The characteristic aromas at each stage of chocolate processing change in quantity and quality depending on the cocoa variety, the chemical composition of the beans, the specific protein storage content, and the polysaccharides and polyphenols determining the type and quantity of the precursors formed during the fermentation and drying process, leading to the formation of specific chocolate aromas in the subsequent roasting and conching processes. Bean aroma is frequently profiled, identified, and semiquantified by headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPMEGC-MS) and by gas chromatography olfactometry (GC-O). In general, the flavors generated in chocolate processing include fruity, floral, chocolate, woody, caramel, earthy, and undesirable notes. Each processing stage contributes to or depletes the aroma compounds that may be desirable or undesirable, as discussed in this report.

Slaughterhouse solid waste is one of the sources of greenhouse gas (GHG) today. Crop residue decomposition or incineration has a great impact on global warming. Therefore, it is urgent to study the possibility of better environmentally friendly approaches to solid waste management and its safe disposal. The digestion of this type of solid waste in a decomposing process from organic content allows the recovery of valuable resources (such as biogas) and the use of the digestate in various fertilizer industries. In this study, two substrates were studied to determine their biomethane (BMP) potential in anaerobic digestion. The substrates were fermented and digested anaerobically and biogas production was measured. Methane yield of the slaughterhouse substrates had a lower methane yield between 232.2 and 250.8 mL/gVS and 53.6 to 57.9% biodegradability. Harvest substrates produce between 167.1 and 274.9 mL/gVS with a biodegradability of 39.1 to 64.3%. Co-digestion of both substrates at a ratio of IS 1:2 (RR:WS 3:1) generated a higher yield 289.1 ml/gVS and 66.9%. biodegradability of A kinetic analysis was carried out using Gompertz models, transfer and logistic function for methane production biodegradation.

The anaerobic digestion (AD) of rumen residues and wheat straw in different proportions of inoculum (66.67, 50, 33.33 %V/V) to substrate and their co-digestion potential have been exhaustively studied in this research. It explores variation of feedstock characteristics such as biodegradability and methane potential during AD and anaerobic co-digestion (ACoD) of rumen residues (RR) with wheat straw (WS), under mesophilic conditions. Comparative performance was made with a organic load rate of 18 gVS/L (with 2:1, 1:1 and 1:2 g/g VS of inoculum to substrate). Among different kinetic models studied (modified Gompertz model, transfer model and logistic function), the AD and ACoD of substrates showed better fit to Gompertz model (R2: 0.977–0.997) indicating variation in lag phase and methane production rate depending on the substrate characteristics. During AD, the methane yield improved as the inoculum ratio increased, however, there was no significant difference in ACoD yield from RR to WS for the inoculum to substrate ratios studied, due to the synergistic effect as a result. of greater biodegradability and optimal conditions (such as the C/N ratio). A comparison of methane generation means indicated that maximum methane production can be achieved by mixing rumen residue (75 %) and wheat straw (25 %) with a C/N ratio of (38.15).

Food fraud is a persistent global threat estimated to cost the food industry over USD 30 billion annually. The integration of artificial intelligence (AI) with analytical instrumentation has generated significant research activity directed at developing detection systems capable of identifying adulteration, mislabeling, and substitution across diverse food matrices. This systematic review critically examines the extent to which AI-assisted instrumental technologies contribute to food fraud prevention, and identifies the structural limitations that constrain their real-world implementation. A systematic search of peer-reviewed literature published between 2021 and 2026 yielded 72 eligible studies after application of predefined inclusion criteria. Studies were required to report quantitative performance metrics (accuracy, R², RMSE, AUC, sensitivity, specificity), describe methodological limitations, and mention laboratory or industrial implementation contexts. Data were extracted into a structured seven-sheet workbook covering study characteristics, instrumental technologies, AI architectures, performance metrics, industrial validation status, implementation evidence, and methodological quality. The corpus reveals a systematic pattern of high reported analytical accuracy—frequently exceeding 95% and in many cases reaching 100%—under controlled laboratory conditions. However, 75% of studies (54/72) conducted no external validation, 100% of studies reported no pilot-scale or routine monitoring application, and no study achieved inter-laboratory validation. The predominant technology was NIR spectroscopy (26/72 studies, 36%), followed by gas chromatography-based systems (14/72, 19%) and electronic noses (8/72, 11%). Classical machine learning—predominantly SVM, Random Forest, and ANN—dominated methodological approaches (43/72, 60%), with deep learning architectures accounting for 26% of studies. Technology Readiness Levels were unreported in 97% of studies. Methodological quality was predominantly moderate (42/72 studies scoring 3/5), with 19 studies scoring 2/5 and only one achieving the maximum score. This review identifies a structural gap between detection and prevention as the central finding: the scientific literature consistently demonstrates high analytical precision in laboratory settings while providing minimal evidence of real-world industrial deployment, regulatory integration, or measurable impact on the prevention of food fraud events. The findings demonstrate that the limitation is not primarily technological but systemic, highlighting the need for a paradigm shift from performance-driven research toward validation-driven, deployment-oriented frameworks.

The economic impact of food fraud is difficult to quantify precisely, because fraud is structurally designed to evade detection; available estimates are indirect projections rather than direct forensic accounting and are commonly cited in the range of USD 10–15 billion annually. The integration of artificial intelligence (AI) with analytical instrumentation has generated a rapidly expanding body of research aimed at detecting adulteration, mislabeling, and substitution across food matrices. This systematic review examines the extent to which AI-assisted instrumental technologies contribute to food fraud prevention (as distinct from laboratory detection) and characterizes the structural factors that constrain real-world translation. A systematic search of the peer-reviewed literature published between 2021 and 2026 yielded 83 eligible records (80 primary studies and 3 review articles) after applying predefined inclusion criteria. Data were extracted into a structured seven-sheet workbook covering study characteristics, instrumental technologies, AI architectures, performance metrics, industrial-validation status, implementation evidence, and methodological quality. The corpus shows consistently high reported analytical accuracy under controlled laboratory conditions (median of extractable classification accuracies ≈ 99–100%; ≥95% in 86% of studies with an extractable value). At the same time, 68 of 83 studies (82%) reported no external validation, no study (0/83) achieved inter-laboratory validation, no study documented routine-monitoring application, and only one study reported testing in a genuine industrial environment. The most frequently featured platforms were NIR spectroscopy and electronic-nose arrays (each featuring in 30/83 studies, frequently in data-fusion combinations), followed by gas-chromatography-based systems (16/83) and hyperspectral imaging (13/83). Classical machine learning predominated (57/83 studies coded as classical ML, with a further 11 hybrid ML/DL designs and 12 deep-learning-only designs). A direct statistical comparison found no significant difference in reported accuracy between classical-ML and deep-learning studies (median 100% vs. 98.2%; Mann–Whitney U test, p = 0.16). A pre-specified test of the hypothesis that high reported accuracy is itself a marker of overfitting was not supported by the corpus: reported accuracy was not negatively associated with external-validation status (Fisher’s exact p = 0.51) or with methodological-quality score (Spearman ρ = 0.15, p = 0.23). Methodological quality was predominantly moderate (49/83 scored 3/5; 22 scored 2/5; 11 scored 4/5; one study scored 5/5), and 19/83 (23%) carried a high risk of bias. The review’s central observation—a measurable gap between demonstrated laboratory detection and evidenced real-world prevention—is well supported by the deployment, inter-laboratory, and routine-monitoring data. We deliberately separate this strongly evidenced conclusion from weaker inferences (e.g., the overfitting hypothesis) that the corpus cannot currently establish, and we outline a validation-driven, deployment-oriented research agenda.