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中国精品科技期刊2020
王雨莹,戴宇佳,王悦悦,等. 基于近红外光谱技术的香榧蛋白质和脂肪含量无损检测方法研究[J]. 食品工业科技,2024,45(18):1−8. doi: 10.13386/j.issn1002-0306.2023100276.
引用本文: 王雨莹,戴宇佳,王悦悦,等. 基于近红外光谱技术的香榧蛋白质和脂肪含量无损检测方法研究[J]. 食品工业科技,2024,45(18):1−8. doi: 10.13386/j.issn1002-0306.2023100276.
WANG Yuying, DAI Yujia, WANG Yueyue, et al. Research on Non-destructive Detection of Protein and Fat Content in Torreya Based on Near-infrared Spectroscopy Technology[J]. Science and Technology of Food Industry, 2024, 45(18): 1−8. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023100276.
Citation: WANG Yuying, DAI Yujia, WANG Yueyue, et al. Research on Non-destructive Detection of Protein and Fat Content in Torreya Based on Near-infrared Spectroscopy Technology[J]. Science and Technology of Food Industry, 2024, 45(18): 1−8. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023100276.

基于近红外光谱技术的香榧蛋白质和脂肪含量无损检测方法研究

Research on Non-destructive Detection of Protein and Fat Content in Torreya Based on Near-infrared Spectroscopy Technology

  • 摘要: 为丰富香榧品质把控的技术手段,本研究利用近红外光谱分析技术建立了香榧存储时间及条件的定性分析模型,同时结合化学计量方法建立了香榧蛋白质和脂肪的定量分析模型。对光谱进行预处理后选取特征波长,基于遗传算法(Genetic Algorithm,GA)和多元散射校正(Multiplicative Scatter Correction,MSC)算法建立的卷积神经网络模型(Convolutional Neural Networks,CNN)模型鉴别准确率达到99.2%,能够较好区分不同存储时间条件的香榧;建立偏最小二乘法(Partial Least Squares,PLS)、径向基函数神经网络(Radial Basis Function Neural Network,RBF)和极限学习机(Extreme Learning Machine,ELM)模型,对比分析采用竞争性自适应重加权采样法(Competitive Adapative Reweighted Sampling,CARS)作为光谱特征筛选方法,蛋白质和脂肪的特征个数分别为41和56,大大提升运算效率,结果显示,使用二阶导数(second derivative,D2)进行预处理、使用CARS方法建立的D2-CARS-PLS模型为最优模型,在香榧蛋白质和脂肪含量的预测中都取得较好效果,决定系数(Coefficient of Determination,R2)分别为0.977和0.984。研究结果表明,近红外光谱技术在香榧品质的快速无损检测方面具有潜力,提供了一种可靠的香榧品质分析方法。

     

    Abstract: To enrich the technical means for quality control of Torreya, this study utilized near-infrared spectroscopy (NIRS) analysis technique to establish a qualitative analysis model for the storage time and conditions of Torreya. Additionally, a quantitative analysis model for protein and fat content of Torreya was established using chemometric methods. After preprocessing the spectra and selecting feature wavelengths, a convolutional neural network (CNN) model based on genetic algorithm (GA) and multiplicative scatter correction (MSC) algorithm was developed, achieving an identification accuracy of 99.2% in distinguishing Torreya with different storage times and conditions. Furthermore, partial least squares (PLS), radial basis function neural network (RBF), and extreme learning machine (ELM) models were established and compared. Competitive adaptive reweighted sampling (CARS) was used as a spectral feature selection method, resulting in 41 and 56 selected features for protein and fat content, respectively, significantly improving computational efficiency. The results showed that the D2-CARS-PLS model with second derivative (D2) preprocessing and CARS method was the optimal model, achieving good prediction performance for protein and fat content of Torreya with coefficients of determination (R2) of 0.977 and 0.984, respectively. The findings demonstrated the potential of near-infrared spectroscopy technology for rapid and non-destructive detection of Torreya nut quality, providing a reliable analytical method for Torreya nut quality assessment.

     

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