There is an exponential growth in textual content generation every day in today's world. In-app messaging such as Telegram and WhatsApp, social media websites such as Instagram and Facebook, e-commerce websites like Amazon, Google searches, news publishing websites, and a variety of additional sources are the possible suppliers. Every instant, all these sources produce massive amounts of text data. The interpretation of such data can help business owners analyze the social outlook of their product, brand, or service and take necessary steps. The development of a consumer review summarization model using Natural Language Processing (NLP) techniques and Long short-term memory (LSTM) to present summarized data and help businesses obtain substantial insights into their consumers' behavior and choices is the topic of this research. A hybrid approach for analyzing sentiments is presented in this paper. The process comprises pre-processing, feature extraction, and sentiment classification. Using NLP techniques, the pre-processing stage eliminates the undesirable data from input text reviews. For extracting the features effectively, a hybrid method comprising review-related features and aspect-related features has been introduced for constructing the distinctive hybrid feature vector corresponding to each review. The sentiment classification is performed using the deep learning classifier LSTM. We experimentally evaluated the proposed model using three different research datasets. The model achieves the average precision, average recall, and average F1-score of 94.46%, 91.63%, and 92.81%, respectively. The Covid-19 threat has led to an exponential rise in online purchases of food, electronics, clothes, etc. , 23. As The approach of analyzing the behavior of emotions, opinions, and feelings for a specific entity or subject that is expressed in the form of text is known as opinion mining or sentiment analysis. The accomplishment of sentiment analysis can be achieved at the word, phrase, sentence & document level . PerformThe most widely employed approaches to perform sentiment analysis include the Machine Learning/Deep Learning based method, lexicon-based method, and hybrid methodology , 43, 49.In machine learning or deep learning-based approaches, the dataset is separated into training and testing datasets. A training data set is used during the training process so that the model discovers to associate the specific input text to the conforming yield to learn the documents. The testing dataset is used during the prediction process where the transformation of hidden textual inputs into feature vectors is done using a feature extractor. The model is then fed these vectors, which generate prediction tags for the corresponding vectors. The hybrid approach for sentiment analysis is the implementation of a Lexicon-based approach combined with a machine learning-based approach .All these techniques have demonstrated remarkable accomplishment in formal language, conventional text sources, and in well-specified fields where pre-labeled data is accessible for training or the lexicon coverage includes those words that convey specific sentiments within a corpus. But these approaches are incompetent to match up to the large quantity, diversity, and momentum with which the unstructured data is constantly posted online.The performance of sentiment analysis approaches utilizing machine learning techniques has recently improved because of the introduction of various kinds of feature extraction methods that are essential to produce accurate results , 45. As For instance, contemplate the review “Although this mobile phone is too heavy, it is little cheap”, includes implicit aspects: weight (implied by the term heavy), price (implied by the term cheap), and sentiment-bearing word relations. In general, sentiment appears neutral, however, aspects have both negative and positive polarities. Further, these implicit aspects are poorly described and are not articulated using standard terms and definitions. This issue can be solved by grouping very similar aspects into attributes and then analyzing them with the Aspect-based Sentiment Analysis (ABSA) approach , 18, 19.The functionality of the model suggested in this paper is divided into two stages: review mining and review summarization. Each user's reviews are categorized as either negative, positive, or neutral in review mining. In review summarization, a concise summary for corresponding reviews gets automatically formulated based on the results of the review mining step. The scope of this work is restricted to the development of a reliable, accurate, and robust review mining model  that can further result in building an effective consumer review summarization model.In this research, we present a unique sentiment analysis technique based on the robust hybrid analysis of sentiments which is used to solve the challenge of obtaining efficient review mining results for consumer review summarization. To begin, a straightforward and efficient pre-processing technique is used to eliminate stop words, digits, meaningless words, and other undesirable elements. Then, the preprocessed reviews are given as input to the robust and efficient hybrid feature engineering technique, which combines review-related features and aspect-related features and forms a hybrid feature vector (HFV). The hybrid feature extraction methodology suggested in this paper can deal with ambiguous and inconsistent sentences as well while performing sentiment analysis.The method proposed in this work is focused on analyzing sentiments efficiently, thus this segment comprises some recent studies categorized into three fields: Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), and Deep Learning-based SA methods. Towards the end of this section, the motivation and contribution of this research are also discussed.This section focuses on the study of several recent works carried out in the sphere of sentiment analysis , 51, 53.The SentiDiff algorithm proposed by Wang et al.  combinesSentiVec, a kernel optimization method for sentiment word embedding, is suggested by Zhu et al. . The firA new method called the sentiment-based rating prediction method is suggested by Munuswamy et al.  to develA variety of classifiers and feature sets to perform sentiment quantification are explored by Ayyub et al. . Based oA unified framework that helps in bridging the gap between machine learning-based approaches and lexicon-based approaches is presented by Iqbal and Hashmi . The autChiong et al.  proposedAs a robust method for enhancing sentiment analysis accuracy, aspect terms extraction has attracted significant attention from researchers. This section discusses the numerous studies that have been recently suggested on ABSA , 46, 50.Schouten et al.  presenteWang et al.  proposedAn automatic method is presented by Parthi et al.  to calcuThe end-to-end ABSA is investigated by Bie and Yang , and a uAurangzeb et al.  presenteDeep learning-based methods have also been applied to the sentiment analysis domain recently to further improve efficiency , 30, 52.The first end-to-end SEmi-supervised Multi-task Learning framework (SEML) for performing ABSA on user reviews is presented by Li et al. . Both asThe aspect-based sentiment analysis for demonetization tweets is performed using the improved deep learning method by Datta and Chakrabarti . Pre-proKumar et al.  suggesteAn aspect-gated graph convolutional network (AGGCN) is proposed by Lu et al. , which cHuang et al.  introducAygun et al.  employedBased on tweets related to cryptocurrency, the work proposed by Aslam et al.  performsSentiment analysis has recently received substantial attention from researchers for a variety of applications, hence a framework to perform it efficiently while addressing scalability is needed. We investigated a variety of strategies employed to perform sentiment analysis, intended to improve the feature representation and classification using machine learning/deep learning-based techniques. The inclusion of fraudulent, sarcasm, spam, emoticons, negation and other types of content in a significant number of online reviews makes feature extraction challenging. The study carried out in this section shows that deep learning methods are more efficient than machine learning methods for sentiment classification. The accuracy of deep learning algorithms, we believe, is mostly determined by the feature set.According to the findings of the studies mentioned above, sentiment analysis-based approaches are insufficient to handle the issues of accurately representing sentiments, opinions, and emotions, from online reviews. As a result, the ABSA approaches recently proposed are studied, however, their scope or application is also limited as they address the challenge of ambiguity and sarcasm to some extent, but don't consider emotions, negations, and long-term dependencies for accurate classification.Aside from these issues, scalable review datasets have not been used to evaluate ABSA approaches. Some ABSA approaches relied primarily on unsupervised procedures, necessitating a significant number of manual data annotations. Because some SA/ABSA algorithms rely solely on symbolic feature extraction, their accuracy suffers. In comparison to machine learning and lexicon-based techniques, deep learning-based techniques provided superior scalability and efficiency.Based on our observations that hybrid feature sets accomplish effective results, we split the feature extraction phase into two parts: Review-Related Features (RRF) and Aspect-Related Features (ARF). After that, RRF and ARF numerical features are merged into a Hybrid Feature Vector (HFV).As part of RRF, various methods like term frequency-inverse document frequency (TF-IDF), n-gram features & emoticon polarities, are employed for making emotional representations more accurate.As part of ARF, we propose a novel way for extracting aspects terms employing co-occurrence frequencies and then assigning the polarities. The ARF method produces aspect terms with their polarities.For the classification task, a deep learning classifier LSTM is used for the classification of input reviews either