Dataset Information

Artificial Intelligence Sensing: Effective Flavor Blueprinting of Tea Infusions for a Quality Control Perspective.

ABSTRACT: Tea infusions are the most consumed beverages in the world after water; their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for the industry. Any qualification method capable of objectifying the product's sensory features effectively supports industrial quality control laboratories in guaranteeing high sample throughputs even without human panel intervention. The current study presents an integrated analytical strategy acting as an Artificial Intelligence decision tool for black tea infusion aroma and taste blueprinting. Key markers validated by sensomics are accurately quantified in a wide dynamic range of concentrations. Thirteen key aromas are quantitatively assessed by standard addition with in-solution solid-phase microextraction sampling followed by GC-MS. On the other hand, nineteen key taste and quality markers are quantified by external standard calibration and LC-UV/DAD. The large dynamic range of concentration for sensory markers is reflected in the selection of seven high-quality teas from different geographical areas (Ceylon, Darjeeling Testa Valley and Castleton, Assam, Yunnan, Azores, and Kenya). The strategy as a sensomics-based expert system predicts teas' sensory features and acts as an AI smelling and taste machine suitable for quality controls.

SUBMITTER: Caratti A

PROVIDER: S-EPMC10856620 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Artificial Intelligence Sensing: Effective Flavor Blueprinting of Tea Infusions for a Quality Control Perspective.

Caratti Andrea A Fina Angelica A Trapani Fulvia F Bicchi Carlo C Liberto Erica E Cordero Chiara C Magagna Federico F

Molecules (Basel, Switzerland) 20240123 3

Tea infusions are the most consumed beverages in the world after water; their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for the industry. Any qualification method capable of objectifying the product's sensory features effectively supports industrial quality control laboratories in guaranteeing high sample throughputs even without human panel intervention. The current study presents an integrated analytical strategy acting as an ...[more]

PMID: 38338309

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Project description:Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients' view of artificial intelligence in melanoma diagnostics in Germany, with a particular focus on patients with a history of melanoma. Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. Results: 298 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.91-0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85-0.92] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.35-0.46] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.92-0.97] to its use as assistance system for physicians. In sub-group analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians. Conclusions: The vast majority of participants exhibited a positive attitude toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.

Project description:PurposePiling is a critical process for the formation of raw Liupao tea (RLT) flavor quality. However, given the unclear changes in the flavor quality of RLT during the piling process, and the key substances of taste-contributing and aroma components that affecting the flavor quality of RLT was lacking. Herein, the present study aimed at evaluating the changes in flavor quality and its key substances of RLT during the piling process.MethodsSensory evaluation was conducted on RLT samples collected at 11 time points during the piling process. The biochemical and aroma components of these samples were detected using UV spectrophotometer, high-performance liquid chromatography (HPLC), and headspace solid-phase microextraction gas chromatography-mass spectrometer (HS-SPME-GC-MS), respectively. Multivariate analysis was performed to investigate the effects of piling time on the flavor quality of RLT.Key findingsThe sensory quality of RLT increased and then decreased during the piling process. The changes in flavor quality of RLT during the piling process could be divided into four stages: 0-6, 9-15, 18-24, and 27-30 h. The flavor quality of RLT changed significantly with the variations in biochemical and volatile compounds. The key contributors to taste changes included significant decreases in (-)-epigallocatechin gallate, (-)-epicatechin gallate, (-)-epigallocatechin, (-)-catechin gallate, and theobromine content and significant increases in gallic acid, gallocatechin, and theaflavin content. Additionally, alterations in the content of 19 characteristic aroma compounds, such as cedrol, methyl salicylate, methyl palmitate, trans-nerolidol, decanal, 6,10-Dimethyl-2-undecanone, neryl alcohol, α-cedrene, and (E, E)-2,4-heptadienal, significantly influenced the aroma of the tea infusion. This study provides insights into the formation of flavor quality in RLT at different piling stages, which also provide a scientific foundation for optimizing the piling and production processes of RLT.

Project description:BackgroundLocalization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method.ResultsHere we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality.ConclusionsOur deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.

Dataset Information

Artificial Intelligence Sensing: Effective Flavor Blueprinting of Tea Infusions for a Quality Control Perspective.

Publications

Artificial Intelligence Sensing: Effective Flavor Blueprinting of Tea Infusions for a Quality Control Perspective.

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