Classification of transformed anchovy products based on the use of element patterns and decision trees to assess traceability and country of origin labelling

Physicochemical, Antioxidant and Mineral Composition of Cascara Beverage Prepared by Cold Brewing

Research background

Cascara, the dried husk of coffee cherries, has attracted attention as a potential beverage due to its unique flavour profile and potential health benefits. Traditionally, cascara is prepared using hot brewing methods. However, recent interest in cold brewing methods has led to research on how temperature affects the functional properties of cascara beverages.

Experimental approach

Colour (CIE L*a*b*), total dissolved solids and titratable acidity were determined in cascara beverages prepared at 5, 10, 15 and 20 °C. The concentration of phenols and flavonoids, as well as antioxidant properties were evaluated using spectrophotometric methods. Caffeine, chlorogenic acid and melanoidins were quantified by HPLC. The mineral composition was determined using inductively coupled plasma mass spectrometry (ICP-MS). The results were compared with a hot-brewed cascara beverage.

Results and conclusions

Cold brewing resulted in significantly higher concentrations of total phenolic compounds, expressed as gallic acid equivalents (ranging from 309 to 354 mg/L), total flavonoids, expressed as quercetin equivalents (11.8-13.6 mg/L), and caffeine (123-136 mg/L) than the hot-brewed cascara beverage sample (p<0.05). Temperature had a noticeable effect on most variables, although the effect appeared to be random. In particular, concentrations of caffeine (p<0.01) and copper (p<0.001) were highest in beverages prepared at 20 °C and decreased with decreasing brewing temperature. Multivariate analysis showed that minerals (As, Co, Mn, Sn, Mg and Ca), hue and phenolic concentration contributed to the first principal component, which mainly differentiated the hot-brewed sample. Antioxidant-related variables, total titratable acidity and Se contributed most to the second principal component, which facilitated the separation of samples brewed at 5 °C.

Novelty and scientific contribution

To our knowledge, this is the first study to suggest that temperature affects the functional properties of cascara beverage produced by the cold brewing method. Experimental evidence supports the existence of a direct proportionality between caffeine and copper concentrations and brewing temperature.

Artificial intelligence predictability of moisture, fats and fatty acids composition of fish using low frequency Nuclear Magnetic Resonance (LF-NMR) relaxation

Moisture, fats and fatty acids of 14 pelagic and demersal fishes were measured by conventional chemical analysis to relate these with the proton relaxation using Low Frequency Nuclear Magnetic Resonance (LF-NMR). Artificial intelligence was used to assess the predictability of composition using six relaxation parameters of LF-NMR. Multiple linear regression showed significant prediction for moisture (W) (P < 0.00001), total fat (F) (P < 0.0001), ω-6 fatty acid (O6) (P < 0.001), saturated fats (SF), fatty acids (FA), mono-unsaturated fatty acids (MU) and ω-3 fatty acid (O3) (P < 0.01). However, the highest regression coefficient was observed for water (R2: 0.490) and the lowest was observed for SF (R2: 0.224). The low regression coefficients indicated strong non-linear relationships exited between LF-NMR parameters and composition. However, decision tree showed higher regression coefficients for all compositions considered in this study (R2:0.780-0.694). In addition, it provided simple decision rules for the prediction of composition. General Regression Neural Network provided the highest prediction capability (R2:0.847-1.000 for training and 0.506-0.924 for validation).

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-024-05977-3.

Elemental profiles of swine tissues as descriptors for the traceability of value-added Italian heavy pig production chains

The increasing demand for reliable traceability tools in the meat supply chain has prompted the exploration of innovative approaches that meet stringent quality standards. In this work, 57 elements were quantified by inductively coupled plasma mass spectrometry and direct mercury analysis in 80 muscle and 80 liver samples of Italian heavy pigs to investigate the potential of new tools based on multi-elemental profiles in supporting value-added meat supply chains. Samples from three groups of animals belonging to the protected designation of origin (PDO) Parma Ham circuit (conventionally raised; raised with genetically modified organism (GMO)-free feeds; raised with GMO-free feeds plus the supplementation of omega-3 polyunsaturated fatty acids (n-3 PUFA)) and a fourth group of samples from animals not compliant with the PDO Parma Ham production process were analyzed. Hierarchical cluster analysis allowed for the identification of three macro-clusters of liver or muscle samples, highlighting some inhomogeneities among the target groups. Following SIMCA analysis, better classification models were obtained by using liver elemental profiles (95% correct classification rate), with the highest classification accuracy observed for GMO-free livers (100%). The elements contributing the most to the separation of livers by class membership were La, Ce, and Pb for conventional, Li, Cr, Fe, As, and Sr for GMO-free + n-3 PUFA, and Lu for non-PDO samples. Given these findings, the analysis of the elemental profiles of pig tissues can be regarded as a promising method to confirm the declared pig meat label attributes, deter potential complex fraud, and support meat traceability systems.

Unravelling Metabolic Heterogeneity of Chinese Baijiu Fermentation in Age-Gradient Vessels

Fermentation vessels affect the characteristics of food fermentation; however, we lack an approach to identify the biomarkers indicating fermentation. In this study, we applied metabolomics and high-throughput sequencing analysis to reveal the dynamic of metabolites and microbial communities in age-gradient fermentation vessels for baijiu production. Furthermore, we identified 64 metabolites during fermentation, and 19 metabolites significantly varied among the three vessels (p < 0.05). Moreover, the formation of these 19 metabolites were positively correlated with the core microbiota (including Aspergillus, Saccharomyces, Lactobacillus, and Bacillus). In addition, ethyl lactate or ethyl acetate were identified as the biomarkers for indicating the metabolism among age-gradient fermentation vessels by BP-ANN (R2 > 0.40). Therefore, this study combined the biological analysis and predictive model to identify the biomarkers indicating metabolism in different fermentation vessels, and it also provides a potential approach to assess the profiling of food fermentations.

Investigating machine learning models in predicting lake water quality parameters as a 3-year moving average

Lake water quality plays a vital role in the lake ecosystem, including biotic (for living creatures, such as plants, animals, and micro-organisms) and abiotic interactions. In this research, various types of machine learning (ML) methodologies, such as classification and regression tree (CART), chi-squared automatic interaction detector (CHAID), C5 tree, quick, unbiased, and efficient statistical tree (QUEST), along with multilayer perceptron (MLP) neural network, and radial basis function (RBF) neural network, are employed to predict the concentration of water quality parameters (P, EC, TDS, pH, DO, NH3, SO4, and θ). Lake Erie is situated at the international border of the USA and Canada. The C5 tree and QUEST tree are used to classify data and predict the number of groups, while the other methods are used to predict the concentration of water quality parameters in the form of a 3-year moving average. The greater matching between the observed and predicted data of dissolved oxygen (NSE = 0.978, bias = 0.126) shows that the CART decision tree has higher accuracy in correctly detecting the concentration of this parameter. The C5 tree could identify 33 groups correctly out of 36 total groups, which shows better accuracy for the C5 tree in classifying the data for this parameter.

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.