Using a Combination of Spectral and Textural Data to Measure Water-Holding Capacity in Fresh Chicken Breast Fillets

Beibei Jia,Hong Zhuang,Seung-Chul Yoon,Yu-Feng Li,Wei Wang

doi:10.3390/app8030343

Abstract

The aim here was to explore the potential of visible and near-infrared (Vis/NIR) hyperspectral imaging (400–1000 nm) to classify fresh chicken breast fillets into different water-holding capacity (WHC) groups. Initially, the extracted spectra and image textural features, as well as the mixed data of the two, were used to develop partial least square-discriminant analysis (PLS-DA) classification models. Smoothing, a first derivative process, and principle component analysis (PCA) were carried out sequentially on the mean spectra of all samples to deal with baseline offsets and identify outlier data. Six samples located outside the confidence ellipses of 95% confidence level in the score plot were defined as outliers. A PLS-DA model based on the outlier-free spectra provided a correct classification rate (CCR) value of 78% in the prediction set. Then, seven optimal wavelengths selected using a successive projections algorithm (SPA) were used to develop a simplified PLS-DA model that obtained a slightly reduced CCR with a value of 73%. Moreover, the gray-level co-occurrence matrix (GLCM) was implemented on the first principle component image (with 98.13% of variance) of the hyperspectral image to extract textural features (contrast, correlation, energy, and homogeneity). The CCR of the model developed using textural variables was less optimistic with a value of 59%. Compared to results of models based on spectral or textural data individually, the performance of the model based on the mixed data of optimal spectral and textural features was the best with an improved CCR of 86%. The results showed that the spectral and textural data of hyperspectral images together can be integrated in order to measure and classify the WHC of fresh chicken breast fillets.

Highlights

In recent years, the consumption of poultry meat has increased continually and reached high levels worldwide [1]
A hyperspectral imaging system in the visible and near-infrared (Vis/NIR) region of 400–1000 nm was adopted to explore the potential of combination of spectral and textural data for classifying samples with different Water-holding capacity (WHC)
The correct classification rate (CCR) of the prediction set for the model based on the spectra of seven informative wavelengths (441, 469, 506, 566, 596, 617, and 939 nm) was 73%, slightly lower than that of the model based on spectra of the full-band with a value of 78%

Summary

Introduction

The consumption of poultry meat has increased continually and reached high levels worldwide [1]. High-quality and safe chicken meat and meat products are expected. Water-holding capacity (WHC), which refers to the ability of muscle to retain water, is the result of early postmortem biochemical and biophysical changes occurring in muscle tissues [2]. The WHC of fresh chicken meat is important because it influences the retention of vitamins, mineral salts, and water [3]. From an economic point of view, as the meat is sold by weight, poor WHC will lead to high drip loss and a reduction in meat weight [3,4]. Poor WHC will cause a large amount of dripping

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Methods

Conclusion