The quality of raw ham is linked to the loss of essential properties such as color, which must be red and stable. In fact, the intensity of the red color of cured ham is susceptible to a progressive variation over time, which requires the producer to search for technologies to ensure maximum stability of the factors responsible for the red color of raw ham. Nitrites (potassium E249 and sodium E250) and nitrates (sodium E251 and potassium E252) are added during the production phase of raw hams mainly as preservatives. Nitrates may also be naturally present in small quantities in meat products. The aim of this work was to develop a rapid method, based on image analysis techniques, to detect the presence of nitrate and nitrites in raw hams by inspecting the surface of a slide. A number of 160 slices of raw hams of different producers have been acquired by an RGB system composed by a camera and a square array illumination system (color temperature of 6500K) in a black box. An algorithm to read the chromatic coordinates in L*a*b* in CIELAB color space was developed in MATLAB® environment. A numerical classifier capable to discriminate raw ham samples prepared with the use of food additives and without. Chemical determinations of nitrates and nitrites have been carried out to train the numerical classifier. A 5-fold cross validation statistic method has been used to validate the numerical classifier. The image analysis system was able to detect efficiently the chromatic pattern of the raw ham slices. The numeric classifier reached the 100% of correct classification (validation set) of raw hams containing additives. The percentage of correct classification of raw hams without additives was 95.0% (validation set), whit a percentage of false positives of 5%. The overall correct classification was 97.5% of detection. Thus, the developed technique could be used instead of the analytic technique because rapid and easy to use. Furthermore, it should be noted that the analytical determinations carried out on all samples showed that additives were present in traces or in a limited quantity, and this result reinforce the capability of the image analysis procedure developed.

Additives individuation in raw ham using image analysis

Romaniello R.;Perone C.;
2021

Abstract

The quality of raw ham is linked to the loss of essential properties such as color, which must be red and stable. In fact, the intensity of the red color of cured ham is susceptible to a progressive variation over time, which requires the producer to search for technologies to ensure maximum stability of the factors responsible for the red color of raw ham. Nitrites (potassium E249 and sodium E250) and nitrates (sodium E251 and potassium E252) are added during the production phase of raw hams mainly as preservatives. Nitrates may also be naturally present in small quantities in meat products. The aim of this work was to develop a rapid method, based on image analysis techniques, to detect the presence of nitrate and nitrites in raw hams by inspecting the surface of a slide. A number of 160 slices of raw hams of different producers have been acquired by an RGB system composed by a camera and a square array illumination system (color temperature of 6500K) in a black box. An algorithm to read the chromatic coordinates in L*a*b* in CIELAB color space was developed in MATLAB® environment. A numerical classifier capable to discriminate raw ham samples prepared with the use of food additives and without. Chemical determinations of nitrates and nitrites have been carried out to train the numerical classifier. A 5-fold cross validation statistic method has been used to validate the numerical classifier. The image analysis system was able to detect efficiently the chromatic pattern of the raw ham slices. The numeric classifier reached the 100% of correct classification (validation set) of raw hams containing additives. The percentage of correct classification of raw hams without additives was 95.0% (validation set), whit a percentage of false positives of 5%. The overall correct classification was 97.5% of detection. Thus, the developed technique could be used instead of the analytic technique because rapid and easy to use. Furthermore, it should be noted that the analytical determinations carried out on all samples showed that additives were present in traces or in a limited quantity, and this result reinforce the capability of the image analysis procedure developed.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11369/402836
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