An anti-interference colorimetric sensor array (CSA) technique was developed for the qualitative and quantitative detection of target heavy metals in corn oil. This method involves a binding mechanism that triggers changes in atomic energy levels and visible color changes. A custom-built olfactory visualization device was employed to gather spectral data, revealing distinct CSA color difference patterns. Subsequently, three pattern recognition algorithms were used to create an identification model for the target heavy metals. The results showed that the ACO-KNN (Ant Colony Optimization-K-Nearest Neighbor) model outperformed the other models, achieving accuracy rates of 90.28% and 89.58% for the calibration and prediction sets, respectively. The ACO-PLS (Partial Least Square) model was more stable with the lowest root mean square error of prediction (RMSEP), which were 0.1730 and 0.1180, respectively. The limit of detection (LOD) and quantification (LOQ) of Pb and Hg were (0.3, 0.6, 1.1 and 2.2) x 10-3 mg/L, respectively.

Scale-down models (SDM) are pivotal tools for process understanding and improvement to accelerate the development of vaccines from laboratory research to global commercialization. In this study, a 3 L SDM representing a 50 L scale Vero cell culture process of a live-attenuated virus vaccine using microcarriers was developed and qualified based on the constant impeller power per volume principle. Both multivariate data analysis (MVDA) and the traditional univariate data analysis showed comparable and equivalent cell growth, metabolic activity, and product quality results across scales. Computational fluid dynamics simulation further confirmed similar hydrodynamic stress between the two scales.

Quinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize the composition of quinoa, which is influenced not only by the variety type but also by the farming and processing conditions. In this study, we present a rapid and straightforward method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated under conventional and organic farming and processed through boiling and extrusion. The mass spectra of the different protein extracts were processed using the MALDIquant software (version 1.19.3), detecting 49 proteins (with 31 tentatively identified). Intensity values from these proteins were then considered protein fingerprints for multivariate data analysis. Our results revealed reliable partial least squares-discriminant analysis (PLS-DA) classification models for distinguishing between farming and processing conditions, and the detected proteins that were critical for differentiation. They confirm the effectiveness of tracing the agricultural origins and technological treatments of quinoa grains through protein fingerprinting by MALDI-TOF-MS and chemometrics. This untargeted approach offers promising applications in food control and the food-processing industry.

Wheat species with various ploidy levels may be different regarding their immunoreactive potential in celiac disease (CD), but a comprehensive comparison of peptide sequences with known epitopes is missing. Thus, we used an untargeted liquid chromatography tandem mass spectrometry method to analyze the content of peptides with CD-active epitope in the five wheat species common wheat, spelt, durum wheat, emmer, and einkorn. In total, 494 peptides with CD-active epitope were identified. Considering the average of the eight cultivars of each species, spelt contained the highest number of different peptides with CD-active epitope (193 ± 12, mean ± SD). Einkorn showed the smallest variability of peptides (63 ± 4) but higher amounts of certain peptides compared to the other species. The wheat species differ in the presence and distribution of CD-active epitopes; hence, the entirety of peptides with CD-active epitope is crucial for the assessment of their immunoreactive potential.

Principal component analysis (PCA) and partial least squares regression (PLS) were combined in this study to identify key material descriptors determining tabletability in direct compression and roller compaction. An extensive material library including 119 material descriptors and tablet tensile strengths of 44 powders and roller compacted materials with varying drug loads was generated to systematically elucidate the impact of different material descriptors, raw API and filler properties as well as process route on tabletability. A PCA model was created which highlighted correlations between different powder descriptors and respective characterization methods and, thus, can enable reduction of analyses to save resources to a certain extent. Subsequently, PLS models were established to identify key material attributes for tabletability such as density and particle size but also surface energy, work of cohesion and wall friction, which were for the first time demonstrated by PLS as highly relevant for tabletability in roller compaction and direct compression. Further, PLS based on extensive material characterization enabled the prediction of tabletability of materials unknown to the model. Thus, this study highlighted how PCA and PLS are useful tools to elucidate the correlations between powder and tabletability, which will enable more robust prediction of manufacturability in formulation development.

In the last decade, \"expressions\" of grape marc spirits aged in wooden barrels of characteristic amber color and complex sensory attributes have been introduced. Yet studies on constituents migrating from the barrel to the beverage are scarce, and their metabolic profile remains unexplored. Furthermore, the literature on the assessment of their antioxidant activity is limited. NMR metabolomics and spectrophotometry have been implemented in 38 samples to elucidate the impact of the aging procedure on the metabolites\' composition and establish whether these beverages exhibit antioxidant activity. Provenance was related to fusel alcohols, esters, acetaldehyde, methanol, saccharides, and 2-phenylethanol, while ethyl acetate and ethyl lactate contributed to discriminating samples of the same winery. Identified metabolites such as vanillin, syringaldehyde, and sinapaldehyde were related to the aging procedure. The maturation in the barrel was also associated with an increase in xylose, glucose, fructose, and arabinose. The antioxidant potential of the aged Greek grape marc spirits resulting from their maturation in oak barrels was highlighted. The metabolic profiling and antioxidant potential of aged Greek grape marc spirits were assessed for the first time. Finally, the enrichment of the aromatic region was noted with the presence of metabolites with a furanic and phenolic ring derived, respectively, from the polysaccharides\' degradation or the thermal decomposition of lignin.

