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

Quality Assessment and Classification of Codonopsis Radix Based on Fingerprints and Chemometrics

In China, Codonopsis Radix (CR) is frequently consumed both as food and medicine. Here, a comprehensive strategy based on fingerprinting and chemometric approaches was created to explore the influence of origins, storage time and kneading processing on the quality of CR. Firstly, high-performance liquid chromatography with diode array detection was used to obtain the fingerprints of 35 batches of CR from six different origins and 33 batches of CR from varying storage times or kneading procedures. Secondly, chemometric methods including similarity analysis (SA), principal component analysis (PCA), hierarchical clustering analysis (HCA), and two-way orthogonal partial least square with discriminant analysis (O2PLS-DA) were used to evaluate the differences of chemical components in CR so as to identify its source and reflect its quality. Moreover, 13 and 16 major compounds were identified as marker compounds for the discrimination of CR from different origins, storage time and kneading processing, respectively. Furthermore, the relative content of the marker components and the exact content of Lobetyolin were measured, indicating that the contents of these components vary significantly between various CR samples. Meanwhile, the chemical components of CR were identified using Mass spectrometry. According to the findings of our investigation, the quality of CR from Gansu was the best, followed by Shanxi and then Sichuan. The quality of CR from Chongqing and Guizhou was poor. At the same time, the quality of CR was the best when it was kneaded and stored for 0 years, indicating that the traditional kneading process of CR is of great significance. Conclusively, HPLC fingerprint in conjunction with chemical pattern recognition and component content determination can be employed to differentiate the raw materials of different CR samples. Additionally, it is also a reliable, comprehensive and prospective method for quality control and evaluation of CR.

Geographical origin classification of peanuts and processed fractions using stable isotopes

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Stable isotopes of peanuts and their different fractions are investigated.

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Stable C, N, O and H isotopes of peanuts are used to assign production origin.

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Peanuts are leguminous plants and fix nitrogen from the atmosphere.

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Peanut δ¹⁵N is unaffected by processing and indicates soil nitrification processes.

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LDA model achieved higher classification rates than k-NN and SVM models.

This study investigates the use of stable isotopes (C, N, H, and O) to characterize the geographical origin of peanuts along with different peanut fractions including whole peanut kernel, peanut shell, delipidized peanuts and peanut oil. Peanut samples were procured in 2017 from three distinctive growing regions (Shandong, Jilin, and Jiangsu) in China. Peanut processing significantly influenced the δ¹³C, δ²H, and δ¹⁸O values of different peanut fractions, whereas δ¹⁵N values were consistent across all fractions and unaffected by peanut processing. Geographical differences of peanut kernels and associated peanut fractions showed a maximum variance for δ¹⁵N and δ¹⁸O values which indicated their strong potential to discriminate origin. Different geographical classification models (SVM, LDA, and k-NN) were tested for peanut kernels and associated peanut fractions. LDA achieved the highest classification percentage, both on the training and validation sets. Delipidized peanuts had the best classification rate compared to the other fractions.

Intra-regional classification of Codonopsis Radix produced in Gansu province (China) by multi-elemental analysis and chemometric tools

Multi-elemental analysis is widely used to identify the geographical origins of plants. The purpose of this study was to explore the feasibility of combining chemometrics with multi-element analysis for classification of Codonopsis Radix from different producing regions of Gansu province (China). A total of 117 Codonopsis Radix samples from 7 counties of Gansu province were collected. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of 28 elements (³⁹ K, ²⁴ Mg, ⁴⁴Ca, ²⁷Al, ¹³⁷Ba, ⁵⁷Fe, ²³Na, ⁸⁸Sr, ⁵⁵Mn, ⁶⁶Zn, ⁶⁵Cu, ⁸⁵Rb, ⁶¹Ni, ⁵³Cr, ⁵¹ V, ⁷Li, ²⁰⁸Pb, ⁵⁹Co, ⁷⁵As, ¹³³Cs, ⁷¹ Ga, ⁷⁷Se, ²⁰⁵Tl, ¹¹⁴Cd, ²³⁸U, ¹⁰⁷Ag, ⁴Be and ²⁰²Hg). Among macro elements, ³⁹ K showed the highest level, whereas ²³Na was found to have the lowest content value. Micro elements showed the concentrations order of: ⁸⁸Sr > ⁵⁵Mn > ⁶⁶Zn > ⁸⁵Rb > ⁶⁵Cu. Among trace elements, ⁵³Cr and ⁶¹Ni showed higher content and ⁴Be was not detected in all samples. Intra-regions differentiation was performed by principal component analysis (PCA), cluster analysis (CA) and supervised learning algorithms such as linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machines (SVM), and random forests (RF). Among them, the RF model performed the best with an accuracy rate of 78.79%. Multi-elemental analysis combined with RF was a reliable method to identify the origins of Codonopsis Radix in Gansu province.

