ElMasry Gamal, Wold Jens Petter
Agricultural Engineering Department, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt.
J Agric Food Chem. 2008 Sep 10;56(17):7672-7. doi: 10.1021/jf801074s. Epub 2008 Jul 26.
A nondestructive method using online spectral imaging has been developed for quantitative measurements of moisture and fat distribution in six species of fish fillets: Atlantic halibut (Hippoglossus hippoglossus), catfish (Icatalurus punctatus), cod (Gadus morhua), mackerel (Scomber japonicus), herring (Clupea harengus), and saithe (Pollachius virens). A spectral image cube was acquired for each fish fillet, and a subsampling approach for relating spectral and chemical features was applied. Spectral data was first analyzed by partial least-squares regression (PLSR), and then the regression coefficients were applied pixel-wise to convert the pixel spectra to a meaningful distribution map of moisture and fat contents. The resulting images are called "chemical images", which illustrate the distribution of fat and/or water content in the fillets. The pixel-wise prediction models for water and fat content had a correlation value of 0.94 with root-mean-square error estimated by a cross-validation (RMSECV) of 2.73% and a correlation value of 0.91 with RMSECV of 2.99%, respectively. This technique is suitable for high-speed assessment of quality parameters of biomaterials and should thus be implemented in industrial applications. The product could comprehensively be defined not only in terms of its external features such as size, shape, and color but also in terms of its chemical composition and its spatial distribution.
已开发出一种使用在线光谱成像的无损方法，用于定量测量六种鱼片（大西洋大比目鱼、鲶鱼、鳕鱼、鲭鱼、鲱鱼和绿青鳕）中的水分和脂肪分布。为每个鱼片获取一个光谱图像立方体，并应用一种用于关联光谱和化学特征的子采样方法。光谱数据首先通过偏最小二乘回归（PLSR）进行分析，然后逐像素应用回归系数，将像素光谱转换为有意义的水分和脂肪含量分布图。所得图像称为“化学图像”，它展示了鱼片中脂肪和/或水分含量的分布。水分和脂肪含量的逐像素预测模型的相关值分别为0.94，交叉验证估计的均方根误差（RMSECV）为2.73%；相关值为0.91，RMSECV为2.99%。该技术适用于生物材料质量参数的高速评估，因此应在工业应用中实施。该产品不仅可以根据其外部特征（如尺寸、形状和颜色）进行全面定义，还可以根据其化学成分及其空间分布进行定义。
