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

Geographical origins of Angelica sinensis using functional compounds and multielement with machine learning-based fusion approaches

Ensuring food traceability is essential for maintaining safety and authenticity. Angelica sinensis (Oliv.) Diels (AS), a medicinal food prized for its rich nutritional value and tonic effects, is frequently vulnerable to geographic origin fraud. In this study, 16 functional compounds and 40 multielement were utilized to investigate the regional characteristics and the geographical origins authentication of AS samples from 8 different origins. Three algorithms were introduced, and the K-nearest neighbors (KNN) model constructed by the second-level fusion using 22 key variables screened by VIP features performed the best for AS origin classification, with a prediction accuracy of 100.00 % in both the training set and the testing set. Moreover, 5 environmental factors, including longitude, latitude, cation exchange capacity, accumulated temperature of growing degree days above 5 °C and dry or moisture index, were identified as the primary influenced environmental factors.

Integrating mineral elements and metabolite features to distinguish Lotus seeds from different geographic origins

The characteristics of lotus seeds (LS) are influenced by variety and environment. However, it remains unknown the difference of metabolites and elements of LS from different origins. In this study, an accurate quantification method (97-107 %) for 20 mineral elements in LS was developed, and a metabolomic method was established to identify a total of 323 metabolites in LS. Mineral composition analysis revealed significant variations in the mineral element contents among LS samples from seven geographical regions. LS were rich in potassium (14,710 mg/kg), manganese (67.19 mg/kg), with a low level of sodium (210 mg/kg). A total of 10 mineral elements and 117 metabolites (p < 0.05 and VIP > 1) were identified as the potential geographical markers of LS by integration analysis. The linear discriminant analysis model showed high prediction accuracy. This study provides strong experimental evidence to maintain the authenticity and quality of LS in the food industry.

Enhanced food authenticity control using machine learning-assisted elemental analysis

With the increasing attention being paid to the authenticity of food, efficient and accurate techniques that can solve relevant problems are crucial for improving public trust in food. This review explains two main aspects of food authenticity, namely food traceability and food quality control. More explicitly, they are the traceability of food origin and organic food, detection of food adulteration and heavy metals. It also points out the limitations of the commonly used morphology and organic compound detection methods, and highlights the advantages of combining the elements in food as detection indicators using machine learning technology to solve the problem of food authenticity. Taking elements as detection objects has the significant advantages of stability, machine learning technology can combine large data samples, ensuring both the accuracy and efficiency. In addition, the most suitable algorithm can be found by comparing their accuracy.

Pretreatment method for oxygen stable isotope ratio analysis of the sugar-rich fraction in fruit juice via isotope ratio mass spectrometry

RATIONALE

The oxygen stable isotope ratio (δ18O) of the sugar-rich fraction of fruit juice is important as a tracer of the geographical origin of raw material. This study sought to minimize the inter-day variation of δ18O attributable to the influence of water to accurately monitor geographical origin labeling.

METHODS

Two drying devices (freeze dryer and vacuum oven) were compared. Then, two humidity levels (normal and low humidity) at which the samples were placed after drying were compared. The low-humidity environment was constructed using a glove bag and pure argon gas. δ18O was measured using thermal conversion elemental analyzer/isotope ratio mass spectrometry. Improvements were made to the measurement method based on aforementioned analyses results, and the performance of the initial and improved methods was compared.

RESULTS

δ18O of juice dried in a vacuum oven was 3.30‰ lower than that of juice dried in a freeze dryer. Moreover, δ18O of juice samples exposed to normal humidity was 3.74‰ lower than that of samples exposed to low humidity. The combined inter-day and intra-day standard deviation was reduced from 1.20‰ in the initial method to 0.42‰ in the improved method.

CONCLUSIONS

This study describes a pretreatment method for δ18O measurement in the sugar-rich fraction of fruit juice with less inter-day variation, and it will be useful for monitoring geographical origin labeling.

