Dataset Information

Amperometric microbial biosensor for sugars and sweetener classification using principal component analysis in beverages.

ABSTRACT: Sugar and artificial sweeteners are additives in packaged food and beverage products that are widely used, where excessive sugar consumption can cause an increase in various diseases. Detection and classification of natural sugars sucrose, fructose, glucose, and artificial sweetener aspartame are needed to determine the effects of consuming these sweeteners. This study uses an amperometric biosensor integrated biochip-D, which uses Saccharomyces cerevisiae as a bioreceptor through cellular metabolic respiration activity expressed in dissolved oxygen (DO) levels. The variations of sweetener concentration used were in the range of 50 mM to 250 mM. The measurement results showed that the higher the concentration of sugar and artificial sweeteners, the lower DO levels would be measured. It was due to the yeast cell respiration in consuming oxygen (O₂) and producing carbon dioxide (CO₂), where the decrease in DO levels of sucrose was 14.24%, fructose was 18.02%, glucose was 16.59%, and aspartame was 20.45% at a concentration of 250 mM. The measurement data was clustered and classified using principal component analysis (PCA), which resulted in data variance percentages of 92.80% and 89.40% for the two main components. In the application studies of the biosensor, sensitive determination of sugar in the beverage samples was investigated.

Supplementary information

The online version contains supplementary material available at 10.1007/s13197-022-05625-8.

SUBMITTER: Umar L

PROVIDER: S-EPMC9813324 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Amperometric microbial biosensor for sugars and sweetener classification using principal component analysis in beverages.

Umar Lazuardi L Rosandi Vira Annisa VA Setiadi Rahmondia Nanda RN Agustirandi Beny B Linda Tetty Marta TM Kuswandi Bambang B

Journal of food science and technology 20221124 1

Sugar and artificial sweeteners are additives in packaged food and beverage products that are widely used, where excessive sugar consumption can cause an increase in various diseases. Detection and classification of natural sugars sucrose, fructose, glucose, and artificial sweetener aspartame are needed to determine the effects of consuming these sweeteners. This study uses an amperometric biosensor integrated biochip-D, which uses <i>Saccharomyces cerevisiae</i> as a bioreceptor through cellula ...[more]

PMID: 36618051

Similar Datasets

Project description:Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG) signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability), arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA)-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness) using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S) enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18) and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40%) as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing) neither achieved the cross-day classification (the accuracy was around chance level) nor replicated the encouraging improvement due to the inter-day EEG variability. This result demonstrated the effectiveness of the proposed method and may shed some light on developing a realistic emotion-classification analytical framework alleviating day-to-day variability.

Dataset Information

Amperometric microbial biosensor for sugars and sweetener classification using principal component analysis in beverages.

Supplementary information

Publications

Amperometric microbial biosensor for sugars and sweetener classification using principal component analysis in beverages.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets