The study was carried out to evaluate NIRS (Near Infrared Reflectance Spectroscopy) performance in predicting activity water (aw), pH, moisture, expressible drips (ED%) and Total Volatile Nitrogen (TVN) in common sole. Samples (n = 141) were submitted to instrumental analysis and minced wet muscle were scanned in reflectance mode using a NIRSystem 5000. NIRS technique showed a higher precision in predicting ED (fraction of explained variance, R2 = 0.83 and fraction of explained variance in cross validation, 1-VR = 0.55), aw (R2 = 0.84; 1-VR = 0.69) and moisture (R2 = 0.87; 1-VR = 0.67). The performance of calibration evidenced low SECV (standard error of cross validation) values. However, the data evidenced very low correlations between NIR and measurements of TVN. The principal component analysis (PCA), modified partial least square (MPLS) and SVM (support vector machine) were utilized in order to discriminate between fresh and frozen-thawed fish products. Results show that NIRS was able to distinguish fresh (98% samples recognized) and frozen-thawed (80% samples recognized) raw fillet according MPLS. The ConfMatrix (%) values obtained from SVM were 0.93 and 0.83 respectively.

Determinazione di parametri di qualità  e autenticazione in sogliole tramite NIRS

FASOLATO, LUCA;NOVELLI, ENRICO;LOPPARELLI, ROSA MARIA;BALZAN, STEFANIA;MIRISOLA, MASSIMO;SERVA, LORENZO;SEGATO, SEVERINO;
2008

Abstract

The study was carried out to evaluate NIRS (Near Infrared Reflectance Spectroscopy) performance in predicting activity water (aw), pH, moisture, expressible drips (ED%) and Total Volatile Nitrogen (TVN) in common sole. Samples (n = 141) were submitted to instrumental analysis and minced wet muscle were scanned in reflectance mode using a NIRSystem 5000. NIRS technique showed a higher precision in predicting ED (fraction of explained variance, R2 = 0.83 and fraction of explained variance in cross validation, 1-VR = 0.55), aw (R2 = 0.84; 1-VR = 0.69) and moisture (R2 = 0.87; 1-VR = 0.67). The performance of calibration evidenced low SECV (standard error of cross validation) values. However, the data evidenced very low correlations between NIR and measurements of TVN. The principal component analysis (PCA), modified partial least square (MPLS) and SVM (support vector machine) were utilized in order to discriminate between fresh and frozen-thawed fish products. Results show that NIRS was able to distinguish fresh (98% samples recognized) and frozen-thawed (80% samples recognized) raw fillet according MPLS. The ConfMatrix (%) values obtained from SVM were 0.93 and 0.83 respectively.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/2266649
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