School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, PR China; School of Agriculture, Department of Agricultural Engineering, University of Cape Coast, Cape Coast, Ghana.
School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, PR China.
Food Chem. 2015 Jun 1;176:403-10. doi: 10.1016/j.foodchem.2014.12.042. Epub 2014 Dec 18.
Rapid analysis of cocoa beans is an important activity for quality assurance and control investigations. In this study, Fourier transform near infrared spectroscopy (FT-NIRS) and chemometric techniques were attempted to estimate cocoa bean quality categories, pH and fermentation index (FI). The performances of the models were optimised by cross-validation and examined by identification rate (%), correlation coefficient (Rpre) and root mean square error of prediction (RMSEP) in the prediction set. The optimal identification model by back propagation artificial neural network (BPANN) was 99.73% at 5 principal components. The efficient variable selection model derived by synergy interval back propagation artificial neural network regression (Si-BPANNR) was superior for pH and FI estimation. Si-BPANNR model for pH was Rpre=0.98 and RMSEP=0.06, while for FI was Rpre=0.98 and RMSEP=0.05. The results demonstrated that FT-NIRS together with BPANN and Si-BPANNR model could successfully be used for cocoa beans examination.
快速分析可可豆是质量保证和控制调查的重要活动。在本研究中，尝试使用傅里叶变换近红外光谱（FT-NIRS）和化学计量技术来估计可可豆的质量类别、pH 值和发酵指数（FI）。通过交叉验证优化模型性能，并在预测集中通过识别率（%）、预测相关系数（Rpre）和预测均方根误差（RMSEP）进行检查。通过反向传播人工神经网络（BPANN）得到的最佳识别模型在 5 个主成分下达到 99.73%。通过协同间隔反向传播人工神经网络回归（Si-BPANNR）得到的有效变量选择模型在 pH 和 FI 估计方面表现更优。Si-BPANNR 模型的 pH 值预测相关系数（Rpre）为 0.98，预测均方根误差（RMSEP）为 0.06，FI 的 Rpre 为 0.98，RMSEP 为 0.05。结果表明，FT-NIRS 结合 BPANN 和 Si-BPANNR 模型可成功用于可可豆检测。