into negative, positive, or neutral classes.We suggest a novel technique of SA with special reference to consumer review summarization, based on the above-mentioned concerns of the state-of-the-art approaches. The suggested method comprises three stages: pre-processing, feature extraction, and sentiment classification. To denoise input reviews, the pre-processing step employs a standard procedure. The feature extraction step is ingeniously built as a hybrid step to overcome the difficulties of accurately representing the features of input reviews. In particular, the proposed model has the following contributions:According to the findings of the aforementioned studies, sentiment analysis has become a popular study area due to several intriguing and challenging research topics. An enormous number of start-up organizations are aiming to provide sentiment analysis/review mining services due to their diverse functional uses. Every firm wants to know how its products and services are perceived by its customers. This domain will remain active and dynamic in the coming years due to scientific challenges and functional components. The goal of this work is to develop a hybrid model to perform sentiment analysis accurately from the perspective of consumer review summarization.The different steps followed in this study are shown in Fig. The SemEval-2014 restaurant reviews data set , SentimeThe pre-processing of the dataset consisting of raw reviews is done by deleting & fixing the intricate and pointless text. The data pre-processing functionality used in the proposed work is beginning with tokenization and finishes with the removal of meaningless words, digits, and words less than three characters. Using the tokenization function, we subdivided the input reviews into tokens. Then we applied stemming to each token to cut it down to its singular form . Then, to limit the number of tokens, stop-words like ‘I’, 'a', 'an', 'am', and so on are deleted. Dates, special characters like #, @, etc., URLs, and meaningless words  are all found and eliminated. Furthermore, words with less than three letters and integers are also found and eliminated. This algorithm assures that the dimensional space of raw reviews is effectively reduced. Table There have been several attempts to represent input reviews as features. Precise, robust, & effective feature extraction, still, remains a difficult research challenge for review mining. As a result, developing an efficient and consistent feature set that can yield high categorization accuracy is an important aspect of any sentiment analysis system. Therefore, we propose a robust and effective sentiment analysis architecture employing hybrid feature extraction methodology. In the first step, the extraction of RRF features is done by employing various methods to achieve the polarity for every term within the preprocessed text including negations and emoticons. The aspect terms with their polarities are then extracted using the ARF method. Sarcastic and ambiguous reviews are also addressed and represented by the ARF. Finally, by integrating the results of ARF and RRF, every preprocessed review is expressed as HFV.RRF is a sentence-level feature representation strategy that considers emoticons as well along with text to describe feelings, opinions, and negations in the input reviews. To construct the hybrid form of features, traditional approaches like n-gram, TF-IDF, & emoticons-specific features have been combined. The TF-IDF approach uses BoWs, and the n-gram relies on word embeddings. The usage of solitary words for extracting features limits sentiment analysis in various ways. The negation problems don’t get solved with a single-word feature, and at the same time, it can contribute to the inappropriate classification of sentiments.We addressed these issues first by obtaining a word list from the pre-processed reviews by n-gram feature extraction. The TF-IDF of n-gram words is then calculated using the TF-IDF on the n-gram yield. The combination of n-gram and TF-IDF technique is not just shrinking the dimensionality but resulting in an effective representation of each review as well. The emoticons-specific features are then retrieved for improving the sentiment analysis accuracy further. As a result, RRF is attained as a combination of n-gram, TF-IDF, & emoticons-specific features. The RRF procedure is described in detail below.i is the document comprising the ith review that has been pre-processed. We start with the application of the n-gram approach on a preprocessed document for blended n-gram and TF-IDF. An n-gram is a proximal series of n-words obtained from a given dataset. An n-gram model with n = 1 is known as a unigram, when n is 2, it is referred to as a bigram, when n is 3, it is referred to as a trigram, and so on. "Bad" and "Very Bad," for example, are unigrams and bigrams, correspondingly. From the input sentence, the n-gram approach constructs a sequence of n consecutive words as follows:Ngrami denotes the set of n-grams derived from the preprocessed input document Ngramd We fixed the value of Let us assume that QNgrami signifies the list of words associated with the ith ith review and Ngramd signifies the list of words associated with the complete data set Ngrami consists of 60 terms and the term “good” occurs 5 times, the result of TF is Following the results of the n-gram, TF-IDF is employed for getting the inverse document frequency for the word lists resulting from the training/testing datasets. Term Frequency (TF) indicates how repeatedly a term exists throughout the document and Inverse Document Frequency (IDF) is a measure that finds out if a term is unusual or common among all the documents in a corpus.From then on, the emoticons-specific features are then retrieved from the review dataset and represented as a vector for categorization and subsequent computations. Because every review may or may not contain emoticons, we set the value of the emoticons feature vector (EF) of size 1 × 2 to zero for each review. The discrete probability distribution formula is used to count the emoticons for every review alongside the sentiment label. A positive emoji is represented as 1, while a negative emoji is represented as − 1. Suppose the review comprises of six emoticons, three positive and three negative, then the outcome will be represented as 3, − 3]. When positive, negative, or both emoticons are missing from a review, the emoticons-related features are given a value of zero. The emoticons-specific features are then joined with , − 3. WhWe apply the ARF extraction procedure to training and testing datasets after the extraction of the hybrid version of review-specific features, which uses the mechanism derived from Schouten et al.  solely tThe suggested ARF method is demonstrated in Algorithm I. Let Q denote the training set, which consists of m raw internet reviews. In each input review, we performed the extraction of the categories followed by the estimation of their co-occurrence rates against the lemmas and dependencies forms. The ARF algorithm's initial step is to identify each review's set of lemmas, dependencies, & categories. The lemma represents the dictionary form of a word, while dependencies show the binary grammatical relationship existing between a term called governor or head and another term called modifier or dependent in a sentence.The instance below demonstrates a review comprising dependency relations, with the words 'price' (governor) and 'okay' (dependent) making a dependency relation named nsubj (subject-predicate relation), and the words 'very' (governor) and 'attractive' (dependent) making one more dependency relation termed avdmod .S. For input review, the SC comprises a list of aspect categories. Each review is handled using the Standford CoreNLP framework's dependency parser, POS tagger, and lemmatizer functions as an NLP process [42]. The subsequent action is to count and add the lemma or dependency form's unique occurrences to vector The list of lemmas and dependence forms are contained in set Based on co-occurrence and occurrence frequency values in vectors The explanation of Algorithm I is given in Fig. As demonstrated in Fig. ith review.The primary issue with using neural network classifiers and conventional classifiers is the vanishing gradient or exploding gradient. The vanishing gradient problem hinders the learning of long data sequences. As a result of the exploding gradient problem, the weights oscillate, further degrading the network quality. Therefore, when neural networks like CNNs/RNNs attempt to store information over extended time intervals, it takes prolonged time due to inadequate, crumbling error backflow. LSTM network, on the other hand, is a kind of RNN proficient in learning order dependence in sequence prediction problems. To overcome the vanishing gradient problem, LSTM uses a distinctive additive gradient structure that incorporates direct access to the forget gates' activations at every time step of the learning process. Thus, it defeats the error backflow problems with the minimum computational complexity of O(1). In the proposed work, we use LSTM for the automatic sentiment analysis from the sequential features vector received for each sequential review. It accepts the training dataset and test sample features vector f, Wi, Wo, Wc, Uf, Ui, Uo, Uc) and biases  are not time-dependent which means that the outputs of different timesteps are calculated using the same weight matrices. The input and forget gates work together to refresh the memory cell. The forget gate examines the part of memory to be forgotten, while an input gate approximates new values corresponding to the view currently stored in the memory cell. As a result of the output gate and memory cell, the hidden description is estimated. Due to LSTM activation including summation over time and derivatives distributed over sums, the gradient in LSTM gets spread over extended periods and gets vanishes. The fully connected layer followed by the softmax layer works on classifying the input features into appropriate matching classes according to the training dataset. The output layer generates the final sentiment