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Starch is a polysaccharide that is abundantly found in nature and is generally used as an energy source and energy storage in many biological and environmental processes. Naturally, starch tends to be in miniscule amounts, creating a necessity for quantitative analysis of starch in low-concentration samples. Existing studies that are based on the spectrophotometric detection of starch using the colorful amylose–iodine complex lack a detailed description of the analytical procedure and important parameters. In the present study, this spectrophotometry method was optimized, tested, and applied to studying starch content of atmospheric bioaerosols such as pollen, fungi, bacteria, and algae, whose chemical composition is not well known. Different experimental parameters, including pH, iodine solution concentrations, and starch solution stability, were tested, and method detection limit (MDL) and limit of quantification (LOQ) were determined at 590 nm. It was found that the highest spectrophotometry signal for the same starch concentration occurs at pH 6.0, with an iodine reagent concentration of 0.2%. The MDL was determined to be 0.22 μg/mL, with an LOQ of 0.79 μg/mL. This optimized method was successfully tested on bioaerosols and can be used to determine starch content in low-concentration samples. Starch content in bioaerosols ranged from 0.45 ± 0.05 (in bacteria) to 4.3 ± 0.06 μg/mg (in fungi). 

Electronic cigarette (e-cigarette) market increased by 122% during 2014–2020 and is expected to continue growing rapidly. Despite their popularity, e-cigarettes are known to emit dangerous levels of toxic compounds (e.g., carbonyls), but a lack of accurate and efficient testing methods is hindering the characterization of e-cigarette aerosols emitted by a wide variety of e-cigarette devices, e-liquids, and use patterns. The aim of this study is to fill this gap by developing an automated E-cigarette Aerosol Collection and Extraction System (E-ACES) consisting of a vaping machine and a collection/extraction system. The puffing system was designed to mimic e-cigarette use patterns (i.e., power output and puff topography) by means of a variable power-supply and a flow control system. The sampling system collects e-cigarette aerosols using a combination of glass wool and a continuously wetted denuder. After the collection stage, the system is automatically washed with absorbing and extracting liquids (e.g., methanol, an acetaldehyde-DNPH solution). The entire system is controlled by a computer. E-ACES performance was evaluated against conventional methods during measurements of nicotine and carbonyl emissions from a tank type e-cigarette. Nicotine levels measured using glass fiber filters and E-ACES were not significantly different: 201.2 ± 6.2 and 212.5 ± 17 μg/puff ( p = 0.377), respectively. Differences in formaldehyde and acetaldehyde levels between filter-DNPH cartridges and the E-ACES were 14% ( p = 0.057) and 13% ( p = 0.380), respectively. The E-ACES showed reproducible nicotine and carbonyl testing results for the selected e-cigarette vaping conditions.