Fat content in minced meat
Quality control is crucial in the food industry . By monitoring the nutritive property of the product , it contributes not only in protecting a brand reputation , but also in increasing it . Fat content is one of the properties that consumers look at first when buying meat . Its quantitative level needs to be precisely documented . Also , monitoring the fat content while transforming meat plays a cost-effective role . New automation technologies are therefore needed to improve brand competitiveness .
In this study , 5 minced meat samples were generously provided by Atria ( Kauhajoki , Finland ). 5 extra samples were obtained by mixing the 5 original ones , so that 10 samples are included in this analysis . In order to know accurately their fat content , SPECIM ordered measurements from a 3rd party laboratory , certified in fat analysis , using a butyrometer according to Gerber method ( Seilab in Seinäjoki , Finland ; method NMKL 181 , 2005 ; See Table 1 ).
Samples were measured with SPECIM hyperspectral camera FX17 ( Fig . 1 ). Hyperspectral imaging a surface and non-destructive method which combines spectroscopy and imaging . For each pixel of the acquired image , NIR spectra are collected ( 900 – 1700 nm ). Those can be converted into fat content employing relevant processing algorithms . Here a regression model was built and calibrated on 8 samples , and applied on the 2 remaining ones ( indicated with * in Table 1 ). Data were processed with Mira software ( perClass ):
Conclusions :
The use of FX17 in machine vision systems would provide meat transformers with crucial and accurate information for fat quantification . Besides , this fast and non-destructive method is also suitable to detect other properties such as moisture and freshness . Also , contaminants such as pieces of wood and plastics can be sorted . FX17 is a perfect tool for industrial quality control . Besides , the flexibility of the methods allows a fast adaptation to new regulations .
Hyperspectral imaging offers cost reduction and quick adaptation to new regulations by offering real-time information about the manufacturing process for decision making and real-time control of meat processing factory input and output being within specification .
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Measured value by Seilab
Measured value by FX17 Sample 1 0.6 % 0.9 % Sample 2 16 % 15.2 % Sample 3 * 10 % 10.4 % Sample 4 * 18 % 20.8 % Sample 5 75 % 75.1 % Sample 6 ( mix ) 3 % 2.7 % Sample 7 ( mix ) 6 % 5.5 % Sample 8 ( mix ) 11 % 12.8 % Sample 9 ( mix ) 19 % 19.0 % Sample 10 ( mix ) 24 % 23.5 %
Table 1 : fat content on each sample included in this study . Samples 3 and 4 were used for validation purpose .
Figure 2 : regression plot of the quantitative model for fat content prediction . Red dots relate to calibration samples , whereas green ones relate to validation samples .
Figure 3 : Example of fat distribution on a meat sample ( here Sample 4 ).
The results of the regression model are presented in Table 1 and Fig . 2 . It clearly shows that the FX17 is a suitable tool to measure precisely the fat content in minced meat .
In addition of measuring the fat content in samples , hyperspectral imaging is suitable to measure its distribution ( Fig . 3 ).
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Figure 1 : FX17 on the 40x20 scanner ( left ) and example of a sample on the scanner sample tray ( right ).