Application of multi-element (C, N, H, O) stable isotope ratio analysis for the traceability of milk samples from China

Optimizing geographical traceability models of Chinese Lycium barbarum: Investigating effects of region, cultivar, and harvest year on nutrients, bioactives, elements and stable isotope composition

Lycium barbarum is a type of "medicine-food homology" whose geographical origin has attracted strong interest from consumers due to different regional quality characteristics. A sophisticated OPLS-DA model to verify Lycium barbarum origin was developed using 266 samples gathered from five cultivars in two regions between 2020 and 2022, which was based on 67 indices, including nutrients, bioactives, elements and stable isotopes. Twelve variables (fructose, δ2H, glucose, tartaric acid, Mo, Na, Sr, His, Phe, Mn, Lys and Rb) were selected to refine models that could distinguish Lycium barbarum origin without being impacted by cultivar or year. The model of training set and testing set samples had discrimination rates of 100 % and 94.71 % to 98.28 %, suggesting an optimized multi-variate analysis strategy using OPLS-DA model could correctly predict the origin of blind Lycium barbarum samples. This study provides new evidence for constructing a reliable traceability model for the geographical origins of Lycium barbarum.

Several Feature Extraction Methods Combined with Near-Infrared Spectroscopy for Identifying the Geographical Origins of Milk

Milk is a kind of dairy product with high nutritive value. Tracing the origin of milk can uphold the interests of consumers as well as the stability of the dairy market. In this study, a fuzzy direct linear discriminant analysis (FDLDA) is proposed to extract the near-infrared spectral information of milk by combining fuzzy set theory with direct linear discriminant analysis (DLDA). First, spectral data of the milk samples were collected by a portable NIR spectrometer. Then, the data were preprocessed by Savitzky-Golay (SG) and standard normal variables (SNV) to reduce noise, and the dimensionality of the spectral data was decreased by principal component analysis (PCA). Furthermore, linear discriminant analysis (LDA), DLDA, and FDLDA were employed to transform the spectral data into feature space. Finally, the k-nearest neighbor (KNN) classifier, extreme learning machine (ELM) and naïve Bayes classifier were used for classification. The results of the study showed that the classification accuracy of FDLDA was higher than DLDA when the KNN classifier was used. The highest recognition accuracy of FDLDA, DLDA, and LDA could reach 97.33%, 94.67%, and 94.67%. The classification accuracy of FDLDA was also higher than DLDA when using ELM and naïve Bayes classifiers, but the highest recognition accuracy was 88.24% and 92.00%, respectively. Therefore, the KNN classifier outperformed the ELM and naïve Bayes classifiers. This study demonstrated that combining FDLDA, DLDA, and LDA with NIR spectroscopy as an effective method for determining the origin of milk.

Effects of sampling time and location on the geographical origin traceability of protected geographical indication (PGI) Hongyuan yak milk: Based on stable isotope ratios

Hongyuan yak milk is a protected geographical indication (PGI) product of rich nutritional value, which is popular among consumers. Stable isotope ratio analysis (SIRA) is an effective way to protect the authenticity of the geographical origin of PGI products, and it is crucial to study the factors affecting stable isotopes. Firstly, we proved that the SIRA could be used to identify the geographical origin of Hongyuan yak milk, and that the identification accuracy in combination with δ13C and δ18O was 100 %. Secondly, we analyzed the effect of sampling selection on the stable isotopes of Hongyuan yak milk in practical applications, which showed that sampling time influenced the δ13C, δ2H, and δ18O, while the sampling locations did not. There were interactions between the effect of sampling time and location on δ2H and δ18O. These results provide a reliable method for identifying PGI products and also provide new guidance on sampling models.

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Quality control of Ganoderma lucidum by using C, H, O, and N stable isotopes and C and N contents for geographical traceability

Rationale

Ganoderma lucidum (G. lucidum) is a popular medicinal fungus that has been used in traditional medicine for decades, with its provenance influencing its medicinal and commercial worth. The amount of active ingredients and the price of G. lucidum from different origins vary significantly; hence, fraudulent labeling is common. Reliable techniques for G. lucidum geographic verification are urgently required to safeguard the interests of consumers, producers, and honest dealers. A stable isotope is widely acknowledged as a useful traceability technique and could be developed to confirm the geographical origin of G. lucidum.

