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Background: Early biomarkers of prodromal Alzheimer’s disease (AD) are critical both to initiate interventions and to choose participants for clinical trials. Odor threshold, odor identification and odor familiarity are impaired in AD. Methods: We investigated the relative abilities of standard screening (MMSE) and olfactory measures to predict transitions from cognitively normal (CN) to mild cognitive impairment (MCI), from CN to AD, and MCI to AD. The archival sample of 497, from the UCSD ADRC, included participants who were CN, MCI, AD and converters to MCI or AD. Apoe ε4 status, a genetic risk factor, was available for 256 participants, 132 were ε4 carriers. A receiver operating characteristic curve (ROC) curve plots the trade-off between sensitivity and specificity. Area under the ROC curve (AUC) was used to determine diagnostic accuracy. Results: Different measures were better predictors at specific stages of disease risk; e.g., odor familiarity, odor identification and the combination showed higher predictive value for converting from MCI to AD in ε4 carriers than the MMSE. Combining odor familiarity and odor identification produced an AUC of 1.0 in ε4 carriers, MMSE alone was 0.58. Conclusions: Olfactory biomarkers show real promise as non-invasive indicators of prodromal AD. The results support the value of combining olfactory measures in assessment of risk for conversion to MCI and to AD.

Natural and man‐made chemicals affect the odor characteristics of drinking water, creating the need to set guidelines for treatment, consumer communication, monitoring, and other considerations. Limits can be based on estimates of odor detection or recognition threshold concentrations by humans. Informed guidance, however, is needed to use threshold testing to identify the levels of odor‐causing chemicals humans can detect. In this article, we touch on this issue, discuss challenges associated with human threshold measurement, and provide examples of how procedures can be made more reliable and less dependent on large numbers of subjects and trials. For example, the single ascending method of limits, a once‐through procedure recommended by ASTM International, can be extended to become a staircase procedure that repeatedly samples the perithreshold region with relatively few trials. Multiple data analyses can be used to determine individual and group thresholds, thereby increasing confidence in the selection of odor threshold concentrations for setting guidelines and standards. Clearly, further research and additional guidance are needed to address how to best measure the sensitivity of humans to odor‐causing chemicals in water.

Background/Objectives: Aroma is a key determinant of crop quality and consumer acceptance, and aroma contribution and odor threshold are critical attributes for the identification of aroma compounds. Because the experimental determination of aroma contribution and odor thresholds is time-consuming and complex, most volatiles lack contribution and/or threshold data. Methods: We compiled odor thresholds for 716 volatile compounds and 31,459 aroma contribution records, and trained machine-learning models that took molecular fingerprints and physicochemical descriptors (e.g., molecular weight, logP, TPSA) as inputs to predict aroma contribution and odor threshold. We evaluated multiple fingerprint–model combinations, optimized hyperparameters via 5-fold cross-validation on the training set, and assessed the best models on a held-out validation set. Results: The ECFP6–GBDT combination performed best for predicting aroma contribution (macro-F1 = 0.732; weighted-F1 = 0.912). The ECFP4–GBDT model performed best for predicting odor thresholds (R2 = 0.94; RMSE = 0.44). Applying the models to volatiles in passion fruit juice identified 2-phenylethyl acetate as a potential new contributor to passion fruit aroma, whereas menthyl acetate likely exerted a negative influence; both findings were confirmed by serial dilution and sensory evaluation. The developed models provided both a GUI and a CLI, were easy to use, and supported straightforward upgrades by retraining with user-provided data. Conclusions: This work provided a methodological foundation for identifying crop aroma compounds and supported the genetic improvement of aroma traits.

Odor analysis by olfactometry relies on the use of n-butanol as a reference compound for standardizing the selection of human panelists. This requires that human sensitivity towards n-butanol is correlated to sensitivity towards other odorants as well as complex odor mixtures. However, there is limited evidence in the literature of such correlations. In this work, datasets from three odor laboratories were investigated in order to clarify this. All panels routinely analyzed n-butanol and H2S samples. Two of the laboratories analyzed samples from pig production or industry, whereas one laboratory determined odor threshold values for typical pig production odorants. Non-significant correlations were observed in most cases and odor threshold values for structurally related compounds were not well correlated. The work presented strongly indicates that the sensitivity of odor panelists towards n-butanol is not well transferred to other odorants or odor samples. Furthermore, minimization of variance by using n-butanol is not transferable to other odorants or environmental samples. Thus, the harmonization of human panelists for odor analysis based on n-butanol does not appear to result in harmonization with respect to other odorants or odor samples.

A study of organic compounds that caused a serious taste and odor episode of water supply in two residential areas in Catalonia (N.E. Spain) was carried out. Sweet and paint/solvent odor were the main descriptors used by consumers. Some cases of sickness and nausea were also associated with drinking water consumption by the consumers. Closed-loop stripping analysis (CLSA) combined with sensory gas chromatography and gas chromatography mass spectrometry detection were used to study the problem. As a result, 3-(trifluoromethyl)phenol (CAS number 98-17-9) was for the first time identified as a responsible of an odor incident in drinking water. Concentration levels of this compound were up to 17,000 ng/L in groundwater and up to 600 ng/L in distributed water. Odor threshold in water for 3-(trifluoromethyl)phenol was determined as 13 ng/L (45 °C).

