Wine Classification via Infrared Spectroscopy and Computer Vision
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Solution Overview
Problem
Conventional systems for evaluating food and beverages are limited by their calibration to specific chemical compounds, making it difficult to detect and classify samples with unknown chemical makeup efficiently and effectively.
Innovation Solution
A computer vision classification system is developed using visual image representations derived from raw spectroscopy data, employing transfer learning with pre-trained models to classify samples, such as wine, by analyzing the full spectra data, enabling the detection of unknown chemical compounds and environmental perturbations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine-based systems are calibrated to detect specific chemical compounds, then measurement precision for known compounds is improved, but the ability to detect and classify samples with unknown chemical makeup deteriorates
Solution Approach 1:
The system uses a single spectroscopy device that can perform multiple functions: detecting both known and unknown chemical compounds through full spectra analysis. The computer vision model is trained to recognize patterns across the entire spectral range, enabling the system to identify various wine characteristics (alcohol content, fruitiness, oak aging, defects) without requiring separate calibrated detectors for each compound
Solution Approach 2:
The system transforms the chemical composition data into visual image representations, changing the parameter space from chemical concentration values to pixel intensity values. This transformation allows the application of computer vision techniques to spectroscopy data, enabling the detection of patterns and features that were not previously detectable using traditional chemical analysis methods
2Reliability
If expert evaluations are used to classify wine quality, then subjective quality assessment is improved, but objectivity and repeatability deteriorate due to personal biases and availability limitations
Solution Approach 1:
The system replaces the mechanical process of human sensory evaluation (tasting, smelling) with an automated spectroscopy-based detection system. The spectroscopy device objectively measures chemical composition, and the computer vision model automatically classifies the wine characteristics, eliminating human subjectivity and availability constraints while maintaining or improving assessment quality
Solution Approach 2:
The system enables the wine sample to 'evaluate itself' through spectroscopy analysis. Instead of requiring external experts to assess the wine, the system extracts and analyzes the wine's own spectral signature to determine its chemical composition and quality characteristics, making the evaluation process self-contained and automated
3Measurement precision
If traditional analytical tools are used to evaluate food and beverages, then detection of specific chemical compounds is improved, but efficiency and data-driven classification of complex samples deteriorates
Solution Approach 1:
The system merges spectroscopy data acquisition with computer vision analysis into a unified workflow. The full spectra data is directly transformed into visual images and processed by the trained model in an integrated pipeline, eliminating the need for separate analysis steps for different chemical compounds and significantly improving evaluation efficiency
Solution Approach 2:
The computer vision model is pre-trained on a large dataset of spectroscopy images with known wine characteristics. This preliminary training enables the model to quickly and accurately classify new wine samples without requiring time-consuming manual analysis or recalibration, dramatically improving evaluation productivity
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
This approach allows for efficient, data-driven detection and classification of samples, providing robust and repeatable results beyond the limitations of expert evaluations, and identifying chemical compounds not previously detected, such as flavor-imbuing compounds in wine.
Implementation Method 1
wave spectra data generated by Fourier-transform infrared spectroscopy (FI-RT) analysis of a sample
Data Source
AI summary
Some embodiments of the present disclosure relate to systems and methods including generating, by infrared spectroscopy, spectra data identifying quantities and associated wavelengths of radiation absorption for each of a plurality of wine samples as determined by the infrared spectroscopy; converting the spectra data for each wine sample to a set of discretized data; transforming the discretized data into a visual image representation of each respective wine sample, the visual image representation of each wine sample being an optically recognizable representation of the corresponding converted set of discretized data; and storing a record including the visual image representation of each wine sample in a memory.