Feng Yu, Jinfang Ma, Yi Qi, Han Song, Guiliang Tan, Furong Huang, Maoxun Yang
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引用次数: 2
摘要
在此基础上,建立了山核桃种子来源鉴别模型。首先,从3个种子产地采集了81份核桃样品,获得了其近红外光谱。接下来,对81份核桃样品的近红外光谱进行主成分分析(PCA)。然后,对C. nutans光谱进行乘法散点校正(MSC)、标准正态变量(SNV)、一阶导数和二阶导数预处理,并结合支持向量机(SVM)算法进行建模和分析。其中,一阶导数预处理的SVM模型效果最好,训练集准确率为93.44%(57/61),测试集准确率为85.00%(17/20)。为了进一步提高模型的识别精度,采用网格搜索(GS)、遗传算法(GA)和粒子群优化(PSO)三种优化算法对SVM模型进行最佳c和g参数的识别。结果表明,PSO优化算法得到的最佳参数为c = 0.8343, g = 57.8741,相应的模型训练集准确率为96.36%(60/61),测试集准确率为95.00%(20/21)。因此,建立基于近红外光谱与化学计量学相结合的核桃种子来源分类模型是可行的,且具有简单、快速、绿色的优点。
Geographical Traceability of Clinacanthus nutans with Near-Infrared Pectroscopy and Chemometrics
In this study, a seed origin discrimination model for Clinacanthus nutans was developed. First, 81 C. nutans samples from three seed origin locations were collected, and their Near-Infrared (NIR) spectra were obtained. Next, Principal Component Analysis (PCA) was performed on the NIR spectra of the 81 C. nutans samples. Then, MSC (multiplicative scatter correction), SNV (stand-ard normal variate), first derivative, and second derivative pre-treatments of the C. nutans spectra were performed and combined with the Support Vector Machine (SVM) algorithm for modelling and analysis. Among these methods, first-order derivative pre-treatment achieved the best SVM model effectiveness, with a training set accuracy of 93.44% (57/61) and a test set accuracy of 85.00% (17/20). In order to further improve the discrimination accuracy of the model, three optimization algorithms Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) were employed to identify the best c and g parameters for the SVM model. The results demonstrated that the PSO optimization algorithm yielded the best parameters of c = 0.8343, g = 57.8741, with corresponding model training set the accuracy of 96.36% (60/61) and test set the accuracy of 95.00% (20/21). Therefore, developing a seed origin classification model for C. nutans based on NIR spectroscopy combined with chemometrics is feasible and has the advantages of being simple, rapid, and green.