detection outcome.Let’s suppose that the LSTM input layer receives the t gate f . In everWe used MATLAB tool, Windows 11 operating system with an i5 processor for investigational evaluation of the proposed work. The SemEval-2014 restaurant reviews dataset, Sentiment140, and STS-Gold datasets have been used to evaluate the proposed model and compare it to the recent state-of-the-art methods. We have divided all three datasets into 70% training and 30% testing datasets. To demonstrate the efficacy of the hybrid feature engineering methodology, HFV performance is first compared to the proposed RRF and ARF methods employing the LSTM classifier using the SemEval-2014 restaurant reviews dataset. In addition, the comparison of the proposed hybrid feature extraction approach with the recent state-of-the-art methods is also done using all three datasets. The different metrics used to evaluate performance of ARF, RRF, and HFV are F1-measure, accuracy, recall, precision, and Mean Sentiment Analysis Time (MSAT). The MSAT represents the average processing time, i.e., the computing time of the sentiment categorization. For the estimation of the MSAT evaluation parameter, we executed 50 instances of sentiment analysis classification.Based on SemEval-2014 restaurant reviews dataset, the performance evaluation of RRF, ARF, and HFV using LSTM is presented in this section Figs. It can be observed from the examination of F1-measure, accuracy, precision, and recall that the hybrid feature engineering method gave much better results for sentiment classification than ARF and RRF using LSTM. The HFV strategy encompasses the issues associated with sarcasm, negation, ambiguity, emotions, opinions, and feelings regarding restaurants and their aspects, which is the primary cause of this performance improvement. The combination of n-gram, TF-IDF, and emoticons-specific features results in the attainment of the RRF vector. It successfully constructs the hybrid feature vector, which conveys the frequency of words occurring in every review alongside emoticon-specific features. To further limit sentiment analysis errors, the ARF approach employs the co-occurrence frequency strategy for managing sarcastic and ambiguous phrases. The RRF outperformed the ARF in terms of results because it successfully addressed negations, opinions, and emotions.The performance of F1-measure and MSAT with varied training data set sizes is shown in Figs. Using the SemEval-2014, STS-Gold and Sentiment140 datasets, we compared the proposed hybrid feature extraction approach employing LSTM with the below-mentioned recent state-of-the-art methodologies for investigating its quantitative representation.Supervised ABSA (SABSA) [ (SABSA) : This meSentiVec [SentiVec : The newN-gram + TF-IDF + SVM [DF + SVM : Our perSEML [SEML : It's anMTMVN [MTMVN : Since iTables In comparison to SentiVec and N-gram + TF-IDF + SVM, the ABSA methods: SABSA, SEML, and MTMVN are found to exhibit poor performance since they lack negation handling and do not extract review-specific features. The model presented in this paper focuses on delivering an integrated solution that combines the advantages of both aspect-specific and review-specific features to increase performance.The paper aims to accurately perform sentiment analysis from the point of view of the consumer review summarization model for capitalists. We outlined several research concerns and possible solutions for the challenges that occur when performing sentiment analysis for raw online reviews. Using the hybrid feature extraction method proposed in this paper, the input pre-processed reviews can be transformed into meaningful feature vectors, allowing efficient, reliable, and robust sentiment analysis to take place. The results reveal that as compared to individual methodologies; the hybrid approach greatly improves sentiment analysis performance. We have also compared the proposed model’s performance with the recent state-of-the-art methods for F-1 measure, accuracy, precision, recall, and AUC evaluation parameters. All five evaluation parameters are found to improve significantly. The HFV + LSTM model's future directions include (1) applying sentiment classification results to extend the proposed model for consumer review summarization, (2) investigating the proposed model's performance using diverse datasets, and (3) exploring more NLP techniques to make the model language independent.


Rapid and highly sensitive quantitative analysis of chlorpromazine (CPZ) in human whole blood is of great importance for human health. Herein, we utilize the screen-printed carbon electrodes (SPCE) as the electrode substrates for growth of highly electroactive and antifouling nanocomposite materials consisting of vertically ordered mesoporous silica films (VMSF) and electrochemically reduced graphene oxide (ErGO) nanosheets. The preparation of such VMSF/ErGO/SPCE could be performed by using an electrochemical method in a few seconds and the operation is controllable. Inner ErGO layer converted from graphene oxide (GO) in the growth process of VMSF provides oxygen-containing groups and two-dimensional π-conjugated planar structure for stable fabrication of outer VMSF layer. Owing to the π-π enrichment and excellent electrocatalytic abilities of ErGO, electrostatic preconcentration and antifouling capacities of VMSF, and inherent disposable and miniaturized properties of SPCE, the proposed VMSF/ErGO/SPCE sensor could be applied for quantitative determination of CPZ in human whole blood with high accuracy and sensitivity, good stability, and low sample consumption. H-phenothiazin-10-yl)-N,N-dimethyl-propan-1-amine, CPZ), as a class of phenothiazine derivatives with aliphatic side chains, is an important antipsychotic drug, which has been widely used to treat personality disorders and various psychotic disorders, such as adult manic depression or schizophrenia ), potassium ferrocyanide (K4[Fe(CN)6]), sodium phosphate dibasic dodecahydrate (Na2HPO4·12H2O), chlorpromazine (CPZ), citric acid (CA), glucose (Glu), L-threonine (Thr), L-leucine (Leu), hydroquinone (HQ), ascorbic acid (AA), uric acid (UA), 3-hydroxytyramine hydrochloride (DA), and bovine serum albumin (BSA) were obtained from Aladdin Chemistry Co. Ltd. . Sodium dihydrogen phosphate dehydrate (Na2H2PO4·2H2O) was bought from Macklin . Screen-printed carbon electrodes (SPCE) (DRP-C110-U75) were received from Metrohm . Among them, the working electrode (4 mm in diameter) and counter electrode of the SPCE were prepared from carbon, and Ag was used as a pseudo-reference electrode. Human blood serum  was provided from Center for Disease Control and Prevention  for real sample analysis. All reagents used in the experiment were of analytical grade without further treatment. Deionized water (18.2 MΩ cm) was prepared from Mill-Q system .Monolayer graphene oxide (GO) aqueous dispersion (1 mg/g) was purchased from Hangzhou GaoxiTech . Tetraethyl orthosilicate (TEOS), hexadecyl trimethyl ammonium bromide (CTAB), potassium ferricyanide (KX-ray photoelectron spectroscopy (XPS) was performed on PHI5300 electron spectrometer using 250 W, 14 kV, Mg Kα radiation . The surface morphology of VMSF was characterized by HT7700 Transmission electron microscope  at an acceleration voltage of 100 kV. VMSF was prepared by gently scraping from the surface of SPCE and was dispersed in the ethanol. The dispersion was further dropped onto the copper grids after ultrasonication for 2 h to obtain the TEM specimen. All electrochemical experiments, including cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS), were carried out on an Autolab  electrochemical workstation. In this article, the scan rate used in CV was 100 mV/s, and DPV parameters used were as follows: step, 0.005 V; modulation amplitude, 0.025 V; modulation time, 0.05 s; interval time, 0.2 s.2SO4 for 10 cycles. After being washed by ultrapure water and dried under nitrogen, 20 μL of GO (0.1 mg/mL) was dropped onto the SPCE and dried at 60 °C. The electrode was named as GO/SPCE. VMSF was grown onto the GO/SPCE surface according to the protocol reported previously [3 and EtOH (v/v = 1:1), 1.585 g CTAB, and 3050 μL TEOS under vigorous stirring for 2.5 h. Subsequently, GO/SPCE was placed into the above precursor solution and subjected to a constant voltage of –2.1 V for 5 s. Then, the electrode was quickly washed by deionized water, dried by nitrogen, and further aged at 80 °C. Thus, the resulting electrode with surfactant micelles (SM) inside the nanochannels was designed as SM@VMSF/ErGO/SPCE. In order to remove SM from the silica nanochannels, SM@VMSF/ErGO/SPCE was put into 0.1 M HCl-EtOH under stirring for 5 min. The obtained electrode was called VMSF/ErGO/SPCE.Prior to use, SPCE first underwent electrochemical pretreatment to remove the impurities absorbed on the electrode surface by applying a potential range of 0.4–1.0 V in 0.05 M Heviously . BrieflyThe received human whole blood was diluted or undiluted with 0.1 M PBS (pH 6.0) without any complex pretreatment. Upon the addition of known concentration of CPZ into the human whole blood samples, electrochemical responses of CPZ were determined by the VMSF/ErGO/SPCE sensor. The volume of samples is very small (<50 µL).In summary, we present a simple and efficient method for preparation of disposable VMSF/ErGO/SPCE electrochemical sensor and investigate its analytical performance of CPZ detection. Such VMSF/ErGO/SPCE could be obtained by a rapid and controllable one-step electrochemical method, which combines the reduction of GO and growth of VMSF. Arising from the electrostatic preconcentration ability of outer VMSF layer and electrocatalytic capacity and π-π enrichment ability of inner ErGO layer, the proposed VMSF/ErGO/SPCE shows excellent analytical performance towards CPZ. Furthermore, combining the inherent characteristics of VMSF (antifouling ability) and SPCE (disposable property), direct electrochemical analysis of CPZ in human whole blood was successfully realized with rather low sample consumption and good anti-interference, stability, reproducibility, and regeneration abilities, which could be extended to detect various analytes related to diseases.