Methods

G. lucidum samples from various sources and in varying stages were identified by using δ 13C, δD, δ 18O, δ 15N, C, and N contents combined with chemometric tools. Chemometric approaches, including PCA, OPLS-DA, PLS, and FLDA models, were applied to the obtained data. The established models were used to trace the origin of G. lucidum from various sources or track various stages of G. lucidum.

Results

In the stage model, the δ 13C, δD, δ 18O, δ 15N, C, and N contents were considered meaningful variables to identify various stages of G. lucidum (bud development, growth, and maturing) using PCA and OPLS-DA and the findings were validated by the PLS model rather than by only four variables (δ 13C, δD, δ 18O, and δ 15N). In the origin model, only four variables, namely δ 13C, δD, δ 18O, and δ 15N, were used. PCA divided G. lucidum samples into four clusters: A (Zhejiang), B (Anhui), C (Jilin), and D (Fujian). The OPLS-DA model could be used to classify the origin of G. lucidum. The model was validated by other test samples (Pseudostellaria heterophylla), and the external test (G. lucidum) by PLS and FLDA models demonstrated external verification accuracy of up to 100%.

Conclusion

C, H, O, and N stable isotopes and C and N contents combined with chemometric techniques demonstrated considerable potential in the geographic authentication of G. lucidum, providing a promising method to identify stages of G. lucidum.

Effect of cocooning conditions on the structure, carbon and nitrogen isotope ratios of silks

The stable isotope technique provides the possibility to trace ancient textiles because the technique is associated with advantages such as trace indication, fast detection, and accurate results. Since different cocooning conditions may impact cocoons even under identical habitats, it is important to investigate the effects of different cocooning temperatures and humidity on the isotope incorporation values in the cocoons. In this study, silk fibers were reeled under different conditions of temperature and humidity, followed by analysis of the secondary structure of cocoon proteins and isotope incorporation patterns. We found that the deviations in carbon isotope values of silk under different cocooning conditions could reach up to 0.76‰, while the deviation in carbon isotope values at different locations of a single silk was 2.75‰. Further, during the cocooning process, depletion of the 13C-isotope at different locations of the silk fibers was observed, reducing the δ13C values. We proposed that the changes in carbon isotopes in silk were related to the content of sericin and silk fibroin in silk. Finally, we did not observe a significant difference in isotope ratios in degummed cocoons. In summary, the 13C isotope was enriched in sericin, whereas 15N was enriched in fibroin, and these findings provide basic information for tracing the provenance of silks.

Isotope Fingerprinting as a Backup for Modern Safety and Traceability Systems in the Animal-Derived Food Chain

In recent years, due to the globalization of food trade and certified agro-food products, the authenticity and traceability of food have received increasing attention. As a result, opportunities for fraudulent practices arise, highlighting the need to protect consumers from economic and health damages. In this regard, specific analytical techniques have been optimized and implemented to support the integrity of the food chain, such as those targeting different isotopes and their ratios. This review article explores the scientific progress of the last decade in the study of the isotopic identity card of food of animal origin, provides the reader with an overview of its application, and focuses on whether the combination of isotopes with other markers increases confidence and robustness in food authenticity testing. To this purpose, a total of 135 studies analyzing fish and seafood, meat, eggs, milk, and dairy products, and aiming to examine the relation between isotopic ratios and the geographical provenance, feeding regime, production method, and seasonality were reviewed. Current trends and major research achievements in the field were discussed and commented on in detail, pointing out advantages and drawbacks typically associated with this analytical approach and arguing future improvements and changes that need to be made to recognize it as a standard and validated method for fraud mitigation and safety control in the sector of food of animal origin.

