Departamento de Biofísica, Escuela Nacional de Ciencias Biológicas, IPN Prolongación de Carpio y Plan de Ayala S/N Col Santo Tomás CP 11340, DF México, Mexico.
Meat Sci. 2010 Oct;86(2):511-9. doi: 10.1016/j.meatsci.2010.05.044. Epub 2010 Jun 20.
Chemometric MID-FTIR methods were developed to detect and quantify the adulteration of mince meat with horse meat, fat beef trimmings, and textured soy protein. Also, a SIMCA (Soft Independent Modeling Class Analogy) method was developed to discriminate between adulterated and unadulterated samples. Pure mince meat and adulterants (horse meat, fat beef trimmings and textured soy protein) were characterized based upon their protein, fat, water and ash content. In order to build the calibration models for each adulterant, mixtures of mince meat and adulterant were prepared in the range 2-90% (w/w). Chemometric analyses were obtained for each adulterant using multivariate analysis. A Partial Least Square (PLS) algorithm was tested to model each system (mince meat+adulterant) and the chemical composition of the mixture. The results showed that the infrared spectra of the samples were sensitive to their chemical composition. Good correlations between absorbance in the MID-FTIR and the percentage of adulteration were obtained in the region 1800-900 cm(-1). Values of R(2) greater than 0.99, standard errors of calibration (SEC) in the range to 0.0001-1.278 and standard errors of prediction (SEP estimated) between 0.001 and 1.391 for the adulterant and chemical parameters were obtained. The SIMCA model showed 100% classification of adulterated meat samples from unadulterated ones. Chemometric MID-FTIR models represent an attractive option for meat quality screening without sample pretreatments which can identify the adulterant and quantify the percentage of adulteration and the chemical composition of the sample.
化学计量学中红外（MID-FTIR）方法被开发出来，以检测和量化肉末与马肉、脂肪牛肉屑和组织化大豆蛋白的掺假情况。此外，还开发了一种 SIMCA（软独立建模分类分析）方法来区分掺假和未掺假的样品。纯肉末和掺杂物（马肉、脂肪牛肉屑和组织化大豆蛋白）根据其蛋白质、脂肪、水和灰分含量进行了特征描述。为了为每种掺杂物建立校准模型，在 2-90%（w/w）的范围内制备了肉末和掺杂物的混合物。使用多元分析对每种掺杂物进行了化学计量分析。测试了偏最小二乘（PLS）算法来对每个系统（肉末+掺杂物）和混合物的化学成分进行建模。结果表明，样品的红外光谱对其化学成分敏感。在 1800-900 cm(-1) 范围内，获得了 MID-FTIR 吸光度与掺假百分比之间的良好相关性。对于掺杂物和化学参数，获得了大于 0.99 的 R(2) 值、校准标准误差（SEC）在 0.0001-1.278 范围内和预测标准误差（SEP 估计值）在 0.001-1.391 之间。SIMCA 模型对未掺假的肉样进行了 100%的分类。化学计量学 MID-FTIR 模型代表了一种有吸引力的选择，用于无需样品预处理的肉类质量筛选，可以识别掺杂物并量化掺假百分比和样品的化学成分。
