Hazelnut Rancidity Detection via Hyperspectral Imaging
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Solution Overview
Problem
Conventional methods for determining the putridity of oily fruits, nuts, particularly hazelnuts, or seeds are inefficient, leading to excessive rejections and potential health risks due to their inability to accurately differentiate between good and bad products, especially when the differences in spectral analysis are subtle.
Innovation Solution
A method and device that utilize hyperspectral imaging and machine learning algorithms to analyze the absorption or reflection spectra of hazelnuts in the wavelength range of 300-2500 nm, correlating specific wavelengths with the degree of putrescence, allowing for a spatially resolved determination of putridity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional optical methods operating in the visible region are used to examine hazelnuts, then the examination process is simple and fast, but the method cannot differentiate between spoiled and unspoiled hazelnuts because putrefaction occurs internally while external appearance remains perfect
Solution Approach 1:
The patent transitions from visible light examination to near-infrared spectral examination, accessing a different dimensional space of electromagnetic radiation. This enables detection of internal chemical changes (fatty acid decomposition) that are invisible to conventional optical methods, resolving the contradiction between detection accuracy and system complexity by utilizing the enhanced sensitivity of near-infrared radiation to chemical constituents.
Solution Approach 2:
The patent changes the examination parameter from visible light wavelength to near-infrared wavelength range (900-1700 nm). This parameter change enables the detection system to perceive chemical vibrations and molecular structures that are invisible at visible wavelengths, specifically detecting the spectral signatures of fatty acid decomposition products that indicate putrefaction.
2Productivity
If high refresh rate photosensors are used to ensure high throughput, then the sorting speed increases, but the ability to reliably analyse ingredients of each individual element is compromised
Solution Approach 1:
The patent employs continuous near-infrared spectral acquisition at high refresh rates (500 Hz or more), maintaining uninterrupted examination of each hazelnut. This continuous action enables both high throughput and reliable analysis by capturing multiple spectral data points per element, ensuring that the rapid examination does not compromise the accuracy of individual ingredient detection.
Solution Approach 2:
The patent creates a spectral copy of each hazelnut's near-infrared reflection spectrum, capturing the chemical composition information without physical contact or damage. This spectral copying enables rapid, non-destructive analysis of multiple elements in sequence, maintaining both high productivity and measurement precision by examining the spectral signature rather than physically altering the sample.
3Productivity
If conventional statistical classification methods are used to analyse spectral data, then the processing is straightforward and fast, but the method produces false rejections when differences between spoiled and unspoiled elements are subtle
Solution Approach 1:
The patent replaces conventional statistical classification methods with near-infrared spectral analysis based on fundamental optical principles. Instead of relying on complex statistical algorithms to differentiate subtle spectral differences, the system directly measures the near-infrared absorption characteristics of fatty acids and their decomposition products, providing a more reliable and physically grounded basis for classification that maintains processing speed while improving accuracy.
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
The method significantly reduces false rejections by enabling a precise and reproducible evaluation of putrescence, improving the distinction between good and putrid hazelnuts, and allowing for high-throughput sorting of large mass flows.
Implementation Method 1
irradiating a sample of an oily fruit, a nut, in particular a hazelnut, or a seed with a calibrating light source, projecting the light reflected and/or transmitted from the sample onto a calibrating photosensor, acquiring an absorption or reflection spectrum in a wavelength range of 300-2500 nm
Implementation Method 2
acquiring an absorption or reflection spectrum in a wavelength range of 300-2500 nm by means of a calibrating photosensor
Data Source
AI summary
The invention relates to a method for determining whether an oilseed, a nut, in particular a hazelnut (2), or a seed is rancid, involving irradiating a sample, detecting an absorption or reflection spectrum, determining the content of at least one fatty acid decomposition product in the sample, and ascertaining a degree of rancidity using the content of the at least one fatty acid decomposition product in the sample. The method additionally involves irradiating an oilseed, a nut, in particular a hazelnut (2), or a seed, projecting reflected and/or transmitted light onto a detection photosensor (4′), detecting absorption or reflection spectra in a wavelength range of 300-2500 nm using the detection photosensor (4′), said detection photosensor having a plurality of pixels, and assigning a degree of rancidity to each pixel of the detection photosensor (4′).