## 3 Results and Discussion
### Development of the Calibration Model
The majority of data points for all measured components were clustered along the regression lines. The accuracy of the estimation is defined as good when the cross-validation correlation coefficient (RMSECV) lies in the range between 0.70-0.89, whereas coefficients higher than 0.90 are related to an excellent prediction of the chemical composition [26]. Values of both standard calibration errors (SEC) and standard cross-validation errors (SECV), for all examined variables, were within the range of those reported in the bibliography [10,24,25,27] on the use of NIR spectroscopy to assess the chemical composition of meat and meat products. The highest standard calibration (SEC) and cross-validation (SECV) errors were observed in the estimation of the moisture and fat contents, and the lowest values were observed in the estimation of the protein content. Reference data showed that the range was wider for moisture and fat (31.87-59.94 and 20.72-53.76 respectively) contents and narrower for protein (11.08-22.41) content, and this fact could account for differences in the accuracy of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **SEC** & **RMSEC** & **SECV** & **RMSECV** \\ \hline Moisture (\%) & 1.58 & 0.92 & 2.13 & 0.86 \\ Ash (\%) & 0.18 & 0.77 & 0.26 & 0.56 \\ Protein (\%) & 0.89 & 0.87 & 1.17 & 0.78 \\ Fat (\%) & 1.73 & 0.93 & 2.17 & 0.88 \\ \hline \hline \end{tabular}  
* SEC = standard calibration error; RMSEC = root mean square standard calibration error; SECV = standard cross-validation error; RMSECV = root mean square standard cross-validation error.  
\end{table} 
Table 2: Calibration statistics for estimating the chemical composition of traditional sausages for the calibration samples. Figure 1: Near infrared spectra of traditional sausages. the estimation since variability can positively affect the NIR spectroscopy predictability [28]. The limited ability of NIR spectroscopy to estimate the protein content of traditional sausages was also confirmed in the studies by [PERSON] et al. [10; 25] and [PERSON] et al. [29] and is due to the relatively small range of values for the protein content that hampers the detection of differences between samples. NIR spectroscopy is not usually applied for the estimation of the ash content in meat and meat products because inorganic compounds, in ionic forms, show no absorption in the near infrared spectrum and, therefore, such a calibration model would have minimal use [10; 30]. Furthermore, [PERSON] et al. [17] also reported poor accuracy in the prediction of ash content in Thai steamed pork sausages. The latter workers related the poor accuracy to very small range of ash content. [PERSON] et al. [31] reported lower accuracy in the prediction of NaCl content, a major component of ash, in fermented pork sausages. Due to endogenous variances in the meat and fat used for the production of sausages, a considerable number of samples is required for the development of an adequate calibration model. These variations are related to the type of meat, which includes different animal species and anatomical parts of the carcass [10]. However, [PERSON] et al. [24] reported that between 30 and 150 samples are necessary to construct a calibration model with sufficient range for the individual components. The samples used for the development of the model must have significant variability in their chemical composition because high variability results in a robust calibration model [18]. This is feasible to a certain extent since retail samples such as the ones used in the present study should comply with the various regulations for food production and meet consumer criteria for their acceptability. Finally, in foods produced from muscle tissue (meat and fish), the primary source of variability arises from their compact and heterogeneous nature, making it difficult to collect high-quality spectra.