Non-destructive and rapid analysis of chemical compositions in Thai steamed pork sausages by near-infrared spectroscopy

• Published in: Food chemistry Vol. 129; no. 2; pp. 684 - 692
• Main Authors: Ritthiruangdej, Pitiporn, Ritthiron, Ronnarit, Shinzawa, Hideyuki, Ozaki, Yukihiro
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 15.11.2011; Elsevier
• Subjects: Biological and medical sciences; Chemical compositions; Food industries; Fundamental and applied biological sciences. Psychology; Meat and meat product industries; Near-infrared (NIR) spectroscopy; Partial least squares (PLS) regression; Steamed pork sausage; Near-infrared (NIR) spectroscopy; Steamed pork sausage; Chemical compositions; Partial least squares (PLS) regression; Sausage; Chemical analysis; Meat product; Pork product; Water vapor; PLS regression; Near infrared spectrometry; Chemical composition; Pork
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2011.04.110

► Plastic casing provides little influence in the partial least squares (PLS) model. ► NIR spectrum of the sausages are strongly influenced by physical properties of samples. ► MSC is a powerful pretreatment method to eliminate baseline fluctuations in spectra. ► The prediction accuracy for validation samples was greatest for the moisture model. The objective of the present study was to evaluate the ability of near-infrared (NIR) spectroscopy to predict chemical compositions of Thai steamed pork sausages in relation to different types of sample presentation forms of NIR measurements (with and without plastic casing). NIR spectra of sausages were scanned to predict the chemical compositions, protein, fat, ash and carbohydrate non-destructively. NIR spectrum features of the sausage samples were strongly influenced by physical properties of the samples, such as the presence of plastic casing and inhomogeneous physical structure inside the samples, yielding significant baseline fluctuations. Thus, regression models were developed using partial least squares (PLS) regressions with two pretreatment methods, namely multiplicative scatter correction (MSC) and second derivative, which overcame the baseline problems. The prediction results suggest that the contents for the protein, fat and moisture can be estimated well with the proper selection of the pretreatment method.