Geographical traceability of gelatin in China using stable isotope ratio analysis

Geographical traceability is crucial to the quality and safety control of gelatin. However, currently, methods for gelatin traceability have not been established anywhere in the world. This study aimed to investigate the possibility of differentiating the geographical origins of gelatin from different regions in China using stable isotope technology. To achieve this objective, 47 bovine stick bone samples from three different regions (Inner Mongolia, Shandong, and Guangxi, respectively) in China were collected, and gelatin was extracted from these bones using the enzymatic method. The fingerprint characteristics of stable isotopes of δ13C, δ15N, and δ2H of gelatin from different regions in China were studied. Moreover, isotopic changes from the bone to gelatin during the processing were examined to evaluate the effectiveness of these factors as origin indicators. The results of the one-way analysis of variance (ANOVA) showed that the δ13C, δ15N, and δ2H of gelatin from different regions display significant differences, and using the linear discriminant analysis (LDA), the correct differentiation of origin reached 97.9%. Certain differences in stable isotope ratios were observed during the processing of bone to gelatin samples. Nonetheless, the fractionation effect caused by the processing of bone to gelatin samples was not sufficient to influence the identification of gelatin from different origins, which proves that δ13C, δ15N, and δ2H are effective origin indicators of gelatin. In conclusion, the stable isotope ratio analysis combined with the chemometric analysis can be used as a reliable tool for identifying gelatin traceability.

Identification of Geographical Origin of Milk by Amino Acid Profile Coupled with Chemometric Analysis

This study aimed to establish a method to identify the geographical origin of milk based on its amino acid profile. High-performance liquid chromatography (HPLC) was carried out to measure amino acid contents. The significant differences of amino acid profiles of milk samples from four regions in China (Hebei, Ningxia, Heilongjiang, and Inner Mongolia) were analyzed by ANOVA. Furthermore, the principal component analysis (PCA) demonstrated the feasibility of geographical origin identification using an amino acid profile, which the first 2 principal components account for 65.62% of total variance. The predictive model for the geographical origin of milk samples was established by orthogonal partial least squares-discriminant analysis (OPLS-DA) with a classification accuracy of 100% and the performance parameters of R2X 0.98, R2Y 0.82, and Q2 0.75. The excellent predictive ability of the model was validated using the validation data set. The analysis of variable importance in projection (VIP) showed that seven amino acids played a key role in the geographical origin identification. This method is a reliable strategy to identify the geographical origin of milk for protecting consumers against mislabeling fraud.

Determining the geographical origin of flaxseed based on stable isotopes, fatty acids and antioxidant capacity

BACKGROUND

Flaxseed is an economically important oilseed crop whose geographic origin is of significant interest to producers and consumers because every region may exhibit particular quality characteristics. The lipid/fatty acid method of determining the geographic origin of flaxseed has not been found to be adequate.

RESULTS

To improve the discrimination rate and the geographical traceability of this crop, the chemical profiles of the flaxseed samples were characterized via lipids/fatty acids, stable isotopes, and antioxidant capacity. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were also performed. A satisfactory discrimination rate of 98.6% was obtained after combining fatty acids, stable isotopes, and antioxidant capacity to trace the origin of flaxseed from five regions in northern China.

CONCLUSION

This study provides an effective method for distinguishing the geographic origin of flaxseed. © 2021 Society of Chemical Industry.

Stable Isotope and Element Profiling for Determining the Agroclimatic Origin of Cow Milk within a Tropical Country

Information on the geographic origin of milk is important in determining quality attributes and for economic gain through building brand value associated with origin. Stable isotope signatures and trace element concentrations have been increasingly used in authentication of milk, though information on the power of such technology in verifying agroclimatic origin in small continents with diverse climatic, environmental conditions, and animal management practice is scarce. Therefore, the main objective of this study was to investigate the possibility of using a stable isotope composition of C, N, O, and H and element fingerprints to determine the agroclimatic origin of milk produced in different agroclimatic zones of Sri Lanka. Stable isotopes ratios of C, N, H, and O, and elemental fingerprints of milk samples were determined by IRMS and ICP-MS, respectively. Significant variations were observed in stable isotope ratios, especially δ¹⁸O and the mean content of Li, Al, Cr, Mn, and Sr in the bulk milk samples obtained from different agroclimatic zones. A linear discriminant analysis differentiated cow milk produced from four agroclimatic zones based on stable isotope ratios, and the inclusion of elemental ratios enhanced the discriminating ability.

