Origin Determination of Walnuts (Juglans regia L.) on a Worldwide and Regional Level by Inductively Coupled Plasma Mass Spectrometry and Chemometrics

Exploring Deep Learning Approaches for Walnut Phenotype Variety Classification

The efficient classification of agricultural commodities like walnuts is crucial for assessing quality and managing the supply chain. This scholarly article analyses various deep learning and data science methods for walnut fruit classification. For this purpose, first, a dataset comprising images of walnuts from Chandler, Fernor, Howard, and Oguzlar varieties was collected. Two different experiments were conducted. In the first experiment, only deep learning methods were used as classifiers. In this experiment, InceptionV3 demonstrated the highest classification accuracy, followed by VGG-19 and VGG-16. In the second experiment, deep learning algorithms were used for feature extraction, followed by support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (k-NN) algorithms for classification. These models resulted in an improvement in overall success rates. The most effective classification was achieved with the InceptionV3 and LR combination, achieving the highest success rate. These results highlight the efficacy of deep learning methodologies in swiftly and accurately classifying agricultural products based on visual information, indicating the potential to strengthen classification systems within the agricultural sector.

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.

Elemental Profiling for the Detection of Food Mixtures: A Proof of Principle Study on the Detection of Mixed Walnut Origins Using Measured and Calculated Data

Element profiling is a powerful tool for detecting fraud related to claims of geographical origin. However, these methods must be continuously developed, as mixtures of different origins in particular offer great potential for adulteration. This study is a proof of principle to determine whether elemental profiling is suitable for detecting mixtures of the same food but from different origins and whether calculated data from walnut mixtures could help to reduce the measurement burden. The calculated data used in this study were generated based on measurements of authentic, unadulterated samples. Five different classification models and three regression models were applied in five different evaluation approaches to detect adulteration or even distinguish between adulteration levels (10% to 90%). To validate the method, 270 mixtures of walnuts from different origins were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Depending on the evaluation approach, different characteristics were observed in mixtures when comparing the calculated and measured data. Based on the measured data, it was possible to detect admixtures with an accuracy of 100%, even at low levels of adulteration (20%), depending on the country. However, calculated data can only contribute to the detection of adulterated walnut samples in exceptional cases.

Exploring the potential of high-resolution LC-MS in combination with ion mobility separation and surrogate minimal depth for enhanced almond origin authentication

Almonds (Prunus dulcisMill.) are consumed worldwide and their geographical origin plays a crucial role in determining their market value. In the present study, a total of 250 almond reference samples from six countries (Australia, Spain, Iran, Italy, Morocco, and the USA) were non-polar extracted and analyzed by UPLC-ESI-IM-qToF-MS. Four harvest periods, more than 30 different varieties, including both sweet and bitter almonds, were considered in the method development. Principal component analysis showed that there are three groups of samples with similarities: Australia/USA, Spain/Italy and Iran/Morocco. For origin determination, a random forest achieved an accuracy of 88.8 %. Misclassifications occurred mainly between almonds from the USA and Australia, due to similar varieties and similar external influences such as climate conditions. Metabolites relevant for classification were selected using Surrogate Minimal Depth, with triacylglycerides containing oxidized, odd chained or short chained fatty acids and some phospholipids proven to be the most suitable marker substances. Our results show that focusing on the identified lipids (e. g., using a QqQ-MS instrument) is a promising approach to transfer the origin determination of almonds to routine analysis.

Superior Valorisation of Juglans regia L. Leaves of Different Maturity through the Isolation of Bioactive Compounds

Extracts rich in bioactive compounds from natural sources have received great interest due to their great impact on human health. The aim of this research is focused on the obtaining and characterization of several extracts from Juglans regia L. leaves in four different maturity phases: young green leaves (YGL), green leaves (GL), mature green leaves (MGL), and yellow leaves (YL), using different solvents: ethanol (e), water (w), or water:ethanol (1:1 (v/v)-m) by employing several methods: magnetic stirring (MS), ultrasound-assisted (UA), as well as maceration (M). The obtained extracts were quantitatively evaluated through spectrophotometric methods: Total Polyphenol Content (TPC-Folin-Ciocalteu assay) and Total Antioxidant Capacity (TEAC assay). Phytochemical screening by means of Fourier-Transform Ion-Cyclotron-Resonance High-Resolution Mass Spectrometry (FT-ICR-MS) indicated the presence of 40 compounds belonging to different phytochemical classes: phenolic acids, flavonoids, flavones, flavanones, flavonones, flavanols, vitamins, tereponoid, steroid, anthocyanidin, and other compounds. Based on TPC and TEAC assays, the water-ethanol mixture was found to be the proper extraction solvent, with the best results being obtained for YL plant material: 146.29 mg GAE/g DM (TPC) and 11.67 mM TE/g DM (TEAC). This type of extract may be used in various domains, such as the cosmetics industry, the biomedical field, and/or the design of functional foods, relying on their phytochemical composition.

Techniques for Food Authentication: Trends and Emerging Approaches

Food producers and retailers are obliged to provide correct food information to consumers; however, despite national and international legislation, food labels frequently contain false or misleading statements regarding food composition, quality, geographic origin, and/or processing [...]

