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

Area of Science:

• Biotechnology and Biosensor Development
• Food Science and Analysis
• Analytical Chemistry

Background:

• Excessive consumption of sugars and artificial sweeteners in packaged foods and beverages is linked to various health issues.
• Accurate detection and classification of sweeteners like sucrose, fructose, glucose, and aspartame are crucial for understanding their health impacts.
• Current detection methods may require improvement in sensitivity and specificity for complex food and beverage matrices.

Purpose of the Study:

• To develop and validate an amperometric biosensor for the detection and classification of natural sugars and artificial sweeteners.
• To investigate the correlation between sweetener concentration and dissolved oxygen (DO) levels using yeast metabolic activity.
• To assess the biosensor's performance in analyzing real beverage samples.

Main Methods:

• An amperometric biosensor integrated with a biochip-D utilizing *Saccharomyces cerevisiae* as a bioreceptor was employed.
• Yeast metabolic respiration activity, measured via dissolved oxygen (DO) levels, was used as the detection signal.
• Principal Component Analysis (PCA) was utilized for data clustering and classification of different sweeteners.

Main Results:

• A decrease in DO levels was observed with increasing concentrations of sugars (sucrose, fructose, glucose) and aspartame.
• At 250 mM, DO levels decreased by 14.24% (sucrose), 18.02% (fructose), 16.59% (glucose), and 20.45% (aspartame).
• Principal Component Analysis (PCA) effectively classified the sweeteners, with the two main components explaining 92.80% and 89.40% of the data variance.

Conclusions:

• The developed yeast-based biosensor demonstrates sensitive detection of sugars and artificial sweeteners.
• The biosensor can differentiate between various sweeteners based on their impact on yeast metabolic activity and DO levels.
• This biosensor shows potential for application in analyzing sweetener content in beverage samples.