The sixth Total Diet Study (TDS) of China included a countrywide study to assess the health effects of MSG (monosodium glutamate). MSG detection, consumption analysis, and risk assessment were conducted on 168 samples from seven food categories of the most typical Chinese daily diet. The highest value of MSG in the daily diet of the Chinese population was 8.63 g/kg. An MSG intake of 17.63 mg/kg bw/d for the general population of China was obtained from content measurements combined with food consumption, while the data from the apparent consumption survey alone gave 40.20 mg/kg bw/d. The apparent consumption did not consider the loss of MSG during food cooking, resulting in an overestimate. To offer a global perspective, MSG content, food category contributions, and ingestion levels across nations were summarized and thoroughly investigated. A realistic, logical, and precise risk assessment protocol for MSG daily intake was developed in this article. Monosodium glutamate (MSG), a flavor enhancer, is typically utilized in food processing in homes and the catering sector worldwide. The dangers of excessive MSG consumption on human health have drawn researchers’ attention repeatedly ,10,11,12Hence, the European Food Safety Authority (EFSA) re-evaluated the safety of glutamic acid and its derived glutamates in June 2017 and presented a population-acceptable daily intake (ADI) of 30 mg/kg bw/d, expressed as all inorganic salt forms of glutamic acid . AccordiIn this study, we investigated the types of daily diet and consumption in the general population of Chinese residents to obtain more exact information on the actual intake of MSG in the daily diet based on the sixth Total Diet Study (TDS) of China. The MSG content of all consumed foods in the ready-to-eat state was detected by our laboratory UPLC-MS/MS assay. The MSG daily intake was calculated by multiplying the food consumption and the related content. The daily intake of MSG in the general population of China was assessed at provincial and national levels based on an ADI value of 30 mg/kg bw/d for a standard Chinese man’s bw of 63 kg, on the risk of human health effects. Meanwhile, to compare the variability of risk assessment results between apparent MSG consumption and actual intake, a 3-day, 24-h dietary survey was conducted using a CHNS-like survey of over 10,000 households in a selected area covering 70% of China’s land area and 85% of its population and assessed in terms of apparent consumption. The real daily dietary intake of MSG in the general Chinese population was substantially lower than the apparent consumption, as shown by the comparison between the findings of the intake analysis and the apparent consumption analysis. This finding suggests that to appropriately assess the risk of MSG intake and prevent overestimation, losses of MSG during cooking and trans-oral processes should be taken into consideration.TDS, a continuous national study, monitors the levels of trace elements and harmful compounds in foods and is also used to estimate hazards of dietary exposure in Chinese populations. The China National Center for Food Safety and Risk Assessment (CFSA) conducted the sixth China TDS from 2016 to 2021 in twenty-four provinces, namely, Heilongjiang, Liaoning, Jilin, Hebei, Beijing, Henan, Shanxi, Shaanxi, Ningxia, Neimenggu, Qinghai, Gansu, Jiangxi, Fujian, Shanghai, Jiangsu, Zhejiang, Hubei, Sichuan, Guangxi, Hunan, Shandong, Guizhou, and Guangdong. In the sampling setting, provinces with a population above fifty million randomly set two different urban sampling points and four rural sampling points. Conversely, one urban sampling site and two rural sampling sites were randomly selected in provinces with a population of fifty million or fewer. The number of participants in each sampling site was not less than 30 households. According to the mixed food sample method used in the earlier TDS in China, the surveyed per capita food consumption was divided into thirteen categories, namely, cereals, legumes, potatoes, meats, eggs, aquatic foods, milk, vegetables, fruits, sugar, beverages, alcohol, and condiments (including cooking oil). The dietary composition of the surveyed areas or populations was collected by grouping the different food items consumed into the categories to which they belonged. Various food samples were collected from food purchasing points such as vegetable farms, side stores, grain stores, farmers’ markets, or farmers’ homes, near the neighborhood committee or village where each survey site was located. Food and condiments for cooking were prepared in designated restaurants, kitchens, or laboratories according to the cooking utensils and methods customarily used locally. All seasonings were added to other food groups as required by the recipe during the cooking process. Then, the ready-to-eat dietary samples were stored at −20 °C and shipped to CFSA.3·H2O was added for cartridge equilibration. After sample loading, water (4 × 0.5 mL) and methanol (2 × 0.5 mL) were used to remove impurities. Then, 0.5 mL of 2% formic acid (v/v) in methanol was used to elute the target. All eluent fractions were combined and dried by nitrogen flow, and the residue was reconstituted in 0.25 mL of 50% aqueous acetonitrile. Finally, depending on the level of MSG content in the dietary sample, 1 mL of the eluate was diluted 10−3 times and then detected for content. MSG identification and quantification were performed using Shimadzu LC 8050 UPLC-MS/MS . The positive ion mode was used in the ESI ionization. The ion spray voltage was +4.5 kV. Nitrogen was used as the atomization gas and the drying gas, with a flow rate of 3 L min−1 and 10 L min−1, respectively. The heating gas velocity was 10 L min−1 of air. Additionally, meanwhile, the desolvation line and heating module temperatures were 300 °C and 400 °C, respectively.One hundred and sixty-eight samples from twenty-four provinces of seven food items, namely, cereals, legumes, potatoes, meats, eggs, aquatic foods, and vegetables, were examined for MSG content determination. Following the local food recipe, these foods used MSG or other analogous flavors during the cooking procedure. MSG was extracted using 4 mL of 0.1 M HCl from 1.0 g of the dietary sample thawed at room temperature during the sample pretreatments. The extraction solution was centrifuged above 4000 rpm for 10 min. Before the sample was cleaned, 1 mL of supernatant was aliquoted from the extraction solution and neutralized with 1 mL of 0.1 M NaOH. An ion exchange cartridge  was used for sample clean-up. Firstly, 2 mL of 5% NH™ UPLC HSS PFP column  was used for MSG chromatographic separation. The temperature of the column oven was 40 °C. The mobile phase was water (phase A) and acetonitrile (phase B) containing 0.2% formic acid (v/v). The steps of the gradient elution program were: 0–3 min: 50% phase B and 50% phase A, 3–6 min: phase B content increasing linearly to 100% and kept for 2 min. The flow rate was 0.20 mL/min, and the injection volume was 1 μL. The retention time of MSG was 2.748 min under these conditions.An ACQUITY+ > 84.05+ and 147.90+ > 56.05+, with collision energies of 16 eV and 23 eV, respectively, in MS separation.Quantitative and qualitative ion pairs on the multiple reaction monitor (MRM) scanning modes were 147.90The dietary intake of each food category was calculated by multiplying the MSG contents and their consumption. The whole MSG intake was the accumulated sum of every food intake. Additionally, then, the sum was divided by 63 kg of the standard Chinese man for MSG intake risk assessment. The items with “not detected” data represented half of the Limits of Detection (LODs) when processing the dietary intake. The risk assessment was based on whether the general population’s intake in each province was above 30 mg/kg bw/d, and areas where MSG intake exceeds this value need to be a cause for concern.The parameters of the assay, such as LODs, Limits of Quantitation (LOQs), and spiked recovery experiment, were jointly validated by three other national laboratories. All methodological sensitivities, reproducibility, and specificities are reflected in the additional information. The LODs of MSG in the food composites ranged from 1.5 to 3.5 μg/kg in different food matrices. Additionally, the LOQs of MSG were 5.0 μg/kg in cereals, legumes, and potato products and 10 μg/kg in meat, egg, aquatic, and vegetable products, respectively. In the recovery test, blank samples were selected from the seven food matrixes in which the MSG concentrations were below the LODs. Three spiking levels of 10, 100, and 1000 μg/kg were evaluated. The validation results of this determination method are seen in It is clear from There was a wide range of MSG intakes, from 4.27 to 32.35 mg/kg bw/d, among different regions in China. The mean (±SD) was 17.63 ± 2.50 mg/kg bw/d at the national level. The top three highest intake provinces were Shanghai, Heilongjiang, and