Food Authentication Goes Green: Method Optimization for Origin Discrimination of Apples Using Apple Juice and ICP-MS

Apples are among the most important fruits worldwide and the most consumed fruit in Germany. Due to higher energy and personnel costs, domestic apples are more expensive and thus offer an incentive for mixing with foreign goods. Moreover, imported apples have a higher carbon footprint, which is an obstacle regarding sales in times of climate change. Not only the transport of the goods but also the analysis influences the carbon footprint. Inductively coupled plasma mass spectrometry (ICP-MS) is a powerful tool for origin discrimination. In this study, 85 apple juice samples were analyzed, whereby sample preparation for ICP-MS was optimized by eliminating the freeze-drying step and thereby reducing CO2 emissions. The CO2 emission was lowered by around 97%. The optimized method was applied to 272 apple juice samples from seven countries to create models for origin determination. The differentiation of European and non-European apples provided an accuracy of 90.9% ± 2.4%. German samples can be differentiated from other countries with an accuracy of 83.2% ± 1.4%. The regional differentiation of German samples (north vs. south) achieved an accuracy of 92.3% ± 5.4%. The results show that the optimized ICP-MS method, in which freeze-drying is not required is well suited for determining the origin of apples from apple juice.

Geographical verification of Pleuropterus multiflorus thunb. by functional compounds, stable isotope ratios, and multielement combined with machine learning methods

The dry roots of Pleuropterus multiflorus Thunb. (PM) have been traditionally utilized as functional foods and medicines China and various Asian countries. They are extensively cultivated in multiple provinces in China, with variations in prices and qualities. This study aims to investigate the regional characteristics of PM by 4 stable isotope ratios, 40 multielement and 16 functional compounds contents, using a total of 357 samples from 8 different geographical origins. Machine learning methods were developed to authenticating the geographical origins of PM, yielding the accuracy range from 94.44 % to 100 % in the test set. Notably, the protected designation of origin, Deqing PM, exhibited a high accuracy of 100 % in most models, A total of 30 significant prediction variables, encompassing 16 functional compounds, δ2H, 12 rare earth elements, and Cu, were identified. Additionally, the study identified altitude, high temperature, and dry or moisture index as the primary influenced environmental factors.

Green and Sustainable Forward Osmosis Process for the Concentration of Apple Juice Using Sodium Lactate as Draw Solution

China is the world's largest producer and exporter of concentrated apple juice (CAJ). However, traditional concentration methods such as vacuum evaporation (VE) and freeze concentration cause the loss of essential nutrients and heat-sensitive components with high energy consumption. A green and effective technique is thus desired for juice concentration to improve product quality and sustainability. In this study, a hybrid forward osmosis-membrane distillation (FO-MD) process was explored for the concentration of apple juice using sodium lactate (L-NaLa) as a renewable draw solute. As a result, commercial apple juice could be concentrated up to 65 °Brix by the FO process with an average flux of 2.5 L·m-2·h-1. Most of the nutritional and volatile compounds were well retained in this process, while a significant deterioration in product quality was observed in products obtained by VE concentration. It was also found that membrane fouling in the FO concentration process was reversible, and a periodical UP water flush could remove most of the contaminants on the membrane surface to achieve a flux restoration of more than 95%. In addition, the L-NaLa draw solution could be regenerated by a vacuum membrane distillation (VMD) process with an average flux of around 7.87 L∙m-2∙h-1 for multiple reuse, which further enhanced the long-term sustainability of the hybrid process.

Authenticating the geographical origin of the Chinese yam (Tiegun) with stable isotopes and multiple elements

The Chinese yam, an important orphan crop with both high nutrient and health promoting value, is mainly produced in the Yellow-Huai-Hai plain near the river basins in China. The protected designation of origin (PDO)-labeled Chinese yam differs greatly from others in market acceptance and price, which has led to fakes and the need for reliable authentication methods. Hence, stable isotope ratios of δ ¹³C, δ ¹⁵N, δ ²D, and δ ¹⁸O and 44 multielemental contents were used to explore the authentication of geographical origins and the effect of environmental factors. Twenty-two elements and δ ¹⁵N were selected as the key variables to authenticate Chinese yams from three river basins as well as to authenticate them among traditional PDOs and others in the Yellow River basin. Moreover, six environmental factors, including the moisture index, maximum temperature, photosynthetically active radiation, soil organic carbon, total nitrogen and pH, were found to be highly related to these variances.

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.