Fermentation stage is a crucial factor for flavor profiles formation of hawthon wine. Thus, comprehensive knowledge of dynamic relationship between nonvolatile (NVOCs) and volatile aroma compounds (VOCs) from hawthorn wine at different fermentation stages was investigated by GC-MS and HPLC coupled with multivariate analysis. The increase of alcohols/esters/acids but decrease of terpenes/aldehydes/ketones was observed as fermentation extension. Specifically, OAV of ethyl acetate, ethyl caprylate, and ethyl caprate was > 50 from the 3rd day to 10th day, giving more fruity properties. Multivariate analysis showed that 1-hexanol, ethyl myristate, isobutyric acid, et al., were linked to the sensory evaluation of \"sweet\", \"floral\" and \"fruity\", and fructose, glucose and bitter amino acids were responsible for reduction of \"bitterness\" and \"astringency\". Additionally, VOCs were positively correlated with organic acids while negative to amino acids/soluble sugars, probably due to metabolization as precursors, providing references for aroma enhancement by regulating NVOCs precursors.

Gas chromatography ion mobility spectrometry (GC-IMS) is a robust and sensitive benchtop technique commonly used for non-target screening of volatile organic compounds. It has been applied to authenticity analysis by generating characteristic \"fingerprints\" of food samples, well suited for chemometric data analysis. This dataset contains headspace GC-IMS spectra from 50 monofloral honey samples from three different botanical origins, 18 acacia honeys (Robinia pseudoacacia), 19 canola honeys (Brassica napus) and 18 honeydew honeys (forest flowers). Honeys were sourced from the beekeepers directly or obtained from governmental food inspectors from Baden-Wuerttemberg, Germany. Authenticity was confirmed by pollen analysis in the framework of the official control of foodstuffs. The data was acquired using a setup based on an Agilent 6890N gas chromatograph (Agilent Technologies, Palo Alto, CA) and an OEM Standalone IMS cell from G.A.S Sensorsysteme m. b. H. (Dortmund, Germany). All samples were recorded in duplicates and spectra are presented as raw data in the .mea file format. The dataset is available on Mendeley Data: https://data.mendeley.com/datasets/jxj2r45t2x.

Among the severe foodborne illnesses, listeriosis resulting from the pathogen Listeria monocytogenes exhibits one of the highest fatality rates. This study investigated the application of near infrared hyperspectral imaging (NIR-HSI) for the classification of three L. monocytogenes serotypes namely serotype 4b, 1/2a and 1/2c. The bacteria were cultured on Brain Heart Infusion agar, and NIR hyperspectral images were captured in the spectral range 900-2500 nm. Different pre-processing methods were applied to the raw spectra and principal component analysis was used for data exploration. Classification was achieved with partial least squares discriminant analysis (PLS-DA). The PLS-DA results revealed classification accuracies exceeding 80 % for all the bacterial serotypes for both training and test set data. Based on validation data, sensitivity values for L. monocytogenes serotype 4b, 1/2a and 1/2c were 0.69, 0.80 and 0.98, respectively when using full wavelength data. The reduced wavelength model had sensitivity values of 0.65, 0.85 and 0.98 for serotype 4b, 1/2a and 1/2c, respectively. The most relevant bands for serotype discrimination were identified to be around 1490 nm and 1580-1690 nm based on both principal component loadings and variable importance in projection scores. The outcomes of this study demonstrate the feasibility of utilizing NIR-HSI for detecting and classifying L. monocytogenes serotypes on growth media.

The composition of honey is mostly determined by the species-specific characteristics of flowering plants, which is reflected in the significant deviations in composition of honey varieties. The high-quality acacia honey is assessed based on both physical-chemical parameters and melissopalynology. The appearance of rape pollen in acacia honey makes the acacia honey be sorted into the multifloral honey category. Over carrying out melissopalynology, the149 samples of various honeys (acacia, rape and multifloral) have also been analysed by using physical-chemical and elemental analysis. Multivariate data analysis revealed that multifloral honey is much closer to acacia honey than to rape honey, as it can be observed from the examined unique parameters. By the PCA (Principal Component Analysis) analysis based on united set of physico-chemical and melissopalynology results the acacia and rape honey samples are entirely separated for each other, while multifloral honey samples are very close to acacia honey group and partially overlap with it. On ignoring the pollen analysis and based on the rest of the results, the multifloral honey category is almost indistinguishable from the declared and verified acacia honey category.