Hunan, with 32.35, 27.78, and 27.56 mg/kg bw/d. The province with the lowest intake was Shandong Province, with a value of only 4.27 mg/kg bw/d, as The risk evaluation included an investigation into the dietary contribution of MSG consumption. This data analysis enabled us to comprehend the variations in dietary practices between regions and to lower risk by limiting items with a high proportion of contaminants. Vegetables were the primary source of essential intake at the national level in our contribution margin analysis, contributing at a rate of 44%, as shown in Since MSG was first used in flavoring, there have been different opinions on its safety in various countries and institutions. The US Food and Drug Administration (FDA) considers the addition of MSG to foods to be “generally recognized as safe” (GRAS). In some countries and regions, MSG is even seen as the main alternative to reduce sodium salt intake. Although our group has reservations about the suitability of this ADI for the Chinese population, it has been used as a tentative evaluation criterion for the risk assessment of MSG. A uniform unit of measurement was needed for MSG intake to be consistent with the criteria used to determine results when comparing different regions of the world. The MSG intake units mentioned previously in In this study assessment, animal-based foods such as meats and aquatic had higher MSG levels than plant-based foods. Compared to plant foods, animal-based foods have more protein amino acid groups, and these free proteins may undergo hydrolysis to produce glutamate under complex external conditions. Many high-protein animal foods have high levels of MSG, and a large proportion of this should be contributed by these free glutamates . This fiWe also carried out a comparative analysis of the global MSG food content. As can be seen in To further explore the influence of the geographical distribution and dietary consumption habits of the Chinese general population on MSG intake, we mapped the geographical distribution of total dietary MSG in China . In geneIn addition, we conducted inter-country comparisons of MSG-related or similar data on the MSG dietary intake of the world population, and the results are shown in Comparatively, the European diet was dominated by high-quality baked goods, soups, broths, sauces, meat, meat products, seasonings, and condiments, all of which were high in MSG and consumed in significant amounts ,35. The In this study, we considered the intake derived by multiplying the food content and the corresponding consumption as the risk assessment value to be accurate. This strategy takes full account of factors such as the low levels of MSG added to fresh or home-cooked foods, and the fact that MSG is dissolved in the water of the dish during cooking and consumption of the food, and this water reduces the imported intake of MSG as a non-edible fraction in the actual intake. Therefore, a realistic approach to measuring MSG intake has the advantage of authenticity and precision. Although a risk assessment of health effects based on MSG consumption would lead to overestimation, high MSG consumption should be a concern in China.In summary, our group conducted a comparison of apparent MSG consumption surveys and real intake using the TDS study to analyze the risk of MSG quickly and accurately for the public using vast data covering over 70% of China’s land and 85% of its population. This is the most representative and up-to-date MSG intake and risk assessment for China to date. We have, for the first time, examined the geographic variation of MSG intake for different food categories in China. This analysis will serve as a local point of reference and offer information on how to restructure diets healthily. However, in this paper, the results of our risk assessment were carried out mainly based on MSG intake in the general population. Therefore, we have shortcomings in the analysis of individual consumption and risk of MSG. In a subsequent study, we will reinforce the probabilistic assessment of the risk of MSG intake in the Chinese population on specific indicators such as gender, age group, and BMI for the individuals participating in the survey, to more precisely target the population groups at high risk of MSG intake.In summary, we conducted a comparison of apparent MSG consumption surveys and real intake using TDS study to analyze the risk of MSG quickly and accurately for the public using vast data covering over 70% of China’s land and 85% of its population. A risk analysis of MSG intake in the general population of China was obtained from both assessments. This is the most representative and up-to-date MSG intake and risk assessment study for China to date. Additionally, considering the MSG loss caused by MSG in whole food cooking and non-edible parts of the dishes, we believe that the strategy of obtaining the maximum intake with MSG apparent consumption through a 3 d, 24 h room survey causes overestimation. This provides a more reliable technical solution for the future risk assessment of relevant condiments. We have, for the first time, examined the geographic variation of MSG intakes for different food categories in China. This analysis will serve as a local guideline and offer information on how to restructure diets healthily. Last but not least, a global comparative analysis of food content and population intake in dominant studies around the world was conducted. This provides a global view of MSG population intake and risk assessment for our colleagues around the world, providing a strategy for a rapid, accurate, and realistic MSG risk assessment of daily household dietary intakes for related work.


However, without reliable source identification,the origin of this decades old problem has been uncertain. Here, wetarget radiocesium contamination in wild boars from Bavaria. Our samples(2019–2021) range from 370 to 15,000 Bq·kg–1137Cs, thus exceeding the regulatory limits (600 Bq·kg–1) by a factor of up to 25. Using an emerging nuclearforensic fingerprint, 135Cs/137Cs, we distinguishedvarious radiocesium source legacies in their source composition. Allsamples exhibit signatures of mixing of Chornobyl and nuclear weaponsfallout, with 135Cs/137Cs ratios ranging from0.67 to 1.97. Although Chornobyl has been widely believed to be theprime source of 137Cs in wild boars, we find that “old” 137Cs from weapons fallout significantly contributes to thetotal level (10–68%) in those specimens that exceeded the regulatorylimit. In some cases, weapons-137Cs alone can lead to exceedancesof the regulatory limit, especially in samples with a relatively lowtotal 137Cs level. Our findings demonstrate that the superpositionof older and newer legacies of 137Cs can vastly surpassthe impact of any singular yet dominant source and thus highlightthe critical role of historical releases of 137Cs in currentenvironmental pollution challenges.Radionuclides released from nuclear accidents or explosionsposelong-term threats to ecosystem health. A prominent example is wildboar contamination in central Europe, which is notorious for its persistentlyhigh  137Cs from nuclear weapons falloutcontributes significantly to the notorious contamination levels inwild boars in Central Europe that were previously believed to be dominatedby Chornobyl.Sixtyyears old  It originates from the fallout of atmospheric nuclearexplosions from the mid-20th century (weapons-137Cs) andnuclear accidents, most prominently the Chornobyl4 and Fukushima6 nuclear accidents (reactor-137Cs). For safety regulations, many countries have employedstrict regulatory limits for 137Cs levels in general foodproducts .7 However, although routine radiation surveillance provides essentialquantitative information on 137Cs contamination levels,the attribution of a contamination to its origins remains poorly understoodas the ubiquitous weapons-137Cs cannot be distinguishedfrom any reactor-137Cs. This analytical challenge impedesthe comprehensive understanding of the origin of environmental 137Cs contamination, which is a critical prerequisite for aquantitative assessment of the responsibilities for certain 137Cs legacies and the establishment of more targeted strategies forenvironmental remediation and protection. More than ever, with threatsof nuclear strikes or accidental releases in the course of the Russo-Ukrainianwar, it is now imperative to be able to identify the source of anyrelease of 137Cs and evaluate their environmental consequences.In the face of climate change, nuclearenergy is experiencing arenaissance as a low-carbon option to feed humanity’s hungerfor energy.9 radiocesium isotopic fingerprintshave not yet been applied routinely for source identification. Cesium-135is an ideal and long-lived candidate (T1/2 = 2.3 My) after a release, better suited than fast-fading 134Cs (T1/2 = 2.07 y). Also, the productionmechanism of 135Cs provides more detailed information onthe nuclear