Evaluation of chicken freshness using a low-cost colorimetric sensor array with AdaBoost–OLDA classification algorithm

• Published in: Food science & technology Vol. 57; no. 2; pp. 502 - 507
• Main Authors: Chen, Quansheng, Hui, Zhe, Zhao, Jiewen, Ouyang, Qin
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.07.2014; Elsevier
• Subjects: algorithms; Biological and medical sciences; Chicken; chicken meat; Classification algorithm; Colorimetric sensor array; colorimetry; discriminant analysis; dyes; Food industries; Freshness; Fundamental and applied biological sciences. Psychology; neural networks; prediction; silica gel; volatile compounds; Classification algorithm; Freshness; Chicken; Colorimetric sensor array; Evaluation; Costs; Poultry; Colorimetry; Algorithm; Vertebrata; Meat animals; Classification; Aves; Sensor array
• ISSN: 0023-6438; 1096-1127
• DOI: 10.1016/j.lwt.2014.02.031

This paper attempted to evaluate chicken freshness using a low-cost colorimetric sensor array with the help of a classification algorithm. We fabricated a novel and low-cost colorimetric sensors array, with a specific colorific fingerprint to volatile compounds, using printing chemically responsive dyes on a C2 reverse silica-gel flat plate. In addition, we proposed a novel classification algorithm for sensors data classification – orthogonal linear discriminant analysis (OLDA) and adaptive boosting (AdaBoost) algorithm, namely AdaBoost–OLDA. And we compared it with two classical classification algorithms – linear discriminant analysis (LDA) and back propagation artificial neural network (BP-ANN). Experimental results showed classification results by AdaBoost–OLDA algorithm is superior to BP-ANN and LDA algorithms, the classification results by which are both 100% in the calibration and prediction sets. This study sufficiently demonstrated that the colorimetric sensors array with a classification algorithm has a high potential in evaluating chicken freshness, and AdaBoost–OLDA algorithm has a strong performance in solution to a complex data classification. •Developed a low-cost colorimetric sensor array using chemical dyes printed on a plate.•Chicken freshness was successfully evaluated by the colorimetric sensor array.•A novel AdaBoost + OLDA algorithm was used for sensors data classification.