Proposing Two Local Modeling Approaches for Discriminating PGI Sunite Lamb from Other Origins Using Stable Isotopes and Machine Learning

For the protection of Protected Geographical Indication (PGI) Sunite lamb, PGI Sunite lamb samples and lamb samples from two other banners in the Inner Mongolia autonomous region were distinguished by stable isotopes (δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O) and two local modeling approaches. In terms of the main characteristics and predictive performance, local modeling was better than global modeling. The accuracies of five local models (LDA, RF, SVM, BPNN, and KNN) obtained by the Adaptive Kennard–Stone algorithm were 91.30%, 95.65%, 91.30%, 100%, and 91.30%, respectively. The accuracies of the five local models obtained by an approach of PCA–Full distance based on DD–SIMCA were 91.30%, 91.30%, 91.30%, 100%, and 95.65%, respectively. The accuracies of the five global models were 91.30%, 91.30%, 91.30%, 100%, and 91.30%, respectively. Stable isotope ratio analysis combined with local modeling can be used as an effective indicator for protecting PGI Sunite lamb.

Determination of macro, micro and toxic element concentrations in peanuts from main peanut producing areas of China by ICP-MS: a pilot study on the geographical characterization

Peanut is an important crop grown worldwide. The geographic origin of peanuts has been a topic of substantial attention since their prices can vary according to their geographic origins. This study evaluated the main macro (K, Ca, Mg, Na, and Al), micro (Fe, Zn, Mn, Ni, Sr, Mo, Cu, Se, V, Co), and toxic (As, Cd, Cr, and Pb) element concentrations in peanuts collected from six different Chinese provinces. Multi-element analysis of peanuts from different regions was carried out to develop a reliable method to trace the origin of peanuts. After microwave digestion, the element concentrations were determined through inductively coupled plasma mass spectrometry (ICP-MS). Certified reference material (CRM, GBW10011) was used to ensure accurate results. The profile of contents of major elements obtained in the current study showed the order: K > Mg > Ca > Al > Na > Zn > Fe > Mn > Ni > Sr > Mo. The average concentrations of toxic elements such as Pb, Cd, As, and Cr were very low and within the safe limits. Correlation analysis showed that there was a strong correlation between individual elements in peanut samples. The data were processed by means of the chemometric approach of linear discriminant analysis (LDA) and 97.0% of samples were correctly predicted.

The study determined 19 elements in peanuts of different regions in China by ICP-MS, and explored a method of identifying peanut origin by multi-element analysis.

Recent techniques for the authentication of the geographical origin of tea leaves from camellia sinensis: A review

Tea is one of the most important beverages worldwide, is produced in several distinct geographical regions, and is traded on the global market. The ability to determine the geographical origin of tea products helps to ensure authenticity and traceability. This paper reviews the recent research on authentication of tea using a combination of instrumental and chemometric methods. To determine the production region of a tea sample, instrumental methods based on analyzing isotope and mineral element contents are suitable because they are less affected by tea variety and processing methods. Chemometric analysis has proven to be a valuable method to identify tea. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most preferred methods for processing large amounts of data obtained through instrumental component analysis.

The Role of Soil Mineral Multi-elements in Improving the Geographical Origin Discrimination of Tea (Camellia sinensis)

The combination of mineral multi-elements with chemometrics can effectively trace the geographical origin of tea (Camellia sinensis). However, the role of soil mineral multi-elements in discriminating the origin of tea was unknown. This study aimed to further validate whether the geographical origin of tea can be authenticated based on mineral multi-elements, the concentrations of which in tea leaves were significantly correlated with those in soil. Eighty-seven tea leaves samples and paired soils from Meitan and Fenggang (MTFG), Anshun, and Leishan in China were sampled, and 24 mineral elements were measured. The data were processed using one-way analysis of variance (ANOVA), Pearson correlation analysis, principal component analysis (PCA), and stepwise linear discriminant analysis (SLDA). Results indicated that tea and soil samples from different origins differed significantly (p < 0.05) in terms of most mineral multi-elemental concentrations. Conversely, the intra-regional differences of different cultivars of the same origin were relatively minor. Seventeen mineral elements in tea leaves were significantly correlated with those in soils. The SLDA model, based on the 17 aforementioned elements, produced a 98.85% accurate classification rate. In addition, the origin was also identified satisfactorily with 94.25% accuracy when considering the cultivar effect. In conclusion, the tea plant cultivars unaffected the accuracy of the discrimination rate. The geographical origin of tea could be authenticated based on the mineral multi-elements with significant correlation between tea leaves and soils. Soil mineral multi-elements played an important role in identifying the geographical origin of tea.