origin of a contamination, which hence allows attributionof a radiocesium contamination to its source via its distinct 135Cs/137Cs ratio. Its mother nuclide (135Xe) has a large cross-section for thermal neutron capture, resultingin suppressed onset of 135Cs under the high neutron fluxdensity of a reactor core.10 By contrast,despite the intense but short neutron flux at the moment of a nuclearexplosion, 135Xe mostly “survives” the explosionbecause most primary fission products of the 135 isobar are 135Te and 135I, which have yet to decay to 135Xe.11 A nuclear explosion hence yieldsa relatively high 135Cs/137Cs ratio, whereasa reactor yields a low ratio. Nowadays, analytical protocols for commercialtriple quadrupole inductively coupled plasma mass spectrometry (ICP-QQQ-MS)as well as thermal ionization mass spectrometry (TIMS) are availablefor the precise determination of 135Cs/137Cs,thus allowing the application of the 135Cs/137Cs ratio as an isotopic fingerprint in nuclear forensics and environmentaltracing studies.19 In any case, the application of 135Cs/137Csas a forensic fingerprint is still far from routine as it requiresmeticulous chemical separation and sophisticated analytical procedures.While isotopic signatures of actinides have been used successfully to distinguish the contributions betweenvarious sources,137Cs contamination following the Chornobyl nuclear accident.20 It was reported that 137Cs inventoryin surface soil ranged from 102 to 105 Bq·m–2 in April 1986 . As a potent accumulator of 137Cs,22 regional wild boars (Susscrofa) were subsequently contaminated, and the 137Cs activity concentrations in their meat exceeded the regulatorylimit by approximately 1–2 orders of magnitude. However, unlikemost forest species, which initially also exhibited high 137Cs contamination in their bodies followed by a decline with time,24137Cs levels in wild boars have not shown a significant decline trendsince 1986.25 In certain locations and instances,the decline in contamination levels is even slower than the physicalhalf-life of 137Cs.26 This phenomenonhas been termed “wild boar paradox” and is generallyattributed to the ingestion of 137Cs accumulating hypogeousfungi  by wild boars.28 Depending on the soil composition,especially clay mineral content,29 theseunderground mushrooms are a critical repository of the downward migrating 137Cs. They are one major food item for wild boars, particularlyduring winter when food on the surface is scarce.30 However, due to the lack of convincing evidence for identifyingthe sources of 137Cs, the origins of the persistent contaminationin wild boars remains unclear.Bavaria, southeastern Germany, is notorious for its heavy 137Cs activities together with 135Cs/137Cs ratiosin wild boar meat samples, collectedfrom 11 Bavarian districts during 2019–2021. Reporting thelargest environmental sample set of 135Cs/137Cs to date (n = 48), we undertook a critical comparisonwith the published values and validated the feasibility of utilizing 135Cs/137Cs for source identification. Using a mixingmodel, we estimated the contribution of weapons-137Cs andreactor-137Cs, which not only deepens our understandingof the “wild boar paradox” but may also allow a futurelocation-specific prediction of the evolution of the 137Cs contamination in wild boars with time. Lastly, our method canbe applied for the traceability of 137Cs in any environmentalsamples in the future.Here, we analyzed the 137Cs fallout due to atmospheric nuclearexplosions led to local 137Cs deposition of about 103 Bq·m–2.31 However, in 1986, considerable additional 137Cs falloutfrom the Chornobyl nuclear accident was deposited on the ground inBavaria after a long-distance atmospheric dispersion, although thestudy area is located approximately 1300 km away from the accidentsite. The input of Chornobyl-137Cs immediately increasedregional 137Cs inventories to 0.5–50 kBq·m–2 . Additionally, the local alpine, pedological, and climaticcharacteristics created a favorable condition for slow 137Cs downward migration in the terrestrial environments. For instance,the topsoil classification map created by the Federal Institute forGeosciences and Natural Resources (BGR) showed that clay silt, sandyclay, loamy sand, and loam are the major topsoil textures in the investigatedregions.32 It is well-known that Cs+ ions are strongly bound to the soil’s clay and finesilt fraction, thus preventing rapid vertical migration.33 In addition, the historical data recorded byGermany’s national meteorological stations showed that theannual average local rainfall generally varied from about 630 to 1211mm (median: 776 mm) in the period from 2006 to 2017.34 In comparison to the rest of Germany, Southern Bavariaexperiences relatively high precipitation rates (>1000 mm) duetothe proximity to the Alps. This also caused increased wet deposition(washout of particles) after Chornobyl as well as during global fallout,resulting in a gradual increase of the radiocesium inventory fromnorth to south.Samples of wild boar meat were collectedfrom forested regions of 11 Bavarian districts in southern Germany1. At theFigure S1 in part 1 of the Supporting Information) was sampled by local Bavarianhunters between fall 2019 and spring 2020, as well as in early 2021.At each site, the fresh muscle samples from targeted wild boar wereseparated, frozen, and transported to Leibniz University, Hannover,after hunting. Most hunters submitted tongue tissue as an easily availabletype of tissue with high contamination levels.35 In our laboratory, the wild boar muscle samples were thawedat room temperature and then cut into smaller pieces (diameter <2 cm), while ensuring complete removal of any foreign matrix. In ouranalytical protocol, briefly, the samples were first dried at 110°C for 24 h and then heated to 420 °C for 36 h in an ovenfor final ashing. After necessary cooling, the ashed samples wereseparately transferred to sealed containers for further storage.Wild boars are traditional game animalsin Bavaria. The meat of wild boars  and guaranteedgrade regents, including HNO3 , HCl , and NH3 , were used to preparesolutions, which were then utilized in sample digestion and radiochemicalanalysis. Ammonium molybdophosphate powder  purchasedfromAlfa Aesar was used for the Cs extraction. To remove the potentiallyinterfering elements from the ashed samples, Dowex 1-X8 (100–200mesh) anion exchange resin purchased from Alfa Aesar and the cationexchange resin AG 50 W-X8 (100–200 mesh) from BioRad Laboratories,Inc. were prepared for sample purification.Two IAEA referencematerials, IAEA-330 (spinach) and IAEA-372 (grass), originating fromPolessko, Kyiv, Ukraine, were used for verifying the measured 137Cs activity was measuredby a high-purity germanium (HPGe) gamma detector, using the 661.7keV γ-peak. Gamma ray efficiency calibration was performed usingEckert & Ziegler’s certified “Mixed nuclide solution7601.” The detector has a counting volume of 131 cm3 with a relative detection efficiencyof 28% and a resolution of 1.9 keV at the 1332 keV 60Coγ-ray peak. The software Genie 2000 was used for evaluatingthe γ-ray spectra of each sample. Based on the calibration filesprepared in our laboratory, the measured data were corrected withthe counting geometry and energy. Gamma-ray self-attenuation was correctedby using the “top-down method” (see part 2 of the Supporting Information). Besides, a physicaldecay correction was also performed for all measured data to the samplingday.The ashed samplewas homogenized and filled into a plastic container for γ-raymeasurement. The sample’s 11 was used in this section, which mainly encompassesthree steps: (I) cesium extraction; (II) cesium purification; and(III) 135Cs/137Cs ratio determination by ICP-QQQ-MS.The detailed process is described in part 3 of the Supporting Information(Table S1). In part 4 of the Supporting Information, the amounts of referencesamples (IAEA-330 and IAEA-372) used for the analysis are listed (Table S2), and the cross-comparison of 135Cs/137Cs ratios in reference materials with publishedvalues39 (QA/QC) is described .The analysis protocol established by Zok et al.137Cs data. One-way analysis of variance (ANOVA) was appliedto evaluate the difference between significant differences in 137Cs activity concentration and 135Cs/137Cs among wild boar characteristics groups. All the statistical analysiswas implemented in SPSS v.22.