Non-Destructive AI Food Profiling from Overtone Reflectance
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Solution Overview
Problem
Existing methods for food analysis, such as gas chromatography and mass spectrometry, require destructive sampling and lengthy processing times, while human expert inspection is unreliable for achieving consistent and reliable food product quality due to variations in agricultural products.
Innovation Solution
A non-destructive system using machine learning and optical analysis with a portable apparatus that captures overtone spectra from non-homogenized food samples, employing a receptacle, light source, optical device, and detector to predict molecular characteristics through machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gas chromatography or mass spectrometry is used for food analysis, then measurement precision is improved, but the sample is destroyed and processing time increases
Solution Approach 1:
The patent replaces mechanical/chemical destruction-based analysis methods (gas chromatography, mass spectrometry) with optical sensing (NIR spectroscopy) combined with machine learning. The optical device detects molecular vibrations through light reflectance without physical or chemical alteration of the sample, enabling non-destructive analysis while maintaining measurement precision through advanced algorithms that interpret spectral data.
Solution Approach 2:
The patent changes the measurement parameter from direct chemical identification (requiring sample destruction) to optical property detection (light reflectance at different wavelengths). By measuring how light interacts with molecular bonds in the food sample and using machine learning to interpret these optical signatures, the system achieves accurate food profiling without destroying the sample.
2Measurement precision
If gas chromatography or mass spectrometry is used for food analysis, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent replaces time-consuming mechanical separation and identification processes (chromatography columns, mass spectrometry scanning) with instantaneous optical detection. The NIR spectroscopy system captures reflectance data across multiple wavelengths simultaneously, and machine learning algorithms rapidly process this data to provide accurate food analysis results in real-time, dramatically reducing processing time while maintaining precision.
3Adaptability or versatility
If human experts visually inspect and taste food products, then adaptability to different products is maintained, but reliability and consistency deteriorate
Solution Approach 1:
The patent replaces human sensory inspection (visual, tactile, taste) with automated optical sensing and machine learning. The system uses NIR spectroscopy to objectively measure molecular properties of food samples and applies trained machine learning models to consistently interpret the data, eliminating human subjectivity and variability while maintaining the ability to handle diverse food products through flexible algorithm configuration.
Solution Approach 2:
The patent enables the system to automatically adapt to different food products through machine learning models that are trained on product-specific data. Once trained, the system independently performs accurate classification and analysis without requiring human expert intervention for each product type, achieving both reliability through consistent automated processing and adaptability through programmable model selection.
4Adaptability or versatility
If human experts manually inspect food products, then adaptability to different products is maintained, but productivity deteriorates
Solution Approach 1:
The patent replaces slow manual human inspection with automated optical sensing and computational analysis. The NIR spectroscopy system rapidly captures spectral data from multiple samples in sequence, and machine learning algorithms instantly process this data to provide classification results, achieving high-speed automated food profiling that maintains product-specific accuracy while dramatically increasing inspection throughput compared to manual methods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid, accurate prediction of food properties like taste and composition without destruction, improving consistency and efficiency in food profiling.
Implementation Method 1
an optical device having an input port and an output port, the input port being configured to sense reflectance from at least a part of the sample in the volumetric sampling space
Implementation Method 2
a detector coupled to the output port, the detector being configured to convert the component of the reflectance into captured data
Data Source
AI summary
A system and method for non-destructive food rapid profiling in terms of taste, variant classification, adulteration, etc., using artificial intelligence. The system includes: a receptacle configured to move a non-homogenized sample in a path to intersect a volumetric sampling space; a sensor configured to sense reflectance from at least a part of the sample in the volumetric sampling space, the sensor being configured to output a component of the reflectance as captured data, the captured data being characterised by an overtone spectrum over a range of wavelengths; and a computing device configured to apply at least one first machine learning model to the captured data to: predict at least one facet corresponding to predictively determined selected wavelengths; and provide a signature data using the at least one facet.