Kuswandi Bambang, Putri Fitra Karima, Gani Agus Abdul, Ahmad Musa
Chemo and Biosensors Group, Faculty of Pharmacy, University of Jember, Jl. Kalimantan 37, Jember, 68121 Indonesia ; Faculty of Science & Technology, USIM, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan Malaysia.
Chemo and Biosensors Group, Faculty of Pharmacy, University of Jember, Jl. Kalimantan 37, Jember, 68121 Indonesia.
J Food Sci Technol. 2015 Dec;52(12):7655-68. doi: 10.1007/s13197-015-1882-4. Epub 2015 Jun 7.
The use of chemometrics to analyse infrared spectra to predict pork adulteration in the beef jerky (dendeng) was explored. In the first step, the analysis of pork in the beef jerky formulation was conducted by blending the beef jerky with pork at 5-80 % levels. Then, they were powdered and classified into training set and test set. The second step, the spectra of the two sets was recorded by Fourier Transform Infrared (FTIR) spectroscopy using atenuated total reflection (ATR) cell on the basis of spectral data at frequency region 4000-700 cm(-1). The spectra was categorised into four data sets, i.e. (a) spectra in the whole region as data set 1; (b) spectra in the fingerprint region (1500-600 cm(-1)) as data set 2; (c) spectra in the whole region with treatment as data set 3; and (d) spectra in the fingerprint region with treatment as data set 4. The third step, the chemometric analysis were employed using three class-modelling techniques (i.e. LDA, SIMCA, and SVM) toward the data sets. Finally, the best result of the models towards the data sets on the adulteration analysis of the samples were selected and the best model was compared with the ELISA method. From the chemometric results, the LDA model on the data set 1 was found to be the best model, since it could classify and predict 100 % accuracy of the sample tested. The LDA model was applied toward the real samples of the beef jerky marketed in Jember, and the results showed that the LDA model developed was in good agreement with the ELISA method.
探索了运用化学计量学分析红外光谱以预测牛肉干（den deng）中猪肉掺假情况的方法。第一步，通过将牛肉干与5%-80%水平的猪肉混合，对牛肉干配方中的猪肉进行分析。然后，将它们磨成粉末并分为训练集和测试集。第二步，使用衰减全反射（ATR）池的傅里叶变换红外（FTIR）光谱仪，在4000-700 cm⁻¹频率区域的光谱数据基础上，记录两组的光谱。光谱被分为四个数据集，即：（a）整个区域的光谱作为数据集1；（b）指纹区域（1500-600 cm⁻¹）的光谱作为数据集2；（c）经过处理的整个区域的光谱作为数据集3；（d）经过处理的指纹区域的光谱作为数据集4。第三步，对这些数据集采用三种分类建模技术（即线性判别分析（LDA）、软独立建模类比法（SIMCA）和支持向量机（SVM））进行化学计量分析。最后，选择模型对样品掺假分析数据集的最佳结果，并将最佳模型与酶联免疫吸附测定（ELISA）方法进行比较。从化学计量学结果来看，数据集1上的LDA模型被发现是最佳模型，因为它能够对测试样品进行100%准确的分类和预测。将LDA模型应用于在任抹销售的牛肉干实际样品，结果表明所开发的LDA模型与ELISA方法结果吻合良好。
