A case of milk traceability in small-scale districts-Inner Mongolia of China by nutritional and geographical parameters

Can stable carbon isotope fingerprints be competent for geographic traceability of rice?

This study aimed to improve the traceability of rice-producing areas to address the increasing demand for accurate methods to confirm food quality and safety. Compound-specific δ13C of fatty acids, δ13C of starch and bulk of rice were measured. PCA, PLS-DA and VIP value analysis of the obtained data were performed to track the source of rice from the six regions. The PLS-DA model established with bulk δ13C, starch δ13C, and fatty acid δ13C, which clearly separated the rice from six regions. The VIP graph showed the value of starch, C18:0 and C18:2 δ13C values (VIP > 1) were important to distinguish the origin of rice. Also, according to loading plots the contribution of starch δ13C was the largest. The findings indicate that the introduction of starch δ13C improves the precision of rice traceability and provides an effective method for identifying rice origin.

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.

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.

A New and Effective Method to Trace Tibetan Chicken by Amino Acid Profiling

As a "rare bird on the plateau", the Tibetan chicken is rich in nutrition and has high medicinal value. In order to quickly and effectively identify the source of food safety problems and to label fraud regarding this animal, it is necessary to identify the geographical traceability of the Tibetan chicken. In this study, Tibetan chicken samples from four different cities in Tibet, China were analyzed. The amino acid profiles of Tibetan chicken samples were characterized and further subjected to chemometric analyses, including orthogonal least squares discriminant analysis, hierarchical cluster analysis, and linear discriminant analysis. The original discrimination rate was 94.4%, and the cross-validation rate was 93.3%. Moreover, the correlation between amino acid concentrations and altitudes in Tibetan chicken was studied. With the increase in altitude, all amino acid contents showed a normal distribution. For the first time, amino acid profiling has been comprehensively applied to trace the origin of plateau animal food with satisfactory accuracy.

How Chemometrics Can Fight Milk Adulteration

Adulteration and fraud are amongst the wrong practices followed nowadays due to the attitude of some people to gain more money or their tendency to mislead consumers. Obviously, the industry follows stringent controls and methodologies in order to protect consumers as well as the origin of the food products, and investment in these technologies is highly critical. In this context, chemometric techniques proved to be very efficient in detecting and even quantifying the number of substances used as adulterants. The extraction of relevant information from different kinds of data is a crucial feature to achieve this aim. However, these techniques are not always used properly. In fact, training is important along with investment in these technologies in order to cope effectively and not only reduce fraud but also advertise the geographical origin of the various food and drink products. The aim of this paper is to present an overview of the different chemometric techniques (from clustering to classification and regression applied to several analytical data) along with spectroscopy, chromatography, electrochemical sensors, and other on-site detection devices in the battle against milk adulteration. Moreover, the steps which should be followed to develop a chemometric model to face adulteration issues are carefully presented with the required critical discussion.

Geographical Origin Determination of Cigar at Different Spatial Scales Based on C and N Metabolites and Mineral Elements Combined with Chemometric Analysis

In this paper, five C and N metabolites and eighteen mineral elements were used to identify the cigar's geographical origin on a country scale (Dominica, Indonesia, and China) and on a prefecture scale (Yuxi, Puer, and Lincang in China). The results show that the best origin traceability method is the combination of C and N metabolites and mineral elements method. Its. Its accuracy of cross-validation can achieve 95% on a country scale and 94% on a prefecture scale. Determination accuracy is ranked as identification by combination > mineral elements > C and N metabolites. For geo-origin determination of cigars, mineral element identification is better than that metabolite identification. The algorithm and factors for origin determination are selected. The results can be used to guide cigar agricultural practices and monitor and regulate the cigar in production and circulation.

