-induced water gain). A quality grades classification method by PC1 score was proposed. With this method, 150 chicken fillets were properly classified into three quality grades, i.e., DFD (dark, firm and dry), normal, and PSE (pale, soft and exudative). Furthermore, PC1 score could be predicted using partial least squares regression (PLSR) model based on Vis/NIRS (R2p'='0.78, RPD'='1.9), without the measurement of any quality traits. Thresholds of PC1 classification method were applied to classify the predicted PC1 score values of each fillet into three quality grades. The classification accuracy of calibration and prediction set were 85% and 80%, respectively. Results revealed that PC1 score classification method is feasible, and with Vis/NIRS, this method could be rapidly implemented.