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Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification 时间:2018-09-07 22:52   来源:未知   作者:admin   点击: 次   Abstract:Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIE, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.
1. Introduction
   Near-infrared spectroscopy (NIRS) has been used for prediction of physicochemical properties of food, being applied to objective control and monitoring of food quality [1]. Also, it is a sustainable alternative as it requires no chemicals that might harm the environment and are hazardous to human beings. The near-infrared spectrum comprises a large set of overtones and combination bands. Selecting a few essential wavelengths related to the response information can reduce significantly the amount of data to be analyzed, providing information for the development of multispectral systems. In this way, multivariate statistical methods could be used for extraction of detailed information of the spectra [2].
  Implementation of NIRS as a process analytical technology (PAT) to the food industry involves a multidisciplinary approach in which computational intelligence (CI), particularly machine learning (ML) [3–10], has been investigated. The main advantage of CI is its capacity of handling multiple parameters, facilitating fast and accurate evaluation of samples in an industrial environment [11].
   Recently, ML techniques application has been investigated for several food processing needs, including prediction and assessment of food quality [3, 4, 12–18]. Wang et al. [13] predicted the total viable counts (TVC) in pork using support vector machines (SVM), showing the advantage of a rapid and readily performed analysis obtaining coefficient of correlation of .
Multilayer perceptron (MLP) neural network was used to correlate Fourier transform infrared (FTIR) spectral data with beef spoilage, with good performance of the classifier with 10 neurons in the hidden layer providing a overall correct classification [12].
  Argyri et al. [15] explored SVM applied to beef samples under different packaging conditions by spectroscopy and sensory analysis in order to predict fresh, semifresh, and spoiled samples. It was reported that the ML techniques (including artificial neural networks (ANN)) provided improved prediction models with accuracy, for the various groups when compared to multivariate statistical methods. Qiao et al. [18] predicted beef eating quality attributes, namely, colour, ultimate pH, and slice shear force (SSF) using spectroscopy methods, achieving prediction results over using SVM on three datasets.