Discrimination of Geographical Origin of Agricultural Products From Small-Scale Districts by Widely Targeted Metabolomics With a Case Study on Pinggu Peach

Geographical indications of agricultural products are characterized by high quality and regional attributes, while they are more likely to be counterfeited by similar products from nearby regions. Accurate discrimination of origin on small geographical scales is extremely important for geographical indications of agricultural products to avoid food fraud. In this study, a widely targeted metabolomics based on ultra-high-performance liquid chromatography-tandem mass spectrometry combined with multivariate statistical analysis was used to distinguish the geographical origin of Pinggu Peach of Beijing and its two surrounding areas in Heibei province (China). Orthogonal partial least squares-discriminant analysis (OPLS-DA) based on 159 identified metabolites showed significant separation from Pinggu and the other adjacent regions. The number of the most important discriminant variables (VIP value >1) was up to 62, which contributed to the differentiation model. The results demonstrated that the metabolic fingerprinting combined with OPLS-DA could be successfully implemented to differentiate the geographical origin of peach from small-scale origins, thus providing technical support to further ensure the authenticity of geographical indication products. The greenness of the developed method was assessed using the Analytical GREEnness Metric Approach and Software (ARGEE) tool. It was a relatively green analytical method with room for improvement.

Application of stable isotopic and mineral elemental fingerprints in identifying the geographical originof concentrated apple juice in China

Food traceability is an important component of food safety and quality. Currently, there is no authentic established technique to identify the origin of concentrated apple juice (CAJ) in China. In this study, the isotopes of δ¹³C, δ¹⁸O and the contents of 32 elements in CAJ from five production areas (BHB, NWR, SCH, LP and YRAR) were determined. The δ¹³C, δ¹⁸O and 28 elements were significantly different (P < 0.05: post-hoc Duncan's test) in the five production areas. PCA, PLS-DA and OPLS-DA were employed for regional classification of samples. The results show that ten key variables (Tl, Se, δ¹⁸O, B, Mg, Sr, Nd, Mo, As, and Na) are more relevant for discrimination of the samples. These findings contribute to understanding the variations of stable isotopic and element compositions in Chinese CAJ depending on geographic origins and offer valuable insight into the control of fraudulent labeling regarding the geographic origins of CAJ.

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.

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.

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.

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.

Chemical Analysis Combined with Multivariate Statistical Methods to Determine the Geographical Origin of Milk from Four Regions in China

Traceability of milk origin in China is conducive to the implementation of the protection of regional products. In order to distinguish milk from different geographical distances in China, we traced the milk of eight farms in four neighboring provinces of China (Inner Mongolia autonomous region, Hebei, Ningxia Hui autonomous and Shaanxi), and multivariate data analysis was applied to the data including elemental analysis, stable isotope analysis and fatty acid analysis. In addition, orthogonal partial least squares discriminant analysis (OPLS-DA) is used to determine the optimal classification model, and it is explored whether the combination of different technologies is better than a single technical analysis. It was confirmed that in the inter-provincial samples, the combination of the two techniques was better than the analysis using a single technique (fatty acids: R² = 0.716, Q² = 0.614; fatty acid-binding isotopes: R² = 0.760, Q² = 0.635). At the same time, milk produced by farms with different distances of less than 11 km in each province was discriminated, and the discriminant distance was successfully reduced to 0.7 km (Ningxia Hui Autonomous Region: the distance between the two farms was 0.7 km, R² = 0.771, Q² = 0.631). For short-distance samples, the combination multiple technologies are not completely superior to a single technique, and sometimes, it is easy to cause model over-fitting.

Discrimination of mutton from different sources (regions, feeding patterns and species) by mineral elements in Inner Mongolia, China

The traceability of mineral element fingerprints to mutton in a small area of China was studied. The element data of 104 sheep and 24 goat samples from Inner Mongolia were measured, and the data were analyzed by multivariate statistical analysis from different origins, species and feeding patterns. The results shows that 11 elements (Mg, Al, K, Ca, Mn, Fe, Cu, Zn, Rb, Sr, Ba) in sheep meat had significant differences between different regions (P < 0.05), and the results of linear discriminant analysis (LDA) showed that the accuracy of the original classification rate was 95.2%, and the cross-validation rate was 85.9%. Goat meat and sheep meat samples from Alxa League were also clearly identified with LDA results showing that the cross-validation accuracy of the two species was 70.2%. Then the feeding patterns of sheep meat were effectively classified. The results showed that the multi-element analysis has certain potential as a method to distinguish mutton in a small area.

