Wang Fuxiang, Wang Chunguang, Song Shiyong
School of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010000, China.
Mongolia Lvtao Detection Technology Company Limited, Hohhot 010000, China.
Foods. 2022 Jun 22;11(13):1841. doi: 10.3390/foods11131841.
Traditional chemical methods for testing the fat content of millet, a widely consumed grain, are time-consuming and costly. In this study, we developed a low-cost and rapid method for fat detection and quantification in millet. A miniature NIR spectrometer connected to a smartphone was used to collect spectral data from millet samples of different origins. The standard normal variate (SNV) and first derivative (1D) methods were used to preprocess spectral signals. Variable selection methods, including bootstrapping soft shrinkage (BOSS), the variable iterative space shrinkage approach (VISSA), iteratively retaining informative variables (IRIV), iteratively variable subset optimization (IVSO), and competitive adaptive reweighted sampling (CARS), were used to select characteristic wavelengths. The partial least squares regression (PLSR) algorithm was employed to develop the regression models aimed at predicting the fat content in millet. The results showed that the proposed 1D-IRIV-PLSR model achieved optimal accuracy for fat detection, with a correlation coefficient for prediction (Rp) of 0.953, a root mean square error for prediction (RMSEP) of 0.301 g/100 g, and a residual predictive deviation (RPD) of 3.225, by using only 18 characteristic wavelengths. This result highlights the feasibility of using this low-cost and high-portability assessment tool for millet quality testing, which provides an optional solution for in situ inspection of millet quality in different scenarios, such as production lines or sales stores.
传统的用于检测广泛食用的谷物——小米脂肪含量的化学方法既耗时又昂贵。在本研究中，我们开发了一种低成本且快速的小米脂肪检测和定量方法。使用连接到智能手机的微型近红外光谱仪从不同产地的小米样品中收集光谱数据。采用标准正态变量变换（SNV）和一阶导数（1D）方法对光谱信号进行预处理。使用包括自助软收缩（BOSS）、可变迭代空间收缩方法（VISSA）、迭代保留信息变量（IRIV）、迭代可变子集优化（IVSO）和竞争性自适应重加权采样（CARS）在内的变量选择方法来选择特征波长。采用偏最小二乘回归（PLSR）算法建立预测小米脂肪含量的回归模型。结果表明，所提出的1D-IRIV-PLSR模型在脂肪检测方面达到了最佳精度，仅使用18个特征波长时，预测相关系数（Rp）为0.953，预测均方根误差（RMSEP）为0.301 g/100 g，剩余预测偏差（RPD）为3.225。这一结果凸显了使用这种低成本、高便携性评估工具进行小米质量检测的可行性，为不同场景（如生产线或销售商店）下的小米质量现场检测提供了一种可选方案。
