Mycotoxins are frequently found in fruits and fruit juices. However, data about occurrence and fate of mycotoxins along the fruit juice processing chain are currently insufficient. Herein, a liquid chromatographic/tandem mass spectrometric (LC–MS/MS) multi-mycotoxin method was developed and applied to investigate the effect of technological unit operations on the fate of three of the most relevant mycotoxins along the processing chain for cloudy and clear apple juice, namely patulin (PAT), ochratoxin A (OTA), and alternariol (AOH). Raw juice obtained directly after dejuicing was spiked with the aforementioned mycotoxins at pilot-plant scale prior to subjecting it to different technological unit operations. Regarding clear apple juice production treatment with a pectinolytic enzyme preparation, and pasteurization were insignificant for mycotoxin reduction, but fining with subsequent filtration was effective, although the mycotoxins showed different affinity towards the tested agents. The most effective fining agent was activated charcoal/bentonite in combination with ultrafiltration, which removed OTA (54 µg/L) and AOH (79 µg/L) to not quantifiable amounts (limit of quantification (LOQ) 1.4 and 4.6 µg/L, respectively), while PAT was reduced only by 20% (from 396 to 318 µg/L). Regarding cloudy apple juice production, all studied processing steps such as centrifugation and pasteurization were ineffective in reducing mycotoxin levels. In brief, none of the common steps of clear and cloudy apple juice production represented a fully effective safety step for minimizing or even eliminating common mycotoxins. Thus, ensuring the sole use of sound apples should be of utmost importance for processors, particularly for those manufacturing cloudy juices.

Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes.

Through its suggestive name, non-targeted methods (NTMs) do not aim at a predefined "needle in the haystack". Instead, they exploit all the constituents of the haystack. This new form of analytical methods is increasingly finding applications in food and feed testing. However, the concepts, terms, and considerations related to this burgeoning field of analytical testing needs to be propagated for the benefit of ones associated in academic research, commercial development, and official control. This paper addresses the frequently asked questions around notations and terminologies surrounding NTMs. The widespread development and adoption of these methods also necessitates the need to develop approaches to NTM validation, i.e., evaluating the performance characteristics of a method to determine if it is fit-for-purpose. This work aims to provide a roadmap to approaching NTM validation. In doing so, the paper deliberates on the different considerations that influence the approach to validation and provides suggestions thereof.

Non-targeted methods (NTMs) require robust methods for comparing spectral data for reliable classification and identification. Traditional approaches using match factors reduce complex spectral relationships to single values, limiting their utility in quality assurance. This study presents an evaluation of spectral comparison methodologies, contrasting classical Mahalanobis distance (MD) with neural network approaches, namely, neural classification distance (NCD). Using matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry data from bacterial isolates, we systematically assessed these methods across varying levels of spectral variability. The MD approach exhibited consistent performance under controlled conditions but showed limitations with increasing spectral complexity. In contrast, the NCD demonstrated adaptability across all scenarios, revealing its capability in handling complex spectral relationships. Through this exemplary example, we present the mathematical framework for quantifying spectral variations and establish criteria for method selection in different analytical scenarios. This work provides a foundation for proposing data quality metrics in NTMs and offers practical implementations for routine quality assurance. The methodology developed here extends beyond mass spectrometry applications and contributes to the broader field of analytical quality control in complex spectral analysis.

Standardized and validated analytical methods are essential for reliable official control of food and feed, ensuring consumer health protection and legal compliance. The Official Collection of Methods of Analysis and Sampling (ASU) addresses the need for uniform testing procedures across Germany’s official control laboratories. It was established in 1980 and is currently published by the Federal Office of Consumer Protection and Food Safety (BVL). Here, we present the ASU’s origin, legal framework, organizational structure, and its processes for method validation and harmonization. The ASU encompasses over 1,000 methods across 6 topic-specific volumes, covering diverse analytical techniques and facilitating continuous advancement through expert working groups. It also serves as a key interface between national, European, and international standardization efforts. The ASU significantly contributes to a harmonized, high-quality official control, supporting legal certainty and effective consumer health protection in the evolving landscape of food and feed safety.

Non-targeted methods (NTMs) using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) show promise in bacterial resistance detection, yet temporal variations in spectral features pose significant challenges. These proteomic patterns, which characterize bacterial phenotypes and pathological functions, may vary over time due to bacterial adaptation, virulence, or resistance mechanisms, resulting in large prediction uncertainties and potentially degrading NTM performance. We present a comprehensive screening method to detect temporal changes in MALDI-TOF spectral patterns, demonstrated using methicillin-resistant and -susceptible Staphylococcus aureus (MRSA/MSSA) isolates collected over several years. Our approach combines convolutional neural networks (CNNs) with statistical methods, including significance testing, kernel density estimation, and receiver operating characteristics for dataset shift detection. We employ Gradient-weighted Class Activation Mapping (Grad-CAM) for post hoc feature description, enabling biochemical characterization of temporal changes. This analysis reveals crucial insights into the dynamic relationship between spectral data patterns over time, addressing key challenges in developing robust NTMs for routine applications.