Identification of iron ore brands by multi-component analysis and chemometric tools

Identification of iron ore brand is one of the most important precautions against fraud in the international iron ore trade. However, the identification of iron ore brand can be sophisticated, due to fact that the role played by multi-component in iron ore brand identification was unclear. This study aims to establish an objective approach to identify iron ore brands based on their multi-component content. A total of 1469 batches of iron ore samples, covering 16 commonly consumed iron ore brands from 3 countries, were analyzed for multi-component content. It was investigated that 10 primary, minor, and trace chemical components varied significantly in contents according to different iron ore brands. This prospective relationship between the multi-component contents and the iron ore brand was then used to place 16 brands into 12 groups and 8 brands of them were correctly identified by a flowchart. Furthermore, chemometric tools such as linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and support vector machine (SVM) were applied to construct models to simultaneously discriminate 16 iron ore brands. Both the training and test results proved that LDA performed best in this circumstance. In the LDA method, MgO, Fe, SiO₂, and P are the feature components contributing the most to the identification of 16 brands of iron ore. Based on the findings, the multi-components are distinct variables to establish an internationally recognized model of iron ore brand identification.

Discrimination of goat, buffalo, and yak milk from different livestock, regions, and lactation using microelement contents

This study aims at investigating whether the livestock, lactation stage, or geographical origin of specialty milk in China could be classified by the elemental contents. Samples of goat, buffalo, and yak milk were collected from main production provinces in China and the contents of nine elements in milk from different livestock, regions, and lactation stages were determined. Statistical analysis indicated that the contents of Mn, Cu, As, Se, Cs, and Mo in mature milk were significantly different between the three livestock (p < 0.05). The trends of Mn and Cs contents of the goat milk and buffalo milk increased first and then decreased gradually during lactation. The contents of Se, Cs, Ba, and Mo in mature milk of three livestock were significantly different between the three regions (p < 0.05). The result of linear discriminant analysis (LDA) and partial least squares discrimination analysis (PLS-DA) showed that the total correct classification rates in different livestock for mature milk reached 86.7 and 90%. For goat milk, the total correct classification rates of different lactations and regions reached 88.9 and 77.8% for LDA (100 and 87.5% for PLS-DA), and followed by yak milk of different regions which achieved 88.9% for LDA (88.9% for PLS-DA). In summary, the microelements in milk showed a significant correlation with livestock, region, and lactation of goat, buffalo, and yak milk. The elemental fingerprints combined with the LDA could be potential for classifying the livestock, lactation stage, and geographical origin of milk. PRACTICAL APPLICATION: According to study the contents of miroelements in milk from different livestock, regions, and lactation stages, provide evidence and support for classifying the livestock, lactation stage, and geographical origin of milk.

Traceability in food processing: problems, methods, and performance evaluations-a review

Processed food has become an indispensable part of the human food chain. It provides rich nutrition for human health and satisfies various other requirements for food consumption. However, establishing traceability systems for processed food faces a different set of challenges compared to primary agro-food, because of the variety of raw materials, batch mixing, and resource transformation. In this paper, progress in the traceability of processed food is reviewed. Based on an analysis of the food supply chain and processing stage, the problem of traceability in food processing results from the transformations that the resources go through. Methods to implement traceability in food processing, including physical separation in different lots, defining and associating batches, isotope analysis and DNA tracking, statistical data models, internal traceability system development, artificial intelligence (AI), and blockchain-based approaches are summarized. Traceability is evaluated based on recall effects, TRUs (traceable resource units), and comprehensive granularity. Different methods have different advantages and disadvantages. The combined application of different methods should consider the specific application scenarios in food processing to improve granularity. On the other hand, novel technologies, including batch mixing optimization with AI, quality forecasting with big data, and credible traceability with blockchain, are presented in the context of improving traceability performance in food processing.