This work tested near-infrared (NIR) and UV-visible (UV-Vis) spectroscopy coupled with chemometrics to characterize flours from different starch origins. In particular, eighteen starch-containing flours (e.g., type 00 flour, rye, barley, soybean, chestnut, potato, spelt, buckwheat, oat, millet, rice, durum wheat, amaranth, chickpea, sesame, corn, hemp and sunflower flours) were analyzed with a twofold objective: chemically characterizing the investigated flours and laying the groundwork for the development of a fast and suitable method that can identify the botanical source of starch in food. This could ensure ingredient traceability and aid in preventing/detecting food fraud. Untargeted approaches were used for this study, involving the simultaneous acquisition of a large amount of chemical information (UV-Vis on extracted starch and NIR signals on raw flours) coupled with chemometric techniques. UV-VIS spectra were acquired between 225 and 800 nm after sample pretreatment to extract starch. NIR spectra were acquired between 900 and 1700 nm using a poliSPEC NIRe portable instrument on the flours without any kind of pretreatments. An initial exploratory investigation was conducted using principal component analysis and cluster analysis, obtaining interesting preliminary information on patterns among the investigated flours. In particular, the UV-Vis model successfully discerned samples such as potato, chestnut, sunflower, durum wheat, sesame, buckwheat, rice, corn, spelt and 00-type flours. PCA model results obtained from the analysis of NIR spectra also provided comparable results with the UV-Vis model, particularly highlighting the differences observed between hemp and potato flours with soybean flour. Some similarities were identified between other flours, such as barley and millet, rye and oats, and chickpea and amaranth. Therefore, some flour samples underwent surface analysis via scanning electron microscope (SEM) using the Nova NanoSEM 450 to detect distinctive morphology.

In the present feasibility study, SPME Arrow-GC-MS method coupled with chemometric techniques, was used for investigating the impact of two different storage conditions, namely freezing and refrigeration, on volatile organic compounds (VOCs) of different commercial breads. The SPME Arrow technology was used as it is a novel extraction technique, able to address issues arising with traditional SPME fibers. Furthermore, the raw chromatographic signals were analysed by means of a PARAFAC2-based deconvolution and identification system (PARADISe approach). The use of PARADISe approach allowed for an efficient and rapid putative identification of 38 volatile organic compounds, including alcohols, esters, carboxylic acids, ketones, and aldehydes. Additionally, Principal Component Analysis, applied on the areas of the resolved compounds, was used to investigate the effects of storage conditions on the aroma profile of bread. The results revealed that the VOC profile of fresh bread is more similar to the one of bread stored in the fridge. Furthermore, there was a clear loss of aroma intensity in frozen samples, which could be explained by phenomena related to different starch retrogradation that occurs during freezing and refrigeration. However, considering the limited number of investigated samples, this study must be considered as a proof of concept; a more statistically representative sampling and further examinations of other properties, such as bread texture, need to be performed to better understand whether samples destined for eventual analysis should be frozen or refrigerated.

Supercritical fluid chromatography coupled to high-resolution mass spectrometry (SFCHRMS) is gaining attention for non-target screening (NTS), yet systematic evaluations of data-processing workflows remain scarce. Here, we present the first comprehensive comparison between a component-profile (CP) approach based on Region of Interest analysis combined with Component Detection Algorithm and Multivariate Curve Resolution (ROI-CODA-MCR) and a feature-profile (FP) workflow, MS-DIAL. Using extracted ion chromatograms (EICs) as ground truth, both workflows were optimized and benchmarked for standard detection, peak intensity reliability, blank filtering performance, and suspect screening for wastewater samples. Both detected a comparable number of standards (33-35 out of 37) but ROI-derived intensities closely matched EICs (average ratio ≈1, RSD = 5.8%), while MS-DIAL intensities deviated substantially (up to two orders of magnitude, RSD >200%). In non-target analysis, MS-DIAL produced ≈1000 features with many false positives, while ROI-CODA-MCR yielded ≈200 components with lower blank contributions and superior resolution of in-source fragments and adducts. MS-DIAL detected more suspect compounds, highlighting a higher risk of false negatives in the CP approach, particularly for low-intensity or sparse signals. Our results demonstrate that SFCHRMS datasets, often including intense background signals, challenge key assumptions underlying both FP and CP workflows and necessitate careful parameter optimization.