Prediction of chicken meatball quality through NIR spectroscopy and multivariate analysis

Abstract

Near Infrared (NIR) Spectroscopy leads a great opportunity to replace the expensive and time-consuming chemical conventional analysis for determination of the quality of meat products. This study was conducted aiming to evaluate the feasibility of NIRS and to establish a rapid assessment method to easily predict the quality of chicken meatball. Samples of meatball (n=123) were collected from Golden Harvest Company of Bangladesh. After collecting sample, spectra were obtained prior to analysis and a total of 369 NIRs were collected and stored in computer by DLP NIR scan Nano Software. To generate reference data 123 meatball samples were analyzed for proximate components, instrumental color CIE L*, a*, b*, and pH of meatball. After that a partial least square regression model for calibration and cross validation were developed for data analysis using The Unscrambler X software. Accuracies of the calibration models were evaluated using the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), coefficient of calibration (R²C) and coefficient of cross validation (R2 CV). Calibration equations were developed from reference data using partial least squares regressions. The standard deviation is 2.41, 0.14, 2.1, 0.41, 1.31, 0.31, 1.26, 0.38, and 0.38 for L*, a*, b*, pH, DM, moisture, CP, EE and ash respectively which indicates that all values are adequate for analytical purposes. Predictive ability of the models was assessed by coefficient of determination of cross-validation (R2 CV) and root mean square error of cross-validation. Predictions were good (R2 CV=0.84) for lightness (L*), (R2 CV=0.72) for redness (a*), (R2 CV=0.77) for yellowness (b*), (R2 CV=0.78) for pH, (R2 CV=0.73) for CP, (R2 CV=0.83) for EE (R2 CV=0.72) for moisture, (R2 CV=0.72) for DM and (R2 CV=0.74) for ash. From the results, it can be concluded that NIRS can be used for the rapid assessment of physico-chemical traits of chicken meatball.

Prediction of chemical compositions of crushed maize used in meat animal ration using near infrared spectroscopy and multivariate analysis

“…In Table 2, NIRS calibration and prediction statistics for different parameters is shown. A wide and even distribution in composition, along with precise reference analysis techniques are recognized as important characteristics of the calibration set of samples, in order to obtain a successful equation (Hashem et al, 2022a(Hashem et al, , 2022b(Hashem et al, and 2022cCen and He, 2007). Calibration co-efficient were 0.979, 0.978, 0.986, 0.977, 0.965, 0.967, 0.975 for DM, Moisture, CP, EE, CF and ash, respectively, where Egesel and Kahriman, (2012) showed that CP, EE, ash for maize grain were 0.990, 0.823, 0.926 respectively.…”

Characteristics of beef patties affected by adding soya meat Effect of soya meat on the quality of beef patties

“…Among the essential nutrients protein is the major as it plays the vital role in body building but majority of the population in developing countries is suffering from protein shortage (Apata et al, 2011). Meat and meat products can minimize this problem as a contributor of high-quality protein (Hashem et al, 2021(Hashem et al, , 2022(Hashem et al, and 2023Hasan et al, 2022;Hossain et al, 2021 andTalukdar et al, 2014). Among the processed meat products e.g.…”