Exploring a fusion identification model combining FTIR and GC-MS for rice origin with a 97.4% accuracy, enhancing food authentication.

In order to establish a fusion identification model of infrared spectrum fingerprint information and volatile component information, and improve the model’s identification rate of rice origin. Fourier transform infrared spectroscopy and gas chromatography-mass spectrometry were used to analyze the mid-infrared spectrum absorbance and volatile component content of 20 samples of Panjin rice, 19 samples of Sheyang rice, and 15 samples of Wuchang rice, and the characteristics were screened out using variance analysis. Spectra and volatile components, combined with the partial least squares-discriminant analysis (PLS-DA) method, are used to establish an identification method that fuses these two types of fingerprint information. The results show that the rice origin identification accuracy of the information fusion model is 97.4%, which is 4.5% and 8.5% higher than that of the single spectral fingerprint information model (92.9%) and the volatile fingerprint information model (88.9%) respectively. Therefore, information fusion technology improves the identification effect of the model, and it is feasible and effective to use the PLS-DA method information fusion model to identify rice origin.

Introduction to Rice Origin Identification

In the contemporary landscape of agricultural product authentication, establishing the origin of products with precision is not only pivotal for consumer trust but also for maintaining the integrity of local brands. Among such products, rice, a staple food for a significant portion of the global population, holds considerable importance. In this context, the development of a robust identification model that leverages both infrared spectrum fingerprint information and volatile component information emerges as a crucial endeavor. This study explores the efficacy of integrating Fourier Transform Infrared Spectroscopy (FTIR) and Gas Chromatography-Mass Spectrometry (GC-MS) to enhance the identification rate of rice origin, focusing on samples from Panjin, Sheyang, and Wuchang.

Methodological Framework: Combining FTIR and GC-MS

The novel approach centers on analyzing the mid-infrared spectrum absorbance and the volatile component content from 20 Panjin rice samples, 19 Sheyang rice samples, and 15 Wuchang rice samples. The process begins with the meticulous extraction of these samples’ unique spectral fingerprints and volatile component profiles. Variance analysis plays a critical role in screening out the characteristic features from the collected data, ensuring that only the most discriminating and relevant information is utilized for subsequent analysis.

Data Analysis: Variance Analysis and Feature Screening

Building on the foundational data obtained from FTIR and GC-MS, the study employs Partial Least Squares-Discriminant Analysis (PLS-DA) to establish a fusion identification model. This model adeptly integrates the two distinct types of fingerprint information—spectral and volatile—into a coherent framework for rice origin authentication. The rationale behind this fusion is the hypothesis that combining multiple analytical perspectives can offer a more comprehensive and accurate identification mechanism than relying on a singular data source.

Establishing the Fusion Identification Model with PLS-DA

The results of this innovative approach are compelling. The integrated information fusion model achieves an impressive 97.4% accuracy rate in identifying the origin of rice samples. This performance marks a significant improvement over models that utilize singular data sources, with the spectral fingerprint information model achieving a 92.9% accuracy rate and the volatile fingerprint information model reaching an 88.9% accuracy rate. Such findings underscore the added value of merging infrared spectrum and volatile component analyses through the PLS-DA method, enhancing the model’s capability to discriminate between rice origins with high precision.

Results: Superior Accuracy of the Fusion Model

The superiority of the information fusion technology in this context is not merely a quantitative leap in accuracy rates but also a qualitative enhancement in the model’s identification capabilities. The increase of 4.5% and 8.5% in accuracy, compared to the single spectral and volatile information models respectively, signifies a substantial advancement in the field of agricultural product authentication.

Conclusion: Advancing Food Authentication with Information Fusion

In conclusion, the feasibility and effectiveness of employing an information fusion model, anchored by PLS-DA, for rice origin identification are convincingly demonstrated through this study. The methodology outlined offers a promising pathway for stakeholders in the agricultural sector to authenticate the origin of rice, reinforcing trust in the product while protecting the heritage and economic interests of local producers. This study not only contributes to the body of knowledge in agricultural science but also paves the way for future research and application of information fusion technology in food authentication and beyond.

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Original research was done by Fang Yong, Li Peng, Du Mengjia, Mao Bo, Shen Fei, Hu Qiuhui, Pei Fei

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