Olfactometry is globally acknowledged as a technique to determine odor concentrations, which are used to characterize odors for regulatory purposes, e.g., to protect the general public against harmful effects of air pollution. Although the determination procedure for odor concentrations is standardized in some countries, continued research is required to understand uncertainties of odor monitoring and prediction. In this respect, the present paper strives to provide answers of paramount importance in olfactometry. To do so, a wealth of measurement data originating from six large-scale olfactometric stack emission proficiency tests conducted from 2015 to 2017 was retrospectively analyzed. The tests were hosted at a unique emission simulation apparatus—a replica of an industry chimney with 23 m in height—so that for the first time, conventional proficiency testing (no sampling) with real measurements (no reference concentrations) was combined. Surprisingly, highly variable recovery rates of the odorants were observed—no matter, which of the very different odorants was analyzed. Extended measurement uncertainties with roughly 30–300% up to 20–520% around a single olfactometric measurement value were calculated, which are way beyond the 95% confidence interval given by the widely used standard EN 13725 (45–220%) for assessment and control of odor emissions. Also, no evidence has been found that mixtures of odorants could be determined more precisely than single-component odorants. This is an important argument in the intensely discussed topic, whether n -butanol as current reference substance in olfactometry should be replaced by multi-component odorants. However, based on our data, resorting to an alternative reference substance will not solve the inherent problem of high uncertainty levels in dynamic olfactometry. Finally, robust statistics allowed to calculate reliable odor thresholds, which are an important prerequisite to convert mass concentrations to odor concentrations and vice versa.

Threshold assessments for the reference odorant n-butanol are an integral part of various research, clinical, and environmental sensory testing procedures. However, the practical significance of a high or low threshold for n-butanol beyond a particular testing environment and procedure are often unclear. Therefore, this study aimed to determine between-method correlations and to investigate the association between the n-butanol threshold and perceptual/behavioral odor effects in natural breathing scenarios in 35 healthy adults. The thresholds for n-butanol derived from the Sniffin’ Sticks test and determined by the ascending limit dynamic dilution olfactometry procedure were significantly correlated (∣r∣ = 0.47). However, only the thresholds determined by olfactometry were significantly correlated to the odor detection of n-butanol in an exposure lab. Moreover, participants with a higher sensitivity for n-butanol in the olfactometer-based assessment rated ammonia, during a 75 min exposure, to be more unpleasant and showed better performance in a simultaneous 3-back task than participants with lower sensitivity. The results of this study suggest that beyond the strict parameters of a certain psychophysical procedure, the threshold for n-butanol can be a meaningful indicator of odor detection and effects in some cases.

Literature data on odour thresholds of volatile food constituents, and, in particular on their odour quality, may differ significantly. In order to obtain more reliable sensory data, the odour thresholds of eighty-four compounds previously characterised as key food odorants were re-evaluated and compared to literature results. In addition, the odour thresholds of ten odorants are reported here for the first time. On the basis of a distinct protocol, also the aroma attributes of the odorants were evaluated in order to define an aroma language, which can be used for specific purposes, e.g., training of panellists for GC-Olfactometry.

Contamination with 2,4,6-trichloroanisole (TCA) often causes taste and odor (T&O) problems in drinking water due to its low odor threshold concentration. Microbial O-methylation of the precursor 2,4,6-trichlorophenol (TCP) would be the dominant mechanism for TCA formation. Simple and rapid measurement of TCP in the low concentration range is necessary to control the problems induced by TCA. In this study, the combination of microbial conversion and instrumental analysis was proposed as a method of TCP quantification. Fungi and bacteria were isolated from various water samples and examined for their ability to produce TCA from TCP. As a result, a strain exhibiting quantitative TCA production and a high growth rate was obtained and named Mycolicibacterium sp. CB14. The conversion rate of TCP to TCA by this strain was found to be high and stable (85.9 ± 5.3%), regardless of the applied TCP concentration, although within the range of 0.1–10 µg/L. The limits of detection and quantification for TCP by this proposed method were determined to be 5.2 ng/L and 17.3 ng/L, respectively. By improving the methods, Mycolicibacterium sp. CB14 could be used for the quantification of TCP at very low concentration levels, which is sufficient to manage the T&O problem caused by TCA.

We recently reported that subjects with a higher olfactory identification threshold for rose odor declined more in attentional ability in the elderly. This study focuses on discrimination ability and olfactory identification threshold in twelve elderly subjects living in a community (age: 80.9 ± 1.6). Olfactory function was assessed by the rose odor identification threshold. We assessed the discrimination ability by distinguishing 5 similar odor pairs. Our results showed that the subjects with a higher olfactory identification threshold (≥ 5) declined more in discrimination ability (14% ± 14%, p = 0.03) compared to those with a lower threshold (≤ 4) (averaged value set at 100%). As discrimination ability is related to the basal forebrain cholinergic system, our results suggest that olfactory impairment links to the decline in cognitive function relating the cholinergic system.