Study on the origin traceability of Tibet highland barley (Hordeum vulgare L.) based on its nutrients and mineral elements

The potential of traceability by nutrients and mineral elements in highland barley (Hordeum vulgare L.) from five cities in Tibet were investigated. The results showed that there were significant differences in nutrients and mineral elements in highland barley from different regions (P < 0.05). The original classification accuracy of linear discriminant analysis (LDA) was 78.3%, and the discrimination accuracy of training set samples based on partial least-squares discriminant analysis (PLS-DA) model was over 65%. The results of correlation analysis show that five elements (Fe, Zn, K, Mn and P) in highland barley are related to the concentration of elements in soil, while three elements (Ca, Cu and Mg) in highland barley have no obvious correlation with soil, because the special natural environment in Tibet affecting the growth of highland barley. This indicates that the origin traceability of highland barley can be achieved by measuring its nutrients and mineral elements.

Enhanced Cadmium Accumulation and Tolerance in Transgenic Hairy Roots of Solanum nigrum L. Expressing Iron-Regulated Transporter Gene IRT1

Solanum nigrum L., a hyperaccumulator of cadmium (Cd), is regarded as a promising candidate for phytoremediation of heavy metal pollution. In the present study, the hairy roots of Solanum nigrum L. were selected as a model plant system to study the potential application of Iron-regulated Transporter Gene (IRT1) for the efficient phytoremediation of Cd pollution. The transgenic hairy roots of Solanum nigrum L. expressing the IRT1 gene from Arabidopsis thaliana were successfully obtained via the Agrobacterium tumegaciens-mediated method. Expression of IRT1 reduced Cd stress-induced phytotoxic effects. Significantly superior root growth, increased antioxidant enzyme activities, decreased reactive oxygen species (ROS) levels, and less cell apoptosis were observed in the transgenic hairy roots of Solanum nigrum L. compared to the wild-type lines under Cd stress. Enhanced Cd accumulation was also carried out in the transgenic hairy roots compared to the control (886.8 μg/g vs. 745.0 μg/g). These results provide an important understanding of the Cd tolerance mechanism of transgenic IRT1 hairy roots of Solanum nigrum L., and are of particular importance to the development of a transgenic candidate for efficient phytoremediation process.

A Two-Step Ferric Chloride and Dilute Alkaline Pretreatment for Enhancing Enzymatic Hydrolysis and Fermentable Sugar Recovery from Miscanthus sinensis

A two-step process was proposed to enhance enzymatic hydrolysis of Miscanthus sinensis based on a comparative study of acid/alkaline pretreatments. Ferric chloride pretreatment (FP) effectively removed hemicellulose and recovered soluble sugars, but the enzymatic hydrolysis was not efficient. Dilute alkaline pretreatment (ALP) resulted in much better delignification and stronger morphological changes of the sample, making it more accessible to enzymes. While ALP obtained the highest sugar yield during enzymatic hydrolysis, the soluble sugar recovery from the pretreatment stage was still limited. Furthermore, a two-step ferric chloride and dilute alkaline pretreatment (F-ALP) has been successfully developed by effectively recovering soluble sugars in the first FP step and further removing lignin of the FP sample in the second ALP step to improve its enzymatic hydrolysis. As a result, the two-step process yielded the highest total sugar recovery (418.8 mg/g raw stalk) through the whole process.

One-Step or Two-Step Acid/Alkaline Pretreatments to Improve Enzymatic Hydrolysis and Sugar Recovery from Arundo Donax L

Energy crops are not easily converted by microorganisms because of their recalcitrance. This necessitates a pretreatment to improve their biodigestibility. The effects of different pretreatments, as well as their combination on the enzymatic digestibility of Arundo donax L. were systematically investigated to evaluate its potential for bioconversion. Dilute alkaline pretreatment (ALP) using 1.2% NaOH at 120 °C for 30 min resulted in the highest reducing sugar yield in the enzymatic hydrolysis process because of its strong delignification and morphological modification, while ferric chloride pretreatment (FP) was effective in removing hemicellulose and recovering soluble sugars in the pretreatment stage. Furthermore, an efficient two-step ferric chloride-alkaline pretreatment (FALP) was successfully developed. In the first FP step, easily degradable cellulosic components, especially hemicellulose, were dissolved and then effectively recovered as soluble sugars. Subsequently, the FP sample was further treated in the second ALP step to remove lignin to enhance the enzymatic hydrolysis of the hardly degradable cellulose. As a result, the integrated two-step process obtained the highest total sugar yield of 420.4 mg/g raw stalk in the whole pretreatment and enzymatic hydrolysis process; hence, the process is a valuable candidate for biofuel production.