Origin traceability of peanut kernels based on multi-element fingerprinting combined with multivariate data analysis.

Area of Science:

• Agricultural Science
• Analytical Chemistry
• Food Science

Background:

• Multi-element analysis is a common method for determining the geographical origin of agricultural products.
• Investigating the feasibility of using multi-element fingerprinting to identify peanut kernel origins across different regions.

Purpose of the Study:

• To assess the effectiveness of multi-element fingerprinting for identifying the geographical origins of peanut kernels.
• To compare the performance of various statistical methods in classifying peanut samples based on their elemental composition.

Main Methods:

• Determination of 20 element concentrations in 135 peanut samples from Jilin, Jiangsu, and Shandong Provinces, China.
• Statistical analysis including analysis of variance (ANOVA), principal component analysis (PCA), k-nearest neighbors (k-NN), linear discriminant analysis (LDA), and support vector machine (SVM).

Main Results:

• Distinct elemental fingerprints were observed for peanut kernels from different regions.
• k-NN, LDA, and SVM achieved high classification rates (91.1-91.2%) for provincial origin prediction.
• Support Vector Machine (SVM) demonstrated superior accuracy (91.3%) in identifying peanut kernels at the city level compared to k-NN (72.2%) and LDA (78.3%).

Conclusions:

• Multi-element fingerprinting combined with SVM is an effective method for identifying peanut kernel producing areas at various regional scales.
• This approach enhances regional capabilities for quality assurance and control of agricultural products.