基于近红外光谱技术的食用油酸值和过氧化值定量分析研究

目的 建立基于傅里叶近红外光谱技术的定量分析模型, 实现快速测定食用油中酸值和过氧化值含量, 保证食用油的品质安全以及跟踪食用油储藏期间的品质变化。方法 首先采用傅里叶近红外光谱仪采集食用油样品漫反射光谱, 接着采用归一化(Normalize)和标准正态变换(standard normal variate, SNV)对光谱数据进行预处理, 降低原始光谱中噪声的影响; 其次通过随机森林(random forest, RF)和引导软收缩(bootstrapping soft shrinkage, BOSS)算法提取特征波长; 最后结合径向基函数(radial basis function, RBF)神经网络和极限学习机(extreme learning machine, ELM)建立食用油酸值和过氧化值的预测模型, 并与全波段的模型进行对比分析。结果 经过BOSS算法所提取的特征波段建立的模型预测效果优于RF算法以及全波段模型, 酸值模型的决定系数(determination coefficient, R2)达到0.98, 均方根误差(root mean square error, RMSE)达到0.08; 过氧化值模型的R2达到0.96, RMSE达到0.63。结论 BOSS算法有效的提取了食用油酸值和过氧化值的特征波段, BOSS-RBF模型能够适用于食用油中酸值和过氧化值含量的快速、无损检测。利用近红外光谱技术对食用油酸值和过氧化值进行定量分析是可行的, 可通过该方法实现对食用油品质的分析研究。

Quantitative analysis of acid value and peroxide value of edible oil based on near-infrared spectroscopy

Objective To establish a quantitative analysis model based on Fourier near-infrared spectroscopy for rapid determination of acid value and peroxide value in edible oils, ensure the quality and safety of edible oils and track changes in quality during storage. Methods The diffuse reflectance spectrum of edible oil samples was collected by Fourier near-infrared spectrometer. In addition, normalized and standard normal variate (SNV) was used to pre-process the spectral data to reduce the influence of noise in the original spectra. Furthermore, feature wavelengths were extracted by random forest (RF) and bootstrapping soft shrinkage (BOSS) algorithms. Eventually, Radial Basis Function (RBF) neural network and Extreme Learning Machine (ELM) were combined to build prediction models for the acid value and the peroxide value of edible oil, then compared them with the full waveband models. Results The model built from the feature bands extracted by the BOSS algorithm outperforms the RF algorithm and the full-band model. The determination coefficient (R2) of the acid value model reached 0.98, and the root mean square error (RMSE) reached 0.08; the R2 of the peroxide model reached 0.96, and the RMSE of prediction reached 0.63. Conclusion The BOSS algorithm effectively extracts the characteristic bands of acid value and peroxide value of edible oil, and the BOSS-RBF model can be applied to the rapid and nondestructive detection of acid value and peroxide value content in edible oil. It is feasible to quantitatively analyze the acid value and peroxide value of edible oil by near-infrared spectroscopy, which can be used to analyze the quality of edible oil.