Drying is a key method for food preservation; however, conventional techniques often lead to the degradation of bioactive compounds, compromising nutritional quality. This systematic review, following the PRISMA protocol, examines emerging food drying technologies designed to enhance process efficiency while preserving nutritional and functional properties. A bibliometric analysis was conducted to identify research trends from 2014 to 2024. Searches were performed in the Scopus and Web of Science databases accessed on 17 January 2025, including only original research articles published in English focusing on food drying applications. Reviews, editorials, and studies unrelated to the food sector were excluded. Due to the technological nature of the outcomes, a formal risk of bias assessment was not applicable. This review highlights several emerging drying technologies, such as microwave, radiofrequency, infrared, ultrasound, freeze-drying, and cold plasma. The qualitative synthesis indicates these technologies improve the retention of phenolics, flavonoids, and vitamins, thus enhancing nutritional stability. Nevertheless, challenges remain in industrial-scale implementation, particularly regarding the economic feasibility and optimization of operational parameters. This review received no funding and was not registered in any public database. The findings underscore the need for continued research to develop sustainable and functional dried food products that meet current market demands and consumer expectations.

This study aimed to develop and validate models to develop and validate models for screening for high concentrations adulterations in cocoa powder using high-performance liquid chromatography (HPLC) and multivariate techniques. Scanning electron microscopy (SEM) complemented the study, which involved binary mixtures of pure cocoa powder mixed with sugar, cocoa husk, coffee husk, or corn flour at various adulteration levels, along with 93 commercial samples from 12 countries. SEM assessed the morphology of the samples, and HPLC quantified methylxanthines and flavonols, and the resulting data were used in multivariate statistics. SEM revealed that particle size and similarity posed challenges for effective visual screening, underscoring the necessity for the proposed chromatographic and chemometric approach. Principal component analysis (PCA) effectively separated pure samples from adulterated ones. Linear discriminant analysis (LDA) distinguished between pure samples and those adulterated with sugar, coffee husks, and corn flour, achieving balanced accuracies exceeding 78%. Partial least squares (PLS) regression predicted the levels of adulteration for these three adulterants, with a coefficient of determination (R2) greater than 0.82. However, the intrinsic chemical similarity between pure cocoa and cocoa husks made it difficult to classify and predict outcomes for this particular byproduct. When applying the models to commercial samples, significant profile deviations was observed, highlighting their potential as an initial screening tool for unusual compositions.