“Median ± standarddeviation” is used for the description of the data distribution.Spearman correlation analysis and linear regression analysis wereused to quantitatively study the relationship between variables and Tables S5 and S6 in part5 of the Supporting Information. Overall, the fresh weight activityconcentration of 137Cs in wild boar meat collected from11 Bavarian districts varied between 0.37 and 14 kBq·kg–1 , in which about 88% ofmeasured data were above the regulatory limit according to Germanlaw and all data exceeded the Japanese limit. In addition, a spatialheterogeneity of 137Cs contamination levels was observedbetween various Bavarian districts, with the range of coefficientsof variation (CVs) from 23 to 113% (excluding regions with a samplesize < 3). In the Garmisch-Partenkirchen region , we observed minimum and maximum 137Cs levelsin wild boars with the widest range (14 kBq·kg–1) and the highest CV . In northernBavaria , we found a relatively narrow 137Cs variability  and the lowest 137Cs contamination (0.67± 0.16 kBq·kg–1). By contrast, regionC  contributed the heaviest 137Cslevels in this study . From a temporal perspective, hardly anysignificant decline trend can be found in 137Cs activityconcentrations between our samples (2019–2021) and the historicalrecord of 137Cs contamination in wild boar during similarseasons since 2001 ,which is consistent with the observation of persistent 137Cs contamination in wild boars from Austria.25 To explore the potential sources responsible for the persistent 137Cs contamination in wild boars, we compared measured 137Cs with the inventories in the study regions. With negligiblecontributions from Fukushima,40 we consideredreactor-137Cs from Chornobyl and weapons-137Cs as the major sources in Bavaria. For weapons-137Cs,the cessation of atmospheric tests resulted in no noteworthy new weapons-137Cs fallout after the last test in 1980. Nowadays, the baselineof airborne 137Cs (<10 μBq·m–3 in Germany) contributes insignificantly to the relatively high inventoryin soil41 and consequently in wild boars. Consideringthat the above-mentioned contamination level is orders of magnitudegreater than the 137Cs inventories reported in less contaminatedregions,42 it suggests a substantial impact of Chornobyl-137Cs on the Bavarian ecosystem. This is consistent with reportsof Chornobyl-137Cs dominating the total 137Csinventory in Austria by 90%.43 Besides,we found notable geographical differences in 137Cs inventoriesamong investigated districts with a declining trend from south tonorth . Moreover, these inhomogeneous 137Cs spatial patternsare also discovered in any specific district with a range of CVs from12%  to 63% . However, a simpleregression analysis showed no latitudinal pattern for 137Cs levels in wild boars and no obvious correlation between the 137Cs activity concentrations and topsoil inventories ,47 animal access to agricultural areas with lower 137Cslevels in crops than in wild plants or mushrooms growing in forests,20 heterogeneity of deposition, season of sampling,30 etc.,48 no simplemodel can correlate topsoil 137Cs inventories and the resultingactivity concentration in the animal tissue.The detailed information about sampling and the measured resultsare summarized in kBq·kg–1 2a, withentories 2b. This 135Cs/137Cs studies. The measured 135Cs/137Cs ratios ranged from 0.67 to 1.97 , with the highestratio found in the northern region  and the lowestratio from the central part of Bavaria . Similarly,like the radiocesium inventory in Bavaria decreasing from south tonorth, there is a pronounced geographical pattern in 135Cs/137Cs ratios, in which the latitude-dependent declineis confirmed , whereas a relatively poor significantcorrelation between the topsoil 137Cs inventories and 135Cs/137Cs ratios  is apparent. Unlike the measured 137Cs activity concentrations, the 135Cs/137Cs ratio’s variability is relatively narrow, with the CV varyingfrom 4 to 24% (median: 9%). More interestingly, correlation analysisshowed a significant positive relationship between the 135Cs/137Cs ratio’s CV and topsoil 137Csinventories’ CV in these regions , suggestingthe dependence of 135Cs/137Cs ratios’difference on the non-uniform spatial distribution of topsoil 137Cs inventories. Considering that such non-uniform patternsare typically attributed to the deposition of Chornobyl-137Cs, we here used the map of Chornobyl-derived 137Cs fromMeusburger et al.49 and found a significantheterogeneity in Chornobyl-derived 137Cs in our study regions,with the CV ranging from 48 to 105%. The great variability of the 135Cs/137Cs ratios in this study is thus thoughtto reflect the variable contributions of 137Cs sourcesin Bavaria.For a convenient comparison, allratios were corrected to March 11, 2011, which is the date of thelast major radiocesium source affecting the environment (Fukushimanuclear accident). We propose this date as the reference for decaycorrection in 135Cs/137Csratios for the discrimination of the two radiocesium sources, we systematicallycompared our values with all reported 135Cs/137Cs ratios over the last decades  and plotted the measuredvalue with their locations, as shown in 135Cs/137Cs ratios obtained from countries that experiencedmajor nuclear accidents  are much lower with a narrowrange than those obtained from regions far away from any locationswith releases from nuclear accidents . By contrast, the 135Cs/137Cs ratios in samples from other European countries  were in between the fingerprint signature ofweapons-137Cs and reactor-137Cs , thus indicating mixing.In the case of the present study, there is a significant gap in therange of 135Cs/137Cs ratios between the minimumvalue (0.67) and the maximum value (1.97) measured in Bavarian wildboars. This inconsistency may be explained by the uptake of radiocesiumfrom mixed sources.To better apply the measured 135Cs/137Cs ratioin different countriesmay be affected by the weapons’ and test’s characteristics.53 For instance,the median ratio in the USA  is about 41% higher than that in Canada . Therefore, we here adopted the ratio obtained fromthe historical human lung tissue (Vienna)54 as the 135Cs/137Cs fingerprint for centralEurope, which likely represents the integral signature in a Europeansetting. These samples were collected in the 1960s, so its 135Cs/137Cs ratio is only governed by weapons fallout . In this scenario, the higher 135Cs/137Cs ratios observed in Kronach , compared to that in otherregions, and the relatively low 137Cs contamination inwild boars together suggest that, here, Chornobyl-137Cswas not the dominant source in this region. By contrast, some regionshave high 137Cs contamination and a relatively low mean 135Cs/137Cs ratio, such as Cham , which implies thatChornobyl-137Cs mostly accounts for the local 137Cs contamination. To better display the spatial distribution of 135Cs/137Cs ratios in the study area, we plottedthe measured values on the 137Cs deposition map derivedfrom BfS data .The 135Cs/137Cs ratios observed in wild boars are negatively relatedto the amount of Chornobyl-137Cs ingested from their habitat,we expected that there would be a negative relationship between the 137Cs activity concentrations and 135Cs/137Cs ratios in samples collected from Bavaria. To validate this idea,we applied the regression analysis for two arrays, and as expected,we found a negative correlation (P < 0.01). However,the 135Cs/137Cs ratios estimated by the fittingcurve do not match the measured value in most cases, and the R2 is only about 0.25. Metabolic variabilitiesmay explain why certain specimens of wild boars may accumulate ormaintain 137Cs levels more efficiently than others, whereasthe 135Cs/137Cs ratio will be unaffected, leadingto such a mismatch between the 137Cs activity concentrationand the ratio. Nevertheless, statistical analysis showed no significantdifference in either the 137Cs activity concentration orthe 135Cs/137Cs ratio among our wild boars’characteristics .Considering that the 137Cs is rapidly adsorbed onto (clay) mineralsand graduallymigrates vertically,55 until it reaches further potentialaccumulators such as underground fungi.27 The global fallout from atmospheric nuclear explosions peaked in1964, more than 20 years prior to Chornobyl’s fallout. Dueto the time span between both events, the actual 137Csinventory in greater soil depths differs from that in the topsoilacross the regions. Moreover, the availability of 137Csin subterranean species  may further amplify the differencesin the spatial pattern of 137Cs as its downward migrationrate and accumulation process varies with the local environmentalconditions, such as the increased likelihood for precipitation inthe alpine regions of Southern Bavaria. Thus, another interpretationfor the poor correlation between 137Cs activity concentrationsand 135Cs/137Cs ratios is that the regionaldifference in 137Cs availability complicates the correlationof two variables using a single model. In other words, different contributionpercentages of two independent 137Cs sources may resultin identical 137Cs contamination levels, thus weakeningthe explanation of the observed phenomenon using a single regressionmodel. This hypothesis has been tested in Figure S7 (part 10 of the Supporting Information) where significantdifferences in the relationship between 137Cs levels and 135Cs/137Cs ratios (sample size ≥ 2) wereobserved in the studied regions.It is well-known from Chornobyl and Fukushima thattopsoil 137Cs sources  have mixed inthe Bavarian soil, the