Assessment of intramuscular fat content of pork using NIR hyperspectral images of rib end
Assessment of intramuscular fat content of pork using NIR hyperspectral images of rib end
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DOI：
10.1016/j.jfoodeng.2016.07.005
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发表时间：
2017-01-01
影响因子：
5.5
通讯作者：
Ngadi, Michael O.
中科院分区：
文献类型：
--
作者：
Huang, Hui;Liu, Li;Ngadi, Michael O.
开发并测试了一种快速有效的近红外（NIR；900-1700 nm）高光谱成像方法，用于测定猪肉圆肋末端最后 6 根肋骨的肌内脂肪（IMF）含量。应用Gabor滤波器、灰度共生矩阵（GLCM）和宽线检测器（WLD）等模式分析技术将高光谱图像处理为数据，从中提取光谱和图像特征，包括纹理光谱、纹理和线条特征。在6根肋骨图像的所有光谱和图像特征中，Gabor滤波平均光谱的一阶导数显示出与IMF含量最强的相关性，因此被选为在任何给定肋骨数量下预测IMF含量的最佳特征。利用多元线性回归 (MLR) 建立肋末端和 6 个不同肋位置的 IMF 含量之间的预测模型。使用留一法交叉验证来测试所建立模型的稳健性。校准、交叉验证和完全验证的相关系数（分别为 R-e、R-ev、R-fv）用于评估模型准确性。所有六个 MLR 模型均表现出良好的性能（R-c >= 0.90、R-cv >= 0.87、R-fv >= 0.81），其中最后 2/3 肋骨处的 IMF 含量显示出最佳预测能力（R-c = 0.96、R-cv = 0.95、R-fv = 0.83）。 Gabor 滤波器 MLR 模型应用于图像中的每个像素，以成功导出分布图并可视化腰部中的 IMF 内容分布。结果表明，沿单个背最长肌的不同肋骨处猪肉 IMF 含量的常规测定可以用模式分析技术处理的肋骨端部高光谱图像代替。 (C) 2016 年由爱思唯尔有限公司出版。
A rapid and effective near infrared (NIR; 900-1700 nm) hyperspectral imaging method to determine intramuscular fat (IMF) content of pork at the last 6 ribs of the round rib end was developed and tested. Pattern analysis techniques including Gabor filter, grey-level co-occurrence matrix (GLCM), and wide line detector (WLD) were applied to process hyperspectral images into data from which spectral and image features, including texture-spectral, texture and line features were extracted. Among all spectral and image features of the 6 ribs' images, the first derivative of Gabor filtered mean spectra showed the strongest correlation with IMF content, and was therefore selected as the optimal feature for prediction of IMF content at any given rib number. Multiple linear regression (MLR) was exploited to build prediction models between IMF content at rib end and at 6 different rib locations. Leave-one-out cross validation was used to test the robustness of established models. The correlation coefficients of calibration, cross validation and full validation (R-e, R-ev, R-fv, respectively) were used to assess model accuracy. All six MLR models showed a good performance (R-c >= 0.90, R-cv >= 0.87, R-fv >= 0.81), with that for IMF content at the 2nd/3rd last rib showing the best predictive ability (R-c = 0.96, R-cv = 0.95, R-fv = 0.83). The Gabor filter-MLR models were applied to each pixel in an image to successfully derive distribution maps and visualize IMF content distribution in the loins. Results demonstrated that the conventional determination of IMF content of pork at different ribs along a single Longissimus dorsi could be replaced by pattern analysis technique-processed hyperspectral images of rib ends. (C) 2016 Published by Elsevier Ltd.