Rapid colorimetric and artificial intelligence-based methods for determining the microbial quality of raw milk, processed milk, and milk products
ABSTARCT :
Background: Milk is perishable product, it requires immediate processing after few hours (2-4Hrs) of production, otherwise, bacterial load increases and milk gets deteriorate. At present Methytene Blue Dye Reduction Test, commonly known as MBRT test is used as a quick method to assess the microbiological quality of raw and pasteurized milk.
This test is based on the fact that the blue colour of the dye solution added to the milk get decolourized when the oxygen present in the milk get exhausted due to microbial activity.
The sooner the decolourization, more inferior is the bacteriological quality of milk assumed to be. This test is widely used at the dairy reception dock, processing units and milk chilling centres where it is followed as acceptance/rejection criteria for the raw and processed milk.
Further, processed dairy products may also get spoiled in the supply chain for microbiological reasons. Description: The above mentioned MBRT has to be done under sterile conditions. Take 10 ml milk sample in sterile MBRT test tube. Add 1 ml MBRT dye solution (dye concentration 0.005%).
Stopper the tubes with sterilized rubber stopper and carefully place them in a test tube stand dipped in a serological water bath maintained at 37t1oC.
Record this time as the beginning of the incubation period. Decolourization is considered complete when only a faint blue ring (about 5mm) persists at the top. However, for processed dairy products, the enumeration of bacterial load is time-consuming and requires different methods.
Expected solution: The above mentioned MBRT takes 4 to 6 Hours for completion of the test. Therefore, it is proposed to develop cost effective, rapid and portable method/Machine to deduct microbial load in Raw and Pasteurized Milk as compared to present Methylene blue reduction test (MBRT).
Meanwhile, smart packaging tools such as freshness indicators could be a solution for processed dairy products.ln this context, artificial intelligence, machine vision, systems, etc. could be explored.
EXISTING SYSTEM :
There has always been a need to establish alternative methods to rapidly assess microbial quality which are robust and reliable yet simple and inexpensive. Some rapid methods include density centrifugation and continuous flow epifluorescent microscopy (Cunningham and Saunders, 1988), use of a commercial instrument, BioSysTM, manufactured by MicroSys, Inc.
which detects metabolic changes in microorganisms during incubation. Bioluminescence assay for determining total bacterial contamination (TBC) of raw milk (Frundzhyan et al. 1999), direct epiflourescent filter technique (DEFT) requires separation of bacteria from milk sample through centrifugation and then staining by acridine orange, impedance measuring instrument to estimate microbial population in raw milk (Cady et al. 1978).
Attempts have been made to assess microbial quality by measuring dissolved oxygen (DO) in milk sample and its reduction over time. The idea was to relate the rate of change in (DO) levels with the population of bacteria in the sample (Homhual and Jindal 2001).
DISADVANTAGE :
Sensitivity Limitations: Colorimetric methods may not detect low levels of microbial contamination, potentially leading to false negatives.
Interference from Components: The presence of other substances in milk (like fats, proteins, or dyes) can interfere with colorimetric reactions, affecting accuracy.
Limited Range of Microbial Detection: These methods may not differentiate between types of bacteria, limiting their utility for specific microbial identification.
Data Requirements: AI methods require large datasets for training, which can be challenging to obtain, especially for specific milk products.
Potential Biases: AI models can inherit biases from training data, leading to skewed results or misinterpretation of microbial quality.
PROPOSED SYSTEM :
This system begins with a sample preparation module that ensures consistent and accurate preparation of milk samples. The colorimetric detection module employs specific reagents to quickly identify microbial contamination, providing immediate visual results through optical sensors.
Simultaneously, the AI analysis module leverages machine learning algorithms to process data, enabling real-time predictions of microbial quality and risk assessment based on historical trends.
The data management module organizes and stores test results, allowing for easy access and trend visualization, while the user interface module offers an intuitive dashboard for seamless interaction.
Quality control measures are embedded throughout the system to ensure accuracy and reliability, including regular calibration of instruments and validation of AI models.
ADVANTAGE :
Speed of Results: These methods provide quick results, often within minutes, allowing for timely decisions in processing and quality control.
Cost-Effectiveness: Many colorimetric tests are relatively inexpensive, making them a cost-effective option for quality assessment.
Simplicity and Ease of Use: Colorimetric tests are generally straightforward and can often be performed without complex equipment, making them accessible for routine testing.
Automated Analysis: These methods can automate data processing and analysis, reducing the labor burden and minimizing human error in interpreting results.
High Accuracy and Precision: AI algorithms can analyze large datasets and identify patterns that may be missed by traditional methods, leading to improved accuracy in microbial detection.
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