release maxima of which were about 20–30years apart. To visualize this mixing process more intuitively, wepropose a conceptual mechanism diagram , downward migration in the pedosphere depends on thebiogeochemical system’s complexity , resulting in significant regional differencesin 137Cs migration, mixture, and accumulation. The situationfor wild boars is further complicated by their mobility, which allowsthem to cover areas with higher and lower contamination, possiblywith a variable mixing degree. Instead of focusing on food sources, it hence appears more practical to focus on the actual,integral contamination in the wild boar itself as it represents thefinal outcome of an equation with virtually countless factors .We propose that two  diagram 5. Up unt137Cs and Chornobyl-137Cs in wildboars using the characteristic 135Cs/137Cs ratios(Table S7). With the signature of fallout and Chornobyl , the cesium from fallout (Ps) can be estimated by putting the measuredratio (Rs) into the equation Ps = (Rs – Rc)/(Rf – Rc). Moreover, we plotted the 137Cs activityconcentrations and radiocesium contributions in wild boars for allsamples on the basis of the 137Cs deposition map derivedfrom BfS . Compared with the Chornobyl-137Cs contributions in top soils estimated by plutonium isotopes(ca. 60–90%),32 although there isa good linear relationship between the two data sets , the predicted Chornobyl contributionpercentages estimated by cesium isotopes are found slightly lower.Particularly in northern Bavaria (region A), the contributions fromChornobyl-137Cs are significantly lower than in the restof Bavaria , but the 137Cs contamination level in wild boars still exceeds the regulatorylimit in 62.5%. Although it is still debated whether or not the persistent 137Cs contamination in Bavarian boars spanning decades hasits roots in fungal species, we can now present isotopic evidencethat the atmospheric weapons fallout that has been residing in ourenvironment for more than 60 years is still affecting radioactivecontamination levels in wild boars. More unexpectedly, in Cham (regionC), Kelheim (region E), and Garmisch-Partenkirchen (region K), wefound that about 40–50% of 137Cs contamination insome wild boar samples originated from the weapons fallout. Therefore,our findings provided visual evidence for the interpretation thatpersistent 137Cs contamination in Bavarian wild boars isalso related to the six decades old global weapons fallout in ourecosystem.The mixing model showed that the median 137Cs contribution to the radioactive contamination in Bavarian wildboars, we calculated the weapons-137Cs activity concentrationby using the estimated contribution percentage and the total 137Cs concentration (Table S10).As shown in 137Cs remains the overall more significantcontributor to wild boar contamination, about 25% of wild boar samplesexhibit such significant contributions from weapons-137Cs that the fraction of weapons-137Cs alone is high enoughto exceed the European regulatory limit (600 Bq·kg–1). Spatially, these samples originated from regions A (n = 1), C (n = 3), E (n = 1), I(n = 2), and K (n = 5), respectively, with the relativecontribution of weapons-137Cs between 12% (region C) and29% (region K). On the other hand, a similar pattern is observed viaan alternative pathway in certain regions where the total contaminationlevel is relatively low (<3000 Bq·kg–1)but the relative contribution of weapons-137Cs is high. In anycase, no assumptions about the contribution of old weapons-137Cs should be made just by a given 137Cs deposition. Forexample, both of the above patterns were observed in region K, wherethe 137Cs deposition exceeds 30,000 Bq·m–2. In contrast, the contribution of weapons-137Cs to thetotal activity concentration was found to be below 20% in region Bwith a relatively low 137Cs deposition inventory (∼9000Bq·m–2). We suggest that this phenomenon maybe caused by a certain randomness in the food selection and uptakeof 137Cs by wild boars since regional 137Csavailability and environmental factors can generate multiple combinationsof the two 137Cs legacies. Despite great challenges inrevealing a detailed picture of the persistently high 137Cs contamination in Bavarian wild boars, our findings demonstratethat the six decades old weapons-137Cs alone is still capableof yielding significant contamination levels that exceed the regulatorylimit in wild boars today. Therefore, for the scientific communityof radioecology, the disproportionate weapons-137Cs contributionmay provide new insights into the use of effective half-lives to describe 137Cs behavior in the terrestrial environment as the valueis also governed by the various 137Cs legacy sources andtheir mixing process in the region.In order to further evaluate the weapons-ectively7b. Furth135Cs/137Cs as adirect isotopic fingerprint, we were able to show that the mixed 137Cs legacy from Chornobyl and nuclear weapons fallout isresponsible for the persistence of high contamination levels. Withthe effective half-lives of 137Cs in wild boars being longerthan the physical half-life of 137Cs, this phenomenon sometimesmust have appeared like a violation of the law of radioactive decay.By implementation of a mixing model, our findings demonstrate thatweapons-137Cs contributed between 12 and 68% in those samplesthat exceeded the regulatory limit. The unusually high levels in wildboars not only legitimate rigid regulatory control for human foodsafety, they are partly (21% of samples) also above the conservativescreening benchmark levels  for boars themselves.21Our work reveals deeperinsights into the notorious radiocesiumcontamination in Bavarian wild boars beyond the total radionuclidequantification only. Using 137Cs has resided in the environmentfor at least 60 years  and its contribution as a pollutant of central Europe has generallybeen regarded as negligible compared to that of Chornobyl, our workprovides the forensic evidence showing that this underestimated 137Cs legacy can accumulate in certain environmental mediaalong with more recent reactor-137Cs releases. Both contributorsform an intense 137Cs source that exceeds the contributionfrom any singular, yet dominant source in the area (like Chornobylin the case of Bavaria). This mixed source is the main supplier towild boars in the winter season and in turn the main reason for thepersistent 137Cs contamination in Bavarian wild boars.After several single-source studies, this is the first time that 135Cs/137Cs has been used to demonstrate the accumulationof radiocesium legacies from different nuclear sectors in ecosystemspecies and that the effects of such “superimposed”radioactive contaminations have been caught while they are transmittedthrough the food chain of biological communities, eventually to humanconsumers of game meat. The recognition of this deleterious environmentalimpact thus provides new insights for the radioecological researchcommunity as policy makers may need to consider a multitude of 137Cs contributors to the total inventory in an ecosystem andtake them all into account for holistic risk assessment.Althoughthe weapons-137Cs release from nuclear accidents or nuclearexplosions will add to the historical 137Cs legacy overtime and further aggravate the current contamination situation. Accordingto the International Atomic Energy Agency, 56 nuclear power reactorsare currently under construction across the world,59 thus underscoring the role of nuclear power in the futureglobal energy portfolio. With the intensifying war between Ukraineand Russia, much concern has been expressed about the terrible consequencesof a nuclear war or a combat-triggered nuclear accident. Once released,radiocesium will remain in the environment for generations and impactfood safety immediately and, as shown in our study, for decades. Anyadditional releases will cause further accumulation and mixing witholder sources, making it necessary to understand the underlying mechanismsof the biogeochemical cycling of radiocesium. For example, the impactof soil properties on mixing of different radiocesium sources hasnot yet been understood sufficiently. Consequently, more efforts arestill needed to better understand the sources, inventories, environmentalfates, and ecological risks of radiocesium.Anyfuture 135Cs/137Cs for the distinction of present or future 137Cs sources. Possible applications include other environmental 137Cs repositories such as mushrooms,61 honey,44 or sediments.62 Lastly, this study illustrates that strategic decisionsto conduct atmospheric nuclear tests 60–80 years ago stillimpact remote natural environments, wildlife, and a human food sourcetoday. A similar, long-lasting consequence can be expected from Chornobyl-137Cs deposited in central Europe that will have a longer impactthan the relatively short ecological half-lives of 137Cssuggest.Having proven apowerful tool in complex radioecological questions,this study highlights the outstanding potential of