Exploring rice origin traceability through FT-NIR spectroscopy and chemometrics, using PCA and LDA methods for high accuracy in food authenticity analysis.

For 169 rice samples from four provinces, Jiangsu, Liaoning, Hubei, and Heilongjiang, a Thermo Antaris II Fourier transform near-infrared analyzer with a wave number measurement range of 10 000 to 4 000 cm-1 was used, and chemometrics pattern recognition was used. Component analysis (PCA) and linear discriminant analysis (LDA) methods are used to conduct origin traceability analysis. The results show that the PCA method can basically distinguish the rice origin based on the first two principal components, but there is partial overlap among various types of samples; the PCA-LDA method can more effectively distinguish the rice origin, and the Monte Carlo simulation method is used to randomly and repeatedly select the training set and prediction The accuracy rate of identifying the rice origin of four provinces is above 93.00%, and the recognition accuracy rate is relatively high. Therefore, the chemometric pattern recognition method combined with infrared spectroscopy has certain feasibility and application value for rice origin traceability analysis.

Introduction to Food Traceability and Study Objective

In the realm of agricultural product authentication, establishing the geographic origin of food items has become increasingly vital due to consumer demand for traceability and authenticity. This paper presents a sophisticated analytical approach focusing on rice, a staple food consumed globally, sourced from four provinces in China: Jiangsu, Liaoning, Hubei, and Heilongjiang. Utilizing a Thermo Antaris II Fourier Transform Near-Infrared (FT-NIR) analyzer with a wavenumber measurement range of 10,000 to 4,000 cm-1, this study applies chemometrics pattern recognition techniques, including Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), for the traceability analysis of 169 rice samples.

Chemometric Techniques and Spectral Data Acquisition

Chemometrics, a field of chemistry concerned with the extraction of relevant information from chemical data, is employed here to analyze the spectral data obtained from the FT-NIR spectroscopy. PCA, a statistical procedure that uses orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components, was initially used to discern the origin of the rice samples. The results from PCA indicated that, based on the first two principal components, it was possible to generally distinguish the rice samples according to their origins. However, a degree of overlap among the various sample types was observed, suggesting that while PCA could provide basic differentiation, the precision of origin determination was not fully optimal.

Principal Component Analysis for Rice Origin Determination

To enhance the accuracy of the origin traceability, PCA was followed by LDA, a method that, unlike PCA, is supervised and considers the labels of the data points (i.e., the origins of the samples). The combination of PCA for dimensionality reduction and LDA for classification, referred to as the PCA-LDA method, showed a significant improvement in distinguishing the origins of the rice samples. Furthermore, the robustness of this analytical strategy was evaluated using the Monte Carlo simulation method, which involves randomly selecting multiple training sets and prediction sets to assess the model’s performance under varied conditions. This step is crucial for validating the reliability and generalizability of the model across different data sets.

Enhancing Accuracy with PCA-LDA Method and Monte Carlo Simulation

The findings revealed that the PCA-LDA method, supported by the Monte Carlo simulation for validation, achieved an impressive accuracy rate of over 93.00% in identifying the rice’s origin among the four provinces. This high level of accuracy underscores the effectiveness of combining chemometric pattern recognition methods with infrared spectroscopy for the purpose of food traceability.

Implications and Applications in Food Traceability Technologies

The implications of this study are significant, not only for the field of food science and technology but also for stakeholders across the food supply chain, including producers, regulators, and consumers. By establishing a reliable method for rice origin traceability, this research contributes to the broader goal of enhancing food authenticity and safety, ultimately fostering consumer trust and satisfaction. The success of this approach also suggests its potential applicability to other agricultural products, paving the way for further innovations in food traceability technologies.

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Original research was done by Li Yong, Yan Huangqian, Long Ling, Yu Xiangyang

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