PDF(Pubmed)
Abstract：
The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly affected by climate, environment, geology and other factors. By identifying the fluorescence characteristic absorption peaks of the same rice seed varieties from different origins, and comparing them with known or standard samples, this study aims to authenticate rice, protect brands, and achieve traceability. The study selected the same variety of rice seed planted in different regions of Jilin Province in the same year as samples. Fluorescence spectroscopy was used to collect spectral data, which was preprocessed by normalization, smoothing, and wavelet transformation to remove noise, scattering, and burrs. The processed spectral data was used as input for the long short-term memory (LSTM) model. The study focused on the processing and analysis of rice spectra based on NZ-WT-processed data. To simplify the model, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to screen the best wavelengths. These wavelengths were used as input for the support vector machine (SVM) prediction model to achieve efficient and accurate predictions. Within the fluorescence spectral range of 475-525 nm and 665-690 nm, absorption peaks of nicotinamide adenine dinucleotide (NADPH), riboflavin (B2), starch, and protein were observed. The origin tracing prediction model established using SVM exhibited stable performance with a classification accuracy of up to 99.5%.The experiment demonstrated that fluorescence spectroscopy technology has high discrimination accuracy in tracing the origin of rice, providing a new method for rapid identification of rice origin.
摘要：
农产品的来源对其质量和安全至关重要。本研究利用荧光检测技术探讨了不同产地水稻化学成分和结构的差异。这些差异主要受气候的影响，环境,地质和其他因素。通过鉴定同一品种不同产地的水稻种子的荧光特征吸收峰,并将它们与已知或标准样品进行比较，这项研究旨在鉴定水稻，保护品牌，并实现可追溯性。本研究选取同一年种植于吉林省不同地区的同一品种水稻种子作为样品。荧光光谱法用于收集光谱数据，通过归一化预处理，平滑，和小波变换来去除噪声，散射,和毛刺。经处理的光谱数据用作长短期记忆(LSTM)模型的输入。该研究集中在基于NZ-WT处理的数据的水稻光谱的处理和分析。为了简化模型，无信息变量消除（UVE）和连续投影算法（SPA）用于筛选最佳波长。这些波长被用作支持向量机(SVM)预测模型的输入以实现有效和准确的预测。在475-525nm和665-690nm的荧光光谱范围内，烟酰胺腺嘌呤二核苷酸(NADPH)的吸收峰,核黄素(B2),淀粉，并观察到蛋白质。使用SVM建立的原点追踪预测模型表现出稳定的性能，分类准确率高达99.5％。实验表明,荧光光谱技术在大米产地溯源中具有较高的鉴别精度,为水稻产地的快速鉴定提供了一种新的方法。