Multivariate analysis for organic milk authentication

To differentiate organic milk (OM) from conventional milk (CM), an orthogonal projection to latent structure-discriminant analysis (OPLS-DA) model was constructed using δ¹³C, δ¹⁵N, δ¹⁸O, 51 elements and 35 fatty acids (FAs) as the variables. So far, most reported studies barely use three or more types of variables, but more variables could avoid one-sidedness and get stabler models. Our multivariate model combines geographical and nutritional parameters and displays better explanatory and predictive abilities (R²X = 0.647, R²Y = 0.962 and Q² = 0.821) than models based on fewer variables for differentiating OM and CM. In particular, δ¹⁵N, Se, δ¹³C, Eu, K and α-Linolenic acid (ALA) are found to be critical parameters for the discrimination of OM. These results show that the multivariate model based on stable isotopes, elements and FAs can be used to identify OM, and can potentially expand the global databases for quality and authenticity of milk.

Classification of organic and ordinary kiwifruit by chemometrics analysis of elemental fingerprint and stable isotopic ratios

Elemental fingerprint, stable isotopic analysis, and chemometrics were combined to identify organic kiwifruit from ordinarily cultivated kiwifruit. Samples of organic (n1 = 78) and ordinary kiwifruit (n2 = 85) were collected from neighboring areas. For elemental fingerprint, the contents of 15 elements in fresh fruits, including Al, Cr, Mg, Pb, Zn, Ca, Cu, Mn, Se, Cd, Fe, Na, Sr, Co, and K, were determined by inductively coupled plasma optical emission spectrometry (ICP-OES). Three stable isotopes, including δ¹³ C, δ¹⁵ N, and δ¹⁸ O, were analyzed using an isotope-ratio mass spectrometer (IRMS). Different classification methods including soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLSDA), and least squares support vector machines (LS-SVM), were used to discriminate the organic and ordinary kiwifruits by fusion of elemental and stable isotopic. As a result, the sensitivity, specificity, and overall accuracy of SIMCA model were 0.885, 0.857, and 0.864, respectively. PLSDA and LS-SVM obtained 0.950 and 0.983 classification accuracy of organic and ordinary kiwifruits, respectively. It was demonstrated that elemental fingerprint and stable isotopic analysis would provide useful chemical information for the identification of organic fruits, and the capacity of these methods could be enhanced by chemometrics. PRACTICAL APPLICATION: The classification of kiwifruit usually relies on the label assigned by the merchant, which is prone to deceive consumers. This research has developed an accurate and effective classification method based on stable isotopes and mineral elements for the identification of ordinary kiwifruit and organic kiwifruit, providing a tool for the quality monitoring of organic food.

Determining the geographical origin of milk by multivariate analysis based on stable isotope ratios, elements and fatty acids

To construct a reliable discrimination model for determining milk geographical origin, stable isotope ratios including δ¹³C, δ¹⁵N and δ¹⁸O, 51 elements and 35 fatty acids (FAs) in milk samples from Australia, New Zealand and Austria were detected and analyzed. It is found that all of the stable isotope ratios in the milk samples of Australia are the highest, followed by those of the samples from New Zealand and Austria. In addition, 14 elements and 8 FAs show different contents in the samples of different countries at the significance level of P < 0.05. Based on these results, a multivariate model with good robustness and predictive ability for authenticating milk origin (R²X = 0.693, Q² = 0.854) was successfully constructed. Element contents and stable isotope ratios are more reliable variables for milk origin discrimination and Rb, δ¹⁸O, Tl, Ba, Mo, Sr, δ¹⁵N, Cs, As, Eu, C20:4n6, Sc, C13:0, K, Ca and C16:1n7 are the critical markers in the multivariate model for verifying milk origin.

