Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information

Abstract

The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky–Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products.

What the fish? Tracing the geographical origin of fish using NIR spectroscopy

“…When the uncorrelated and non-linear variables of NIR spectra are removed with feature selection, the prediction ability and robustness of the final model is improved ( Yun, 2022 ). It has also been proven to improve the prediction accuracy in fish ( Currò et al, 2021 ; Lv et al, 2017 ; O'Brien et al, 2013 ; Varrà et al, 2021 ), quinoa flour ( Wang et al, 2022 ), asparagus ( Richter et al, 2019 ), garlic ( Han et al, 2024 ), tea ( Jin et al, 2022 ), mushrooms ( Chen et al, 2022 ) and many other food products. It involves the identification and selection of relevant spectral regions or features that carry the most discriminatory information about the geographical origin of the food.…”

“…While there are only a handful of instances of feature selection for fish traceability, it has been used extensively for authentication of other food products. Feature selection while reducing the bulk of data can also improve the model accuracy as demonstrated by Han et al (2024) for determining the geographic origin of garlic. They applied feature selection after initial to modelling with machine learning algorithms to pick the features with most relevant input for the model.…”