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products' and Foods' Origin

Fraudulent practices are the first and foremost concern of food industry, with significant consequences in economy and human's health. The increasing demand for food has led to food fraud by replacing, mixing, blending, and mislabeling products attempting to increase the profits of producers and companies. Consequently, there was the rise of a multidisciplinary field which encompasses a large number of analytical techniques aiming to trace and authenticate the origins of agricultural products, food and beverages. Among the analytical strategies have been developed for the authentication of geographical origin of foodstuff, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) increasingly dominates the field as a robust, accurate, and highly sensitive technique for determining the inorganic elements in food substances. Inorganic elements are well known for evaluating the nutritional composition of food products while it has been shown that they are considered as possible tracers for authenticating the geographical origin. This is based on the fact that the inorganic component of identical food type originating from different territories varies due to the diversity of matrix composition. The present systematic literature review focusing on gathering the research has been done up-to-date on authenticating the geographical origin of agricultural products and foods by utilizing the ICP-MS technique. The first part of the article is a tutorial about food safety/control and the fundaments of ICP-MS technique, while in the second part the total research review is discussed.

Population identification strategies for counterfeit coin detection

Advances in manufacturing, 3-d imaging, and globalization have led to a rise in fraudulent coinage and a world-wide interest in coin authentication. Modern manufacturing methods allow the alloy, construction, and struck image of coins to be more readily reproduced. Larger coin denominations and efforts to reduce the cost of coining add additional incentive. Detection of fraudulent coinage can parallel authentication of food, beverages, and manufactured goods by studying product-inherent features. Reverse-quality-engineering provides clues to authenticity. One promising method is in the use of finite mixture models to compare individual measurements of groups of coins to assist in authentication. An example is provided using the coin weights of two groups of coins. Authentication of a Questioned set of coins is explored, comparing the weight population of Example coins drawn from circulation with the weights of a Questioned set drawn from an unknown origin. In the test, just over half of the Questioned coin set matched the distribution of the Example coin set. The other portion, nearly half of the coin sample, did not match the Example coins drawn from circulation. If this were combined with a similar analysis of other coin properties, similar results would help validate the finding. The example shows that groups of coins can be authenticated by using one or more measures of properties of populations of Questioned coins versus Example coins that are largely authentic.

Food Authentication of Almonds (Prunus dulcis Mill.). Fast Origin Analysis with Laser Ablation Inductively Coupled Plasma Mass Spectrometry and Chemometrics

Food fraud is a growing problem, especially misdeclaration due to regional price differences offering a wide field. Fast, powerful, and cost-effective analytical methods are therefore essential to counteract food fraud. The isotopolome is suitable for origin discrimination and was analyzed in this study using laser ablation inductively coupled plasma mass spectrometry (ICP-MS). A total of 250 almond samples from six countries and four crop years were analyzed and evaluated by chemometric methods. By using a ratio-based assessment, calibration problems were avoided and an origin predictive accuracy of 85.2 ± 1.2% was achieved. Compared to ICP-MS with solution nebulization, the analysis time could be reduced to about one-fifth.

Glass authentication: Laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS) for origin discrimination of glass bottles

Counterfeiting is an omnipresent issue, among others in the cosmetics industry or on the art market. Particularly in the case of very expensive perfumes or very valuable art objects, counterfeits are strongly represented and are steadily increasing. Typically, the content of perfumes is analyzed, but the bottle offers another level of authentication, as it is an essential part of the product. For art objects made of glass, glass is an essential part of the artwork and thus provides an important contribution to the authenticity of the object. In the present pilot study, we developed a laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS) method to classify glass using perfume bottles manufactured at different production facilities, Germany, India, Peru and Poland as an example. Using minimally invasive laser ablation invisible to the eye, we were able to detect counterfeit flacons without having to open the vials. A total of 63 elements could be recorded during method development. After statistical evaluation (t-test, ANOVA, principal component analysis (PCA)), 15 (Li, Na, Al, Ti, V, Co, Rb, Sr, Mo, Ba, La, Ce, Pr, Er and Pb) significant marker elements were extracted from the data sets to differentiate the different glass origins. By using LDA, six different production sites from four different countries could be differentiated over a sample period of six months with a prediction accuracy of 100%.

Determination of the Geographical Origin of Walnuts (Juglans regia L.) Using Near-Infrared Spectroscopy and Chemometrics

The prices of walnuts vary according to their geographical origin and, therefore, offer a financial incentive for adulteration. A reliable analysis method is required to quickly detect possible misdeclarations and thus prevent food fraud. In this study, a method to distinguish between seven geographical origins of walnuts using Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics as a fast, versatile, and easy to handle analytical tool was developed. NIR spectra of 212 ground and afterwards freeze-dried walnut samples, harvested in three consecutive years (2017-2019), were collected. We optimized the data pre-processing by applying and evaluating 50,545 different pre-processing combinations, followed by linear discriminant analysis (LDA) which was confirmed by nested cross-validation. The results show that in the scope of our research minimal pre-processing led to the best results: By applying just multiplicative scatter correction (MSC) and median centering, a classification accuracy of 77.00% ± 1.60% was achieved. Consequently, this complex model can be used to answer economically relevant questions e.g., to distinguish between European and Chinese walnuts. Furthermore, the great influence of the applied pre-processing methods, e.g., the selected wavenumber range, on the achieved classification accuracy is shown which underlines the importance of optimization of the pre-processing strategy.