Stable isotopes verify geographical origin of Tibetan chicken

Due to the particularity of origin area, the yield and price of Tibetan chicken are different from those in the plain areas, which leads to chickens from the plains being disguised as Tibetan chickens. In present study, Tibetan chicken and chicken from other four provinces in plain areas were distinguished by stable isotopes and chemometric methods. The cross-validation rate was 97.6%. Secondly, correlation between mean δ¹³C, δ¹⁵N, δ²H and δ¹⁸O values in chicken samples and environmentalparameters was investigated. These indicators are closely related to geographical environment. δ¹³C and δ¹⁵N values were positively correlated with longitude (R = 0.955) and altitude (R = 0.796), respectively. δ¹⁸O and δ²H values were negatively correlated with latitude (R = 0.903) and altitude (R = 0.938), respectively. At last, we used stable isotopes to trace Tibetan chicken from the different area of Tibet. When four stable isotope ratios were combined, we can distinguish them.

Traceability and Authentication of Manila Clams from North-Western Adriatic Lagoons Using C and N Stable Isotope Analysis

In the Adriatic lagoons of northern Italy, manila clam (Ruditapes philippinarum) farming provides important socio-economic returns and local clams should be registered with the Protected Designations of Origin scheme. Therefore, there is a need for the development of rapid, cost-effective tests to guarantee the origin of the product and to prevent potential fraud. In this work, an elemental analysis (EA) coupled with isotope ratio mass spectrometry (IRMS) was employed to identify the isotopic fingerprints of clams directly collected onsite in three Adriatic lagoons and bought at a local supermarket, where they exhibited certification. In particular, a multivariate analysis of C/N, δ¹³C and δ¹⁵N in manila clam tissues as well as δ¹³C in shells and Δ¹³C (calculated as δ¹³C_shell–δ¹³C_tissues) seems a promising approach for tracking the geographical origin of manila clams at the regional scale.

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.

Identifying Counterfeit Cigarettes Using Environmental Pollen Analysis: An Improved Procedure

Traditional pollen preparation techniques provide clear residues for pollen identification; however, such methods are time-consuming, requiring repeated centrifugation, heating, and digestion with high-concentration hazardous chemicals. Tobacco leaves can effectively trap environmental pollen due to hairy surface and terpene-rich exudates. A new tobacco sample processing method was developed by using different extraction chemistry with surfactant. Marlboro Gold cigarettes were employed as model samples for method development. Parameters critical for pollen extraction, which include number of cigarette sticks used, extraction solution, and extraction temperature, were optimized. By using 1% dishwashing detergent to treat three cigarettes at room temperature, the improved method was able to recover sufficient pollen for microscopic analysis in three repeated centrifuge-washing steps and omit hazardous chemicals involved in traditional methods. We focused on the pollen of common ragweed (Ambrosia artemisiifolia), a plant native to North America, as an indicator to differentiate genuine and counterfeit U.S. brand cigarettes. Results from analyzing randomly purchased genuine (authenticated by forensic examination) and known counterfeit Marlboro Gold provided by law enforcement revealed that a significant amount (39%) of Ambrosia were consistently present in all genuine samples, while counterfeit contained none or only trace count. Similar results were found in other counterfeit U.S. brand cigarettes (all seized in the U.S.) involved in this study as well. Lack of Ambrosia in cigarette strongly indicates the product was not originated in the United States.

Mass Spectrometry for Analysis of Changes during Food Storage and Processing

Many physicochemical changes occur during food storage and processing, such as rancidity, hydrolysis, oxidation, and aging, which may alter the taste, flavor, and texture of food products and pose risks to public health. Analysis of these changes has become of great interest to many researchers. Mass spectrometry is a promising technique for the study of food and nutrition domains as a result of its excellent ability in molecular profiling, food authentication, and marker detection. In this review, we summarized recent advances in mass spectrometry techniques and their applications in food storage and processing. Furthermore, current technical challenges associated with these methodologies were discussed.